Random changes without a well-thought-out plan often lead to neutral or even negative results, leaving you frustrated and wondering if your efforts were wasted. 

Success in A/B testing doesn’t have to be defined solely by immediate KPI improvements. Instead, by shifting your focus from short-term gains to long-term learnings, you can turn every test into a powerful tool for driving sustained business growth. 

This guest blog was written by Trevor Aneson, Vice President Customer Experience at 85Sixty.com, a leading digital agency specializing in data-driven marketing solutions, e-commerce optimization, and customer experience enhancement. In this blog, we’ll show you how to design A/B tests that consistently deliver value by uncovering the deeper insights that fuel continuous improvement. 

Rethinking A/B Testing: It’s Not Just About the Outcome 

Many people believe that an A/B test must directly improve core e-commerce KPIs like conversion rates, average order value (AOV), or revenue per visitor (RPV) to be considered successful. This is often due to a combination of several factors: 

1. Businesses face pressure to show immediate, tangible results, which shifts the focus toward quick wins rather than deeper learnings. 

2. Success is typically measured using straightforward metrics that are easy to quantify and communicate to stakeholders.

3. There is a widespread misunderstanding that A/B testing is a one-size-fits-all solution, which can lead to unrealistic expectations. 

However, this focus on short-term wins limits the potential of your A/B testing program. When a test fails to improve KPIs, you might be tempted to write it off as a failure and abandon further experimentation. However, this mindset can prevent you from discovering valuable insights about your users that could drive meaningful, long-term growth. 

A Shift in Perspective: Testing for Learnings, Not Just Outcomes 

To maximize the success and value of your A/B tests, it’s essential to shift from an outcome-focused approach to a learning-focused one. 

Think of A/B testing not just as a way to achieve immediate gains but as a tool for gathering insights that will fuel your business’s growth over the long term. 

The real power of A/B testing lies in the insights you gather about user behavior — insights that can inform decisions across your entire customer journey, from marketing campaigns to product design. When you test for learnings, every result — whether it moves your KPIs or not — provides you with actionable data to refine future strategies. 

Let’s take a closer look at how this shift can transform your testing approach. 

Outcome-Based Testing vs. Learning-Based Testing: A Practical Example 

Consider a simple A/B test aimed at increasing the click-through rate (CTR) of a red call-to-action (CTA) button on your website. Your analytics show that blue CTA buttons tend to perform better, so you decide to test a color change. 

Outcome-Based Approach 

Your hypothesis might look something like this: “If we change the CTA button color from red to blue, the CTR will increase because blue buttons typically receive more clicks.”

In this scenario, you’ll judge the success of the test based on two possible outcomes: 

1. Success: The blue button improves CTR, and you implement the change. 2. Failure: The blue button doesn’t improve CTR, and you abandon the test. 

While this approach might give you a short-term boost in performance, it leaves you without any understanding of why the blue button worked (or didn’t). Was it really the color, or was it something else — like contrast with the background or user preferences — that drove the change? 

Learning-Based Approach 

Now let’s reframe this test with a focus on learnings. Instead of testing just two colors, you could test multiple button colors (e.g., red, blue, green, yellow) while also considering other factors like contrast with the page background. 

Your new hypothesis might be: “The visibility of the CTA button, influenced by its contrast with the background, affects the CTR. We hypothesize that buttons with higher contrast will perform better across the board.” 

By broadening the test, you’re not only testing for an immediate outcome but also gathering insights into how users respond to various visual elements. After running the test, you discover that buttons with higher contrast consistently perform better, regardless of color. 

This insight can then be applied to other areas of your site, such as text visibility, image placement, or product page design. 

Key Takeaway: 

A learning-focused approach reveals deeper insights that can be leveraged far beyond the original test scenario. This shift turns every test into a stepping stone for future improvements. 

How to Design Hypotheses That Deliver Valuable Learnings

Learning-focused A/B testing starts with designing better hypotheses. A well-crafted hypothesis doesn’t just predict an outcome—it seeks to understand the underlying reasons for user behavior and outlines how you’ll measure it. 

Here’s how to design hypotheses that lead to more valuable insights: 1. Set Clear, Learning-Focused Goals 

Rather than aiming only for KPI improvements, set objectives that prioritize learning. For example, instead of merely trying to increase conversions, focus on understanding which elements of the checkout process create friction for users. 

By aligning your goals with broader business objectives, you ensure that every test contributes to long-term growth, not just immediate wins. 

2. Craft Hypotheses That Explore User Behavior 

A strong hypothesis is specific, measurable, and centered around understanding user behavior. Here’s a step-by-step guide to crafting one: 

● Start with a Clear Objective: Define what you want to learn. For instance, “We want to understand which elements of the checkout process cause users to abandon their carts.” 

● Identify the Variables: Determine the independent variable (what you change) and the dependent variable (what you measure). For example, the independent variable might be the number of form fields, while the dependent variable could be the checkout completion rate. 

● Explain the Why: A learning-focused hypothesis should explore the “why” behind the user behavior. For example, “We hypothesize that removing fields with radio buttons will increase conversions because users find these fields confusing.” 

3. Design Methodologies That Capture Deeper Insights 

A robust methodology is crucial for gathering reliable data and drawing meaningful conclusions. Here’s how to structure your tests:

● Consider Multiple Variations: Testing multiple variations allows you to uncover broader insights. For instance, testing different combinations of form fields, layouts, or input types helps identify patterns in user behavior. 

● Ensure Sufficient Sample Size & Duration: Use tools like an A/B test calculator to determine the sample size needed for statistical significance. Run your test long enough to gather meaningful data but avoid cutting it short based on preliminary results. 

● Track Secondary Metrics: Go beyond your primary KPIs. Measure secondary metrics, such as time on page, engagement, or bounce rates, to gain a fuller understanding of how users interact with your site. 

4. Apply Learnings Across the Customer Journey 

Once you’ve gathered insights from your tests, it’s time to apply them across your entire customer journey. This is where learning-focused testing truly shines: the insights you gain can inform decisions across all touchpoints, from marketing to product development. 

For example, if your tests reveal that users struggle with radio buttons during checkout, you can apply this insight to other forms across your site, such as email sign-ups, surveys, or account creation pages. By applying your learnings broadly, you unlock opportunities to optimize every aspect of the user experience. 

5. Establish a Feedback Loop 

Establish a feedback loop to ensure that these insights continuously inform your business strategy. Share your findings with cross-functional teams and regularly review how these insights can influence broader business objectives. This approach fosters a culture of experimentation and continuous improvement, where every department benefits from the insights gained through testing. 

Conclusion: Every Test is a Win 

When you shift your focus from short-term outcomes to long-term learnings, you transform your A/B testing program into a powerful engine for growth. Every

test—whether it results in immediate KPI gains or not—offers valuable insights that drive future strategy and improvement. 

With AB Tasty’s platform, you can unlock the full potential of learning-focused testing. Our tools enable you to design tests that consistently deliver value, helping your business move toward sustainable, long-term success. 

Ready to get started? Explore how AB Tasty’s tools can help you unlock the full potential of your A/B testing efforts. Embrace the power of learning, and you’ll find that every test is a win for your business.

The post A/B Testing: It’s Not Just About the Outcome appeared first on abtasty.

Recently, they shared this ethos at the CX Circle event by Contentsquare featuring Mel Parekh, Head of E-commerce at Hotel Chocolat. Mel took the stage to unravel the complexities of customer loyalty—a subject that has never been more critical in the fast-evolving world of eCommerce. The discussion centered around how Hotel Chocolat has navigated the challenges of a changing world while staying true to its brand values using the power of experimentation.

The Secret Ingredient: Authenticity and Quality

Hotel Chocolat stands out in the chocolate industry for its commitment to authenticity and quality. While most chocolate brands are content to source their cocoa, Hotel Chocolat went all-in, growing their own on the lush Rabot Estate in Saint Lucia. This direct control over their supply chain ensures that they use only the highest quality ingredients while helping craft a brand that’s as genuine as the cocoa it cultivates. 

Hotel Chocolat has witnessed a constant change in the e-commerce landscape. They’ve learned to adapt to these changes while staying true to their brand identity. One of their key initiatives has been to clearly define who they are as a brand and to create compelling reasons for customers to return to their site time and time again.

A Changing Landscape

It’s no secret that the world of eCommerce is in constant flux. Prices are rising across the board—from raw materials to operating costs—and the competition for customers is fiercer than ever.  In this environment, retailers must do more with less, finding innovative ways to stand out. 

As customers increasingly engage with various digital platforms and experiences, the range of choices available to them has become almost overwhelming. In this crowded marketplace, standing out from the competition requires more than just eye-catching design elements.

Moreover, the explosion of data in recent years has made it possible for even smaller companies to leverage insights that were once only accessible to larger players. However, the real challenge lies in capturing this data, interpreting it effectively, and, most importantly, implementing it in ways that drive meaningful results. Hotel Chocolat has embraced this data-driven approach, using insights to refine their strategies and create a more personalized experience for their customers with both Contentsquare and AB Tasty.

