An e-commerce recommendation engine is essentially a way of automatically pointing customers to suggested products beyond what they have currently searched. It can follow them throughout various stages of the shopping process. When it comes to machine learning, this typically uses user data and engagement (clicks, search, preferences, etc) to make informed recommendations. This is usually handled via an algorithm. 

Let’s discuss each type of recommendation engine along with different technologies for recommendation systems, and the best context to use them in. As with anything, there are advantages and disadvantages to the solutions offered, which will be briefly covered below. Read on to learn more.

Category management

To improve the relevancy of product recommendations, it’s advised to group products together by categories. With this, it’s worth mentioning that creating complementary clusters is more impactful than grouping similar items together. A good product recommendation strategy is built upon knowing your customers’ needs. To learn more about this topic, consult our related article on e-commerce category management.

Product recommendation engine types

As mentioned above, there are different types of product recommendation engine approaches. Regardless of which one is used, historical customer data is highly suggested to make sure your website visitors have personalized recommendations. The more data and behavior that you have, the more meaningful the suggestions will be.

The idea is to make relevant predictions on what users want outside of search-based methods. With that, the amount of information available will determine the best approach to integrate. Each type is briefly explained below.

Collaborative filtering

To enable effective collaborative filtering on an e-commerce website, it’s necessary to have pre-existing data. In fact, the more historical insight, the better. This is because collaborative filtering uses past behaviors to suggest what a similar customer will need. In general, this type uses product ratings to highlight relevant items.

Content-based filtering

Content-based filtering differs from collaborative filtering in that it’s more personalized. It also doesn’t depend on previous user behavior and makes suggestions based on preferences. These are indicated by customers when setting up an online account with your brand.

In addition, this type continues to fine-tune suggestions based on website interactions – typically handled with an algorithm. That said, it’s advised to pull in preferences from other data sources, aside from shopping behavior. For example, a profile could also include entertainment-based and news article engagement.

This requires a hybrid approach, which is further explained in the following section.

Hybrid filtering

When discussing hybrid filtering, these methods are usually a combination of both collaborative and content-based approaches. Also, other techniques may be incorporated. Hybrid filtering can use different types together or independent of one another. This type of recommendation engine is good for e-commerce websites with minimal historical data.

Of course, there are advantages and disadvantages to each type of product recommendation engine. Collaborative, content-based, and hybrid filtering are all good at suggesting items in different ways.

Advantages vs. disadvantages

Essentially, the advantages and disadvantages of different product recommendation engine types come down to how much data is available, and suggestion specificity. If there is minimal customer information, it’s best to use content-based filtering. This is because of its highly specific nature.

That said, if data is available, collaborative filtering might be better. Ideally, the products suggested should be similar enough to the customers’ interests while allowing for some flexibility. Since collaborative filtering is less based on the inclinations of a particular user, it can make suggestions outside the realm of content-based filtering.

The right approach will depend on the type of products being suggested and the availability of historical information. In general, it’s good to A/B test the best model, which will be explained following information on product recommendation engine technologies. There is no one-size-fits-all approach.

Context-aware recommendations

In addition to the types of recommendation engines listed above, it’s important to consider the context of where the customer is engaged. This means different suggestions should be used for a product detail page vs a homepage, for example. Additionally, recommendations increasingly require more personalization, through related content beyond products.

Product recommendation technologies

To support the different types of recommendation engines, there are different technologies utilized. These include session-based, reinforcement learning, multicriteria, risk-aware, mobile, and artificial intelligence (AI). They are outlined below with a brief description of each type of technology.

• Session-based

With session-based, the suggestions are given based on individual browsing behavior. This technology doesn’t use past data and is only relevant to the interactions within a single session. It’s good for sites that lack prior user knowledge.

• Reinforcement learning

As the name suggests, reinforcement learning is based on positive reinforcement to make informed decisions for users. The recommendation agent is rewarded for certain interactions and uses this insight to optimize website performance.

• Multicriteria

Using multiple criteria to make recommendations, multicriteria technology understands users across a set number of preferences. This is based on how a customer will rate various aspects of a product. Examples include sizing, color, fabric, and price.

• Risk-aware

As customers are more responsive to recommendations based on different contexts, risk-aware technology understands when it’s best to suggest products. The goal is to not turn off customers by interrupting them at inopportune moments.

• Mobile

This recommendation technology uses mobile data to provide location-based suggestions. Since mobile phones contain GPS and other functionalities not found on computers, they can recommend products differently.

• AI

The most recent type of technology, AI uses past data to automatically calibrate product recommendations. This is based on machine learning and relies less on the interactions of customers.

Looking for an AI-powered recommendation engine to create new revenue opportunities and higher your AOV? Inspire the right customers at the right time with AB Tasty. From A/B testing to product recommendations, AB Tasty is the only platform your experience optimization strategy needs.

How to A/B test with product recommendation engines

When A/B testing a product recommendation engine, it’s important that conversions increase. This will indicate the right type to use. There are various ways to A/B test, including algorithm vs curated, placement, personalization, format, social proof, and dynamic vs. static.

Of course, when choosing the right method, it’s important to consider the resources available. For example, hand-curation will take more time than automatically populating recommendations with an algorithm. Similarly, researching social proof, including reviews, and testimonials takes time. The goal is to find the right A/B testing approach while optimizing operations.

Conclusion

We hope this page provides a good starting point for learning more about what a product recommendation entails. As you implement your strategy, we want you to feel confident in choosing the best approach.

The post What is a Product Recommendation Engine? appeared first on abtasty.

As you know, marketing (and shopping) has moved online. Whether you operate an e-commerce store or a business-to-business operation, you are constantly driving your prospects to take specific actions, like buying a product, signing up for a newsletter, or downloading a free e-book. Whenever a user completes an action, this is seen as a conversion. Conversion rate optimization is the practice of raising the number of users interacting with your site. While this is usually done through a series of small, gradual improvements, the end goal is to optimize your entire online marketing process.

First, let’s get back to basics about conversion rate: what it is, how to calculate it, how to improve it, and how to make sure you don’t get in your own way. Ready? Let’s go!

What is Conversion Rate Optimization?

Before we get into conversion rate optimization, let’s take a deep dive into what conversions and conversion rates are and how to track them.

What is a conversion?

A conversion can refer to virtually any action taken online. It doesn’t refer to a specific action, like a sale, but to any action that you consider valuable to your business. A conversion (also called an event) can be a click, a purchase, a swipe, or a download.

Your conversion rate is the number of conversions that occur in relation to the total possible conversions in a given timeframe. (More on that later!)

Conversion Rate Definitions

It doesn’t matter what business you’re in; you will always try to increase your conversions. While you might be tempted to spend more money on advertising, greater awareness doesn’t always lead to more sales.

Conversion rate optimization is a much more affordable and effective way of acquiring more traffic because you can make educated, data-driven adjustments that focus on the traffic you already have. You can concentrate on micro-conversions (for example, getting your customers to fill out forms so your sales team can give them a call and push them through the funnel) or macro-conversions, like confirmed purchases.

What is a conversion rate?

The easiest example to illustrate conversion rate is in the context of e-commerce. Conversion rate is often used by e-commerce sites to measure the percentage of visitors that end up purchasing products. In other words, how many go through the entire conversion funnel.

If you’re an e-commerce company, your goal will be to optimize your conversion rate, which should lead to increasing your bottom line. Conversion tracking can be done through any web analytics platform, like Google Analytics, Adobe Analytics, or Mixpanel, for any period of time.

CRO

CRO is an acronym for Conversion Rate Optimization.

Conversion Rate

A conversion rate is the number of conversions (or desired actions) taken by visitors divided by the total number of visitors to your site. 

Conversion Rate Optimization

Conversion rate optimization is the strategic process of increasing the percentage of visitors that perform a desirable action on your site.

Conversion Funnel

A conversion funnel refers to the different steps prospects take before becoming full-fledged customers of your business.

CRO test

A CRO test in digital marketing is an online experiment that involves adding, removing, re-arranging, or redesigning different elements on your website.

Macro vs micro-conversions

You may have heard references being made to macro and micro-conversion.

Macro-conversions (or website goals) usually refer to the conversion of a visitor into a paying client or subscriber of a web service, e.g., an online magazine membership, streaming service, or software-as-a-service (SaaS) monthly subscription. These are sometimes called website goals.

On the other hand, micro-conversions are seen as smaller, secondary actions that a visitor takes on a website that indicates that they will convert, for example, clicking through to the site, watching a promotional video, or adding an item to your cart.

Can a user convert twice?

Should you pay for conversion if a single user performed the same action twice? Deduplication is the method we use to ensure that the right partner is credited and that clients aren’t overcharged for conversions.

Until now, all of the conversions measured by AB Tasty have followed a deduplication method. If a user accessed your conversion URL twice, we would only count one conversion for that web user. This was the case for URL and event-type objectives, such as click tracking. The transaction objectives, set explicitly by our e-commerce tag, were the only exceptions. For these objectives, you have the option of displaying the conversions in the report in deduplicated (by default) or duplicated mode.

The deduplication method is best suited if you want to track macro-conversions. You want to know whether or not your modifications impacted your ability to, more or less, convert your users into subscribers.

For micro-conversions (e.g., add to cart, access to content), the duplication method offers a complementary perspective. For example, in the case of user interactions with your interfaces, you may wish to measure whether your tests allow more users to use any element of the interface, but also whether it generates a lot of use. The conclusions of a test consist of emphasizing whether a new functionality would be different if, despite the increase in users accessing it, it does not create any repeated use (the concept of “stickiness”). The deduplication vision responds to the initial question, while the duplication vision responds to the second.

How do you calculate conversion rate?

Now that you know what a conversion rate is, you want to calculate your conversion rate and measure the effectiveness of your site. Don’t worry – you don’t need crazy math skills. All you have to do is divide the number of actions completed in a defined period of time by the total number of visitors to your website, then multiply the result by 100.

In other words:

Conversion rate = (Conversions or goals achieved / Total visitors) * 100

Imagine that your e-commerce website got 25,746 visitors during a chosen time frame; of those 25,746 visitors, 4,832 completed a transaction. Then, your conversion rate is 18.76%. Pretty good!

Depending on what you’re looking to measure, you can also calculate the conversion rate in the following ways:

You can also find an automatic conversion rate calculator to get a precise calculation of your website’s conversion rate for those not doing their own math.

What is a good conversion rate? (with examples)

Benchmarking conversion rates isn’t easy. An FMCG (fast-moving consumer goods) e-commerce site might have a completely different conversion rate to a site that sells insurance. Two to five percent is considered a good conversion rate; remember that even a minor jump in conversions can have a significant impact.

Try to compare a site that is most similar to yours. The ADI Consumer Report for recent years shares the following stats:

Benefits of Conversion Rate Optimization

There are many reasons why CRO is such an important part of your online marketing strategy. While strategies like SEO and advertising can generate additional traffic, your conversion rate optimization strategy can help turn your traffic into quality leads and sales. You’ll also gain valuable insights into customer preferences and behaviors. Let’s look at how CRO can benefit your business.

1. CRO creates a seamless shopping experience

Conversion rate optimization drives sales, but it can also remove any obstacles on your site that might prevent customers from completing a purchase. Small improvements in UX can have a big impact and improve the overall shopping experience of your customers.

Creating a seamless shopping experience without these niggling roadblocks will increase the customer lifetime value, i.e., keep customers coming back for more. This is done by analyzing feedback and testing solutions to ensure that the decisions you make are rooted in real data.

2. CRO helps you understand your customers

The days of “educated guesses” are over. A CRO specialist will provide you with real insights based on the data collected, getting to the root of any problems you may be experiencing. Let’s look at the issue of shopping cart abandonment.

You might find that your users are landing on your site, browsing around, and adding items to their cart but simply not taking the final step of checking out. This could be due to cognitive dissonance, a simple psychological barrier to purchase. You might add a pop-up message that states that the item will only be on sale for 24 hours — or that there are only three left in stock — to create urgency or add a badge that indicates that checkout is secure to see if that makes a difference.

Maybe you want to know more about your customers so that you can tailor your messaging according to their needs. CRO can provide real insight into buyer personas and user psychographics.

3. CRO improves marketing ROI and lowers acquisition costs

You might be aware of the adage that it costs more to acquire a new customer than to retain or convert an existing one. It’s true! Luckily, the higher the conversion rate is on your site, the lower your customer acquisition costs.

We know that you have to spend on advertising, and we know that paid advertising is expensive. Popular keywords aren’t cheap, so identifying the problems on your site and the phrases that lead to the most conversions will save you a lot of money on ineffective paid advertisements and improve your marketing ROI.

How to create a CRO strategy

According to Forrester Research, 90% of firms rated their CRO program as valuable or extremely valuable on a Likert Scale when it comes to achieving their strategic goals. Let’s examine how you can create your winning CRO strategy.

Why your CRO strategy matters

CRO can transform your business for good. A recent survey by marketing firm Outgrow found that among 3,000 companies, 5% of companies investing in CRO tools reported an ROI larger than 1000%. While your business might not enjoy that level of growth, the average ROI for the companies surveyed stands at 223%; and more than 70% of marketers use CRO campaign results to inform their campaigns.

If executed correctly, CRO strategies can improve landing page conversions, identify buyer personas, reduce cart abandonment and increase sales.

Setting clear CRO goals

Before setting up your CRO campaign, define your Key Performance Indicators (KPIs). Try to be as detailed as possible. Good examples include:

• Decreasing your bounce rate by 10%
• Increasing the average browsing time by 1 minute
• Reducing cart abandonment rates by 25%
• Increase site speed by 1.5 seconds
• Increase subscriptions by 5%
• Increase page views
• Increase newsletter subscribers

A good KPI is specific, measurable, and limited to a specific timeframe. Once you know what you are aiming for, you can start executing.

Phases of your CRO strategy

You’ll soon discover that the entire process of optimizing conversion rates will provide a wealth of information that you can explore and utilize in your future campaigns. While A/B testing is the most common experiment that CRO experts run, true optimization is more robust.

Phase 1: Research and discovery

our first step will be trying to identify opportunities for improvement. This could be anything that prevents a visitor from converting, such as bad copy to UX bottlenecks. You’ll start by analyzing your existing site in Google Analytics and choosing a limited number of opportunity pages to optimize.

From there, you can choose the metric that you want to compare and improve upon, e.g., high bounce rates or low average session duration. The pages you want to test should have sufficient traffic and be important enough to your business that making improvements will deliver a real impact. Once you’ve identified the pages, you can hone in on why users aren’t converting as well as they should.

Try to answer the questions, “How are people finding this page?” and “What is their pre-click experience like?”, Knowing this will help you hone in on the user intent (i.e., what value they hope to get out of their visit). You can see this by visiting Google Analytics and examining the Default Channel Group of the URL you want to analyze. This will reveal the sources that are leading people to your page.

