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Article

Sep 13, 2024

5min read

Unlocking Hidden Revenue – A/B Testing within Single Page Applications

AB Tasty

If your organization is having trouble successfully running A/B tests in areas of your site where customers are going through the purchase flow, the issue may be due to Single Page Applications (SPAs) on your site. As customers move through the process, your A/B testing tool might not recognize their progress in an SPA environment.

There is enormous value in A/B testing critical areas of the web experience that are often operating in SPA environments, such as an eCommerce checkout.

This guest blog post was written by Jason Boal, The Principal Analyst & Optimization Strategist at 33 Sticks – a leading American analytics agency. Let’s address this common issue and uncover ways to overcome this to unlock hidden revenue on your site.

1. What is a SPA and how can I tell if the web experience uses one?

In a Single-Page Application (SPA) environment, content is loaded dynamically without requiring a full page refresh or reload. User interactions occur on a single page, with new content being loaded as the user navigates. Gmail is a prime example of an SPA. At a high level, an SPA functions similarly to a standard client-server interaction, but the key difference lies in what is returned to the browser.

To determine if you are operating in a Single-Page Application (SPA) environment on your site, pay attention to whether the page reloads as you interact online. If you see the page load indicator—such as the spinning icon in the browser tab (in Chrome)—it means the page is reloading, and you are likely using a traditional multi-page application (MPA).

Many websites are hybrids, meaning that only certain sections, like the checkout process, function as an SPA. To find out which parts of your site are SPAs, you can ask your development team for clarification.

2. Does my testing tool work within SPA environments and what do I do if it doesn’t?

Visual editors are becoming extremely popular in the A/B testing space, for many reasons. One is that Marketers are developing and launching more tests compared to the DEV team. If your testing tool has a difficult time loading and testing content in the visual editor, the reason could be that the tool is either not equipped or not set up to properly handle SPAs. This often happens in a secure checkout flow, where the customer is required to step through items like shipping address, billing, etc. The page you are attempting to A/B test on will not properly load in the visual editor and you will receive an error message.

FIGURE 1 – VISUAL EDITOR SPA ERROR MESSAGE

Ask your vendor if your testing tool can detect changes in the DOM and if it has a mechanism to look for timing.

Here are two challenges that some A/B testing tools face:

Visual Editors: Some A/B testing tools rely on the initial page load to determine what content to modify. These tools may struggle when content needs to change without a page reload. For example, if your test content is on page 3 of your site’s checkout flow, which is an SPA, the tool might not detect the need to inject content changes because there are no page loads as users navigate through the checkout flow.
Timing: As content on the page changes, it can be tricky for an A/B testing tool to insert test variations at the right moment. Variations can’t be applied before the content starts loading, but waiting until the content has fully loaded can result in users seeing the content change, a phenomenon known as “flicker.”

AB Tasty has extensive experience creating A/B tests in Single-Page Application (SPA) environments. We recommend implementing a delay on our tag’s execution so that it only triggers when the page is fully ready. This is achieved using a proprietary locking mechanism. This is just one example of how AB Tasty stands out in the A/B testing industry.

3. How do I take it to the next level?

Once you’ve unlocked A/B testing in SPAs, it is time to brainstorm testing ideas and develop a strategic roadmap to uncover ways to increase revenue for your organization. Here are a few ideas to help you jump-start that process!

Test various methods of updating cart quantity.
Test product detail page functions such as color variant selection methods.
Test buy box functions such as stock amount and store information.
Test different shipping messages based on cart value.
Test reordering flow steps.
Test navigation patterns or menu structures to optimize user flow within the SPA.
A/B test various UI/UX elements like buttons, forms, or interactive features specific to your SPA.
Test personalization strategies based on user behavior and interactions within the SPA.

Key Takeaways

There is enormous value in A/B testing critical areas of the web experience that are often operating in SPA environments, such as an eCommerce checkout flow. This is usually the last stage of any digital customer journey and vital to get right.
Determine whether or not your site leverages SPAs anywhere on your site.
Dig into your testing tool to ensure it can properly load test content changes with SPA environments.
Understand what other AB testing tools are out there and how they handle SPAs.
Develop an optimization roadmap based on your new knowledge!