Building Lasting Relationships with Customers in a Phygital World

Loyalty is the cornerstone of Hotel Chocolat’s strategy in this new era. As a premium brand, they understand that their customers aren’t just looking for a product; they’re looking for an experience that resonates with their values and desires. This understanding has led Hotel Chocolat to focus on building a brand that not only meets customer expectations but exceeds them by offering a unique, personalized experience.

One of the key strategies they’ve implemented is their “phygital” approach, which blends the digital and physical worlds to create a more personalized, engaging shopping experience. This approach is centered on three key principles:

• Instant: Reducing delay or lag to ensure a smooth customer experience.
• Connected: Creating a more personal connection with each customer.
• Engaging: Giving customers a sense of control over their shopping journey.

 Make the Experience Personal

With over 120 different chocolate recipes, Hotel Chocolat faced this challenge: how do you help customers find the perfect product without overwhelming them? Their solution was gamification—a method that makes the shopping experience more fun and interactive. In Spring 2023, they launched the “Chocolate Love Match,” a quiz that matches customers to one of six flavor profiles. This not only narrows down the selection from 120 options to 20 or 30, making it easier to shop but also helps customers find the perfect gift for friends and family based on their flavor preferences.

The personalization doesn’t stop there. 

Hotel Chocolat also leverages machine learning and tools like AB Tasty to improve their customer experience further. For instance, they’ve been experimenting with “Add to Bag” personalized recommendations. This initiative is crucial, especially as acquisition costs rise, making it more important than ever to maximize the value of each customer interaction.

Using AB Tasty, they tested two variations: one that showed products frequently bought together and another that displayed recently viewed items for easy access. Both approaches tested positively, resulting in a 5.31% increase in average order value and a 2.87% boost in revenue. 

Embracing Data for Optimization

Hotel Chocolat has also focused on optimizing its digital presence, particularly their website. Working with AB Tasty, they undertook a redesign of their homepage, recognizing that the layout and user experience across devices play a critical role in customer engagement. The goal was to create a more visually appealing and intuitive experience that could better connect with customers online—especially when you can’t taste or smell the products.

The results speak volumes. By optimizing the homepage, they saw a 10% reduction in bounce rate, a 1.67% increase in visiting time, and significant improvements in conversion rates—up 0.54% overall and a substantial 7.24% on desktop. This uplift was largely due to better highlighting the most attractive elements on the homepage, such as category tiles that drive higher conversion and revenue.

Loyalty from a Brand Perspective

Mel Parekh left us with three takeaways for building a brand that stands the test of time:

1. Embracing Change: It shows that your brand is up-to-date and ready to adapt. Staying agile ensures that your brand remains relevant and continues to serve your customers, no matter the circumstances.
2. Listening and Understanding Customers: If loyal customers aren’t heard and understood, they’ll lose their preference for your brand and start considering others.
3. Sticking to Your Values: Clearly reward loyal customers for their loyalty, and make sure to differentiate between who is loyal and who isn’t.

Conclusion

Loyalty isn’t just about offering a great product; it’s about creating connections that resonate. Hotel Chocolat has perfected this recipe by blending their commitment to quality with a data-centric culture. Experimentation and data from AB Tasty have allowed them to be able to improve in all areas – whether that is personalization, gamification of their loyalty scheme, or the link between their online and physical shops. Experimentation has improved more than just their CRO but has helped define who they are and what they stand for.

Find out more in Mel’s talk below:

The post Hotel Chocolat at CX Circle: Sweetening Loyalty with Experimentation appeared first on abtasty.

Since recommendations depend on personalization and user data, it’s possible to choose how to suggest items. In the following article, you’ll learn about various recommendation systems and how to choose the right one for your shop.

Relevant recommendations through personalization

Product recommendations are most impactful when they are relevant to the customer. It is possible to achieve maximum results by ensuring that the recommendations are personalized and account for the individual preferences of customers.

With this, it should be noted that the most effective type of personalization depends on the e-commerce product recommendation system and strategy used.

Personalization requires dialog

Personalization is very complex. It can be used at various touchpoints and implemented using different systems. It means presenting online visitors with product recommendations that are as close as possible to their preferences.

To do this, you first need data on the user’s click and purchase behavior. This can be collected through a dialog with the customer, which is established via their engagement with the online shop. Once data is collected, it can be combined with product data and expert knowledge in a knowledge base.

In this knowledge base, all data is processed and evaluated. One approach is to use artificial intelligence (AI). This allows data (click and purchase behavior) to be processed into information by experts via data mining. The information is then turned into knowledge using algorithms (reinforcement learning).

This knowledge is ultimately used to provide customers with relevant recommendations. Product data, click and purchase behavior, and expert knowledge all converge in the knowledge base.

Dialog-based AI

To provide customers with the best results, an algorithm must first determine what the customer needs. The term dialog-based AI is used for such processes.

A so-called response engine, which uses sensors to record and analyze click and purchase behavior, plays a crucial role here. This takes on the task of identifying a customer’s goals and interests from their engagement with the online store.

Various possibilities for dialog

There are many ways to track customer engagement with an online shop. These are distinguished in the form of:

• Reactions

When a customer browses an online store, numerous reactions can occur. Based on user engagement with the content (clicks, product selection, purchases, etc.), you can find out what they want. Once you have this information, you can present customized content to customers.

• Language

We naturally think of language when we hear the word “dialog.” That said, a dialog between humans and machines based on language has pitfalls. Many people who use voice-activated assistants understand this.

When discussing online stores, human-to-machine dialog occurs through search terms. This allows language to be used for a personalized search engine. Therefore, a product search can be understood as a personalized search system triggered by linguistic input.

With this, the following question arises. How can e-commerce operators use customer-driven engagement and dialog to provide suitable recommendations? There are various systems for achieving this, which are discussed in more detail in the following sections.

Classic recommendation systems (static)

There is a clear distinction between different e-commerce recommendation systems. They are primarily distinguished by the data and methods used to determine relevant suggestions to customers.

With this, there are two classic variants: collaborative and content-based systems. In addition, recommendation systems can incorporate other elements, including demographic data and time spent shopping. We’ll look into two “classic” recommendation systems below.

Collaborative recommendation systems

When online shop customers share click and purchase behavior, a collaborative recommendation system can be used. This analyzes data from different customers and finds similarities to suggest relevant products for a consumer group.

Recommendations generated by this system can follow a headline like “customers who were interested in this product found these relevant.” This is because the system will recommend items to multiple customers with similar patterns. On a related note, algorithms that deliver product lists with this approach are known as collaborative filtering systems.

Collaborative recommendation systems are used by major retailers like Amazon, among others. It is the method of choice when little or no personalization information is available for a customer. It’s also good when the product catalog contains minimal characteristics.

• Advantages of collaborative systems

In e-commerce, the advantage of collaborative product recommendation systems is they can reveal relationships between users and items that aren’t explicitly apparent. Additionally. collaborative filtering can show customers products that differ from previous preferences, but may still be of interest. This means you can surprise your customers.

• Disadvantage of collaborative systems

However, there is a disadvantage. It is referred to as the “cold start problem,” and occurs primarily with new users and products. With this type of e-commerce product recommendation system, it is necessary that there is a large number of customers with similar behaviors. If there is minimal customer engagement, it can be difficult to generate recommendations.

Content-based recommendation systems

Unlike the above type, content-based recommendation systems do not work on the basis of users with similar engagement patterns. Instead, product attribute commonalities are used as a basis. In addition, individual customer engagement plays a role.

Content-based recommendation systems suggest items that are relevant to products a customer expressed interest in purchasing. To calculate recommendations of this kind, content analysis is required to determine product similarities.

When providing such recommendations, you can preface them with something like “similar products from your favorite brand.”

• Advantages of content-based systems

Content-based recommendation systems have both advantages and disadvantages. One major advantage is that content-based recommendation systems can suggest items even if there are no clicks or purchases on the site. This counteracts the “cold start problem” of collaborative recommendation systems.

• Disadvantages of content-based systems

A disadvantage of a content-based e-commerce product recommendation system is that it can be overly specialized. There’s no element of surprise regarding product recommendations. They are only based on the preferences of the individual customer.

If we look back at the example of “similar products from your favorite brand,” we can see another problem with content-based recommendation systems. The customer may wish to see products that have their favorite color, for example.

This makes it clear how important it is to understand preferences. In other words, a customer should be presented with a wide range of recommendation factors. It’s the best way to determine which, of the similar products, the customer actually wants.

Context-aware recommendation systems (dynamic)

Personalization goes beyond the matter of providing customers with desired content. Users increasingly expect content to be presented in the “right” context, one that’s familiar. This presents a challenge for personalization services.

To present customers with relevant recommendations in a context that’s suitable for them, dynamic information is required. This is in addition to static information (such as product similarities). Context-aware recommendation systems process this information.

Within this system, the context is another input for the recommendation system. It conveys what the customer is doing and where recommendations are displayed. The dynamic context-aware information and its interrelationships significantly improve recommendation quality.

Multiple recommendation contexts

When discussing e-commerce, multiple recommendation contexts refer to suggestions beyond products. This typically occurs in a personalized section of the website dedicated to the customer. It’s useful for keeping consumers engaged beyond their purchase(s).

It is possible to show a variety of recommendations in multiple contexts that are tailored to the customer’s preferences. This includes interactive elements and offers a mix of inspiration from similar products to content and entertainment.