You can use various on and off-site testing methods to reveal possible bottlenecks or barriers to conversion, including user testing, session replays, heat maps, click maps and heuristic analysis. The insights you gather should give you enough information to create a detailed hypothesis to test.

Phase 2: Hypotheses and prioritization

While (theoretically) there’s no limit to the number of hypotheses you can test for, companies have finite resources and a limited amount of time to devote to testing, which is where prioritization comes in. You don’t want to focus on hypotheses that won’t move the needle.

To combat this, we can follow two guidelines:

• Come up with testable hypotheses (follow this guide on how to do this).
• Ruthlessly prioritize ideas based on effort and ease.

There are plenty of prioritization models out there you can follow, the most popular being ICE, PIE, and PXL. You want to determine which actions will generate the greatest impact on the effort and resources you’re putting in. Deciding on an objective way to choose between hypotheses will go a long way toward creating predictable, repeatable CRO processes. All prioritization frameworks have pros and cons, so don’t waste too much time picking the right one. Choose one, stick to it, and get started.

Phase 3: Experimentation

Finally, we have experimentation. This is the stage that everyone associates with CRO: A/B testing. A/B testing is a common CRO technique that involves changing one aspect of your website — the color of a CTA, the length of a form, etc. — and observing whether this change positively or negatively impacts your chosen KPI. If your variation provides better results, you can hard code it into your site.

Testing is a process that requires statistical knowledge to get it right. You don’t necessarily need to have a data scientist looking over your shoulder, but it wouldn’t hurt to have some guidance on your first few experiments. (We’ll get into best practices a little later on).

There are several tools you can use to run A/B tests. AB Tasty is a solid solution for running a website, blog, or product experiments and any web personalization.

If you’re experimenting with your pop-ups or your email list, it’s almost certain that your tool of choice will have some sort of A/B testing feature native to the product. That’s certainly true of something like HubSpot or Mailchimp.

In any case, just make sure you can:

• Set up experiments correctly (i.e., the page has enough traffic to draw meaningful insights)
• Randomize and deploy experiences (with the help of a testing tool)
• Analyze the experiments correctly

If you don’t have enough traffic to the page you want to test, you can:

• Validate using qualitative research
• Roll it out and watch the time-series data

You can validate copy changes through Five Second Testing. Show users a design or copy for five seconds, and then ask follow-up questions about what they saw and remember. You can validate usability changes through user testing, session replays, or polls.

Additionally, keep an eye on the data before and after you roll out the changes. If the change is big enough, you can see the bump in the data over time. The numbers certainly shouldn’t go down. You can also try a Bayesian time series model to see if your changes produced significant results, given other implicit trends like seasonality.

It’s not necessarily the best approach, but it is a quick and more scientific way of measuring changes and improvements.

Phase 4: Analysis and repeat

The analysis is tricky, and your best bet would be to engage with a specialist to help you make sense of the data. If you don’t have an analyst, you should take some time to go over the basics. There are many books you can read on the subject of great online resources:

• Statistical Analysis and A/B Testing
• Data science you need to know! A/B testing
• Guidelines for A/B Testing
• How to Calculate, Track, and Analyze Conversion Rates

Important note: before you run the test, you should decide upfront what action you will take if the test wins, loses, or is inconclusive. That way, you mitigate the effects of confirmation bias and cherry-picking.

Components of CRO

While there isn’t a set CRO process that all companies use, most contain the same or similar components. We’ve compiled a list of commonly used CRO elements, as well as a glossary of terms you can consult if you come across a phrase you don’t know.

A/B Testing

A/B testing is the process of verifying your conversion hypothesis. It involves comparing two or more versions of your site and conversion rates to determine which is the most effective. To do this, one version is given to one group and another to the other group. From this, you can identify how each version performs.

Call to action (CTA)

Your call to action tells visitors exactly which action you would like them to take (e.g., “Click here for more information” or “Download your free e-book here”)!

Analytics

Analytics is the various tools to measure and explore visitor information to improve conversion rates.

User or session recordings

User or session recordings are software programs that can track the movement of users and visitors as they navigate through your site. You can use the information to find out which areas they are most likely to click on, which barriers exist that prevent them from taking action, and where they would like to go on the site. 

User experience

User experience (UX) is defined as the overall experience your users have throughout the use of an interface, digital appliance, or, more generally, interaction with any device or service. We say that a user experience is poor if a user experiences an obstacle that prevents them from doing what they’d like to do (for example, if they find it difficult to find or use the search bar, or if the search function delivers irrelevant results). UX and CRO go hand-in-hand because CRO involves identifying obstacles, testing them, and optimizing them so that you perform better.

Heatmaps

Heatmapping is a data visualization technique that shows where visitors click on your page using colors. Hot colors (usual hues of red) show you where visitors are clicking or interacting on your page most frequently, whereas cold colors (blue or green) show you the areas they least click on. You may want to move your most important call to action buttons to the “hot zones” as part of your CRO process.

Multivariate testing

Multivariate testing is similar to A/B testing but compares more variables and reveals more information about how these variables interact. In an A/B test, the traffic is split between different versions of the design of a page. Multivariate testing compares the data from each variation to determine which method is the most successful and which elements will significantly impact a visitor’s interaction with the site. A good example would be creating two different sign-up forms, two different calls to action, and two different headers. You would then send visitors to all possible combinations of these elements to see which combination would be most effective. Whether you use multivariate testing or A/B testing depends on your site traffic. You need a significant amount of traffic to your site to obtain meaningful data from multivariate testing.

Survey and NPS

You don’t always have to use on-site behavior to guide CRO. Many companies use surveys and NPS (Net Promoter Score) surveys to gather the opinions of their users. Net Promoter asks customers variations of the same simple question: Based on your recent interaction, how likely are you to recommend our brand to others? This is an excellent way to determine the sentiment about your site and identify areas for improvement.

Best CRO Practices

It’s hard to compile a list of best (and worst!) CRO practices because every company is different. In fact, that’s why we have to test every assumption we make — there simply isn’t a one-size-fits-all methodology that works for every site, every time. You have to spend time understanding how your customers think and behave and what their preferences are.

Here are a few strategies you can try as you embark on your optimization journey:

1. Run A/B tests on your landing pages

Landing pages are designed specifically to convert your users to a specific action, like capturing a lead or buying a product. An optimized landing page might be the only thing you need for business to boom, so make sure you thoroughly test and improve the landing pages that have the most impact on your bottom line.

To run an A/B test, you should put at least two different landing pages against each other, differentiated by a single element. This could include different calls to action, different designs, copy lengths, and images. Make sure that your landing page is visually appealing, and be prepared to redesign the entire page if it’s not converting. Remember, it’s not about your preferences: it’s about your customers.

2. CTA copy and color combinations and placement

Experimenting with a CTA button (including changing the wording, font, and copy length as well as color combinations and where the buttons are placed) is a great way to test your site and improve conversion.

For example, let’s say you’re an AB Tasty client and you’d like to increase clicks to the “Confirm purchase” CTA on your basket page. Using our visual editor, you can test if changing the color of the CTA from blue to green will help. Since you’ve already got AB Tasty’s tag on your website, you can get this test up and running in minutes.

Unbeknownst to them, a shopper on your site will either be presented with the original version or the variation. This process is randomized so that the pool seeing version A is more or less identical to those visiting version B. Since you’ll have set your test’s KPI to be “Clicks on CTA,” you can easily see in the report which variation performed best once enough visitors have been exposed to the test to make it statistically significant.

3. Page layout testing

Website navigation has a significant impact on user experience. If you have high bounce rates or low session duration, make sure to test different variations of your page layout. Is it easy for users to find what they are looking for? Are they able to find and use the search bar? Most websites have their search bar located on the top right corner of the site, above the fold. Design-wise, it might look better to place it somewhere else, but non-conventional placement can make it difficult for users to find. Drop menus are popular, but a lot of website visitors find them annoying because their eyes move faster than their mouse, which makes them hard to use. A different menu format can make a big difference.

4. Pop-ups and urgency

Pop-ups have a bad reputation, but they can be extremely useful tools if used correctly. A pop-up letting users know that they can receive an additional 10% off if they sign up for the newsletter before checking out is a great motivator. They can also be used to create a sense of urgency. This is usually found on accommodation sites letting you know that four other people are looking at the same room as you or on e-commerce sites that tell you that there are only two units of the item you are looking at in stock. Personalized offers, time-limited offers, and free delivery offers are valuable conversion tools for customers because the benefits outweigh the intrusiveness of the pop-up.

5. Clean, actionable web copywriting

Copywriting isn’t easy. Often it’s not what you say, but how you say it (and present it). A few simple phrases that clearly state what you want your visitor to do and know about your brand are often more effective than long-winded essays about your years of experience in the business.

Good copywriting for a website is concise and easy to understand, even at a glance. If you are writing a more complex article, such as a troubleshooting guide, try to solve their problem right away by making it easy to find their question and then guiding them through the steps. Remember that most people don’t like to read, so include lots of imagery and video where possible to break up the text.

SEO forms part of your copywriting and CRO considerations as well. Try writing SEO-optimized copy for every step of the buyer journey, whether they are simply looking for information to solve a problem, doing comparison shopping, or ready to make a purchase.

CRO Mistakes to Avoid

In Conversion rate optimization, there’s no secret recipe for achieving higher conversion rates. However, there are a few common mistakes you can avoid in the process of finding what works best for your business.

Check out our list of CRO mistakes to avoid below:

1. Conversion Killers

While there is no magic wand to wave during CRO that will guarantee a better result, there are a few things that are surefire conversion killers. You have to be willing to kill your darlings or, more specifically, kill off pages and posts that are hurting your conversion rates instead of helping.

2. Too many distractions

Too many bells and whistles will only get in the way. Multiple calls to action, gimmicky animation, and pages overstuffed with copy and images will cause more frustration than conversion. Visitors should be able to find what they need easily. Avoid clutter and decision fatigue by presenting options in a neat, intuitive, and organized way.

3. Slow site speed

Consumers are used to websites loading at lightning speed — even a two-second delay can increase bounce rates by 100%! A slow loading time can negatively impact your SEO as well as your UX, so make sure that you keep your site running optimally to prevent your site speed from negatively affecting your conversion rate.

4. Unclear navigation

Have you ever entered a building and found yourself hopelessly lost and frustrated because of the layout? Website visitors have even less tolerance for sites that aren’t easy to navigate because they can simply click away and visit a competitor’s site. If your site isn’t intuitive to use, your bounce rates will go through the roof.

5. Ultra-long forms

Data is powerful and important, but you have to keep your customers’ preferences in mind. If your data collection form is too long and too detailed, you risk your customers abandoning the form altogether. Shorter is always better when it comes to web forms.

6. Forced account creation

Data privacy is a hot topic, and very few people are willing to hand over their email addresses and details to a website. Give customers the option to checkout as a guest and convince them to sign up with compelling offers and freebies (like e-books) instead of forcing them to create an account before they are ready.

FAQs about Conversion Rate Optimization

What is the purpose of CRO?

Conversion Rate Optimization (CRO) aims to persuade more website users or visitors to complete the desired action on your website. This ultimately allows you to lower your customer acquisition costs by gaining more value from existing users, acquiring additional new visitors, and growing your sales and business. 

What is a CRO strategy?

A conversion rate optimization (CRO) strategy is the technique used to improve conversions (such as purchasing an item, signing up for a service for a trial period, or downloading a free e-book) by visitors to a website or app.

What are the best CRO tools?

You will need to use a combination of tools to get the best CRO results. For analytics, we recommend using Google Analytics or Adobe Analytics. Behavioral analysis tools like Microsoft Clarity’s heat mapping tool and feedback tools like Hubspot’s NPS surveys can help gather additional insight that drives improvement. At the same time, AB Tasty makes split and multivariate testing and experimentation seamless and straightforward.

What is a CRO test?

A conversion rate optimization (CRO) test is used to determine which changes to your site will have the best possible outcome. This is usually an A/B or multivariate test, but there are numerous other methods of testing that can be deployed.

How do I improve my conversion rate?

Conversion rates can be improved through many tactics, including shortening your contact forms, adding testimonials and reviews, removing distractions, strengthening your copy, and adding pop-ups that create a sense of scarcity or urgency.

The best CRO resources

You will find plenty of information about conversion rates on our blog, but if you’d like to bookmark a few outside sources, we can recommend the following thought leaders:

• Laura Stude
• Bryan Eisenberg
• Tim Ash
• Peep Laja
• Joanna Wiebe
• Shane Barker
• Brian Massey
• Scott Brinker

The post The Ultimate Conversion Rate Optimization Guide appeared first on abtasty.

Feature flags have become a staple for DevOps and software development teams to release features quickly and safely. Feature flags can be used for a wide range of purposes and are referred to in a number of ways from feature toggles to feature flippers but the core concept remains the same.

This guide will take you through the concept of feature flags, why and how they are used by teams today, their implementation, and some of their pros and cons.

What are feature flags?

Feature flags are a software development tool whose purpose is to turn functionalities on or off in order to safely test in production by separating code deployment from feature release.

Thus, a feature flag ranges from a simple IF statement to more complex decision trees, which act upon different variables.

There are many types of feature flags, categorized based on their dynamism and longevity that serve different purposes.

Short vs long-lived feature flags

When we talk about longevity, we are referring to feature flags that are either meant to stay in your system for a short period of time while other flags could stay for years.

For example, feature flags may be short-lived to temporarily deploy new changes and test in production while other long-term flags, such as kill switches, remain in your system longer. Therefore, you will need to manage different feature toggles or flags depending on how they’re deployed.

Keep reading: Short vs Long-lived Feature Flags

Dynamic vs static feature flags

In terms of dynamism, which is how often you need to modify a flag in its lifetime, there are two categories of flags:

• Dynamic- allow their value to be changed at runtime.
• Static- only change through actual code changes or configuration file changes.

To learn more about the differences between static and dynamic flags and where to store them depending on which category they fall under, read our best practices article on where to store feature flags.

We can further break down feature flags into various categories of toggles, which should be managed differently. In other words, you will need to manage different feature toggles or flags depending on how they’re deployed. The main categories are:

• Release toggles
• Experimental toggles
• Operational toggles
• Permission toggles

For more details on these different categories of feature toggles, refer to this article.

Why use feature flags?

Feature flags can be employed across a wide range of use cases, from the most simple to more advanced uses as various teams such as engineering, devops and production teams across organizations have begun to recognize their benefits in their software release strategy.

Release anytime

With feature flags, you can deploy anytime by separating code deployment from release. As a result, only specific parts of your feature are activated while any unfinished changes can be toggled off with a switch, making them invisible to users.

Test in production

Feature flags offer a foolproof, less risky way to test in production on real, live users. This will help you obtain valuable feedback from your most relevant users ensuring high-quality products. Feature flags, then, allow you to A/B your features in real-world environments faster than ever before.

