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 webinar, we’ll cover

• The Mobile Customer Journey: Understand how smartphones change consumer behavior and how to create a seamless omnichannel experience.
• Optimizing Mobile Space: Discover techniques to enhance user navigation and highlight key content above the fold.
• The Thumb Zone: Design for accessibility by placing important content and CTAs within easy reach.
• Effective CTAs: Learn best practices for creating compelling CTAs that convert.
• Streamlining Processes: Simplify checkouts and forms to reduce friction and encourage purchases.

Register now to save your spot!

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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

The post Achieving Success with E-commerce Category Management appeared first on abtasty.

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

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

Category management

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

Product recommendation engine types

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

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

Collaborative filtering

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

Content-based filtering

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

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

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

Hybrid filtering

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

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

Advantages vs. disadvantages

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

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

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

Context-aware recommendations

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

Product recommendation technologies

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

• Session-based

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

• Reinforcement learning

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

• Multicriteria

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

• Risk-aware

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

• Mobile

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

• AI

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

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

How to A/B test with product recommendation engines

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

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

Conclusion

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

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

With our new report, Decoding Online Shopping: Consumer Trends for E-commerce in 2024, dig into the depths of consumer preferences, exploring how they shop online, what keeps them coming back for more, and where they want to see improvements in e-commerce.

Decisions, decisions…

of consumers compare 1 or more products when shopping online.

The topics covered in this report include:

• Consumer habits: What are they buying and where?
• Influences: What factors do they consider when making a purchase?
• Frustrations: Why are carts abandoned? What are their online shopping pet peeves?
• Data, privacy, and security: Do they feel their data is in safe hands?
• AI tools: Where do shoppers expect AI assistance in the buying journey?
• Personalization: What do they want from a more tailored shopping experience?
  

Your Sneak Peek

Online shoppers contain multitudes, there’s not just one type or a one-size-fits-all solution to cater to them.

Take a look at some of the insights we uncovered in our report.

Oh, where to begin?

of consumers start their shopping journey with Google search or Google shopping.

Let’s dive deeper into the mind of the consumer together. Download Decoding Online Shopping: Consumer Trends for E-commerce in 2024 now.

The post Decoding Online Shopping: Consumer Trends for E-commerce in 2024 appeared first on abtasty.

We want to give our customers ideas for winning CRO strategies and we are going to do it with an added Christmas cheer! Implementing successful digital strategies can drive conversions, enhance user experience, and ultimately lead to a thriving online presence during the busiest times of the year.

The influx of online traffic during the holiday season demands a seamless and engaging website to keep and convert visitors into customers. In this webinar, we’ll delve into the significance of a top-notch digital presence and how it directly impacts sales and customer satisfaction. Additionally, we’ll showcase real-life success stories from our clients, giving the benefits of using winning strategies.

We are joined Alex Dyson from Merkle – a global data-driven performance marketing agency renowned for its expertise in customer relationship management, analytics, and digital marketing strategies.

We’ll look at how changing online behavior means you need to change your CRO strategy, with insights from the Luxury sector as well as predictions for 2024!

Join us as we share our winning client stories from Merkle and AB Tasty in order to make sure that you have a winning CX strategy!

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