If successful, this encourages customers to return to the online shop on their own, increasing consumer loyalty and website engagement. By discovering related content to their favorite brands, styles, etc, users may purchase more items.

This entertainment-driven approach is based on data already collected from the customer’s previous interactions with the website. The overall experience matches what consumers already know, creating a familiar environment for discovery.

Individual recommendation contexts

If your shop can’t host the content necessary for a multiple recommendation environment, you should ask what your customers specifically need. This will help you deliver the best individualized recommendations possible.

To illustrate how crucial it is to develop a suitable recommendation environment, let’s look at the following scenarios.

• Product detail page

An online shop visitor is looking at a product (for example, a pot) on a product detail page. The customer is currently researching information and wants to buy a pot. To best lead the customer in the right direction, you can display similar products or products customers have also purchased. This can be shown below the product information in a recommendation widget.

• Shopping cart layer

In this situation, a shop customer puts a product (for example, a bicycle) in the shopping cart and a pop-up appears with similar products. Here, the customer is one step away from completing the purchase.

They are about to buy a bicycle and have already added it to their shopping cart. When this happens, you should not display similar products under any circumstances. This will confuse the customer and delay the purchasing process.

To avoid them changing their mind at this stage, you should instead present complementary items like a helmet or bicycle lock. This is known as cross-selling and inspires customers to increase the value of their cart.

Compromises for individual recommendations

If only one or two recommendation methods can be presented on a product detail page, you have to select the best one. An example would be “similar products that you may also like.” It is important to take the term “similar” literally.

“Similar” here means products with the same characteristics as the product viewed are understood. This relates to a customer’s personal preferences. If done correctly, it will increase the quality of recommendations and drive sales.

For example: if a customer shows a particular interest in black items, other black items are considered to be very similar. Without this information about customer behavior, the product color would not help inform recommendations.

Hybrid recommendation systems

It may be necessary to mix or modulate recommendation systems. By combining content-based and collaborative recommendation systems, disadvantages can be minimized. This means that high-quality and relevant recommendations can be generated more quickly for online shop customers. If this occurs, this is called a hybrid recommendation system and ensures better results.

That said, truly relevant recommendations cannot be generated with a universal algorithm. They require the dynamic interconnection of a series of intelligent basic algorithms. The prerequisite for this is a modular software system that supports these basic algorithms in a compatible manner. It also requires experts to be able to configure such dynamic architectures with the right parameters.

Selecting the right recommendation system

We’ve covered a wide range of e-commerce product recommendation systems, alongside various methods and data uses. The final question remains. Which one is “right” for generating suitable recommendations?

Understanding your customer’s needs

The above question cannot be answered easily in general terms. The right recommendation system for your e-commerce platform depends on various factors. Recent developments in e-commerce reveal that previously static structures are becoming more dynamic.

In addition, the shopping environment is becoming increasingly important. With this, it goes without saying that it’s necessary to have a wide product and content selection available. The right recommendation strategy depends on the phase of the customer journey and product context. It’s best to explore a mixture of different systems for an optimal experience.

Expert knowledge as a prerequisite

To dynamically provide personalized recommendations in the right context, your website needs the right software architecture. It needs to dynamically combine a wide range of algorithms. With this, an understanding of the shopping environment context is necessary.

Configuring these architectures requires expert knowledge. This is because only trained individuals can identify the requirements of individual touchpoints for selecting the right recommendation system.

They will best know how to choose the best personalization type for the context. By using an expert, you’ll ensure individual recommendations are generated properly for customers.

Targeted combination of different recommendation systems

As you can see in this article, there are various recommendation systems. They each have their own advantages and disadvantages. The development of e-commerce shows that dynamic structures are becoming more important. Shop customers expect product recommendations in a familiar environment.

To meet such demands, different recommendation system processes can be combined with each other. It’s possible to facilitate this in a targeted manner, based on contextual information.

Since these hybrid systems are very complex, expert knowledge is crucial for success. This is because dynamic architectures need to be designed and personalized to generate relevant product recommendations.

With this, it’s important to understand customer preferences and ensure recommendations are appropriate for various stages of the journey. Following these recommendations can make a big difference in presenting optimal recommendations. All of this means more revenue from increased sales.

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Building a great website is, unfortunately, not simply a matter of following best practices. Even within a single industry, users will hold varied expectations based on your brand, communication style, target audience, funnel, etc. And while browsing the same website, users’ expectations can vary, with some knowing exactly what they want and others needing to explore, check your returns policy, learn about your sustainability initiatives, and so on.

We have all heard the hype about how AI has been revolutionizing how marketers approach experimentation. Generative AI offers new opportunities for optimizing every aspect of the user journey, allowing marketers to:

• streamline testing,
• create new online experiences, 
• and create new types of user segments for more precise personalized experiences that drive conversions.

This guest blog post was written by Rodolphe Dougoud, Project Lead at fifty-five—a leading data company that helps brands harness the potential of Generative AI and mitigate associated risks effectively with a comprehensive and pragmatic AI strategy, among other services. 

Below, we’ll explore these three perspectives in depth, with real-life examples gleaned from AB Tasty’s new algorithm, Emotions AI, and fifty-five’s work with its clients around GenAI. 

AI in Action for Experiences that Matter

1. Streamline testing

When thinking about A/B testing, you might immediately picture creating an experiment and launching it live on a website. However, the most time-consuming phases of the A/B testing process generally come before and after that: finding new features to try out in order to create a testing roadmap and analyzing the results of these tests. Here, AI can increase test velocity by helping to reduce bottlenecks hindering both of the aforementioned stages.

Test ideation

Your roadmap must not only be top-down but also bottom-up: pay close attention to insights from your UX designers, based on benchmarks from your competitors and industry trends, and data-driven insights based on your own analytics data. Here, AI can facilitate the process by analyzing large datasets (e.g., on-site Analytics data) to find insights humans might have missed.

Result analysis

Similarly, it’s essential to analyze the results of your tests thoroughly. Looking at one KPI can sometimes be enough, but it often represents only one part of a bigger story. An aptly-calibrated AI model can find hidden insights within your testing results. 

While we generally know what data we want to access, the actual querying of that data can be time-consuming. Applying a GenAI model to your dataset can also allow you to query your data in natural language, letting the model pull the data for you, run the query, and create instant visualizations for major time gains.

Content creation

While not necessary for most tests, creating new content to be included in the testing phase can take a long time and impact your roadmap. While GenAI cannot produce the same quality of content as your UX team, a UX designer equipped with a GenAI tool can create more content faster. The model used can be trained with your design chart to ensure it integrates with the rest of your content. Overall, adding a GenAI tool as a complement to your design arsenal can yield substantial gains in productivity and, therefore, reinforce your testing roadmap timeline.

2. Create new online experiences

Marketers should not hesitate to experiment with AI to create unique and interactive experiences. Generative AI can create personalized content and recommendations that can engage users more effectively. 

Consider, for instance, fifty-five’s recent work with Chronodrive, a grocery shopping and delivery app. We used AI to address a common user challenge (and, frankly, near-universal issue): deciding what to make for dinner. 

With our innovative solution, taking a picture of the inside of your fridge will allow the app to create a recipe based on the ingredients it identifies, while a photo of a dish – taken at a restaurant or even downloaded from social media – will generate a recipe for said dish and its associated shopping list. 

 Artificial Intelligence opens new creative options that weren’t available with previous LLM models. Chronodrive’s solution may not be applicable to most companies, but every business can think back on their typical user’s pain points and conceptualize how GenAI could help ease them.

3. Create new types of user segments for more precise personalized experiences

When a customer enters a store, a salesperson can instantly personalize their experience by checking if they want to be helped or just want to browse, if they are discovering the brand or are already sold on it, if they require guidance or know precisely what they want… A website, on the other hand, necessitates extra effort to present the user with a similarly personalized experience. 

Online, segmentation thus becomes indispensable to deliver the most satisfying user experience possible. Even during testing phases, deploying A/B tests on user segments makes achieving significant results more likely, as increased precision helps mitigate the risk of obtaining neutral results.

AI can analyze a wide array of user interactions on a given website to determine which elements drive the most conversions, or how different users respond to specific stimuli. This analysis can allow brands to classify users into new segments that could not have been available otherwise. For instance, fifty-five applied AI to split Shiseido’s website users between low and high-lifetime value segments. This allowed Shiseido to run differentiated A/B tests and personalize their website depending on the expected lifetime value of the user, resulting in a 12.6% increase in conversions.

Going even further, what if AI could read your emotions? AB Tasty’s new AI algorithm, Emotions AI, can automatically segment your audience into 10 categories based on emotional needs. 

• If a user needs to be reassured, the website can emphasize its free return policy
• If they need clarity, the website can highlight factual information about your product
• And if they need immediacy, the website can hide any unnecessary information to instead focus on its main CTAs

The model estimates the needs of the user by taking into consideration all of their interactions with the website: how long they wait before clicking, whether they scroll through an entire page, where their mouse hovers, how many times they click, etc. This enables stronger personalization, both during testing phases and when deploying online features, by letting you know exactly what your users need. 