Progressive delivery

When you have a feature ready to be released, you control which subset of users have access to this feature through gradual rollouts of releases. Progressive delivery can be implemented through techniques such as canary releases or ring deployments.

User targeting

Feature flags give you the power to choose your users. There are many methods to choose users with feature flags depending on your objectives. For example, you can choose an ‘elite’ group of users for early access to a feature or you can ask them to voluntarily opt-in through beta testing. Some of the most widely used techniques to progressively roll out your features through user segmentation include ring deployment, canary testing, and dark launch. Just as you give access to certain users, you can also block some users, for example, from a particular country or organization.

Kill switch

Sometimes, while testing in production, a feature may not be working as it should. Feature flags can be used as a kill switch to disable these buggy features to turn them off for users until the issue is analyzed and fixed. This comes in handy for ‘sunsetting’ features, which have been in your system for a long time and are no longer in use and so need to be retired to prevent the accumulation of technical debt.

Challenges of feature flags

You can never have too much of a good thing and yet nothing is perfect. There are times when you may run into some pitfalls while using feature flags so while their use is encouraged and brings great benefit to your team’s workflow, proceed with caution, especially if you’re relying on an in-house feature flagging platform.

1. Complexity

As use cases evolve over time, managing feature flags can become complex, especially with in-house systems lacking the necessary sophistication to support products.

2. Messy code & coordination

Accumulating feature flags in your system can result in messy code. Scattered conditional statements disrupt system flow, making it difficult to pinpoint issues during debugging

3. Technical debt

If stale, unused flags remain in your system, this would eventually lead to the accumulation of technical debt so flags will need to be cleaned up once they are used and are no longer active.

Benefits of feature flags

There is no denying that feature flags have become a necessity when releasing new features rapidly and safely. Having an efficient feature management system can help streamline release processes. The following are just some of the ways that they can bring value to your software development and release processes.

1. Continuous delivery

Feature flags allow teams to practice trunk-based development without the risk of long-lived branches and the dreaded merge hell.

2. Increase the rate of production

Feature flags enable risk-free production testing, leading to faster feature releases to users and higher-quality, less buggy outcomes.

3. Continuous experimentation

Feature flags allow you to frequently validate your changes by testing on a subset of users and measuring performance based on the chosen KPIs.

4. Risk mitigation

By decoupling deployment from release and releasing to a small number of users to detect any bugs and if any issues arise during testing, you can simply turn off the feature flag in production by rolling back the buggy feature.

5. Feature rollback

Feature flags empower you to deactivate a feature if it malfunctions by using a kill switch.

6. Give early access

Feature flags enable releasing features to early adopters who provide valuable feedback for product improvement, ideal for risky releases you’re hesitant to share with a wider audience.

7. Bypass app store validation

With feature flags, you can test new releases on smaller segments of your audience without needing to wait for app store approval (which can be lengthy).

Integrate feature flags in software development

Software usually goes through a series of stages or phases to produce high-quality software through the software development life cycle (SDLC). The phases incorporated depend on the methodology used but the basic principles of SDLC are more or less the same. For more information about the software life cycle, you can read more in our article about the different stages of the SDLC in conventional and Agile methodologies. Based on the above, at the beginning of any software development process, teams and organizations must decide what methodology they will adopt to ensure high-quality products. Waterfall and Agile are two of the most popular methods though Agile is rapidly gaining traction over Waterfall.

• Waterfall methodology is more of a traditional model. It adopts a linear approach to software development where each phase flows downward, like a waterfall, to the next; each stage must be completed before the next one begins.
• Meanwhile, Agile methodology is one that takes on a team-based approach to development. Instead of planning for the whole project, it breaks down development into small batches completed in stages. What makes Agile development innovative is it shifts the focus to the user as it relies on a very high level of customer involvement throughout the project.

However, incorporating feature flags into your release is what differentiates an agile roadmap from a Waterfall roadmap, which helps you stay focused on your core user-alignment activities. Read more to find out why an Agile methodology is the right way to go for modern software development and why you need to incorporate feature flags into your agile roadmap to keep your products moving in the right direction. When it comes to release management strategies, there are some strategies to choose from depending on your use cases.

• Trunk-based development is a branching model where developers collaborate in a single branch and make smaller changes more frequently. The idea behind this practice is to limit long-lived branches that may lead to ‘merge hell’. This is usually a good strategy to use to get features out fast.
• Feature branching, on the other hand, is when developers work separately on a branch and then once their changes are completed, they merge them into the mainline. This is an efficient method to manage large-scale projects with a large number of developers working on specific features.

Feature flags: Best practices

There are many practices that need to be applied when working with feature flags. The following are some best practices that you will need to consider and implement to make your feature flag journey as smooth as possible:

1. Control access to flags

Set up logging to know which change was made by who-this is especially important to reduce dependency between product and engineering teams and better productivity as there will be greater transparency particularly when it comes to introducing new changes.

2. Use a standardized naming scheme

In the absence of a naming convention, people in your team could start naming flags with the same name and getting flags mixed up. This could result in your team activating the wrong flag leading to potential disruptions in your system.

3. Manage different toggles differently

We have different types of flags so it would make no sense to, for example, manage and implement a release toggle and kill switch in the same way.

4. Conduct regular clean-up of your flags

You need to make sure that every now and then you are removing flags from your system that are no longer in use. Some flags are only meant to be short-lived and serve a one-time purpose so these need to be cleaned out of your system.

Choosing a feature flagging platform

Once you’ve decided you prefer to get a 3rd party solution, choosing the best feature flag management service to match your needs may prove to be a challenge. Visit our feature flag service comparison section to see how AB Tasty stacks up against other solutions.

AB Tasty’s Feature Experimentation and Experience Rollouts may just be the solution you’re seeking, where it has been dubbed as a leader in the The Forrester New Wave: Feature Management & Experimentation, Q2 2021 report.

AB Tasty not only allows you to turn features on and off but it gives you full control over your feature releases by wrapping your features in flags and rolling them out based on the specific flag values you assign to different user segments. You can then make informed decisions according to the metrics and KPIs you choose.

If you would like more information on getting started with our server-side functionality, check out our documentation to help you get started.

How to implement feature flags

A typical feature flag implementation is based on (1) a management service that defines the flag, (2) a run-time query to figure out the value of the flag, and (3) an if-else programming construct.

There are many ways to implement feature flags. The simplest and often the starting point is to use if/else statements directly in your code.

You can also rely on open source projects and many libraries are available for your preferred programming language. Read more about the Top 10 feature flag related projects on GitHub.

However, feature flags go beyond simple boolean states to more complex logical statements. Therefore, if you want to delve into more advanced uses, you will need more complex feature flag management tools.

Before embarking on your feature flag implementation journey, it is important to consider the following:

• Identify pain points– it is important to consider your objectives and what issues you are trying to tackle in your software development process and in production.
• Determine your use cases– in other words, consider why you want to use feature flags and who will be using them in your organization besides developers.
• Consider whether to build or buy– there are many factors to consider when deciding whether you’re better off building or buying a feature flagging management system.

Feature Flags FAQs

What are feature flags?

Feature flags are a software development practice whose purpose is to turn functionalities on or off without the need to deploy new code, and therefore to separate code deployment from feature release.

Why use a feature flag?

Feature flags enable continuous delivery by allowing teams to practice trunk-based development without the risk of long-lived branches and the dreaded merge hell. They also increase development velocity by making tests in production simple so you can continuously validate your changes on a subset of users while mitigating risks. They are not only used by engineering teams but also by QA as well as product and operations teams.

What’s the difference between feature toggles and feature flags?

Feature flags are often referred to as feature toggles and vice versa. However, it can be argued that as feature toggles evolved from an on/off switch to expand to more use cases, feature flags became a more appropriate term.

Is feature flagging the same as remote config?

Remote config can be implemented through feature flags. With remote config, features added to your mobile app can be wrapped in a feature flag and toggled on and off remotely when deciding who gets to see the new features.

Are feature flags different from feature branches?

A feature branch is when each developer makes a copy of the codebase in the trunk into a branch where they can make their changes and merge back into the trunk once they are done. Read more about Git branching strategies.

Feature branching sometimes is done using feature flags, especially when one developer is not yet finished with their changes but the release can still occur. Feature flippers would allow you to turn off these unfinished changes while completed features can still be released without delay.

Aren’t feature flags basically A/B tests?

To a certain extent. Both help you ship more confidently. With an A/B test, you can run experiments and choose which version of your website to show to your visitors. However, you can also run A/B tests on features of your website using feature flags. Here, you can create two variations of your feature to a subset of users and then you can analyze how the user experience differs with the new feature.

With that said, A/B testing is not the only testing in production technique you can implement using feature flags,  so they can help you run A/B tests but their uses extend beyond that.

The post Your Guide to Feature Flags appeared first on abtasty.

What is A/B Testing?

A/B testing, also known as split testing, is a marketing technique that involves comparing two versions of a web page or application to see which performs better. These variations, known as A and B, are presented randomly to users. A portion of them will be directed to the first version, and the rest to the second. A statistical analysis of the results then determines which version, A or B, performed better, according to certain predefined indicators such as conversion rate.

In other words, you can verify which version gets the most clicks, subscriptions, purchases, and so on. These results can then help you optimize your website for conversions.

In other words, you can verify which version gets the most clicks, subscriptions, purchases, and so on. These results can then help you optimize your website for conversions.

A/B testing examples

Many of you may be looking for ideas for your next A/B tests. Although the possibilities for A/B testing on your website are endless, sometimes a little inspiration from a success story can go a long way.

Here are some links to a few examples of A/B tests and results:

• 5 A/B Test Case Studies and What You Can Learn From Them?
• AB Tasty Case Studies Library
• 100 Conversion Optimization Case Studies
• 50 A/B Split Test Conversion Optimization Case Studies

What types of websites are relevant for A/B testing?

Any website can benefit from A/B testing since they all have a ‘reason for being’ – and this reason is quantifiable. Whether you’re an online store, a news site, or a lead generation site, A/B testing can help in various way. Whether you’re aiming to improve your ROI, reduce your bounce rate, or increase your conversions, A/B testing is a very relevant and important marketing technique.

Lead

The term “lead” refers to a prospective client when we’re talking about sales. E-mail marketing is very relevant to nurturing leads with more content, keeping the conversation going, suggesting products, and ultimately boosting your sales. with A/B testing e-mails, your brand should start to identify trends and common factors that lead to higher open and click-through rates.

Media

In a media context, it’s more relevant to talk about “editorial A/B testing”. In industries that work closely with the press, the idea behind A/B testing is to test the success of a given content category. For example, if you want to see if it’s a perfect fit with the target audience. Here, as opposed to the above example, A/B testing has an editorial function, not a sales one. A/B testing content headlines is a common practice in the media industry.

E-commerce

Unsurprisingly, the aim of using A/B testing in an e-commerce context is to measure how well a website or online commercial app is selling its merchandise. A/B testing uses the number of completed sales to determine which version performs best. It’s particularly important to look at the home page and the design of the product pages, but it’s also a good idea to consider all the visual elements involved in completing a purchase (buttons, calls-to-action).

What A/B tests should you use?

Classic A/B test: The classic A/B test presents users with two variations of your pages at the same URL. That way, you can compare two or several variations of the same element.

Split tests or redirect tests: The split test redirects your traffic toward one or several distinct URLs. If you are hosting new pages on your server, this could be an effective approach.

Multivariate or MVT test: Lastly, multivariate testing measures the impact of multiple changes on the same web page. For example, you can modify your banner, the color of your text, your presentation, and more.

In terms of A/B testing technology, you can:

A/B test on websites

By A/B testing on the web, you can compare two versions of your page. After, the results are analyzed according to predefined objectives—clicks, purchases, subscriptions, etc.

A/B test native mobile apps

A/B testing in apps is complex because you can’t show two versions after download. Yet, quick updates allow easy design changes and direct impact analysis.

Server-side A/B test via APIs

An API is a programming interface for connecting with applications to exchange data, allowing automatic campaign creation or variation from stored data.

A/B testing and conversion optimization

Conversion optimization and A/B testing are two ways for companies to increase profits. Their promise is a simple one: generate more revenues with the same amount of traffic. In light of high acquisition costs and complex traffic sources, why not start by getting the most out of your current traffic?

Surprisingly, average conversion rates for e-commerce sites continue to hover between 1% and 3%. Why? Because conversion is a complex mechanism that depends on a number of factors. This includes things like the quality of traffic generated, user experience, offer quality, the website’s reputation, as well as what the competition is doing. E-commerce professionals will naturally aim to minimize any negative impact the interplay of the above elements might have on consumers along the buyer journey.

A variety of methods exist to help them achieve this, including A/B testing, a discipline that uses data to help you make the best decisions. A/B testing is useful to establish a broader conversion optimization strategy, but it is by no means sufficient all on its own. An A/B testing solution lets you statistically validate certain hypotheses, but alone, it cannot give you a sophisticated understanding of user behavior.

However, understanding user behavior is certainly key to understanding problems with conversion. Therefore, it’s essential to enrich A/B testing with information provided by other means. This will allow you to gain a fuller understanding of your users, and crucially, help you come up with hypotheses to test. There are many sources of information you can use to gain this fuller picture:

• Web analytics data. Although this data does not explain user behavior, it may bring conversion problems to the fore (e.g. identifying shopping cart abandonment). It can also help you decide which pages to test first.
• Ergonomics evaluation. These analyses make it possible to inexpensively understand how a user experiences your website.
• User test. Though limited by sample size constraints, user testing can provide a myriad of information not otherwise available using quantitative methods.
• Heatmap and session recording. These methods offer visibility on the way that users interact with elements on a page or between pages.
• Client feedback. Companies collect large amounts of feedback from their clients (e.g. opinions listed on the site, questions for customer service). Their analysis can be completed by customer satisfaction surveys or live chats.

How to find A/B test ideas?

Your A/B tests must be complemented by additional information to identify conversion problems and offer an understanding of user behavior. This analysis phase is critical and must help you to create “strong” hypotheses. The disciplines mentioned above will help. A correctly formulated hypothesis is the first step towards a successful A/B testing program and must respect the following rules. Hypotheses must:

• be linked to a clearly discerned problem that has identifiable causes
• mention a possible solution to the problem
• indicate the expected result, which is directly linked to the KPI to be measured

For example, if the identified problem is a high abandon rate for a registration form that seems like it could be too long, a hypothesis might be: “Shortening the form by deleting optional fields will increase the number of contacts collected.”

What should you A/B test on your website?

What should you test on your site? This question comes up again and again because companies often don’t know how to explain their conversion rates, whether good or bad. If a company could be sure that their users were having trouble understanding their product, they wouldn’t bother testing the location or color of an add-to-cart button – this would be off-topic.