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Sep 9, 2024

5min read

Failing Forward for Experimentation Success | Shiva Manjunath

AB Tasty

Shiva Manjunath shares how debunking best practices, embracing failure, and fostering a culture of learning can elevate experimentation to new heights.

In this episode of The 1000 Experiments Club, guest host and AB Tasty’s Head of Growth Marketing UK, John Hughes, sat down with Shiva Manjunath, Senior Web Product Manager of CRO at Motive and Host of the podcast From A to B. Shiva’s journey through roles at Gartner, Norwegian Cruise Line, Speero, Edible, and now Motive, has made him a passionate advocate for the transformative power of experimentation.

During their conversation, Shiva discussed the pitfalls of following “best practices” blindly, the importance of creating an environment where failure is seen as a step toward success, and how companies can truly build a culture of experimentation.

Here are some of the key takeaways.

The myth of ‘Best Practices’

Too often, the so-called experimentation best practices become a checkbox exercise, rather than a thoughtful strategy.

“If you’re focused on best practices, you’re likely missing the point of true optimization,” Shiva notes.

He recounted a situation at Gartner where simplifying a form—typically hailed as a best practice—actually led to a sharp drop in conversions. His point? Understanding user motivation and context is far more important than relying on one-size-fits-all rules. It’s this deeper, more nuanced approach to experimentation that drives real results.

“If what you believe is this best practice checklist nonsense, all CRO is just a checklist of tasks to do on your site. And that’s so incorrect,” Shiva emphasized, urging practitioners to move beyond surface-level tactics and truly understand their audience.

Embracing failure in experimentation

A major theme of the discussion was the pivotal role failure plays in the journey to success. Shiva was candid about his early experiments, admitting that many didn’t go as planned. But these “failures” were crucial stepping stones in his development.

“My first ten tests were all terrible. They all sucked,” Shiva admitted, underscoring that even the most seasoned experts start with mistakes. He stressed that organizations must create an environment where employees can experiment freely, learn from their mistakes, and continue to improve.

“If you’re penalized for running a losing test, you’re not in a culture of experimentation,” Shiva insists.

Organizations that punish failure are stifling innovation. Instead, Shiva advocates for an environment where employees can test, learn, and iterate without fear. “The idea that you have the flexibility to discuss failures and focus on, ‘Well, I ran this test. It lost. Now, what do we do next?’—that’s a culture of experimentation.”

Scaling experimentation maturity

Shiva also explored the varying levels of experimentation maturity within organizations. Many companies claim to have a “culture of experimentation,” but few truly practice it at scale. Shiva emphasized the importance of making experimentation accessible to everyone in the organization, not just a select few.

Reflecting on the loss of Google Optimize, Shiva acknowledged its role as a gateway into the world of experimentation. “I got into experimentation through Google Optimize,” Shiva recalled, recognizing the tool’s importance in lowering the barrier to entry for newcomers. He urged companies to lower barriers to entry and enable more people to engage with experimentation, thereby fostering a more mature and widespread culture of testing.

The role of curiosity and data in experimentation

Another critical point Shiva raised was the importance of curiosity in experimentation. He believes that genuine curiosity drives the desire to ask “why” and dig deeper into user behavior, which is essential for effective experimentation.

“If you’re not genuinely curious about the why behind many things, I don’t know if experimentation is the field for you,” Shiva stated, underscoring curiosity as a crucial soft skill in the field.

Shiva also highlighted the foundational role of being data-driven in any experimentation strategy. However, he cautioned that having data isn’t enough—it must be effectively used to drive decisions.

“If you’re in a business setting and the business looks at your program and this is zero test wins, right? And then after two years, they would rightfully say ‘is this the way it’s supposed to go?’” Shiva remarked, pointing out that data-driven decisions are key to sustaining a culture of experimentation.

What else can you learn from our conversation with Shiva Manjunath?

Why it’s crucial to critically evaluate industry buzzwords and ensure they align with real practices.
How true personalization in experimentation goes beyond just adding a user’s name.
The need for thorough analysis to genuinely support data-driven decisions.
Shiva’s take on the future of experimentation after Google Optimize and how companies can adapt.