Want to Learn More?

If you would like to dive deeper into current experimentation trends, watch our webinar replay here, where fifty-five and AB Tasty explored key CRO case studies and more. And if you have any questions or insights you’d like to share, please leave a comment – we would love to hear from you! 

The post From Clicks to Connections: How AI is Shaping the Future of Digital Optimization appeared first on abtasty.

Product recommendations can follow customers throughout their journey in an online shop. This usually includes the homepage, category and product detail pages, and shopping cart. It also extends to e-mail marketing.

In this article, we will show you where product recommendations make sense for e-commerce along with special features to consider. This is relevant for displaying recommendations at the right moments in an online shopping environment.

Ensuring relevant product recommendations for your e-commerce

In e-commerce, product recommendations are displayed through recommendation “reco” engines. As you might imagine, these deliver relevant recommendations tailored to the visitor. While customers are typically suggested products, recommendation engines can also be used for related content.

Recommendations are presented using widgets which provide the framework for relevant product or content suggestions. These can be customized to match the design and branding of your e-commerce platform.

Differentiate between new and existing customers

With the above information, you might be wondering how to effectively use an e-commerce product recommendation widget. You’ll first need to make a distinction between new and existing customers. This is important because you’ll need to tailor your recommendation engine to different audiences, based on shopping behavior.

In this context, it’s useful to know whether a customer has already visited your online shop, clicked through various categories, and completed a purchase. All of this is considered an online shopping history and only applies to existing customers.

Existing customers: online shoppers with history

If your customers have a history with your store, you can use data based on their previous habits or purchases to display suitable recommendations. You’ll want to analyze click and purchase behavior to personalize relevant product suggestions. In doing so, you’ll likely inspire more sales.

New customers: online shoppers without purchase history

While you can’t offer recommendations based on the personal interests of customers new to your shop, you still can make suggestions to inspire their purchase journey. These include using recommendation algorithms like top sellers, sale items, or new products.

Where to position product recommendations on your website?

There are many ways to position product recommendations in an online shop. You can add them to the homepage, product detail page(s), and directly in the shopping cart. It’s important to strategically place these at every step throughout a customer’s journey.

To help you keep track of the various options, we have provided an overview of suitable positions for online shop recommendations:

1. Homepage
2. Category page(s)
3. Product detail
4. Zero results page
5. Shopping cart
6. Wishlist
7. Thank you / confirmation page
8. Content page
9. Personal shopping area
10. Newsletter

It should be noted that recommendations should be displayed in accordance with the best user experience for each page. To illustrate this better, we will discuss a few pages in detail below. Below we’ll show you what to look for on each page regarding optimal recommendation placement.

1. A dynamic homepage

As mentioned above, you’ll need to build different shopping environments for existing and new customers. If executed properly, this will change the look and feel of your homepage. Just like in a brick-and-mortar store where a salesperson knows the behaviors of regular customers, you can show users their preferences are understood.

For example, an online shop for pet products should know who is purchasing for cats vs dogs. If a cat owner is recommended dog food, they will find the shopping experience counterintuitive. The right products should be presented the moment a customer lands on your homepage, and can be as detailed or broad as needed.

However, as previously explained, if a new customer lands on your homepage, past purchasing data won’t be available. In this scenario, it’s advised to showcase top sellers, sale items, or new products.

2. Category pages

On product category pages, you can use personalized recommendations by analyzing online shoppers’ browsing and purchasing behavior. This approach will allow you to highlight products that are relevant to their habits and align with their interests.

Pro tip: Read more about product category marketing in our new guide!

3. Product detail page recommendations

The product detail page contains more specific information than a homepage. Since a customer is looking at one item, this allows for highly relevant product suggestions. With a product detail page, two types of recommendations make the most sense. These are similar and complementary items.

• Similar products

An online shopper navigates to the product detail page because they find a particular item interesting. If they like the selected item, but are still unsure, similar products can help them make a decision. This is where the customer can become aware of products that may better meet expectations. It helps them feel informed about their purchase, leading to a sale.

• Cross-selling complementary items

Complementary items are relevant for the product detail page. If the customer is convinced they need the selected product, it’s worth showing them matching items. With this, you can draw their attention to other products that may interest the customer. It might inspire them to continue shopping.

When doing this, you’ll want to take the customer’s preferences into account. These include sizing, brands, preferred materials, and dietary needs. The latter is particularly valuable if a customer doesn’t purchase items containing allergens or is vegetarian/vegan. Of course, with new customers, this isn’t possible.

It is also important to account for inventory. You don’t want to recommend products that aren’t in stock, as that creates a frustrating experience. The goal is to enable your customers to make additional purchases.

• Create complete looks with product sets

On the product detail page, showcasing a complete product set is popular. These are recommendations that present customers with an entire look based on one product. The idea is that they can easily add complementary items with minimal effort.

For example, customers can create a stylish outfit, or purchase necessary camping gear in an instant. If you’re selling cameras and photographic equipment, you can offer a set with matching lenses, memory cards, batteries, and a carrying bag. With this, you can allow customers to save or purchase these items at once.

4. Showing alternatives on a zero results page

Sometimes users will search for items that you don’t offer in your shop. This will usually display a page showing “zero results.” To prevent customers from clicking off your site, you have the option of using a product recommendation widget on your page. You don’t want visitors to feel discouraged.

Recommendations on the zero results page can be alternatives from your product range. For example, if a user is searching for a particular brand that you don’t offer, you can recommend similar brands or styles. This will inspire your customers to explore these items and potentially discover new products.

5. Recommendations on the shopping cart page

Even though a user is confirming their shopping cart, it’s not too late to recommend other items. You can still increase sales at this stage of the process.

• Add to cart recommendations

As customers click “add to cart,” you can provide quick personalized recommendations with a cart layer widget. This will pop up and show other products of interest.

• Recommendations on the shopping cart page

The shopping cart page itself can display product recommendations. With this, it’s recommended that you don’t show similar products here as that can confuse the customer. Personalized complementary items make the most sense at this stage.

Additional products that complete the original product or relevant low-cost items are suggested for maximum sales potential. You can set up a checkout zone in your shopping cart that encourages customers to purchase small, cheap items. Think of how a supermarket places snacks near a checkout line.

When recommending items, you have different options for what information to collect from the shopping cart. You can either take the entire order into consideration or you can suggest items based on the last product added. The right approach will depend on your particular shop and strategy.

6. Wishlist

What better place to recommend more products than in your shoppers’ wishlist? This page is already a place where buyers keep up with their future purchasing desires. Product recommendations on this page can encourage a higher order value in the future.

7. Thank you page: more than just an order confirmation

A thank you page is a great place for product recommendations. It doesn’t have to just contain information about the order, it can also encourage future purchases. With relevant recommendations, you can lead customers back to a product detail page. This keeps them engaged in your shop for a longer time.

8. Content page: Recommendations based on topics

By now, it’s clear to see how relevance is key when discussing product recommendations for your e-commerce. With certain campaigns, you may want to execute this manually with marketing and thematic landing pages. Combining content and topic-related recommendations, you can use digital storytelling to emotionally engage online shoppers.

The goal is to create a digital story on a topic and tie it to relevant products. For example, you can do this with inspiration for skiing or surfing vacations. The combination of content and products is a popular approach to e-commerce product recommendation strategy. It moves away from the purely functional aspect of sales, creating a much more personalized experience.

Below you can see what a content page in an online shop looks like. In this example, the topic is a surf trip. In addition to information and a story about the perfect vacation, the page offers thematically appropriate product recommendations for inspiration.

9. Personalized shopping: relevant recommendations and more

If you want to offer users the best experience, you can create a personalized shopping area in your online shop. This is a section that is dynamically, intelligently, and fully customized for each customer. It offers a central location in the shop of the customer’s favorite brands, categories, and items.

This personalized area allows users to browse their own product and brand world. Online shoppers can also receive relevant content suggestions, such as blog articles, and receive shopping news in real-time.

Relevant product recommendations also play a role here. For example, recommendations can be integrated in the form of product sets. Additionally, users can easily access desired information via clickable, interactive elements.

With personalized shopping, the site learns sizing and preferences to display available and on sale items. There is also the potential for embedding interactive content for a unique e-commerce environment.

As you can see, a personalized shopping area offers a particularly high level of inspiration and goes beyond product recommendation widgets.

10. Newsletter: recommendations in real time

To keep your customers fully engaged, you’ll want to provide recommendations through email newsletters. Like everything else discussed, it’s important these are personalized to the customer. You’ll want to account for consumer preferences.

Since emails are read at various times, you want to make sure the recommendations are relevant to when the newsletter is opened. This ensures that all information (including inventory) is up to date. There are also various ways of showcasing recommendations.

• Complementary items

With order confirmation emails you can suggest complementary items. This is a similar strategy to what was discussed on the thank you page.

• Alternative products

If you want to remind inactive customers of products abandoned in their shopping cart, you can suggest both similar and alternative items.

• Topic-related recommendations

Newsletters can focus on a specific topic and showcase thematically related items. With this, you can send topic-specific emails to ensure customers receive highly relevant recommendations.