Instead, they would test various wordings of their customer benefits. Every situation is different. Rather than providing an exhaustive list of elements to test, we prefer to give you an A/B testing framework to identify these elements.

Below are some good places to start:

1. Titles and Headers

You can start by changing the title or content of your articles so that they draw people in. Regarding form, a change of color or font can also make a difference.

2. Call to Action

The call to action is a very important button. Color, copy, position, and type of words used (buy, add to cart, order, etc.) can have a decisive impact on your conversion rate.

3. Forms

It’s important to create a clear and concise form. You can try modifying a field title, removing optional fields, changing field placement, formatting using lines or columns, etc.

4. Navigation

You can test different page connections by offering multiple conversion options. For example, you can combine or separate payment and delivery information.

5. Landing Pages

Lead generation landing pages are vital for prompting user action. Split testing compares different page versions, assessing varied layouts or designs.

6. Images

Images are just as important as text. Play with the size, aesthetic, and location of your photos to see what resonates best with your audiences.

7. Page Structure

The structure of your pages, whether home page or category pages, should be particularly well-crafted. You can add a carousel, choose fixed images, change your banners, etc.

8. Algorithms

Use different algorithms to transform your visitors into customers or increase their cart: similar articles, most-search products, or products they’ll love.

9. Pricing

A/B testing on pricing can be delicate. This is because you cannot sell the same product or service for a different price. You’ll need a little ingenuity when testing your conversion rate.

10. Business Model

Think over our action plan to generate additional profits. For example, if you’re selling target merchandise, why not diversify by offering additional products or complementary services?

If you want more concrete ideas based on your unique users’ journeys on your website, be sure to check out our digital customer journey e-book and use case booklet to inspire you with A/B test success stories.

Tips and best practices for A/B testing

Below are several best practices that can help you avoid running into trouble. They are the result of the experiences, both good and bad, of our clients during their testing activity.

Ensure the data reliability for the A/B testing solution

Conduct at least one A/A test to ensure a random assignment of traffic to different versions. This is also an opportunity to compare the A/B testing solution indicators and those of your web analytics platform. This is done to verify that figures are in the ballpark, not to make them correspond exactly.

Conduct an acceptance test before starting

Do some results seem counter-intuitive? Was the test set up correctly and were the objectives correctly defined? In many cases, time dedicated to acceptance testing saves precious time which would be spent interpreting false results.

Test one variable at a time

This makes it possible to precisely isolate the impact of the variable. If the location of an action button and its label are modified simultaneously, it’s impossible to identify which change produced the observed impact.

Run only one test at a time

For the same reasons cited above, it’s advisable to conduct only one test at a time. The results will be difficult to interpret if two tests are conducted in parallel, especially if they’re on the same page.

Adapt the number of variations to the volume

If there’s a high number of variations for little traffic, the test will last a very long time before giving any interesting results. The lower the traffic allocated to the test, the less there should be different versions.

Wait to have statistical reliability before acting

So long as the test has not attained a statistical reliability of at least 95%, it’s not advisable to make any decisions. The probability that differences in results observed are due to chance and not to the modifications made is very high otherwise.

Let tests run long enough

Even if a test rapidly displays statistical reliability, it’s necessary to take into account the size of the sample and differences in behavior linked to the day of the week. It’s advisable to let a test run for at least a week (two ideally) and to have recorded at least 5,000 visitors and 75 conversions per version.

Know when to end a test

If a test takes too long to reach a reliability rate of 95%, it’s likely that the element tested doesn’t have any impact on the measured indicator. In this case, it’s pointless to continue the test, since this would unnecessarily monopolize a part of the traffic that could be used for another test.

Measure multiple indicators

It’s recommended to measure multiple objectives during the test. One primary objective is to help you decide on versions and secondary objectives to enrich the analysis of results. These indicators can include click rate, cart additional rate, conversion rate, average cart, and others.

Take note of marketing actions during a test

External variables can falsify the results of a test. Oftentimes, traffic acquisition campaigns attract a population of users with unusual behavior. It’s preferable to limit collateral effects by detecting these kinds of tests or campaigns.

Segment tests

In some cases, conducting a test on all of a site’s users is nonsensical. if a test aims to measure the impact of different formulations of customer advantages on a site’s registration rate, submitting the current database of registered users is ineffective. The test should instead target new visitors.

Choosing an A/B testing software

Choosing the best A/B testing tool is difficult.

We can only recommend you use AB Tasty. In addition to offering a full A/B testing solution, AB Tasty offers a suite of software to optimize your conversions. You can also personalize your website in terms of numerous targeting criteria and audience segmentation.

But, in order to be exhaustive, and also to provide you with as much valuable information as possible when it comes to choosing a vendor, here are a few articles to help you choose your A/B testing tool with software reviews:

• The 20 Most Recommended AB Testing Tools By Leading CRO: Experts
• Which A/B testing tools or multivariate testing software should you use for split testing?
• 18 Top A/B Testing Tools Reviewed by CRO Experts

Understanding A/B testing statistics

The test analysis phase is the most sensitive. The A/B testing solution must at least offer a reporting interface indicating the conversions saved by variation, the conversion rate, the percentage of improvement compared with the original, and the statistical reliability index saved for each variation. The most advanced ones narrow down the raw data, segmenting results by dimension (e.g. traffic source, geographical location of visitors, customer typology, etc.).

Before it is possible to analyze test results, the main difficulty involves obtaining a sufficient level of statistical confidence. A threshold of 95% is generally adopted. This means that the probability that result differences between variations are due to chance is very low. The time necessary to reach this threshold varies considerably according to site traffic for tested pages, the initial conversion rate for the measured objective, and the impact of modifications made. It can go from a few days to several weeks. For low-traffic sites, it is advisable to test a page with higher traffic. Before the threshold is reached, it is pointless to make any conclusions.

Furthermore, the statistical tests used to calculate the confidence level (such as the chi-square test) are based on a sample size close to infinity. Should the sample size be low, exercise caution when analyzing the results, even if the test indicates a reliability of more than 95%.

With a low sample size, it is possible that leaving the test active for a few more days will greatly modify the results. This is why it is advisable to have a sufficiently sized sample. There are scientific methods to calculate the size of this sample, but from a practical standpoint, it is advisable to have a sample of at least 5,000 visitors and 75 conversions saved per variation.

There are two types of statistical tests:

• Frequentist tests. The chi-square method, or Frequentist method, is objective. It allows for analysis of results only at the end of your test. The study is thus based on observation, with a reliability of 95%.
• Bayesian tests. The Bayesian method is deductive. By taking from the laws of probability, it lets you analyze results before the end of the test. Be sure, however, to correctly read the confidence interval. Check out our dedicated article to see all there is to know about the advantages of Bayesian statistics for A/B testing.

Lastly, although site traffic makes it possible to quickly obtain a sufficiently sized sample, it is recommended that you leave the test active for several days to take into account differences in behavior observed by weekday, or even by time of day. A minimum duration of one week is preferable, ideally two weeks. In some cases, this period can even be longer, particularly if the conversion concerns products for which the buying cycle requires time (complex products/services or B2B). As such, there is no standard duration for a test.

Other forms of A/B testing

A/B testing is not limited to modifications to your site’s pages. You can apply the concept to all your marketing activities, such as traffic acquisition via e-mail marketing campaigns, AdWords campaigns, Facebook Ads, and much more.

Resources for going further with A/B testing:

• A Beginner’s Guide to A/B Testing Your Email Campaigns
• 7 Tips for Implementing A/B Testing in Your Social Media Campaigns
• A Crash Course on A/B Testing Facebook Ad Campaigns
• How to Improve Your Click Rates with AdWords A/B Testing

The best resources on A/B testing and CRO

We recommend you read our very own blog on A/B testing, but other experts in international optimization also publish very pertinent articles on the subject of A/B testing and conversion more generally. Here is our selection to stay up to date with the world of CRO.

Blogs to bookmark:

• HubSpot Blogs
• Unbounce
• ConversionXL
• Bryan Eisenberg
• MarketingExperiments
• Conversion Rate Experts
• Wider Funnel
• The Conversion Scientist
• The Online Behavior Blog
• Moz
• Conversioner
• The Daily Egg
• WordStream

The post The Ultimate A/B Testing Guide appeared first on abtasty.

Continuous integration (CI) and continuous delivery (CD) are essential terms that are used in DevOps and encompass a set of practices that enable modern development teams to deliver code changes more frequently and quickly.

This is done by introducing automation when it comes to building, deploying, and releasing applications.

CI ensures that code changes are regularly tested and released after merging them into a shared repository (version control system) to ensure their stability while CD allows the quick and smooth delivery of these changes, where they can then be deployed into a live production environment.

Both CI and CD facilitate the efficient release of software to get new, high-quality products out into the market faster than ever before.

Continuous Integration

First, we will take a deeper look at the concept of continuous integration.

In modern software development, developers are usually simultaneously working on different features.

Without continuous integration, while developers attempt to merge their code changes made in separate branches, there’s a high chance that these changes may conflict with changes made by other developers, which may result in what is known as ‘merge conflict hell’. This usually happens when developers try to merge multiple feature branches at the same time.

• Keep reading: Pros and cons of different Git branching strategies

First step: Continuous Integration

Continuous integration (CI) allows developers to merge their code changes into a shared trunk. This is known as trunk-based development, which is a key enabler of continuous integration. With this method, developers can integrate their changes, or commit code in small increments, much more frequently, perhaps even several times a day. Each commit will trigger an automated build and test.

In other words, once these changes are merged, a series of automated tests will verify the build to detect any errors so that any bugs can be quickly fixed without disruption to the software.

Benefits of continuous integration

Because developers are integrating small changes frequently, this allows for faster deployment. It also allows for faster feedback so that developers can fix bugs almost immediately.

In trunk-based development, the master branch is the only long-lived branch while all other branches have a limited life span. This is unlike feature branching where developers make a copy of the codebase and then developers can work on their features separately. This usually leads to merge conflict, and in extreme cases merge hell, as developers are simultaneously merging numerous branches.

In this case, since developers are not integrating their changes frequently, they will not get quick feedback. Instead, they will not be able to see any new changes or release their own features until all the other developers’ changes are ready. Afterward, they will attempt to merge several long-lived branches that may contain significant rather than small changes (and hence major bugs) which could have been avoided had they merged to the trunk more often.

Continuous integration, then, results in higher quality releases as bugs can be detected and fixed quickly leading to increased efficiency and productivity since developers no longer have to wait for everyone else to be finished with their own changes.

Read more: which branching strategies are best suited to support continuous integration and continuous delivery processes

Next step: Continuous Delivery

Continuous delivery is a software release approach where teams release quality products frequently through a series of automated tests. For an efficient continuous delivery process, continuous deployment will also need to be built into your pipeline. More on that in a bit.

Therefore, the goal behind continuous delivery is to have software that is always ready for deployment to a production environment from the source repository. In other words, it makes sure that the code is always in a deployable state even as multiple developers are making daily changes through continuous integration. Though this is usually an automated process, the actual release into the production environment may be done manually by teams.

The benefits of continuous delivery are clear

Quicker time to market: Perhaps the most obvious benefit is the quicker time to market, as code is always ready to be deployed to users.

Constant feedback loop: A constant feedback loop allows teams to receive constant feedback on products from their end-users and then incorporate this feedback into the next release.

Enhances productivity: Teams no longer have to deal with tedious tasks, which can be performed by pipelines instead, allowing these teams to focus on building better products resulting in increased customer satisfaction.

Reduces risk: When changes are released more frequently in small increments, errors can be easily and quickly spotted and fixed, thereby reducing the typical risks associated with releases.

Continuous Integration vs Continuous Delivery vs Continuous Deployment

In software development, the process starts with continuous integration then continuous delivery builds on this process to release the changes that have been merged into the shared trunk during continuous integration. This means that continuous delivery enables the automated deployment of the code from development to the production stage.

Consequently, CI/CD represents the process of continuous development, testing, and delivery of new releases.

Often confused with continuous delivery, continuous deployment, in fact, goes one step further than continuous delivery.

During this stage, all changes are automatically released without any human intervention, whereas in continuous delivery, the changes are prepared for delivery but when they are released is determined by the team manually.

In other words, continuous delivery is a partly manual process while continuous deployment is all about automating the entire release process. If an automated test fails at this stage then the change will not be released but if the changes pass the test then they are automatically deployed.

Therefore, continuous deployment is an efficient means to accelerate the feedback loop with your end users. However, all these processes must follow each other, with continuous integration representing the foundation for the other two to take place.

Let’s sum up these three concepts:

• Continuous Integration
  • Short-lived branches that are merged into a shared trunk several times a day where a series of automated tests give feedback about the changes introduced.
• Continuous Delivery
  • After continuous integration, continuous delivery prepares the software for delivery; deployment to production is usually manual.
• Continuous Deployment
  • After CI and CD, changes are automatically deployed into production; fully automated process.

Continuous Testing

We’ve already mentioned that during CI/CD, software goes through a series of automated tests. A CI/CD process, thus, may include the following types of tests:

• Unit tests – to verify single parts of the application. This isolated part of the code base is referred to as a unit.
• Integration tests – since unit tests focus on an individual component and thus may be insufficient by itself, integration tests ensure that multiple components work together correctly and test how parts of the application work together as a whole.
• Functional tests – these tests make sure that the feature is working as it should.
• End-to-end tests – these tests simulate a user experience to ensure that real users have a smooth, bug-free experience.
• Acceptance tests – these verify the behavior of the software under significant load to ensure its stability and reliability

The testing pyramid below depicts the different types of tests you can run. In some cases, you may not need to run all these tests, especially if you’re just getting started.

Since unit tests are the easiest to implement, requiring fewer resources, then they generally make a good foundation for a fast build and get feedback much more quickly to developers.

Meanwhile, UI tests, which ensure that an application works correctly from a user perspective, are much slower and more complex to run. To sum up, not every CI/CD process will have all these tests but it’s worth remembering that continuous testing through automation is a key component of continuous integration and continuous delivery.

What is a CI/CD pipeline?

A CI/CD pipeline is a series of automated tests that follows a software through its delivery lifecycle by taking the source code through to production.

Thus, a typical pipeline builds the code, runs tests, and then deploys the new software into production in a true replica of the software development lifecycle.

Incorporating a CI/CD pipeline is an essential factor in maintaining a DevOps culture as it ensures the fast and efficient release of software with minimal risk. Building a CI/CD pipeline, thus, puts DevOps ideals into practice as it allows developers to commit their changes frequently to gain fast feedback leading to the emergence of a culture of collaboration, increased productivity, and transparency among teams. These fast feedback loops help fulfill the main goal behind building an efficient CI/CD pipeline, which is reducing the risk usually associated with new releases.