About Shiva Manjunath

Shiva Manjunath is the Senior Web Product Manager of CRO at Motive and Host of the podcast From A to B. His insatiable curiosity about user behavior and deep passion for digital marketing have made him a standout in the world of experimentation. With experience at top companies like Gartner, Norwegian Cruise Line, and Edible, Shiva is dedicated to demystifying CRO and pushing the boundaries of what’s possible in the field.

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.

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Aug 29, 2024

8min read

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

AB Tasty

A/B testing is often seen as the magic bullet for improving e-commerce performance. Many believe that small tweaks—like changing the color of a “Buy Now” button—will significantly boost conversion rates. However, A/B testing is much more complex.

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.

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Aug 28, 2024

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Hotel Chocolat at CX Circle: Sweetening Loyalty with Experimentation

John Hughes

Welcome to a world where chocolate isn’t just a treat but an experience—a world crafted by Hotel Chocolat, a British group with nearly 31 years of rich history. At the heart of their journey lies a realization: loyalty isn’t bought with discounts—it’s earned through authentic connections and shared values.

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:

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.
Listening and Understanding Customers: If loyal customers aren’t heard and understood, they’ll lose their preference for your brand and start considering others.
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:

Hotel Chocolat at CX Circle

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Aug 19, 2024

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From Clicks to Connections: How AI is Shaping the Future of Digital Optimization

AB Tasty

Any marketer will tell you that Digital Optimization is crucial to ensure successful e-commerce operations and yield the best possible return on investment (ROI). This practice includes both A/B testing and website personalization: every website presents a unique set of features and designs, which must, in turn, be optimized through A/B testing.

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

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.

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.

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!

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Jul 18, 2024

4min read

Transaction Testing With AB Tasty’s Report Copilot

Hubert Wassner

Transaction testing, which focuses on increasing the rate of purchases, is a crucial strategy for boosting your website’s revenue.

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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Jul 16, 2024

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The Past, Present, and Future of Experimentation | Bhavik Patel

Maddie Ostrander

What is the future of experimentation? Bhavik Patel highlights the importance of strategic planning and innovation to achieve meaningful results.

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

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What is One-to-One Personalization in Marketing? (With 8 Examples)

Emily Healy

It’s no secret that today’s digital marketplace is highly competitive. Consumers are exposed to an increasingly high number of messages each day. How can you make your message relevant to your consumers and break through the noise?

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

one to one personalization in marketing

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.

1-1 personalization customer segmentation

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?

Higher conversion rates
Increased average order value (AOV)
Reduced cart abandonment
Improved brand value and customer loyalty
Higher customer lifetime value
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's recommended for you

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'a customizable shoe

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.

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Jun 18, 2024

14min read

How To Build A Customer Journey Map

Emily Healy

Understanding your customers’ paths is no easy task. Each user has their own unique reason for visiting your site and an individual route that they take as they explore your pages.

How can you gain insights about your customers to improve your website’s usability and understand buying trends?

The answer is simple: build a customer journey map.

In this blog, we’ll dive into a few things: what is a customer journey, a customer journey map, how to map the customer journey visually, templates of different customer journeys, a step by step guide for how to create them, and examples of customer journeys in action. Let’s get started

What is a customer journey?

A customer journey is a combination of all the interactions customers have with your brand before reaching a specific goal.

Creating a compelling journey helps you stand out and shows customers that you care about their experience. An enjoyable customer journey promotes positive engagement, making for more satisfied customers that are more likely to return for repeat purchases.

By better understanding your customers, you’ll be able to provide them with the best possible user experience every time they visit your online store. The best way to do this is by creating visual customer journey maps that present all this information about customers at a glance.

What is a customer journey map?

A customer journey map is a visual representation of a customer’s interaction with your business or website. It’s used to define which parts of this process might not be working as smoothly as they should be, thus improving the customer’s experience.

The customer journey map is a (mostly) visual tool that helps businesses understand what a customer goes through when buying a product or service from them. It maps out in clear, concise, visual terms, the journey each customer is likely to experience through buyer personas and user data.

The best customer journey map is a story, brought to life visually, of the customer’s experience. In essence, the best customer journey map is a story, brought to life visually, of the customer’s experience. It should be noted, however, that more complex information on the map may require text.