Why recommendation visibility matters

Proper positioning is essential for product recommendations to perform optimally. As an example, it is important to not add suggestions “below the fold.” You don’t want customers to have to search for recommended items. There’s data to suggest that only 20% of content “below the fold” is seen by viewers.

This means that even if the right products are recommended, 80% of consumers would miss them if improperly placed. As you can see, this creates a drop-off in potential sales. It also means, since there was no engagement, less data is collected to inform better recommendations in the future. This insight is crucial for optimization.

If your recommendation engine is well set up, but recommendations aren’t seen then you’re missing valuable sales potential. Due to this, recommendation positioning on all pages of your website is important. This is particularly true for product descriptions where there’s a high chance of interaction.

Optimal e-commerce product recommendation placement for maximum potential

As you can see in this article, an effective e-commerce product recommendation strategy is a win-win. Your users become inspired while shopping and discover new products. As a shop owner, you can increase engagement and sales.

To fully benefit from the potential of a recommendation engine in your online shop, you should place product suggestions on the various stages mentioned. With the right data, you can anticipate the needs of your customers.

Also, make sure online shoppers actually see your recommendations and remain informed after they leave your site. It’s important to send personalized emails that contain real-time data.

This article shows the potential and power of useful product recommendations. Now it’s your turn to implement the best strategy on your site.

The post What’s the Best E-commerce Product Recommendation Strategy? appeared first on abtasty.

In this article, you’ll learn more about what’s involved in achieving an effective approach. You’ll also discover how digital marketing plays a role.

We’ll go over how to implement an easy-to-follow 8-step strategy for maximizing engagement and sales. The key is to balance accessibility, unique merchandising, and customer behaviors. This can all be achieved through understanding and implementing data and analytics. 

Read on to learn more, starting with the fundamentals of category management.

What is category management?

With category management, products are grouped together. The focus is placed on the needs of consumers. This structure allows for more efficient purchasing by anticipating customer demand.

The goal is to not cluster similar products together, but instead to group them by complementary items. That said, category management has been standard in physical retail for decades.

For example, you’ll find pasta alongside sauces in a supermarket. Similarly, hiking gear, clothes, shoes, and backpacks are usually in the same aisle of a sporting equipment store. This concept applies to e-commerce environments.

Cooperation between retailers and manufacturers

Optimal category management is based on functional cooperation between retailers and manufacturers. It works when both parties want to achieve a triple-win situation for satisfied customers, higher profits, and more sales. Within this context, product groups or service categories are viewed as strategic business units.

To be effective, a holistic approach that engages various departments in the company is needed. As mentioned above, this also involves input from manufacturers. Retailers control the product groups, while manufacturers provide detailed knowledge of categories. In the end, both sides are interested in finding the best possible solution for customers.

Tasks of a category manager

Typically, an effective strategy is managed by a dedicated person or team. They are usually responsible for the following tasks:

• Analysis of shopping baskets
• Definition, structure, and optimization of product groups (category management)
• Strategic planning of featured products
• Merchandising and purchasing for the respective product segment

Goals of category management

Category management is a strategic approach designed for long-term goals. The aim is to achieve higher customer satisfaction with an easy-to-navigate shopping experience. This generates more sales and improves the overall brand image.

Also, effective category management provides a competitive advantage. If done properly, consumers will come to trust a store for its expertise in certain product groups.

E-commerce category management

As you might imagine, category management not only applies to physical stores, but also to e-commerce. That said, it’s fairly new to the digital environment and there is room for development. Similar to brick-and-mortar retail, an online store can meet customer needs with structured product categories. This, along with an optimal user experience, can increase customer satisfaction and boost sales.

8 step strategy for E-commerce Category Management

Below, you’ll find an 8-step process that applies to both physical and online retail environments. This involves a systematic and structured approach. It’s encouraged to repeat the steps as much as necessary for implementing optimization and growth potential.

The basis for decision-making will come from data related to online marketing analytics. Therefore, close coordination with relevant teams and departments is essential.

Strategic coordination

Before the process begins, you should discuss strategies and goals with manufacturers for your future product groups. This will help avoid potential conflicts.

1. Understanding consumer behavior

This stage is about learning about your customers through purchasing patterns. You’ll want to pay attention to which products or services they choose. To do this, it’s helpful to analyze quantitative and qualitative data provided by market research.

2. Category importance

This step is where you’ll determine how important the selected product category is in your store’s overall portfolio.

3. Category evaluation

As soon as you have defined a product group, you’ll want to analyze its performance. Sales and various key figures on purchasing behavior will help assess where there is potential for development, particularly in comparison to competitors.

4. Category goals

To monitor the success of your product category, you’ll define specific and measurable targets. These relate to customer profiles, financial reports, market share, and performance. This is important for reaching specific audiences, achieving a certain turnover, and determining featured product categories.

5. Strategy development

At this stage, you’ll develop strategies to help achieve the previously defined goals. These are handled through marketing and procurement. Marketing involves conversion rate, average basket value, and profit. Procurement, on the other hand, focuses on more efficient processes.

6. Tactics for your category management

It is recommended that you and your team define a concrete action plan for the product range, placement, and visibility in your online store. With this, you’ll want to implement effective pricing, communication, and advertising strategies. If executed properly, these tactics will all help boost performance.

7. Implementation of your action plan

Now that you’ve defined goals, strategies, and measures, it’s time to implement your plan. For successful e-commerce category management, you’ll want to assign responsibilities and set deadlines.

8. Review

With your plan set, you’ll want to regularly review product group performance. This will help identify potential for growth and optimization. It’s important to implement improvements regularly. The aim is to design your product range as efficiently as possible.

E-commerce category management and digital marketing

There are many overlaps between e-commerce category management and digital marketing. You can use data provided by both areas to inform overall strategies. This will help determine the right campaigns to run to boost sales and performance.

Product allocation using keywords and clickstream data

As mentioned above, useful data is collected by digital marketing efforts. In addition, it’s important to pay attention to user behavior involving clickstream data and UX design.

This information helps determine which products belong to certain categories. It also is useful for identifying product range gaps so you can add relevant items to respective groups. Additionally, you’ll identify opportunities for cross-selling or upselling strategies.

Optimal website category structure

In addition to what’s already discussed, category managers can use data from digital marketing to identify optimal paths for product selection. This helps create an easy-to-navigate website for customers.

If your visitors are shopping through specific products or brands, you’ll want to make these entry points accessible. It should be simple to select paths through corresponding filter attributes in the search function of your website.

In some cases, themed landing pages, personalized product recommendations, or a brand landing page are worth pursuing. The aim is to offer visitors on your site an outstanding customer experience through an optimal website structure.

For example, category management can be used to structure “barbecue” products such as aprons, spices, charcoal, etc. This would be considered a product group. You may also want to create a themed landing page for barbecue season, which would not only feature products but also content for party inspiration.

Growth potential based on conversion rate and consumer behavior

With the help of digital marketing data, including website traffic, conversion rate, bounce rate, and average shopping basket value, there’s potential for growing sales.

For example, if you notice a category isn’t performing so well, you can improve discoverability in your e-commerce environment. With this, it’s possible to improve overall success.

E-commerce category management offers growth potential

As explained, you can increase customer loyalty and generate higher sales with strategically thought-out e-commerce category management. A long-term approach allows for enormous growth potential for your store. All these efforts can improve loyalty and customer engagement.

Frequently asked questions about e-commerce category management

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To begin, it’s essential to differentiate between conversion rate (CR) and average order value (AOV), as they provide distinct insights into customer behavior. Understanding these metrics helps you implement meaningful changes to improve transactions.

In this article, we’ll delve into the complexities of transaction metrics analysis and introduce our new tool, the “Report Copilot,” designed to simplify report analysis. Read on to learn more.

Transaction Testing

To understand how test variations impact total revenue, focus on two key metrics:

• Conversion Rate (CR): This metric indicates whether sales are increasing or decreasing. Tactics to improve CR include simplifying the buying process, adding a “one-click checkout” feature, using social proof, or creating urgency through limited inventory.
• Average Order Value (AOV): This measures how much each customer is buying. Strategies to enhance AOV include cross-selling or promoting higher-priced products.

By analyzing CR and AOV separately, you can pinpoint which metrics your variations impact and make informed decisions before implementation. For example, creating urgency through low inventory may boost CR but could reduce AOV by limiting the time users spend browsing additional products. After analyzing these metrics individually, evaluate their combined effect on your overall revenue.

Revenue Calculation

The following formula illustrates how CR and AOV influence revenue:

Revenue=Number of Visitors×Conversion Rate×AOV

In the first part of the equation (Number of Visitors×Conversion Rate), you determine how many visitors become customers. The second part (×AOV) calculates the total revenue from these customers.

Consider these scenarios:

• If both CR and AOV increase, revenue will rise.
• If both CR and AOV decrease, revenue will fall.
• If either CR or AOV increases while the other remains stable, revenue will increase.
• If either CR or AOV decreases while the other remains stable, revenue will decrease.
• Mixed changes in CR and AOV result in unpredictable revenue outcomes.

The last scenario, where CR and AOV move in opposite directions, is particularly complex due to the variability of AOV. Current statistical tools struggle to provide precise insights on AOV’s overall impact, as it can experience significant random fluctuations. For more on this, read our article “Beyond Conversion Rate.”