Thus, such a pipeline will include the following elements:

• Building, merging then testing the code-continuous integration
• Preparing the code for delivery- continuous delivery
• Deploying the code automatically- continuous deployment

Example of a typical CI/CD Pipeline

Thus, we can deduce that the stages of the CI/CD pipeline include:

1. Source: the CI/CD pipeline is triggered when a new code is committed to the repository.
2. Build: this is where developers put their new code changes and compile them so they may pass through the initial testing phase
3. Test: this is when the new code is tested through automated tests (for example, running unit tests through continuous integration). Depending on the size and complexity of the software, this step could last from seconds to hours. This stage will provide the feedback necessary for developers to fix any issues.
4. Deploy: this is when the code is deployed to a testing or staging environment to prepare it for final release i.e. continuous delivery. Usually, the build will automatically deploy once it passes through a series of automated tests.
5. Deploy to production: here the code is released into a live production environment to reach end-users, either manually or automatically

It is important to have such a pipeline within modern software development teams as such processes allow teams to direct their energy and time to writing code and improving products while more tedious tasks become automated.

This ties into the idea behind a true DevOps culture, which is reducing manual processes by introducing automation. Without CI/CD, integrating changes and then testing and deploying them would require separate processes which require significant time and effort.

Such automated processes, thus, ensure fewer errors and increased collaboration and efficiency through the software development life cycle. Indeed, implementing a CI/CD pipeline promotes an environment of collaboration as development, IT, and operations teams work together to deliver higher quality software more frequently.

CI/CD best practices

Among the best practices for an efficient CI/CD pipeline include the following:

1. Commit early and often

Keep in mind that the more often you commit changes, the more quickly you will receive feedback on the changes introduced into the trunk. This ensures enhanced collaboration and productivity among your team, reducing risk of errors and merge conflicts when integrating major changes instead of smaller ones more frequently.

The general rule is to commit at least once a day so that others within the team remain up-to-date on all changes taking place. Even if the features are not yet complete, any unfinished changes can be hidden from the end-user with the help of feature flags. More on this later.

2. Make it the only way to deploy to production

After building and implementing a reliable and fast pipeline, do not waste your efforts by bypassing the process.

In other words, you need to make sure that any changes introduced go through the pipeline and that this pipeline is the only way for code to be released into the production environment.

3. Continuously review your automation processes

In modern software development, new technologies are constantly evolving and new processes are always being introduced. Thus, it is important to continuously evaluate which processes and tests can be integrated into your pipeline to increase efficiency.

Also, keep in mind that not everything needs to be automated, at least not all at once. It’s sometimes preferable to start out manual to review what actually needs to be automated.

4. Speed up your pipeline

The whole point of designing a CI/CD pipeline is to speed things up to get software out faster than before through automation.

As such, a general rule of thumb is to run the fastest tests first before letting your pipeline tackle the more demanding, time-consuming tests. For example, executing unit tests first then integration followed by functional tests. That way, with these simpler, quicker tests you’ll be able to detect errors fast and fix them in the time it takes to run a more demanding test.

Thus, it is all about knowing how to prioritize the tests in your test suite.

5. Monitor your pipeline

You should also be on the lookout for any areas of improvement to understand whether there are stages within your pipeline that need to be optimized.

Monitor the metrics collected by your CI/CD tool to identify any issues that need to be addressed to ensure the reliability and performance of your infrastructure.

CI/CD tools to design an efficient pipeline

CI/CD can help teams automate the processes of development, testing and deployment. Some tools will focus on handling the continuous integration side of things while other tools will be more focused on continuous delivery.

In this section, we will highlight some of the common tools used to automate these processes as choosing the right tools is key in implementing an efficient CI/CD pipeline that is most suitable for your organization.

Some popular tools include:

1. Jenkins

This is one of the most well-known open-source tools for CI/CD. As an extensible automation server, it can be used as a CI server and can be turned into a continuous delivery hub.

2. CircleCI

A tool that offers flexible environments and thousands of pre-built integrations; CI/CD orchestration in the cloud or option to use self-hosted runners for added flexibility and control.

3. GitLab CI/CD

Streamline and automate your release process offering safe and flexible deployment options. GitLab also acts as the single source of truth for CI/CD and so you can build, test, deploy and monitor your code from a single application.

4. Travis CI

This is an open-source CI/CD platform to help developers quickly and easily develop, test and deploy code. This tool is quick to set up and supports over 30 languages offering great flexibility.

5. Semaphore

this tool supports a number of languages and platforms including iOS apps. Thus, it can be used to accelerate your releases and deploy across web, desktop and mobile apps.

6. Spinnaker

This is an open-source continuous delivery platform that works with a variety of cloud providers with the aim of offering fast, safe, and repeatable deployments.

CI/CD + feature flags: The magic formula for even faster deployments

As we’ve seen, continuous integration and continuous delivery are two essential practices to help you deliver quality software faster.

Implementing feature flags onto these processes provides further value and reduced risk when it comes to integrating new changes and then deploying them.

What are feature flags?

Feature flags are a software development tool whose purpose is to turn certain functionalities on or off to safely test in production by decoupling code deployment from feature release.

Let’s imagine this scenario: there are multiple developers working on several changes over various timelines. What happens when there are developers who have finished their changes while others have not yet finished? Before, this meant that developers would need to wait till everyone on the team was done with their changes before they could finally integrate and deploy the changes.

This may result in dissatisfied customers who would need to wait longer for new releases and in a disruption in the feedback loop as changes are not being merged frequently enough. With feature flags, developers can push their changes without waiting for other developers by simply turning off the portions of the code that are incomplete.

In other words, these incomplete changes can be hidden behind a feature flag while the finished changes can be released. Once they are finished, they can be turned on to become visible to end-users.

This is important as the whole aim of continuous integration is to integrate changes at least once a day and so feature flags help maintain the momentum of continuous integration.

Much in the same way, feature flags help to deliver on the promise of continuous delivery as developers can still proceed with a release while keeping the unfinished changes hidden behind a flag so they don’t affect the user experience.

This means faster time to market as well as the ability to gather continuous feedback to keep improving your products resulting in increased customer satisfaction.

Feature flags are also helpful as kill switches, which means if any bug made it past automated testing, it can be easily turned off or rolled back using feature flags. This way, you disable the feature with the bug and not the entire feature release.

The main takeaway here is that with feature flags, you can deliver releases faster and more safely to end-users.

Conclusion

To sum up, continuous integration and continuous delivery are essential staples in modern software development but with feature flags, they become even better and more powerful by offering significant value to your CI/CD pipeline and eventually your customers.

The post Continuous Integration and Delivery (CI/CD) Explained appeared first on abtasty.

The idea is to modify several elements simultaneously on the same page and then define which one, among all of the possible combinations, has the most impact on the indicators being tracked.

Multivariate testing (MVT) helps test associations of variables, which is not the case with successive A/B (or A/B/C, etc.) tests. Unlike classic A/B testing, multivariate testing allows you to understand which combination of elements works the best for your visitors and their specific needs. Sounds appealing, doesn’t it? Learn all you need to know in this multivariate testing guide and try any combination of your ideas.

What is a multivariate test?

During an A/B test, you may not modify more than one element at a time (for example, the wording of a button) in order to be able to measure the impact. If you modify both the button’s wording and color (for example, a blue “Buy” button vs. a red “Make the most of it” button) and notice an improvement, how will you know if it was the change in wording or color that contributed to this performance? The impact of one change could be negligible or they each could have had an equal impact.

Multivariate testing looks to provide the solution. You can change a title and an image at the same time. With multivariate tests, you test a hypothesis for which several variables are modified and determine which combination from among all possible solutions performed the best. If you create 3 different versions of 2 specific variables, you then have nine combinations in total (number of variants of the first variable X number of variants of the second).

More articles on multivariate testing:

• What is multivariate testing
• Multivariate testing (Wikipedia)

The history of multivariate testing

Testing methods like MVTs started back in the 1700’s. Scurvy was a major problem back then. Without knowing it, a British Royal Navy ship surgeon created the very first multivariate test in history, when he started giving sick crew members different solutions and treated them under different conditions: a high number of variables that, in the end, he could compare to see how these variables interacted with one another.

This multivariate testing led him to measure the effectiveness of each combination and find out the perfect treatment for scurvy: Citrus fruits, fresh air and lots of sleep.

What kind of websites are relevant for MVT?

Multivariate testing can benefit any website that has a purpose behind it. Because technically, the way of reaching a goal can always be improved. And so can any website. Some sites are aiming at lead generation, e-commerce sites are aiming at selling. Media sites, for example, could benefit from multivariate tests by improving editorial features, not a number of transactions.

Most websites do multivariate tests like:

• Testing the different combinations of text and color of a call-to-action button.
• Testing how text and visual elements on a webpage work together the most effective.

What types of multivariate tests are there?

There are 2 main methods for performing multivariate tests:

• “Full Factorial”: This is the method generally referred to when we talk about multivariate testing. With this method, all of the possible combinations of variables are designed and tested over equal parts of traffic. If you test 2 variants of one element and 3 of another, each of the 6 combinations will therefore receive 16.66% of your traffic.
• “Fractional Factorial”: as its name suggests, only a fraction of possible combinations is effectively tested on your traffic. The conversion rate of untested combinations is statistically deduced based on those actually tested. This method has the disadvantage of being less precise, but requires less traffic.

Why run multivariate tests?

There are three benefits to MVT:

• Avoid performing successive A/B tests and save time since multi variant testing can be seen as performing several A/B tests on the same page at the same time.
• Determine the impact of each variable in measured gains.
• Measure the impact of interactions between different elements presumed to be independent (for example, page title and illustration visual).

Limits of MVT

The first limit concerns the number of visitors needed for your multivariate test’s results to be significant. By multiplying the number of variables and versions tested in your multivariate test, you will quickly reach a large number of combinations. The sample assigned to each combination will be reduced proportionally.

Where, for a traditional A/B test, you would assign 50% of your traffic to the original version in the tool and the rest to the variant, you will only assign 5, 10, or 15% of your traffic to each combination in a multivariate test. In practice, this often translates into longer tests and an inability to reach the statistical significance needed to make a decision. This is especially true if you test pages deep within your site with low traffic, which is often the case in order tunnels or landing pages for your traffic acquisition campaigns.

The second limit is linked to the way the multivariate test is defined. In some cases, it’s the result of an admission of weakness: the users don’t know exactly what to test and think that by testing several things at once in a multivariate test, they will eventually find a solution they can take advantage of. We then often find small changes at work in these multivariate tests. A/B testing, on the other hand, requires great rigour and helps better identify test hypotheses, which generally lead to more creative tests, backed up by data, with better results.

The third limit is related to complexity. Conducting an A/B test is often easier than a multivariate test, especially when analysing the results. You don’t have to do complex mental gymnastics to try to understand why a particular element interacts positively with another in one case but not in another. Keeping the process simple and quick to perform helps maintain confidence and rapidly reiterate on optimization ideas.

Multivariate test ideas and hypotheses

The key to a successful MVT approach is a strong hypotheses for every element tested. This hypothesis should later be implemented in the different test modules and combinations of your MVT.

In order to create a strong MVT hypotheses you must:

• Clearly identify the question you are interested in answering with your MVT
• But note: A hypothesis is a statement, not a question. It is a very clear testable prediction about what will happen, if certain changes are being made to a website
• Make it clear and link your prediction to a problem that has identifiable causes
• Mention a possible solution

More articles about creating MVT test hypotheses:

• How to Find a Winning A/B Testing Hypothesis
• What type of hypothesis test for multivariate testing

Testing sample size

In order to test a MVT hypothesis, you need a larger sample size. Think of your multivariate test as several parallel A/B tests and increase the number of tested visitors accordingly.

In short: A good multivariate test requires enough website traffic to test multiple variations simultaneously. Therefore, the required sample size should never exceed your level of website traffic, unless you want to wait forever for your test results to be valid.

What is the ideal length for my MVT?

There is no universal answer to this question, but to give you an idea, here’s a simple calculation. Let’s say your website has 30,000 visitors a day and about 5% of the visitors convert and you want to test three variations, your test should run for 11 days. If your website has only 5,000 visitors a day and an average conversion rate of 2%, the required number of tested visitors per variation is 78.039, which will require your test to run 468 days.

If your average number of visitors is very low, multi variant testing may be inappropriate for you. Check out A/B testing instead! Additionally, here are six techniques for getting started with testing with low traffic.

Want to know where we took those numbers from? Check out our free sample size calculator!

Our online sample size calculator helps you calculate the minimum sample size as well as the duration of your tests based on your audience, your conversions and other information such as the Minimum Detectable Effect. This helps you increase your confidence level before making any decisions to improve your conversion rate.

Tips and best practices

Here are some tips that will help you set up your first multivariate tests and avoid common mistakes.

1. Choose a strong testing tool

Multivariate testing is often assumed to be very technical, so we suggest you go for a testing tool which keeps it simple for you to use. AB Tasty, for example, makes it easy for any marketer to jump into multivariate testing and helps you gain valuable customer insights for you to make the right decisions.

2. Form a good testing team

In a strong CRO team, different tasks should be clearly defined and distributed. For example: A “conversion manager“ could be lead the team and be in charge of QA. Another team member could be responsible for a first analysis of your visitors‘ behaviour and the status quo. A designer could take care of aesthetic modifications on your website and a technical profile (JS and CSS developer) should be responsible for the implementation of advanced tests. A data scientist could be in charge of evaluating your results in the end. One person can be in charge of different tasks at the same time if they have all skills necessary and sufficient time to meet all tasks.

3. Have a plan and clarify a timeline

It’s all about a good structure. Before you start creating your multivariate tests, clearly define what elements should be tested, why they are being tested and define a time frame. Knowing when the MVT results are needed will help you work more efficiently.

4. Set targets and define success and failure

Set annual targets that can be adjusted each year, for example the number of campaigns launched. Quantitative measurement is easy and precise. For your multivariate testing campaigns, clearly define what makes a test successful. Note that even a failed test is worth something because it helps you understand what doesn’t work and needs to be changed in the future.

5. Create a knowledge database

Keep track of the most important things you learned, save testing knowledge in a database and avoid recurring failure in the future. Once knowledge is acquired, it should be made available to everyone in your team. It will also make the onboarding process of new team members more efficient

6. Include all third parties necessary

Workplace loneliness is a real problem. Your CRO team should not be working in an isolated way. Let others know what your CRO department is working on and spread the word about testing results. Also, be open to new ideas and input from others outside your CRO team!

7. Identify and test different audience segments

In your multivariate testing campaigns you may determine returning visitors prefer a different website design than new visitors. Innovative tools like AB Tasty will recognize and automatically suggest visitor segmentation.