The map itself highlights “touchpoints, which are specific elements of the customer’s interaction with a business. Each of these touchpoints – for example, seeking a product, researching its content, buying the product, waiting for delivery, and returning it if unsatisfied – can be judged as negative, neutral, or positive from the customer’s perspective.

Customer journey maps require various research techniques that include hard data, customer feedback, and creative thinking. As such, no two maps are the same and each one will depend on many different factors that can’t be simplified or stereotyped as a matter of course.

The heart of customer journey maps: Buyer personas

Buyer personas are at the heart of a customer journey map tool and are broad representations, presented as fictional characters, based on real-life data and customer feedback. Typically, each project will create between three and seven buyer personas, each of which will require its own customer journey map.

The point of the customer journey map is to understand, as clearly as possible, what a customer will encounter when using your service. It will also help you improve the elements that are not functioning properly, are not easy to navigate, and show you how to make the entire experience more satisfying.

Each persona, and therefore the journey map itself, is not meant to be a perfect illustration of actual interactions. Rather, it’s a broad representation of the experience from the persona’s perspective.

Who Can Benefit From A Customer Journey Map?

There are many reasons why a customer journey map can be useful to a business. Customer satisfaction is more important than ever to a business, and it’s tied to loyalty to an extent that has not previously existed. Customers are more demanding, aware of their options, and willing to shop around.

By mapping each of the previously mentioned touchpoints, a well-designed customer journey map template can highlight any problems that clients might experience in the process of interacting with a business and help foster a relationship with an organization, product, service, or brand. This can occur across multiple channels and over a long period of time.

Once a customer journey map template has been designed, the entire enterprise can keep the customer at the forefront of the decision-making process. With a focus on the customer and their experience, or user experience (UX), any kinks, holes, or brick walls within the timeline’s touchpoints can be ironed out.

Bringing Together All Aspects Of The Business

Customer journey maps can help a business by bringing together departments with a focus on customer experience. To begin with, all departments can be engaged to discuss issues that customers may face when dealing with them. This is no small thing as many departments may not be used to dealing with customers, yet the decisions they take may have a profound effect on UX. By creating an understanding of how each touchpoint affects UX across the entire business, decisions can be made from an empathetic perspective.

Traditional marketing stops at the point of purchase, but customer experience does not necessarily end there. For example, perhaps the purchase was not to their satisfaction and they want to return the goods. Departments that might not typically be involved in touchpoints before purchase now have a central role to play. How easy is it for the customer to find the return information on a website? If they need information on delivery or collection times, how likely are they to get a response that will satisfy them? This all requires forethought and a policy that keeps customer experience central to design and organization.

How to map the customer journey visually

A customer journey map is a visual representation that helps you gain better insight into your customers’ experiences (from start to finish) from their point of view.

There are two vital elements to creating a customer journey map:

Defining your customers’ goals
Understanding how to map their nonlinear journey

By mapping out a customer’s digital journey, you are outlining every possible opportunity that you have to produce customer delight. You can then use these touchpoints to craft engagement strategies.

According to Aberdeen Group (via Internet Retailer), 89% of companies with multi-channel engagement strategies were able to retain their customers, compared to 33% of those who didn’t.

To visually map every point of interaction and follow your customer on their journey, you can use Excel sheets, infographics, illustrations, or diagrams to help you better understand.

Customer journey maps also help brands with:

Retargeting goals with an inbound viewpoint
Targeting a new customer group
Forming a customer-centric mindset

All of these lead to better customer experiences, which lead to more conversions and an increase in revenue.

Want more information on the digital customer journey? Check out our digital customer journey resource kit for a detailed e-book, an editable workbook, a use case booklet, and an infographic.

Examples of Customer Journey Map Templates and Which to Choose

There are four different types of customer journey maps to choose from. Each map type highlights different customer behaviors as they interact with your business at different points in time. Choosing the right template is essential based on your goals.

Current state template

The current state template is the most commonly used journey map that focuses on what customers currently do, their way of thinking, and how they feel during interactions.

It’s great for highlighting existing pain points and works best for implementing incremental changes to customer experiences.