While these concepts may seem intricate, our goal is to simplify them for you. Recognizing that this analysis can be challenging, we’ve created the “Report Copilot” to automatically gather and interpret data from variations, offering valuable insights.

Report Copilot

The “Report Copilot” from AB Tasty automates data processing, eliminating the need for manual calculations. This tool empowers you to decide which tests are most beneficial for increasing revenue.

Here are a few examples from real use cases.

Winning Variation:

The left screenshot provides a detailed analysis, helping users draw conclusions about their experiment results. Experienced users may prefer the summarized view on the right, also available through the Report Copilot.

Complex Use Case:

The screenshot above demonstrates a case where CR and OAV have opposite trends and need a deeper understanding of the context.

It’s important to note that the Report Copilot doesn’t make decisions for you; it highlights the most critical parts of your analysis, allowing you to make informed choices.

Conclusion

Transaction analysis is complex, requiring a breakdown of components like conversion rate and average order value to better understand their overall effect on revenue. 

We’ve developed the Report Copilot to assist AB Tasty users in this process. This feature leverages AB Tasty’s extensive experimentation dashboard to provide comprehensive, summarized analyses, simplifying decision-making and enhancing revenue strategies.

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A thought leader in the worlds of CRO and experimentation, Bhavik Patel founded popular UK-based meetup community, CRAP (Conversion Rate, Analytics, Product) Talks, seven years ago to fill a gap in the event market – opting to cover a broad range of optimization topics from CRO, data analysis, and product management to data science, marketing, and user experience.

After following his passion throughout the industry from acquisition growth marketing to experimentation and product analytics, Bhavik landed the role of Product Analytics & Experimentation Director at product measurement consultancy, Lean Convert, where his interests have converged. Here he is scaling a team and supporting their development in data and product thinking, as well as bringing analytical and experimentation excellence into the organization.

AB Tasty’s CMO Marylin Montoya spoke with Bhavik about the future of experimentation and how we might navigate the journey from the current mainstream approach to the potentialities of AI technology.

Here are some of the key takeaways from their conversation.

The evolution of experimentation: a scientific approach.

Delving straight to the heart of the conversation, Bhavik talks us through the evolution of A/B testing, from its roots in the scientific method, to recent and even current practices – which involve a lot of trial and error to test basic variables. When projecting into the future, we need to consider everything from people, to processes, and technology.

Until recently, conversion rate optimization has mostly been driven by marketing teams, with a focus on optimizing the basics such as headlines, buttons, and copy. Over the last few years, product development has started to become more data driven. Within the companies taking this approach, the product teams are the recipients of the A/B test results, but the people behind these tests are the analytical and data science teams, who are crafting new and advanced methods, from a statistical standpoint.

Rather than making a change on the homepage and trying to measure its impact on outcome metrics, such as sales or new customer acquisition, certain organizations are taking an alternative approach modeled by their data science teams: focusing on driving current user activity and then building new products based on that data.

The future of experimentation is born from an innovative mindset, but also requires critical thinking when it comes to planning experiments. Before a test goes live, we must consider the hypothesis that we’re testing, the outcome metric or leading indicators, how long we’re going to run it, and make sure that we have measurement capabilities in place. In short, the art of experimentation is transitioning from a marketing perspective to a science-based approach.

Why you need to level up your experiment design today.

While it may be a widespread challenge to shift the mindset around data and analyst teams from being cost centers to profit-enablement centers, the slowing economy might have a silver lining: people taking the experimentation process a lot more seriously. 

We know that with proper research and design, an experiment can achieve a great ROI, and even prevent major losses when it comes to investing in new developments. However, it can be difficult to convince leadership of the impact, efficiency and potential growth derived from experimentation.

Given the current market, demonstrating the value of experimentation is more important than ever, as product and marketing teams can no longer afford to make mistakes by rolling out tests without validating them first, explains Bhavik. 

Rather than watching your experiment fail slowly over time, it’s important to have a measurement framework in place: a baseline, a solid hypothesis, and a proper experiment design. With experimentation communities making up a small fraction of the overall industry, not everyone appreciates the ability to validate, quantify, and measure the impact of their work,  however Bhavik hopes this will evolve in the near future.

Disruptive testing: high risk, high reward.

On the spectrum of innovation, at the very lowest end is incremental innovation, such as small tests and continuous improvements, which hits a local maximum very quickly. In order to break through that local maximum, you need to try something bolder: disruptive innovation. 

When an organization is looking for bigger results, they need to switch out statistically significant micro-optimizations for experiments that will bring statistically meaningful results.

Once you’ve achieved better baseline practices – hypothesis writing, experiment design, and planning – it’s time to start making bigger bets and find other ways to measure it.

Now that you’re performing statistically meaningful tests, the final step in the evolution of experimentation is reverse-engineering solutions by identifying the right problem to solve. Bhavik explains that while we often focus on prioritizing solutions, by implementing various frameworks to estimate their reach and impact, we ought to take a step back and ask ourselves if we’re solving the right problem.

With a framework based on quality data and research, we can identify the right problem and then work on the solution, “because the best solution for the wrong problem isn’t going to have any impact,” says Bhavik.

What else can you learn from our conversation with Bhavik Patel?

• The common drivers of experimentation and the importance of setting realistic expectations with expert guidance.
• The role of A/B testing platforms in the future of experimentation: technology and interconnectivity.
• The potential use of AI in experimentation: building, designing, analyzing, and reporting experiments, as well as predicting test outcomes. 
• The future of pricing: will AI enable dynamic pricing based on the customer’s behavior?

About Bhavik Patel

A seasoned CRO expert, Bhavik Patel is the Product Analytics & Experimentation Director at Lean Convert, leading a team of optimization specialists to create better online experiences for customers through experimentation, personalization, research, data, and analytics.
In parallel, Bhavik is the founder of CRAP Talks, an acronym that stands for Conversion Rate, Analytics and Product, which unites CRO enthusiasts with thought leaders in the field through inspiring meetup events – where members share industry knowledge and ideas in an open-minded community.

About 1,000 Experiments Club

The 1,000 Experiments Club is an AB Tasty-produced podcast hosted by Marylin Montoya, AB Tasty CMO. Join Marylin and the Marketing team as they sit down with the most knowledgeable experts in the world of experimentation to uncover their insights on what it takes to build and run successful experimentation programs.

The post The Past, Present, and Future of Experimentation | Bhavik Patel appeared first on abtasty.

Directing shoppers toward their desired products in a physical store is expected. So, what about online stores?

Product categories are the backbone of every online shop. 

Categorizing your products and taking the time to carefully craft your online directory can be the difference between achieving your business goals and falling behind. 

In this article, you will learn how product categories improve the user experience, optimize your marketing investment, and contribute to your business goals.

What is a product category?

Product categories are like a road map that directs traffic. 

Shoppers need guidance when shopping, especially if you have a big product catalog. The easier the signage is to understand, the faster customers get to their goal – the product they need. 

For a first-class shopping experience, shoppers expect products to be correctly categorized and bundled. Whether customers are searching for skin care products, winter coats, kitchen utensils, or Christmas candles, intuitive product categories are a necessity for marketing. 

How can you categorize products for your marketing?

A product category is created by grouping similar products that share similar features. These shared characteristics, which determine how items in your inventory are grouped together, should be considered in your marketing strategy from the start. 

However, it’s important to continuously review and refine these product categories over time. What was relevant for shoppers in 2018, may not have the same success rate today. Timeliness is key to grabbing your user’s attention.

Examples of product category marketing:

• ASOS – online fashion and cosmetic retailer 

In addition to the typical  “Shop by product,”  visitors can also search by occasion, season, trends, body fit, and more. They’ve set up their online shop with unique categories that correspond to the current interests of their target audience. ASOS bundles its products in new contexts to continually show the buyer new possible uses and cater to their preferences.

• Fackelmann – household product designers and manufacturers

In the Fackelmann online shop,  shop customers can = find product categories for specific contexts, such as various  “baking occasions.” This gives website visitors a quick way to find themed baking utensils perfect for the holidays without having to sort through the typical baking tools. 

• Gorillas – Mobile grocery delivery service 

Products can also be categorized according to frequency of use. A product like mayonnaise tends to be purchased in fewer intervals. Therefore, Gorillas, has classified this product in the “pantry cupboard” category. It’s grouped next to jars of Nutella and other pantry staples, which would typically be on a completely different row of shelves in a physical store.

Through user-centered categorization of their own product range, e-commerce operators can increase their conversion rates and reduce bounce rates. A user-centered approach means that your product categorization strategy must be questioned again and again to find what works best.

What are examples of different types of product categories?

You can create as many product categories as there are features, locations, functions, etc. because a product category includes products with similar product properties and a similar benefit for your customers. 

You can build product categories hierarchically or in other words, in the form of a tree structure. For example, in the “clothing” category you will typically find that there’s a distinction between women’s and men’s clothing. These categories in turn include subcategories such as “pants,” “jackets,” etc.