Articles on best practices worth a read:

• Principles and best practice in Multivariate website testing
• Multivariate Testing – Best Practices & Tools for MVT
• A/B, Split or Multivariate Test: How to Choose the Right One

Examples

Looking for ideas for your very own multivariate tests? Below you’ll find some links to a few examples and testing inspiration:

• AB Tasty Case Studies
• When To Do Multivariate Tests Instead of A/B/n Tests
• Kissmetrics

Multivariate testing softwares

Make sure you use a tool that actually addresses the problems you need to solve. When it comes to improving your website’s conversion rates, in a wide-range optimization process there should be much more involved than testing alone. Therefore, choose a tool that helps you fully understand user behavior. We recommend you use AB Tasty, as it offers you numerous sources of information you can use to gain this fuller picture:

• Heatmaps
• Session Recording
• Client feedback
• Numerous targeting criteria and audience segmentation

Other forms of testing

There’s much more than multivariate tests out there! Here’s a list of other testing scenarios:

• A/B/n Testing: Build and compare two or more variations of the same element
• Split testing: Redirect traffic to one or several URLs. A perfect fit for new pages hosted on your servers
• Multi-Page Testing: Display changes consistently across multiple pages (Funnel Testing)

Refer to this article to learn how to choose between this different testing methods.

Website optimization is not limited to testing. You can use advanced audience segmentation personalization to deliver tailored experiences across every customer touchpoint and much more.

The post Multivariate Testing – All you need to know about MVT​ appeared first on abtasty.

To turn your passive website visitors into active customers on your e-commerce site, you have to create a unique user experience for your buyers.   

According to Forbes, 72% of online consumers only engage with personalized messages and 62% of online shoppers have shared that not having personalized messages would most certainly deter them from making a purchase. 

In other words, customers want personalization.

A few years ago, receiving a personalized email or message from a brand seemed completely novel and revolutionary. But, today, it’s expected, wanted, and needed to keep up in the hyper-competitive digital world we live in. 

Thanks to evolving technology (and its ability to gather enormous quantities of deeply personal information about users, their preferences, and past shopping behaviors), modern consumers expect businesses to know them. And they expect to be known for more than just their likes and dislikes.

Customers associate personalization with positive experiences and good customer service. Personalization is important as it proves to customers that the business cares about their needs, not just the revenue they bring.

Carefully and ethically gathering user data also allows businesses to carefully tailor user experiences to meet the needs of their customers at the right time and in the right way. Not only does this create a better experience for your shopper, but it also increases revenue, conversion rates, and average order value for your e-commerce.

Let’s break down all that you need to know about personalization in the e-commerce world: What is personalization, what are the benefits of it, the privacy concerns and best practices for your website that will help you take your online presence to the next level.

What is personalization?

Personalization is the process of creating a unique experience for each visitor that spends time on your website or app. 

This technique is leveraged to offer relevant customer experiences according to their individual needs and desires at any given point during their user journey.

These efforts include tailoring specific landing pages and unique messages, presenting personalized recommendations, crafting individualized ads, and much more.

How do you gather data about customers to enhance their experience?

Each visitor’s preferences can be discovered by analyzing their behavior on your site, interactions with your business, and any other touchpoint with your brand.

 One simple way to gather basic data about each user is to encourage an account sign-up on your website. Here you will have access to a small database filled with relevant factors such as their name, birthday, contact information, demographics, and past purchases.

What’s the goal of e-commerce personalization?

The goal of personalization should be to drive repeat business, build brand loyalty, increase engagement, and ultimately improve the user experience. Companies should learn what truly matters to the customer in order to create a more meaningful and engaging experience. A few examples include:

• Curating product recommendations based on browsing behavior
• Reaching out to unknown or first-time visitors with modal pop-ups and how-to guides
• Sending personalized vouchers and emails on customers’ birthdays
• Including a warm welcome message when users land on your page and adapting it depending on whether they’re new or returning ones

Why is it important for e-commerce brands to personalize user experiences?

Many companies oversimplify personalization. 

There’s a big difference between providing a few personal touches and truly knowing what your customers want (and expect) your business to deliver. The companies that get personalization right are the ones that keep evolving their personalization efforts as they get to know the customer.

Personalization is the key to customer loyalty. When done well, personalization can drive repeat engagement over time because customers feel that the company knows (and makes an effort to provide) exactly what they are looking for. The more a user interacts with the brand, the more data the company can collect and utilize to create even better customer experiences.

But why is personalization important? Let’s find out:

Customers expect it

Customers have come to expect personalization. Online retailers like Amazon and entertainment platforms like Netflix have perfected the art of serving customers personalized recommendations and relevant offers. For many of us, personalization has become the norm. Customers have high expectations and aren’t comparing apples to apples anymore.

If your favorite pizza parlor app opens up with a gallery of your favorites ready to shop and a quick “Add to cart” button so that you can pop them into your cart right away, why can’t your favorite grocer do the same? Your customers are spoiled for choice and will always gravitate to the businesses that seem to know what they want (and are willing to give it to them).

Personalization drives exploration and engagement

The more we get to know other people (and vice versa), the warmer and happier we feel in their company. In the same way, customers gravitate toward the businesses that know them best. 

The more closely a brand can mirror the tastes and personality of a customer back to them, the more engaged the customer will become. This makes them the obvious choice for consumers looking for their next purchase. If you optimize personalization, each customer will enjoy a unique, impactful interaction with your brand every time they visit the site, driving repeat business and increasing your revenue.

Personalization can fuel conversions

At its core, personalization is about relevance. It uses visitors’ data and machine learning to understand consumer preferences and matches new and existing products to each one’s tastes. By constantly learning about their customers and improving their ability to present them with tailored offers and messages, companies create relevant recommendations that increase the likelihood of conversion. After all, the more relevant your recommendations, the more likely customers are to purchase.

What is the difference between personalization and customization?

It’s common for even seasoned marketers to confuse personalization and customization. While the two concepts often go hand-in-hand, there are subtle differences that can help you distinguish them.

Customization revolves around allowing customers to modify a product or service to suit their particular tastes. 

Think of how Vans lets customers customize their sneakers according to their unique preferences. Users can pick colors and styles from a template to create their own product, which the company produces.

In a nutshell, customization is controlled by the customer who takes the first step by stating their preferences upfront. The company then proceeds to provide them with the tools they can use to adjust an existing product, which is then produced or delivered according to their specifications.

Personalization, on the other hand, is a far more complex practice. It uses various methods to gain an understanding of what the (sometimes potential) customer wants, usually without them implicitly stating their preferences, so brands can market the right product to the right user at the right moment. Through a combination of artificial intelligence and segmentation, it works to identify and present relevant messages, offers, and content for each person.

All in all, in order to get the most out of both personalization and customization, it’s essential to understand how they both work and what each one can offer your brand.

How and where to implement personalization for e-commerce?

Personalization is a tricky subject and successful implementation has a number of difficult considerations. How will you implement personalized touches on your website? How will you test whether or not they are effective?

In most cases, brands should take advantage of both client- and server-side personalization since each approach has its own advantages and drawbacks.

While choosing a personalization strategy is all about what’s right for your business and what will enable you to deliver the right experience to your customers, some solutions offer the best of both worlds by connecting client-side insights with server-side data.

What is the difference between client-side and server-side personalization?

Client-side solutions operate in your customers’ browsers, while server-side tools operate on a server. Additionally, client-side solutions usually don’t require any coding skills, making it easier for marketing teams to implement. In contrast, server-side tools call for technical expertise and extensive coding to incorporate into your code and deploy.

These are the benefits of client-side personalization:

• Client-side solutions are often designed to reduce the need for custom coding. Marketing teams can create and manage personalizations and experiments without assistance.
• Client-side solutions are simple, efficient, and can be implemented quickly. They facilitate data collection and hypotheses can be tested in a matter of days.

These are the benefits of server-side personalization:

• Server-side personalization is best deployed when a solution is code-heavy and requires data from external sources such as a customer data platform.
• Server-side solutions require a more technical approach, which means your product or tech teams might have to take the lead.

Want to get started on optimizing your website? AB Tasty is a best-in-class optimization platform that helps you convert more customers by leveraging experimentation and personalization to create a richer digital experience – fast. Whether you want to make client-side or server-side solutions, AB Tasty can help you achieve the perfect digital experience with ease.

Which option is best?

The debate over which is best, client-side or server-side testing, often boils down to the fundamental question: Which is the best option for your company? While choosing the best approach will ultimately depend on your company’s needs, it’s entirely possible to have the best of both solutions.

Factbox: Definitions

The benefits of e-commerce personalization

There are many reasons why personalization is an essential pillar of every successful online marketing strategy. We’ve spoken about the advantages of using personalization in broad strokes, but there are several compelling personalization benefits, particularly when it comes to e-commerce companies. 

Let’s look at how personalization can benefit your business.

Offer relevant product recommendations

One of the primary benefits of personalization, particularly for e-commerce sites, is the ability to generate relevant product recommendations. According to a study by Accenture, 48% of consumers spend more when their experience is personalized. 

You can make accurate recommendations on items your customers may want to purchase next using the data you’ve already collected.

Gain a better understanding of customers (and customer loyalty)

Understanding what customers would want places a business in a better position to meet their expectations. 

You can analyze the information you’ve gathered to develop buyer personas that will help you segment your most common customer profiles and personalize your entire website to meet their needs. This can save a lot of time in the long run because you’ll focus on a handful of defined customer profiles instead of struggling to cover the individual preferences of thousands of unique customers.

Generate high-converting CTAs and landing pages

Personalization plays a big part in generating conversions because it makes your landing pages and calls to action (CTAs) more effective. 

Relevant offers increase the likelihood of customers engaging with an offer and making a purchase. For example, if you’ve just purchased a fancy toy for your children for Christmas and the store suggests adding a pack of batteries so they can play with it, you are far more likely to add them to your cart right then and there. 

Keep in mind that relevance is about matching the right customer with the right product at the right time. It pays off, too: McKinsey found that faster-growing companies that create personalized web experiences drive 40% more revenue from personalization than their slower-growing competitors.

Increased time on site

Have you ever fallen down a YouTube hole or spent hours scrolling through Buzzfeed completing quizzes? 

We tend to spend more time on things that interest us, which is why it’s in your interest to create a website that speaks to your customers and engages their attention. The more you know about your customer’s preferences, the more your site can be tailored to them, thus compelling visitors to explore your site.

Create more relevant product recommendations

What happens when you discover a new song you love? You’ll probably dig a little deeper to find more of that same artist’s work. 

Spotify uses machine learning to categorize music according to your tastes – it goes so far as to predict new genres you didn’t even know you liked! 

You can do the same thing on your site by collecting information and sharing product recommendations that are fresh and relevant to help clients uncover new products and even novel categories they’ll be delighted with.

Generate greater brand affinity

Did you know that 80% of your future profit will come from just 20% of your loyal customers?

Loyalty matters, but consumers have so many choices that loyalty is hard to come by. Personalization makes your customers feel happier and more appreciated, which increases their affinity with your brand. 

When you personalize your users’ experiences, customers immediately feel that you are investing in creating positive experiences for them, which will factor into their decision when it comes to choosing a place to shop. 

Customers care about brands that care about them. If you understand your users and tailor your website according to what you know about them, they’ll stick with you!

What does well-executed personalization look like?

A few businesses have perfected the art of personalization:

• Grammarly, an online grammar and spell-checking application, is on a mission to not only correct users’ writing but help them become better writers. They send their customers weekly reports with suggested areas for improvement and highlight how much customers’ writing has improved over time. This fosters a sense of achievement for their users and demonstrates that Grammarly really wants them to succeed.
• The Whole Foods app keeps a record of every item customers have purchased and makes it easier to re-order items based on past behavior. They also use this data to send personalized offers to clients when they are in the vicinity of a Whole Foods store.
• Nike uses their loyalty program to make shopping easier than ever. Loyalty program users can scan items to find out whether or not they are available in their size and preferred colors.

Privacy, data, and personalization

Collecting data is a sore spot for many consumers who worry that businesses will bombard them with unwanted communication or sell their data to third parties. However, this doesn’t mean that many customers can’t be swayed to hand over their information in exchange for something the company can offer. According to research from Merkle, 71% of users are willing to share personal information with brands if it means that they get a personalized experience.

While collecting user data, keep in mind that there is a fine balance between personalization and invasion of privacy.

Why is data collection important for fueling personalization?

You can use your customer data to determine exactly who your customers are, what they want, and their intentions. You can learn how, when, and why they’ve interacted with your website in the past, and you predict what they will do next. Your customer data allows you to meet their current and future needs, perfectly aligning your offering with their intent at exactly the right time.

Responsible data collection

Data has to be collected ethically, according to GDPR, CCPA, and other regulatory bodies. You need a mix of data types (behavioral, geographic, demographic, and psychographic), a good understanding of your digital traffic levels, and granular customer data. The latter can be collected unobtrusively through a customer data platform (CDP), which can be used to stitch together different sources of data to identify your most valuable customer segments.

Balance personalization with data privacy

Once you have the information, you’ll have to carefully balance personalization and data privacy. 

Think of it this way: If your friend suggests a pair of sneakers you might like, you’ll appreciate the thought, yet if a total stranger walks up to you and does the same, it will feel off-putting. 

If you’re looking for ways to personalize your customer experiences without getting too personal, check out our article about successfully leveraging data for 1:1 personalization.

Best practices for building your personalization strategy

Personalization can transform your business, your marketing campaigns, and your website. Personalization is a must for companies that want to succeed in the experience economy, so let’s take a closer look at how to build your website personalization strategy, as well as some personalization tips.

Set clear and measurable personalization goals

What do you want to achieve through personalization? More conversions? Higher website traffic through improved retargeting? Increased cart size? Once you know what you’d like to achieve and set clear goals to track your KPIs, you can measure and test the success of your personalization efforts.

Here are a few examples of goals you may want to pursue:

• Increasing engagement (more clicks, better open rate, etc.)
• More time spent on the site
• Increased revenue ( more transactions, increased cross-selling, etc.)
• Increased loyalty (a higher percentage of returning customers)

Set simple but specific goals

The key to personalization is to take your time and start small and simple. 

Creating a perfectly tailored digital experience according to the wants and needs of every customer in real-time would be ideal … but it doesn’t happen overnight. It takes careful planning and a good deal of time, resources, and data. We recommend you start small and simple and then scale from there.

Start with the broadest segments

Many companies make the mistake of over-segmentation. Taking a granular approach can be overwhelming and actually limit your understanding of your customer base because you lose sight of the big picture. Start off personalizing for your broader segments and refine your strategy from there.

Use no-code tools for faster turnarounds

Marketers often struggle to use the customer segmentation and personalization data at their disposal because they need developer resources to actually modify the content. A drag-and-drop editor with a pre-packaged widget library can help your marketing team implement changes faster so that your site (and customers) can benefit right away.