2. Future state template

The future state template focuses on what customers will do, think, and feel during future encounters. It’s useful for conveying a picture of how customers will respond to new products, services, and experiences.

3. Day in the Life Template

This template is similar to the current state template because it visualizes present-day customer behaviors, thoughts, and feelings. However, this template assesses how customers behave both with your organization and with peers in your area.

This type of journey map works best for spurring new initiatives by examining unfulfilled needs in the market.

4. Service Blueprint Template

When creating a service blueprint template, you typically begin with an abridged version of a current or future state journey map. Then you add a network of people, methods, procedures, and technologies responsible for giving a simplified customer experience, either in the present or in the future.

Current state blueprint maps are beneficial for recognizing the source of current pain points, whereas future state blueprint maps help create an environment that will be necessary for providing a planned experience.

How to Create a Customer Journey Map (7 Steps)

Creating customer journey maps may feel repetitive, but the design and application you choose will vary from map to map. Remember: customer journeys are as unique as your individual customers.

Step 1: Create Buyer Personas

Before creating a journey map, it’s important to identify a clear objective so you know who you’re making the map for and why. Building personas is the most time-consuming part of the process. It requires detailed research, including qualitative and quantitative data, and is the foundation of the entire process. A persona is a highly relatable and rounded fictional character, generalized, but not stereotyped.

Buyer personas help define customer goals, providing a deeper understanding of their needs and topics of interest. More detail makes for more realistic personas, which means you’ll need to do a fair amount of market research to acquire this data.

Start by creating a rough outline of your buyer’s persona with demographics like age, gender, occupation, education, income, and geography. When you have that in place, you’ll need to get psychographic data on your customers. This kind of information may be harder to collect compared to demographic data, but it is worthwhile to understand customer preferences, needs and wants.

In short, demographics tell you who your customers are and psychographics provide insights into the why behind their behavior. Collecting concrete data on your customers helps you serve them better and deliver a more personalized user experience.

Collecting concrete data on your customers helps you serve them better and deliver a more personalized user experience.

Step 2: Select Your Target Customer

After making several customer personas, it’s time to do a “deep dive” into each to build a more accurate reflection of their experience.

Start by analyzing their first interaction with your brand and mapping out their movements from there.

What questions are they trying to answer? What is their biggest priority?

Step 3: List Customer Touchpoints

Any interaction or engagement between your brand and the customer is a touchpoint.

List all the touchpoints in the customer journey, considering everything from the website to social channels, paid advertisements, email marketing, third-party reviews, or mentions.

Which touchpoints have higher engagement? Which touchpoints need to be optimized?

All customer journey mapping examples are unique. Therefore, touchpoints on one map are unlikely to work for another. In fact, every business needs to update its buyer personas and customer journey maps as their business changes. Even quite subtle changes can have profound effects on the customer journey map template.

Step 4: Identify Customer Actions

Once you have identified all your customer touchpoints, identify common actions your customers make at each step. By dividing the journey into individual actions, it becomes easier for you to improve each micro-engagement and move them forward along the funnel.

Think of how many steps a customer needs to reach the end of their journey. Look for opportunities to reduce or streamline that number so customers can reach their goals sooner. One way to do this is by identifying obstacles or pain points in the process and creating solutions that remove them.

This is a great time to use the personas you created. Understanding the customer will help you troubleshoot problem areas.

Anticipating what your customer will do is another important part of mapping the customer journey. Accurate predictions lead to you providing better experiences, which ultimately leads to more conversions.

Step 5: Understand your available resources

Creating customer journey maps presents a picture of your entire business and highlights every resource being used to build the customer experience.

Use your plan to assess which touchpoints need more support, such as customer service. Determine whether these resources are enough to give the best customer experience possible. Additionally, you can correctly anticipate how existing or new resources will affect your sales and increase ROI.

Step 6: Analyzing the Customer Journey

An essential part of creating a customer journey map is analyzing the results.

Now you have your data, customer journey mapping template, touchpoints, and goals, it’s time to put it all together and define where the UX is meeting expectations and where things can be improved. It is important to note that mapping where things are going well is almost as important as defining what isn’t. Some elements of the journey can be spread to other areas.