Before finally categorizing an article, you should be clear about which classification makes sense. A well-known way of classifying products is to divide them into the following 4 major classifications: 

Please note that the definitions partially overlap

• Consumer goods: These are products that are purchased regularly. An example of this would be toothpaste. The price and quality of the product hardly differ between the different manufacturers. As a result, customers rarely engage with this type of product. They usually choose a brand and stay loyal to it.
• Shopping Goods: Goods in this category are purchased less frequently than consumer goods and are generally more expensive. For this reason, customers collect a lot more information and compare offers before purchasing. An example of shopping goods would be electronic devices.
• Specialty and Luxury Goods: These goods are primarily sought after and purchased by a loyal customer base. Customers know exactly what they want. Often it’s not just about exceptional features and exclusivity, but also about the status that comes with it. A good example of a luxury item would be a limited edition watch.
• Unsought Goods: These products are only sought after when a specific problem occurs. For example, a light bulb is usually only purchased when an old light bulb has burned out.

Looking for a product categorization tool to lower bounce rates and guide customers? AB Tasty is the complete experience optimization platform that allows you to build better experiences for your customers – fast. From A/B testing to online product category management, your optimization strategy only needs one tool. 

How can product categories be analyzed, updated, and used effectively?

An analysis of product categories begins with recording and evaluating product features. The factors that influence the demand for a product positively or negatively are also identified. Not only is your own traffic necessary in the evaluation phase, but competing online shops can also serve as inspiration.

In addition to determining which product categories are assigned, an analysis also includes a more in-depth analysis of each individual product type. The data expands your own understanding of customers. How do they perceive categories and product types? How do they communicate and what is their purchasing behavior?

A comprehensive analysis also helps the shop operator to reflect on current market trends and develop new strategies for category creation.

How to create product categories for marketing successfully?

Now let’s see how you can create effective product categories for your e-commerce marketing strategy using three important steps.

Step 1: Identify the purpose of the product detail pages and product category pages

The most important pages of every online shop are the individual product detail pages and the product category pages. Both types of pages serve a specific purpose.

Product pages are not just the shop windows for the products. Because on these pages, the focus is more on conversion and less on navigation. 

Before visitors get to the product page, they are confronted with the product categories. The purpose of these is to offer customers orientation and guide them smoothly through the range. The categories help the user navigate.

Step 2: Design your product categories with customers in mind

When developing product categories, the customer’s perspective should always be taken. What really motivates them to buy? What information must be integrated into the listing for which product?

To optimize conversion rates in marketing using product categories, online shop operators can turn the following:

Product categories: Summarize and describe

Products can be summarized as described according to the criteria and characteristics presented above. You can then use these groupings for your menu and define main and submenu items. 

(Pro tip: Personalization also makes it even easier for customers to find the product they want)

To give your menu an extra boost, you also have the option to display the categories in a personalized order. i.e. a customer who is primarily interested in the “sneakers” category will see it at the top, while another customer will see “sneakers” further down and “jackets” first because this category is currently particularly relevant to them.

Product category pages: how to design an attractive product category page? 

The product category pages should be clear and attractive. 

• Information displayed about the product: The displayed products on this page should show any details that help visitors differentiate the products (e.g. size, type, or other information related to the product niche).
• Photos and Icons: Use clear images on product category pages. This means your customers can use visualization to get a much quicker overview of whether they are in the right area than if they had to read through long texts.
• Number of products on a page: The key to a good product category page is a balanced number of products. Too few products signal to customers that there is limited variety. Conversely, too many products tend to lead to overwhelm. We recommend between 10 and 50 articles per page.
• Personalized product ranking: To help customers find the right product for them, you can personalize the order of your products on your category pages: i.e. display “relevant” products at the top. (“Relevant” products are a result of the click and purchase behavior of all users.) You also have the opportunity to display the first products in a personalized way by using recommendations.

The product category page “Gartenhaus & Co.” from the provider Gartenhaus GmbH shows a structured and limited number of products.

Step 3: Use options to limit choices

Any of the following methods will reduce the number of items displayed. This makes it easier for customers to choose products and increases the conversion rate.

• Additional product categorization:  Each product category can be divided into subcategories to make the search even more specific. No matter which principle this is used for: the names of the respective categories must be specific and understandable.
• Functional Categorization: Products are classified based on the function they are associated with. This is the most commonly used categorization.
• Demographic categorization: Here the products are classified according to demographic criteria such as gender and age.
• Categorization by area of ​​application:  The key question in this classification is where and how the item is used or installed (e.g. bathroom, kitchen, living room).
• Specific categorization: Within a product category, you can classify the goods again according to specific characteristics (e.g. according to the size of computer monitors).
• Categorization of the solution: This grouping is based on a specific problem that customers want to see solved by a product. A good example would be losing weight or building muscle.
• Filtering: With filters and faceted navigation, shop visitors can narrow down their searches themselves to get to the desired items more precisely.
• Search: With the onsite search function,  customers enter the name of the product or a feature into the search bar to find corresponding results.

What are other benefits of product categories in marketing?

With product categories, you can track and evaluate customers’ surfing habits. Furthermore, you can analyze their purchasing behavior which enables e-commerce companies to make individually tailored product recommendations. This is useful because very few customers have any need or interest in the entire product range. Additionally, the data obtained through individual recommendations provides valuable information about the behavior of shop customers and visitors.

To get even more out of your product categories for marketing, you can also use them to provide category-specific recommendations.  For example, you have the option of providing an overview of certain categories on the homepage and displaying matching articles from the respective category. Based on your customers’ clicking and purchasing behavior, you can personalize the recommendations and present products that are particularly relevant to them.

Personalized product recommendations on the Görtz homepage from a specific category (“New Bags”).

Why product category marketing is an indispensable part of e-commerce marketing

Product categorization is an essential part of conversion optimization for online shops. If the clicking and purchasing behavior of customers is better understood, the guidance of customers through the range can be optimally designed using product categories. This means you can reduce bounce rates and increase conversion rates. The insights from the analysis of product categories are therefore becoming an indispensable part of marketing in e-commerce.

The post A Guide to Successful Product Category Marketing in 2024 appeared first on abtasty.

To capture consumers’ attention, brands need to focus their attention on crafting unique user experiences to deliver 1:1 personalization based on data.

One of the most important focal points to convert visitors into customers and build customer loyalty is 1:1 personalization. More and more customers feel less motivated to complete a transaction when they’re online shopping if their experience is impersonal. Let’s take a look at some data from Forbes:

• 80% of consumers are more likely to complete an online purchase with brands that offer personalized customer experiences.
• 72% of consumers explain that they only interact with personalized messaging.
• 66% of consumers share that coming across content that isn’t personalized would deter them from purchasing.

Customers want personalization. Think about when you walk into a physical store and an employee really listens to your needs, helps you find exactly what you’re looking for, or goes above and beyond your expectations to help you. That is exactly what customers want in the digital marketplace.

A unique, digital one-to-one personalization experience strategy gives companies the potential to customize messages, offers, and other experiences to each website visitor based on data collected about each user.

Digital one-to-one personalization starts with concrete data. Are you leveraging data to better serve and convert your visitors?

To help you answer “yes” to this question, we’ll take a deeper look at:

• What is 1:1 personalization?
• What data helps you know your customers better?
• How to find data?
• How to leverage the data you find 
• What messages should you personalize?
• Examples of one-to-one personalization

What is one-to-one personalization in marketing?

Delivering a unique (or one-to-one) experience to each online consumer is a technique known as one-to-one personalization in marketing.

By mastering the technique of 1:1 personalization, brands can deliver an exceptional level of customer service by providing personalized messages, product recommendations, offers, and specialized content at the right time based on the user’s needs and expectations.

This type of unique user experience is only made possible thanks to the availability of extensive customer data. If you don’t get to know your customers based on their interactions with your brand and user behavior, you’re missing an opportunity to meet your customers’ expectations.

One goal of personalization is to create a “wow” effect. This means you should be making the customer think, “wow, they really know me.” The more information that a company knows about a certain customer, the more personalized the user experience will be.

Without extensive, personalized data, one-to-one personalization isn’t achievable.

What data to collect to improve your customer experience with personalization?

On a wider scale, it’s important to understand the location of your customer, their demographic information (age, gender, education level), purchasing habits, and website browsing information. However, in the hypercompetitive world of personalization, this surface-level data is not enough.

Brands need to move beyond knowing who the customer is and understand how the customer behaves.

Knowing that your customer is a recent college graduate who lives in New York City and spends a lot of time making Pinterest boards will not be enough information to create a strong buyer persona to achieve a unique and pleasant user experience.

Enhancing your customer’s profile will require you to collect relevant data about how your customer interacts with your brand on all channels, what motivates them to purchase, and what makes them tick on top of knowing who they are.

More specifically, robust personalized data will help you better understand:

• Location and demographics
• Interests and hobbies
• Shopping and purchasing habits
• Device and channel frequency
• Where and how they prefer to shop and purchase
• Satisfaction level
• Likes and dislikes

All of this information will allow you to create a sophisticated customer profile. Understanding their motivations, preferences, and expectations helps you characterize users into intricate market segmentations to give them the best possible experience imaginable.

Ideally, the customer will have a positive experience and feel unique based on the information derived from the robust data collection.

How do you find user data?

Extensive data can be found and refined by cross-indexing information stored on separate databases.