Use AI when it makes sense

AI is a useful tool for personalization, but it can’t be used as a black-box solution. For example, if you use natural language processing (NLP), make sure that it’s paired with the interests that you’ve selected. If you want to examine the visitor engagement score, make sure you are the one listing the criteria that will be used to determine engagement levels. AI should always be combined with your unique knowledge and existing data to get the best result.

Optimize your personalization campaigns with A/B testing

Once you’ve committed to running a personalization campaign and set your goals, you must know how to measure if it’s working or not. Here are a few guidelines to consider:

Test, test, and test again

You may already use A/B testing tools to optimize your website or your landing pages, but you can go even further and run A/B tests to identify which personalized version of your website works best for a given segment. Every test will reveal the most effective element to deploy so you can keep improving your site accordingly.

Ensure data reliability for your A/B testing solution

Conduct at least one A/A test to ensure traffic really is randomly assigned to different versions. If there is a dramatic skew between versions, something has gone wrong and will throw off your results.

Test one variable at a time

This is the golden rule of A/B testing! To isolate the impact of a certain variable, you’ll need to make sure this is the only one that changes between different tests.

Conduct one test at a time

When running several tests simultaneously, it can be hard to interpret the results and hone in on which elements have had the biggest impact. Focus on a single test before moving on to the next to ensure you’re measuring the impact of each modification correctly.

Adapt the number of variations to traffic volume

Bear in mind that the greater number of variations you test, the more traffic you’ll need. If you are unable to generate a large enough volume of traffic to test your assumptions, you should test the variation you believe will have the biggest impact first and slowly add variations over time.

Related: Be aware of sample ratio mismatch while A/B testing

Wait for statistical reliability before making definitive changes

Wait until the test attains statistical reliability of at least 95% before you hardcode any changes to your site. You don’t want to jump the gun and implement a modification that doesn’t really improve your existing website.

Measure multiple key performance indicators (KPIs)

Always set a primary objective and secondary objectives to measure your results. This can include your add-to-cart rate, the average cart value, click rates, etc.

Take note of marketing actions during a test

Keep in mind that large-scale marketing campaigns and other external variables can throw off your results. Make sure that you align with your marketing team and are fully aware of which campaigns are running in the background before interpreting the test results.

Examples of personalization done right

While there’s always room for experimentation and no right or wrong method when it comes to personalization, some companies seem to consistently hit the nail on the head with their campaigns and usage of personal preferences. Here are a few of our favorite website personalization examples:

Amazon

Amazon has a product database that contains millions of products, so finding what you are looking for isn’t easy. Luckily, the company uses personalization to determine your interests and instantly promote related content and suggestions in real time. This is a perfect example of how personalization can remove friction for customers by making it easier to find what they are looking for.

Spotify

Spotify uses numerous personalization methods. The latest, Blend, helps users merge their playlists with similar lists from their friends’ accounts. Blend provides match taste scores and shareable data stories. Spotify Blend shows that personalization leads to viral, shareable, and social experiences that cross-promote the platform.

Stitch Fix

Stitch Fix collects information users provide about their size, personal style, and shape, and then selects outfits based on each one’s unique taste and personality. Stitch Fix uses sophisticated AI to categorize style and clearly demonstrates how personalization and machine learning can work together to deliver as good of an experience (if not better) as a personal stylist.

LinkedIn

All social media sites are great at personalization because of the wealth of data users willingly submit. LinkedIn is a shining example of a company that knows how to balance privacy concerns with utility. By gently providing personalized suggestions and links based on users’ current connections and searches, LinkedIn makes it easy to network, job-hunt, or catch up with former co-workers.

Frequently asked questions about personalization

Do you have more questions about personalization? Don’t worry – we have you covered!

The post Everything You Need to Know about Personalization appeared first on abtasty.

For example, we often hear the phrase ‘culture of DevOps’ but we also see it being referred to as an approach or methodology organizations adopt in their everyday practices. So which one is it or can it mean several things at once?

In this guide, we want to investigate DevOps to understand how it came about and the issues this term is trying to address for modern tech teams and why more and more organizations today are incorporating it as the basis of their workflow.

In this guide, we will be looking to answer the following questions:

• What is DevOps?
• How does DevOps work?
• Where did this term come from?
• What is the difference between DevOps and Agile?
• What was life like before DevOps and what pain points is it looking to solve?
• What are the benefits?
• What type of skills are needed?

DevOps- Bringing developers and IT operations together

As you may have guessed, the term DevOps comes from a combination of ‘development’ and ‘operations’.

In that sense, we may consider DevOps as a philosophy that aims to ensure and promote better communication and collaboration between these teams. By combining development and operations processes allows organizations to keep up with the speed of development.

DevOps is not a technology per se. Instead, it can be thought of as an umbrella term that includes the processes, approach and culture that is meant to shorten the software development lifecycle by increasing the speed at which organizations release new software applications.

Read more: How to build a DevOps culture

Therefore, it encompasses a set of practices and tools that increases an organization’s ability to quickly deliver new software as well as improving and optimizing products otherwise not possible with traditional software development practices.

How does DevOps work?

We may deduce from the above that the main underlying premise of the term DevOps is releasing software frequently in small increments to achieve fast feedback loops that would allow teams to continuously improve and optimize software accordingly.

This enables teams to deliver software at greater speed to allow them to compete in a fast-changing market and to meet the ever-evolving demands of consumers.

Through these feedback loops, teams can quickly incorporate consumer feedback so that they’re confident that they are only releasing high quality products that their consumers actually need.

Therefore, you can best visualize the DevOps process as an infinite loop or a continuous cycle of building, testing, deploying and monitoring software releases then the loop resets through continuous feedback that is received as the software moves through these different stages, as illustrated by the image below.

Below are some of the principles that form the fundamentals of adopting a DevOps approach:

Continuous integration– software development practices in which developers commit to a shared repository frequently.

Continuous delivery– it naturally follows that as code is continuously integrated, code is always in deployable state ready to be released to end-users at regular intervals.

Continuous testing– this strategy involves testing at every stage of the software development life cycle in order to obtain valuable feedback to validate the quality of the software and fix any issues as they arise.

Continuous monitoring– this involve continuous monitoring of the code and the underlying structure that supports it in order to quickly identify any failures and fix them in real time

Cross-team collaboration– as we’ve seen, one of the main goals of DevOps is to facilitate collaboration and sharing of feedback to maintain an efficient DevOps pipeline.

Automation– this is the cornerstone of any DevOps strategy as automation is vital to increase velocity and produce more consistent and reliable software. In other words, implementing DevOps involves utilization of technology, mainly automation tools, to automate repetitive processes to allow teams to focus on higher-level work.

In sum, one of the integral practices of DevOps is releasing frequently in small batches to allow room for constant innovation.

This means that teams operating under the DevOps model deploy releases and updates much faster than under the traditional software development model.

Once upon a time…

The term DevOps began to take shape in 2009 during the first DevOpsDays event in Belgium by Patrick Debois, dubbed by some as the ‘father of the DevOps movement’, who had become frustrated with the separation between IT and Development teams after taking on a project with the government ministry doing a large data center migration.   

DevOpsDays has now become a worldwide series of technical conferences that brings together developers and operations professionals to cover a range of topics within modern software development.

The event was inspired by a talk Debois had attended entitled “10+ Deploys per Day: Dev and Ops Cooperation at Flickr” by two Flickr employees, John Allspaw and Paul Hammond. During this presentation, Allspaw and Hammond described how they incorporated continuous integration practices to make frequent deployments possible through improved collaboration between developers and operations.

After the first DevOpsDays event took place, started by Debois himself, DevOps started to gain traction and popularity.

Therefore, the term mainly grew out of frustration from the silos between these two teams and the conflicts that resulted due to this disconnect. This video further explains the history behind DevOps and the image below also gives a great visualization of the history of DevOps and how it evolved over time.

To sum up, DevOps came about as a way to reduce the disconnect between development and operations teams to enhance their productivity and continuously deliver high quality products.

DevOps vs Agile

We often hear DevOps and Agile come up in modern software development. Are they actually one and the same or is there a difference between these two terms after all? If you’re thinking the former then you would be correct.

To truly understand DevOps and Agile and how they may differ, it’s important to highlight their overall philosophy.

DevOps, as already mentioned, aims to enhance collaboration between development and operations teams whereas Agile is an iterative approach that focuses on collaboration, customer feedback and small, frequent releases.

Agile was introduced as a way to replace the more traditional Waterfall methodology, which was considered too rigid and inflexible to fulfil the expectations of a fast-paced world of continuous technological development and innovation.

Therefore, the focus of Agile is about adopting and implementing Agile frameworks such as Kanban and Scrum that help to break down projects into manageable phases or sprints. The goal is to bridge the gap between the development team and end-users to realign focus onto the user.

Both of these concepts, though, provide the framework necessary to achieve faster software delivery. Hence, organizations would do well to make use of both of those methodologies as they complement each other.

Think of Agile as a collection of methodologies to help your team organize their work and deliver releases that can respond to fast-changing consumer needs and DevOps as the foundation and culture that drives delivery of faster and more reliable software.

The following represents the four fundamental values that make up Agile software development, extracted from the Agile manifesto established in 2001 that outlines the central values and principles of this concept:

• Individuals and interactions
• Working software 
• Customer collaboration
• Responding to change

A key takeaway from this section is there is a great deal of overlap between these two concepts as the goals of Agile and DevOps are the same, which is improving the speed and quality of software releases and the software development process in general.

Therefore, it makes little sense to speak of these concepts in isolation as DevOps incorporates Agile principles and practices and takes these further to apply them to operations processes, where the Agile methodology falls short. 

Life before DevOps

Before DevOps, organizations relied more on the traditional Waterfall model but with increasing complexity and speed of today’s digital world, this model was no longer capable of meeting customer demands that are constantly changing and evolving. Therefore, DevOps seeks to address issues and challenges which are often associated with traditional software development practices and techniques. The table below serves to compare how life was before DevOps and how organizations have transformed with DevOps implementation:

Benefits

Software usually goes through a series of stages or phases to produce high quality software through the software development life cycle (SDLC). The phases incorporated depend on the methodology used but the basic principles of SDLC are more or less the same. For more information about the software life cycle, you can read more in our article about the different stages of the SDLC in conventional and Agile methodologies. Based on the above, at the beginning of any software development process, teams and organizations must decide what methodology they will adopt to ensure high quality products. Waterfall and Agile are two of the most popular methods though Agile is rapidly gaining traction over Waterfall.

DevOps skills

As more and more organizations start to recognize the value of DevOps, primarily the ability to drive secure and fast software delivery resulting in reduced time-to-market and increased customer satisfaction, small, medium and large organizations alike are paving their own way to embracing such a culture. 

This will require certain DevOps expertise to make a smooth transition.

One such important role within DevOps is a DevOps Engineer. This is someone who possesses deep knowledge of the software development life cycle and the automation tools for developing CI/CD pipelines.

It goes without saying that a DevOps Engineer works closely across various teams, mainly software developers and IT teams to facilitate code releases.

Read more: The required skill set of a DevOps Engineer and some common tools they work with.

We’ve mentioned that DevOps involves being flexible and moving away from rigid work flows to continuously improve and optimize products to allow for faster innovation. 

Consequently, the key to succeeding in a DevOps role is to be flexible and willing to learn new skills to achieve high performance across an entire organization, especially as the required knowledge will depend on the kind of company the DevOps engineer is working for.

However, one thing is clear, which is that this role is in high demand and is, in fact, among the best paying technology jobs right now.

Takeaway: DevOps at the forefront of digital transformation

To sustain competitive advantage, organizations must adopt the culture, tools and skills necessary to keep up with digital transformation.

You can’t just adopt a set of tools and believe you’re well into your DevOps journey. DevOps involves a cultural change that must occur within your organization in order to implement DevOps successfully.

Devops is not merely a set of tools or technology you adopt but a cultural shift and a tactical approach that empowers teams to collaborate more effectively using technology in order to streamline software delivery to meet or exceed their performance goals.

Therefore, we can conclude that the ultimate goal of DevOps is to:

• Break down communication barriers through the creation of cross-functional teams
• Enhance operational efficiency
• Release high quality products faster

Stay tuned for a future post to explain how you can successfully adopt a culture of DevOps within your own organization.

One thing seems to be clear: DevOps is here to stay and for good reason. 

The post DevOps Guide appeared first on abtasty.

The process must be handled carefully. Afterall, as a product manager, you are delivering something to customers that you have painstakingly researched and know that they need. However, you cannot just release a product and hope for the best. The planning of the release is one of the most important elements to ensure a successful launch and that you’re delivering the very best of the best to your end-users.

The following guide will outline the release process from planning to execution through various deployment strategies based on your objectives and use-cases.

What is release management?

Release management is the system you have in place that allows you to control the software development lifecycle, all the way from planning to testing then release.

It helps your team stay on track and work towards a common goal making sure that every member of the team is aligned.

In simple terms, the release management process ensures that your product is ready for production and release to end-users.

When managed and implemented correctly, it results in high quality releases and enhanced productivity allowing you to release more frequently and with less risk.

At the heart of an effective Agile roadmap is feature release management, which breaks down development into sprints and is more user-centered.

Keep reading: the most important elements to include in your Agile release planning.

Release management phases

The release management process is quite similar to the main steps of the software development process, that is the software development life cycle (SDLC), which consists of:

1. Plan
2. Build
3. Test
4. Deploy
5. Review

At the beginning, the product manager will plan for the new functionality by identifying a consumer pain point and how this feature will relieve it. They will answer questions such as what this release will do, how it will be used and who will use it.

They will then develop a strategy accordingly and start working on the product roadmap. The feature will then be built and managed through the various development cycles.

The manager will then need to decide on an appropriate release strategy depending on the use case, goals and budget.

The role of a product manager, therefore, revolves around managing product releases. In this guide, we’ll go into what we mean by releases and the type of release or deployment strategies that enable product managers to experiment and test their products to ensure optimal customer satisfaction.

First, in the planning stage, the product manager will gather feedback for ideas to start planning for a new feature. The product manager will determine a customer pain point and how it can be solved with this new feature.

The user persona and value proposition will be determined at this stage. The product manager will set up KPIs to assess the product’s success after release. He will then devise a plan on what it is needed to deliver this new feature and the expected release date, according to the following:

• Devise the team needed to build the feature.
• Define requirements and end-goals.
• Build a roadmap.

From product strategy to product delivery: Planning a release

Once a product strategy has been put in place, building a release management process is the next logical step. Such a process requires carefully planning the scope and phases of work and setting clear expectations and requirements.

A release plan will help each member of the team know what they need to do and the timeline of the work. It will lay out the priorities and the flow of the project. Other points to include in a release plan include:

• The product’s goal and roadmap
• New features to add
• Phases of work
• Estimate on deliverables

The product roadmap is an essential component to ensure success of your products. It will outline the strategy, vision and goals and will serve as a guide in order for the different teams involved to execute this strategy and keep everyone aligned and on the right track.