As you assess the data, look for touchpoints that might drive customers to leave before making a purchase or areas where they may need more support. Analyzing your finished customer journey map should help you address places that aren’t meeting customers’ needs and find solutions for them.

Take the journey yourself and see if there’s something you missed or if there is still room for improvement. Doing so will provide a detailed view of the journey your customer will take.

Follow your map with each persona and examine their journeys through social media, email, and online browsing so you can get a better idea of how you can create a smoother, more value-filled experience.

One of the best ways of pinpointing where things are not going to plan is through customer feedback. This is typically done through surveys and customer support transcripts.

Step 7: Take Business Action

Having a visualization of what the journey looks like ensures that you continuously meet customer needs at every point while giving your business a clear direction for the changes they will respond to best.

Any variations you make from then on will promote a smoother journey since they will address customer pain points.

A great way to test your variations to find out what better serves your customers throughout their user journey is by leveraging A/B testing.

AB Tasty is a best-in-class A/B testing solution that helps you convert more customers by leveraging experimentation to create a richer digital experience – fast. This experience optimization platform embedded with AI and automation can help you achieve the perfect digital experience with ease.

Analyzing the data from your customer journey map will give you a better perspective on changes you should make to your site to reach your objective.

Once you implement your map, review and revise it regularly. This way, you will continue to streamline the journey. Use analytics and feedback from users to monitor obstacles.

Customer Journey Map Examples

Customer journey map templates are varied, some appear like works of art, while others are the work of a child, but as long as they are clear and concise, they can be effective.

This customer journey map for the charity ‘The Samaritans’ is a highly empathetic map, focused on the purpose of the charity itself. Note how the text is highly visual and therefore makes it easy to relate to the image of the map itself.

This is an example of a map that gives the impression of a journey, rather than a linear UX. This can help push home the point that customer experience is rarely easy to define as a journey from A to B.

The Truth about Customer Journeys

Customer journeys are ever-changing. Journey maps help businesses stay close to their customers and continuously address their needs and pain points. They provide a visual of different customers which helps to understand the nuances of their audience and stay customer-focused.

Customer journey maps can vary widely, but all maps share the same steps. With regular updates and the proactive removal of roadblocks, your brand can stand out, provide meaningful engagement, improve customer experiences, and see positive business growth.

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Jun 10, 2024

13min read

Customization vs Personalization: What’s the Difference?

AB Tasty

How do you grab a customer’s attention when our world has us on digital information overload?

Capturing attention and standing out in a market saturated with so many options is a challenge now more than ever. To overcome the digital noise, many businesses are aiming to enhance the customer experience with two approaches: customization vs personalization.

What’s the difference between customization and personalization? Are they two terms we can use interchangeably or are they two entirely different concepts?

You guessed it – customization and personalization describe two very different, but not entirely unrelated, practices. Though not the same, (mass) customization and personalization strategies revolve around people’s deep-seated desires for self-expression and recognition – as well as their limited attention spans in a loud, digital world.

Let’s take a deep dive together into the sea of information we have about customization vs personalization.

What’s the difference between customization and personalization?

Customization and personalization are often thought of as synonyms, but while some crossover certainly exists, they are very different terms referring to very different practices. They overlap in their ability to make the customer feel as if they have a unique relationship with the brand in a way that the mass marketing of the past could never achieve.

Both practices have proven particularly well suited to Millennials, the largest consumer age group, who have an instinctive mistrust of older forms of advertisement but are more influenced by both customization and personalization-based marketing. So what exactly are the differences and how do they work?

Customization Definition: Giving the power to the users

The most common form of customization comes in the form of product customization and is typically found online. Product customization is particularly popular for clothing outlets, whereby shoppers can design a piece of clothing from an online template, using different colors, fabrics, and shapes to make their own ‘unique’ product.

Customization examples:

Nike is one of the most important innovators in the field, allowing you to customize every aspect of your sneaker.

Another brand that has used product customization to great effect is Burberry, which has gone from strength to strength in recent years, partly due to being an innovator in the field of product customization.

This is sometimes referred to as ‘mass customization’ whereby online tools allow the customization process to produce bespoke products at mass-produced prices.