For example, you can harvest personalized data from a customer’s interactions with your business by analyzing and storing comments on social media sites, ratings on review sites, mobile app usage vs. desktop usage, customer service interactions, download requests, and more.

How to leverage one-to-one personalization with personalized data

As you can see, personalization cannot exist without data. To achieve one-to-one personalization on your digital channels, your brand must have the ability to transform the collected data into action.

After monitoring and gathering rich data on your customer’s interactions, history, and behavior on your site, it’s time to convert this personalized data into a refined customer buyer persona to serve your customers better.

By segmenting your profiles, you will be able to better understand your customer’s preferences and pain points, which will help you craft these personalized messages and display them at the right time.

How to personalize interactions with customers:

Once you have substantial personalized data collected about your visitors, you can determine the best way to interact with them. There is a fine line between being helpful by displaying personalized messages and being invasive.

The difference in these two feelings will depend on the amount of prior engagement that the customer has with you. For example, a customer who is subscribed to every newsletter has a company discount card and frequently completes transactions on your website will expect you to know their preferences fairly well, like a regular coming into a coffee shop. On the other hand, a first-time visitor will not expect you to know much about them, but they will expect to be welcomed.

The best way to understand how to serve your customers is by asking yourself how you would want to be interacted with at their level of engagement with your brand. What would make you feel welcomed and what would make you feel overwhelmed or uneasy?

What messages should you personalize?

The possibilities for personalized messages can stretch as far as your mind (or your software capabilities) will allow.

Think about personalization in a broad sense. Let’s say a company wants to put its logo onto personalized gifts for its employees. The company’s logo can be put onto t-shirts, pens, stickers, coffee mugs, phone cases, backpacks, sunglasses, golf balls, holiday baskets– the possibilities are nearly endless. The same goes for personalized messages for your own customers.

In marketing communication, some of the most common outlets for 1:1 personalization are:

• Product recommendations
• Emails (subject lines and content)
• Intro and exit banners
• Pop-up messages
• Conversational marketing (chat boxes)
• Offers and discounts
• Language
• Landing pages
• Pricing
• Greetings

To attract and retain your customer’s attention in a market filled with saturated messages, your brand should focus on personalization as much as possible and in as many channels as you can.

What platform to use for one-to-one personalization in marketing?

The journey to a seamless one-to-one personalization, or one-to-one marketing, experience for your customers starts with sophisticated and intuitive software to help transform your ideas into reality.

AB Tasty is the complete platform for experimentation and personalization equipped with the tools you need to create a richer digital experience for your customers — fast. With embedded AI and automation, this platform can help you achieve omnichannel personalization and revolutionize your brand and product experiences.

What is omnichannel personalization?

In marketing, employing one-to-one personalization across multiple channels, platforms, and touchpoints is commonly referred to as omnichannel personalization.

Customers crave personalization wherever they are – on a mobile device, desktop, social media platform, mobile app, or email. When customers receive a personalized experience, they expect this standard of communication across all channels or platforms that they are interacting with.

Achieving omnichannel personalization requires a seamless flow of customer data from one platform or channel to the next. By gathering information on user preferences, behavior, and interests from all virtual touchpoints, your customer’s profile strengthens.

By receiving this consistent level of personalization across all channels, consumers will be inclined to purchase more and to purchase again from the same brand that made them feel seen and heard.

What are the advantages of omnichannel personalization?

1. Higher conversion rates
2. Increased average order value (AOV)
3. Reduced cart abandonment
4. Improved brand value and customer loyalty
5. Higher customer lifetime value
6. Delivering messages at the right time and place

8 Examples of 1-1 Personalization strategies from retail brands

1. ASOS’s Social Connection

Online retailer ASOS prides itself on offering both new and existing customers a range of personalized discounts and deals, which vary depending on if:

• It’s a new customer 
• It’s a returning customer that’s demonstrated a particular interest (e.g. shoes)
• A regular customer (who could then be offered premium next-day delivery, for example)

But how does ASOS get this information? One method they might use is encouraging customers to log in to the site using social media platforms, which would allow ASOS to access further details such as age, gender, and location—which can then be used to tailor even more personalized messages.

Why it works: The ability to use a social platform for account creation makes the process simple for shoppers, while giving ASOS more insight into what deals or promotions would be of the most interest to them.

2. Nordstrom Remembers Your Size

Nordstrom gave its online shopping cart a simple yet effective personal touch: remembering returning customers’ clothing sizes. This may not seem like a massive approach to deliver a personal experience, but it creates a more seamless checkout for the user and brings them one step closer to the purchase. It’s a rather clever move from Nordstrom that hasn’t gone unnoticed.

Why it works: Remembering the customers’ preferred size (based on previous purchases) instantly shows the brand’s attentiveness while making checkout even more simple.

3. Clarins personalization and gamification

Before the booming holiday season, Clarins, a multinational cosmetics company, saw an 89% increase in their conversion rate and a 145% increase in the add-to-basket metric by implementing 1:1 personalization and gamification with AB Tasty.

On Single’s Day, a few weeks before Black Friday, Clarins saw a perfect opportunity to experiment and learn culture by implementing a “Wheel of Fortune” concept in certain countries. The gamification gifts were personalized according to each country’s local culture. Any visitor arriving at their website would play the digital game, spin the wheel, and receive a gift automatically in their inbox. This ease of automatic implementation was a great user experience, especially for mobile visitors.

Read the full story here: How Clarins Uses AB Tasty for Personalization and Retention

4. Amazon’s ‘Recommended For You’ Approach

Amazon is no stranger to personalization marketing. In fact, it could be argued they were the first major e-commerce retailer to really put personalization into action. The company has become known for its product recommendation emails and personalized homepages for logged-in customers. Using their own algorithm, A9, Amazon goes above and beyond to first understand customers’ buying habits and then deliver an experience that’s been deliberately designed for relevance. 

Why it works: Customers feel valued and understood by the retailer when seeing emails and recommended “picks” that are tailored to their interests. Consistency also plays a part in Amazon’s approach, as they continue to deliver an even more granular personalized approach for customers.

5. Nike and Their Customized Approach

Nike always goes the extra mile to personalize the shopping experience, as we’ve seen with their SNKRs app that allows premium (loyalty, Nike+ shoppers) access to a large catalog of products that they can then customize. It’s the perfect way to cement customer loyalty by offering them the unique opportunity to tailor items to their exact liking.

Why it works: By giving customers a certain degree of autonomy with design, Nike is giving customers the freedom to express their individuality, even while the company continues to produce the same style of shoe around the world. Despite being a huge brand, Nike has created a great loyalty program that engages customers and stokes their excitement about buying Nike products.

6. Net-A-Porter’s Personalized Touch

Luxury online retailer Net-A-Porter has adopted the ‘recommended for you’ approach but with a unique twist to appeal to its high-end customers who want a more premium service when they shop. The company gives away freebie products to customers based on previous purchases, adding a personal touch to an otherwise standard online shopping experience. This is not unlike Amazon’s recommended emails, except Net-A-Porter customers receive a physical product — and who doesn’t like a gift!

Why it works: These gifts show the appreciation Net-A-Porter has for its customers and help to bring the luxury shopping experience online.

7. Coca-Cola’s Name Campaign

In 2011, Coca-Cola launched its Share a Coke campaign in Australia, printing thousands of names on their diet and original soft drinks. This simple yet effective campaign made sales skyrocket, supporting the notion that consumers engage with brands that address them by their first name (albeit in a rather broad sense!) Personalized bottles became all the rage, with people trying to find their own names along with those of their friends and family members. The campaign was globally recognized and started the ball rolling for other brands such as Marmite, which also saw great success with a naming campaign.

Why it works: Is it the simple notion of vanity that makes these name campaigns so popular? Consumers love to see their own names on popular products, making them almost ‘gimmicky’ with a collectible edge that makes people feel special!

8. Target’s Guest ID

The US retail giant Target decided to up its personalized campaign game by assigning each customer a guest identification number on their first interaction with the brand. Target then used the data to obtain customer details like buying behavior and even job history! Target used personalized data to understand the consumer habits of its customers and to create a view of their individual lifestyles. Target focused particularly on customers who also had a baby registry with them and even used their marketing data to make ‘pregnancy predictability scores’ for customers who were browsing particular items!

Why it works: Arguably, delivering a personalized experience for every customer visiting a physical store is a tough job for any retailer. By assigning a ‘guest ID’, Target was able to understand buying behaviors and patterns from their customers in-store and use the information to make suggestions on products they may be interested in.

Everyone wins with one-to-one personalization

The data you collect equally benefits your brand and your customers. By understanding what your customers are looking for, you save them time by providing them with informed recommendations, personalized messages, and unique experiences to solve their pain points.

Without proper data collection or genuine segmentation, it’s nearly impossible to provide users with a 1:1 personalized experience. Loyal customers want to feel like their brand really knows them and what they’re looking for. Achieve one-to-one personalized experiences by correctly analyzing and leveraging personalized data. If you’re looking to serve your customers, increase sales, and build brand loyalty at the same time, you’ve found your blueprint with personalization.

The post What is One-to-One Personalization in Marketing? (With 8 Examples) appeared first on abtasty.