Therefore, a product manager is responsible for creating and defining a prioritized list of features to be developed over time according to the organization’s goals so it is usually formed in partnership with senior management and other stakeholders.

Developing an Agile roadmap will be far more flexible than a traditional one, allowing for shorter time frames and adjustments to accommodate changing priorities.

In Agile methodology, the product manager would be expected to release new features on a regular basis, whether quarterly, monthly or even weekly. Because an Agile methodology is user-centered, this means success will be measured based on user feedback, engagement, conversion, etc.

Once the release plan is in place, it is then time to plan for the actual release. There are many release or deployment strategies that a product manager should take into consideration depending on the objectives and the business needs as well as taking into account the impact on end-users.

Which teams are involved in the release process?

As already mentioned, a successful release will require cross-departmental support. Thus, the product team will need to coordinate with other teams, including:

• Product teams- this one is a given but for further clarification, this team may be small or big depending on the organization and consists of several roles with different responsibilities including product owners and product managers. The lines between these two roles often blur but still there are some differences between a product manager and product owner.
• Development- the product team will work closely with developers to draw out the requirements of the release and set the deadlines for each stage of the release. Once the requirements have been defined, the developers will create the documentation and design the functionality.
• Sales- this team will require training and to be provided with materials and documentation to be prepared to sell the product.
• Marketing- they will help develop effective communication around the release to educate and persuade potential customers to try the product.

There are also other more specific, technical roles within release management, besides product managers, which include:

• Release managers– they have a major role in release management, where they manage all aspects of the software delivery lifecycle.
• DevOps engineers– these engineers oversee the release of new code.
• Site reliability engineers– they create a bridge between IT and operations, create scalable and reliable software systems and remain on the lookout for any possible breakdowns in the systems in place.

Choosing a deployment strategy

There are a number of ways to deploy new features to production. Choosing the right strategy is essential and will depend on numerous factors including impact on end-users and the use-case.

There are what are referred to as ‘big bang’ deployments, where the whole application is deployed at once requiring longer development and completion cycles. However, such deployments are no longer practical in modern software development because of the high degree of risks associated such as outages and almost impossible rollbacks.

Therefore, in this guide, we have chosen to focus on four strategies that are within the phased deployments strategies and that are commonly used by organizations and modern teams.

Blue/green deployment

Blue/green deployment is a technique that uses two production environments, blue and green, and traffic is directed to one of these environments once changes are ready.

Therefore, this type of deployment has two production environments but only is active while the other remains idle, which remains as a backup in case any issues arise in the active environment, allowing you to quickly revert to the idle environment while the issue is fixed.

There are many advantages to this practice including rapid release and simple rollbacks. However, blue/green deployment requires a lot of resources to maintain two production environments, especially for product managers who will require special software to track how their features are performing.

Read more on our blog about the pros and cons of blue/green deployment to determine whether it is a viable option for your team.

Canary deployment

Canary deployment is a deployment strategy that decreases the risk of new releases by testing on a subset of users before rolling out to everyone else.

This means that you can choose a percentage of users to test on based on certain attributes and if no problems are discovered then you call gradually roll out to the rest of your users.

Canary deployment differs from blue/green deployment in that the former allows for more targeted releases, which is beneficial if the release is particularly risky. With blue/green deployment, it’s more of an ‘all or nothing’ strategy.

Keep reading: everything you need to know about canary deployments.

Ring deployment

Ring deployment allows you to gradually introduce new features to different user groups to minimize impact. These user groups are represented as an expanding series of rings, hence the name.

Thus, this technique starts with a small group of users, such as internal users, until you gradually start to feel confident enough to target the release to all your users.

A/B testing

A/B testing a common release strategy used by product managers to test features in production with less risk. It is a strategy especially used for experimentation to observe and test which variation works best with users.

Therefore, A/B tests are used in feature testing where product managers can test out their ideas on their target audience by collecting data on feature performance in order to make informed decisions.

In this test, two versions of a feature or system compete against each other and based on preset KPIs, the version that resulted in more conversions or revenue, etc would be the winning variation and all users would then be directed to it.

This method is particularly useful for product managers as it allows them to test out their ideas on their target customers but with less risk. It allows them to keep testing different variations until they’ve found the perfect fit based on live users’ feedback.

Release gradually through phased rollouts

As we’ve seen, deployments could be ‘all or nothing’ or it could be done gradually in the form of progressive rollouts such as the case of canary deployments and A/B tests. Find out how progressive rollouts can help you deliver rapid releases and why they are an essential part of an Agile methodology in our progressive rollout guide.

As we’ve already mentioned, release management guides a product from development to testing and finally to production.

Implementing feature flags in your release management process could help in driving even faster and higher quality releases with even less risk. Through the use of feature flags, all new changes can be deployed and accessed by your end-users while any unfinished changes can remain hidden and wrapped in a flag until it is complete.

With feature flags, you decide the when and to who, giving you complete control.

All the deployment strategies mentioned above can incorporate the use of feature flags when releasing new features, whether through percentage-based releases in the form of canary deployments or releasing or hiding certain features in a ring deployment.

Just as feature flags help in faster deployments, they are also crucial if you want to do rapid rollbacks in case any bugs are discovered that need to be fixed before you release them to everyone else. Such rapid rollbacks give product managers increased control over the deployment process giving them the freedom to test their new features with greater confidence, allowing them to gather valuable feedback to improve their product. Find out when you will need to carry out a feature rollback.

Choosing the right release strategy

As we’ve seen, there are multiple ways to release a new version of an application or new features. Whatever strategy you use will depend on your objectives and budget.

Thus, the release strategy chosen will depend on many factors including:

• Your customer needs
  The type of feature you’re aiming to launch
• The goal or metrics you’re looking to achieve with this release
• Resources at hand

So, for example, if you have the sufficient resources to maintain two environments, as in the case of blue/green deployments, this will offer you ample benefits with little downtime.

However, if you’re limited in the budget and resources you can utilize and your release is more configuration-driven then your best bet is a canary deployment.

It will also largely depend on your business goals. On the one hand, if you want to roll out changes with little to no downtime then blue/green deployment is a viable option. On the other hand, if you want to test your features on a small subset of users then you could consider phased rollouts using feature flags, for example.

Post-release

After release, it is important to keep monitoring releases for any issues that may come up so that they may be fixed immediately.

Third-party services such as AB Tasty’s flagging functionality offer a sophisticated control panel that allows for teams other than development teams to monitor the flags that are being used and get access to reporting to determine if the release has met the KPIs set at the beginning of the experiment.

Such advanced features in a feature flagging platform give product managers more access control over releases without depending on developers.

Feedback is a central pillar to an Agile methodology and the idea of feedback loop, the process of continuously collecting feedback from customers and improving the product accordingly, ensures that the product you’re creating is delivering real value to your customers.

Why continuous delivery matters to product teams

As already mentioned, frequent releases are at the heart of an Agile methodology. Thus, maintaining an environment of continuous delivery is important.

This essentially means that teams release products in shorter batches and reduced time frames to get products out to consumers faster, which in turns allows them to gather valuable feedback to continuously improve their products.

Similarly, a DevOps culture is commonly associated with continuous delivery as both concepts aim to enhance collaboration between developers and operations team and both use automated processes in the software development process to achieve faster and more frequent releases.

Additionally, continuous deployment, which combines continuous integration and continuous delivery, is just as essential to product teams as the idea behind it is to make software readily available without human intervention. This means that it relies on a set of automated processes instead.

Find out how continuous deployment may be added to Agile product management.

Continuous delivery is vital to product teams for the following reasons:

• It allows them to release their products to market faster, which is important in order to respond to the demands of a fast-paced, ever-changing market.
• When teams release to market more frequently, they are able to achieve the aforementioned feedback loop at a faster pace rather than waiting to make a full deployment when it’s more riskier to release rather than releasing in smaller batches.
• This feedback from target customers allows them to improve their products, ultimately resulting in higher quality releases that meet customer requirements exactly.

Final words…

As a product manager, you’ve taken careful steps to building the right product so you also need to be just as particular in how you go about releasing it.

To ensure that you’re delivering a top-notch release, you need to be testing in production on real live users as it is an essential best practice for any product manager.

Testing in production allows you to gather valuable feedback from the users who matter the most, allowing them to improve the product according to their needs. However, it is not only to improve but also gather ideas for future releases for features you might not have considered before.

Learn more about why testing in production is an important component of a product manager’s role nowadays by referring to this article as well as a more lighthearted approach to the importance of testing in production explained through memes.

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What is split testing?

Split testing is a solution for carrying out A/B tests.

By comparing several versions of your web pages, such as your landing pages or homepages, a split test helps you identify which one has a better conversion rate for your visitors.

When the split test is launched, your pages’ traffic is randomly spread over the different versions of your pages. Each one’s performance is tracked and analyzed by the split testing software to identify the version that converts the best, with the highest significance. Split testing determines the version on which the sample converted the best.

Find out more about split testing here:

• The Digital Marketing Glossary
• What is Split Testing?

The difference between conducting a split test and an A/B test

Contrary to A/B testing, which works directly from our AB Tasty software, split url testing hosts the different versions on distinct URLs. Split testing therefore requires intervention from your technical team.

And where A/B testing uses changes that are close to the original, split testing is used where the new version’s page design and content are significantly different from the original.
Split testing is very useful in making major design changes in your website. For example, if you’re thinking about completely changing your home page’s design, split testing is the ideal tool to help you optimize conversions.

But in testing a radically different version of your website, you will obviously make fundamental changes that require back-end operations that your marketing teams cannot make. Split testing, which requires technical teams to intervene, remains the best testing solution when changes are made on the back-end.

A/B, Split or Multivariate Test: How to Choose the Right One?

Why should I run split tests on my website?

To understand why your visitors aren’t converting, it is important to ask yourself which content is keeping your visitors from converting. Your intuition isn’t enough; what matters is the product’s effect on your visitors. By performing a split test, you put your visitors at the heart of your decision-making process. On the one hand, the results obtained from split testing will help you know which page version they interact with the most, and, on the other hand, which information and design transforms them into repeat customers. Split tests promise qualitative feedback on user experience to help you identify barriers and optimise your conversion tunnel. (How to Use Qualitative Data to Plan your Next A/B Test). From what we have learned so far, it is safe to conclude that split tests can be run on every website. With the right a/b testing tools, it is neither complicated nor a high investment. And that’s exactly why you should hop on the split testing train, too! The benefits are:

• It’s perfect for low-traffic sites. One the one hand, if your website has low traffic, split url testing is simply the only method you can use (other testing scenarios like multivariate testing require a certain amount of daily traffic). On the other hand, it makes the analysis of results more than easy. Simply create two variations of a website or landing page and split the traffic accordingly. Afterwards, see which one generated more conversions.
• More money. Let’s put it like it is: After all, split testing will help you find the best version of your website possible. The better your conversion rates are optimized, the higher your average order value and the the higher your turnover will be.

 How to choose the best split testing tool

As improving your site’s conversion rates is a process that does not only involve testing, but also analyzing data, forming strong strong hypotheses (find out more about the steps later), we can only recommend you use a tool that includes all necessary features , like AB Tasty. It offers:

• Data Insights: Session recording, heatmaps and NPS scores to understand the customer journey.
• Split testing & MVT: Easy-to-use visual editor to create experiments quickly.
• Marketing Campaigns: Expandable template library to get back in control of your website.
• Personalized Experiences: Advanced personalization capabilities to reach the right audience.
• Plus it’s easy to use. AB Tasty has designed a WYSIWYG visual editor that allows you to make simple changes by drag & drop and just a few clicks.

Getting started with your very own split test

No matter the goal, before you create a testing campaign, you have to know what you can test and which testing scenarios are ideal for split tests. In general, we suggest split tests whenever there are major changes being made on a site.

Relaunch of a full site or landing page
Split test will guarantee you a better performance and less code to implement, because you can build a new page in your CMS. No additional scripts necessary. Especially useful for pages where you are testing drastic changes – like a complete overhaul of your existing homepage, product detail page or different landing pages.

Back-end heavy tests
We suggest split tests when you want to test back-end heavy tests, like different check out processes that use technically complex page logics. Split testing also makes it easier, as you can build the new checkout process in your CMS and your backend and then split the traffic using AB Tasty. The successful variation can then be deployed almost immediately and there is no additional effort for your developers, as you simply set the new page or checkout live. Split testing works via a simple, lean JavaScript redirect that does not affect your existing SEO.

Page copy length
In general, short-form copy is said to be better than never ending paragraphs. First impressions are made within seconds. However, you will never fully know until you run a split test on your website or landing page yourself. In the end it’s all about data driven decisions here, right?

Page copy position
Page copy is not just where you explain a product or a service. Page copy is also call-to-actions, client testimonials and much more. Finding the perfect position is crucial. The most common rule-of-thumb is the “above the fold rule”, which suggests you put the most important information on the top of your page. It works for most websites, but that doesn’t guarantee it will work for your individual business goals, too. Again: Put it to the test and let split testing decide.

Sign up workflows
When you are testing completely different funnel processes or sign up workflows against each other, again, make sure you use a split test. This will require less code to be implemented on a single page and will improve the overall performance while running those tests.

Crucial steps of a strong split testing scenario

Step 1: Study your website data

Data, data, data! A strong split testing campaign starts your website data. Use a website analytics tool and find weak spots in your conversion funnel, bounces as well as your top landing pages. This will help you determine the right approach and prioritize testing ideas.

Step 2: Form a strong hypothesis

A split test should never be based on a personal opinion or feeling. For your split url testing campaign, make an assumption on which to base the test so that in the end you will know why you are seeing the results you are seeing. A strong testing hypothesis also describes a specific goal.

Here is an example to help you form the perfect hypothesis:

First of all, ask a question and think of a proper answer. “Why are users not signing up for my newsletter?” – “No one is signing up for my newsletter because the form is poorly placed (hidden) on my website.”
Then, propose a solution: “More users will sign up for my newsletter if I place the sign up form on the home page of my website.”
Last but not least, cleary define your success metrics: “I will consider placing the newsletter sign up form on the front page of my website a success, when I record a 10% increase in sign-ups within the next month.”

Step 3: Test your hypothesis

Now it’s up to you to create a variation and split test it against the original page. In order to find out which one is more effective, use your insights from step 1 as a baseline for measuring your results.

To calculate the best test duration, use our a/b test calculator here.

Step 4: Make data-driven decisions

Which variation of your website delivered the highest conversion rate? Advanced split testing tools like AB Tasty will offer baysian a/b testing that helps you weigh up each variant’s impact on the conversion rate.

With a strong testing hypothesis, there will be a clear winner. If there isn’t and the results turn out to be inconclusive, we suggest you go back to step 2 and rethink your hypotheses or have our conversion experts help you out with some advice.

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