What is mass customization?

Mass customization is all about customer experience, sometimes referred to as CX, which marks an enormous shift from shopping habits that were once almost entirely about the product.

Interacting with brands and producing individual styles, or at least appearing to, is at least as important to Millennials as the functionality of a product, and there is little reason to think that subsequent generations will not have similar expectations in the future, especially with technology becoming so ubiquitous and powerful. Wherever retail goes in the near future, CX is going to be an essential part of it. While the cutting edge of customization is not a major preserve of older generations, the need to keep such platforms simple and easy to use should not be ignored.

One of the issues facing businesses offering product customization is how quickly can each purchase be produced? Convenience is also thought to be a major concern for shoppers in the 21st century, and patience is not seen as a virtue. This goes to the heart of the customer experience and will likely inform how successful the model is for each business. The more complex mass customization becomes, the larger on-demand mass production will be.

One thing is for sure, what was once the preserve of the burger joint or ice cream parlor, customization is fast spreading to all aspects of retail, and as a result, is changing everything.

Personalization Definition: Tailor your user’s entire experience

Personalization is one of the most misunderstood marketing terms of all.

Often misunderstood for customization, recommendation, and even optimization, it is instead a set of well-defined practices with an overall purpose or goal. Unlike customization, which offers a very specific set of tools for customer interaction, personalization has far-reaching methods and possibilities and is currently at the very beginnings of its potential. It also touches upon issues of privacy, politics, and generational divides.

In the broadest sense, personalization is marketing designed with the individual in mind. Obviously, there is no single human observing individual tastes to present products or services that are most relevant. This is where highly sophisticated algorithms come in.

One type of personalization is the recommendation engine, although it should be noted that personalization is not a type of recommendation. The most common of these are websites and streaming services, such as YouTube or Netflix. If you have ever used these sites you will know that certain box sets or videos are recommended based on previous viewing habits. To begin with, these were obviously connected programs.

For example, if you had watched a basketball documentary, there’s a good chance that one on baseball would interest you. So far, so simple. But, as recommendation engines become more sophisticated, seemingly unconnected content works just as well. The same type of algorithms are used in retail, most famously by Amazon, and include Email campaigns that are tailored to the individual and addressed personally. This is the beginning of personalization.

Personalization also uses segmentation, for example, individual traits, such as age, gender, or location, all of which can profoundly change how marketing information is presented. Beyond that, a person’s politics, browsing behavior, and even ethical concerns can be taken into account.

We should pause here to consider the two main versions of personalization, rule-based and machine learning.

Rule-based personalization relies on the previously mentioned segmentation model, whereby the audiences are broken down into both broad and granular segments, such as age or location.
Machine learning personalization, on the other hand, uses algorithms. These can be those used in recommendation engines and even parts of segmentation marketing, such as behavior.

Whereas basic algorithms can provide broad data, such as what is trending, recommendation engines, and the like, provide more in-depth information for the individual.

Personalization vs customization in e-commerce

Customization can come in many forms, from fast food restaurants that encourage customers to design their food their way, to online stores that allow visitors to design their own clothing.

Personalization, on the other hand, is a far more complex practice that takes into account customer (or potential customer) behavior in order to market the right products, to the right person, at the right time. Using big data and powerful algorithms, personalization is becoming a more powerful tool every day, with a diverse number of businesses implementing it to great success.

Getting the most out of both practices requires an understanding of how they both work and the major differences between them.

You can certainly see the similarities between customization and personalization:

Both aim to make consumers feel uniquely understood and marketed as individuals.
Both put a high value on self-expression and personal tastes and experiences.
Both create the illusion that consumers are being given white-glove treatment and have a one-on-one relationship with the brand.
Both, in a way, involve the consumer as a co-producer of value – for customization, it means relying on their predilections to craft the product, and for personalization, it’s sharing their data (unbeknownst to them or not) to create personalized marketing messages.

Ultimately, through a product strategy on the part of the company, mass customization is driven by the consumer. Especially appealing to Millennials, it’s a way of validating their own sense of self through ‘build-a-bear’ style product production.

With personalization, the onus is much more on the company to bring value and deliver meaning to the consumer audience they’re targeting.

People naturally crave to be in control of their surroundings and personalization techniques create a cherry-picked environment that feeds into that need. Bargain hunting for low-priced kitchenware from your favorite outlet? If ads for the very items you’re looking for suddenly appear all over the internet, it somehow creates a feeling of empowerment, as if in some impossible way, your wishes and needs automatically manifest.

As for information overload, this is the classic argument that contemporary consumers are exposed to a dizzyingly high number of marketing messages, far too many to consciously register, remember, or act on. Personalization strategies both (hopefully) limit the number of marketing messages a consumer is exposed to in the first place, as well as stimulate the brain to recognize these stand-out strategies, as opposed to letting them sit in the background of our consciousness. This is the idea of selective attention or the fact that your brain will automatically pick up on potentially important stimuli – i.e. those most relevant to you.

Web Personalization And Privacy Concerns: Customization and Personalization

Privacy is a major concern for many consumers and personalization has sometimes seemed more like an unwelcome stalker to some as a result. With legislation being debated in various corners of the world, what is deemed as overstepping the mark should be a concern for all.

All generations have some concerns regarding personalization and privacy, not helped by various data leaks and hacks of major companies. The truth of the matter, however, is that personalization shouldn’t be “creepy.” Transparency is a great way to instil trust with customers. Personalization need not be a back-door practice, where using personal data is hidden behind jargon and misleading declarations. Most website visitors are happy to share data as long as they know how it’s being used, especially if there is some advantage in it for them.

Being able to opt-out is another great way to make customers feel as if they have some control over the process. In some cases, the personalization process itself can be customized. While most users won’t bother engaging, they will appreciate the option to.

Evolution From Mass Marketing To One-To-One Marketing

Mass marketing is where a product or service is marketed to an entire population. It essentially treats everyone the same, with the same needs. Although that is clearly not the case, the philosophy revolves around the idea that the more people who receive the message, the more likely you will reach someone who is interested. It’s essentially the practice of selling low-cost and homogenous items at high volumes. While it’s more miss than hit, mass marketing was well suited to mass media markets, such as television, which had the majority of the population engaging with it on a regular basis.

Mass marketing began in the 1920’s, with the advent of radio. The popularity of this form of media made it ripe for advertisers to market products in a way that wasn’t possible before. As attitudes shifted over the decades, mass marketing’s influence rose and fell until the 1980’s and 1990’s, when it reached its peak.

History of mass marketing timeline:

1920’s – Begins with the advent of radio
1930’s – The great depression reduces its influence
1940’s and 1950’s – With income rising, its effectiveness becomes relevant again through the “Mad men” era
1960’s and 1970’s – A rise in anti-Capitalism sees its influence wane again
1980’s and 1990’s – The peak years of mass marketing during the economic boom

The history of one-to-one marketing is essentially a history of the Internet. When the first HTML dialogue occurred on Christmas Day, 1990, it set in motion the beginnings of personalization. Also in 1993, Webtrends was founded, which was essentially the first commercial web analytics program. Unfortunately, only those well-versed with the technology had any idea of how to read the data, so its effects were minimal.

Things carried on at pace, however, and log file analysis made it possible for non-tech people, most importantly marketers, to make use of the data. This was soon followed by hit counters and Javascript tagging, which became important as the Internet began to use more imagery. With few people using the Internet, however, the advances made during this time were not to be truly helpful for a few years to come.

It wasn’t until 2004 that the type of web analytics we know today began to appear and by 2005 Google had released Google Analytics. This allowed website owners to dig further into the data than had been previously possible, with concise visuals that allowed for easy reading of in-depth information. It’s at this point that personalization becomes more tangible, with conversion rate optimization becoming a particular focus.

Machine learning personalization, such as recommendation engines, soon began to be useful in a way that was not possible before, as algorithms began to exponentiate their capabilities, with Amazon and Netflix leading the way. From Email campaigns to accurate predictions of preferences, the practices of personalization became ubiquitous by 2008.

Mouse tracking and eye tracking also added profound data that improves visitor experience and thus increases interaction. With a deeper understanding of customer habits, personalization is beginning to become more accurate, focused, and effective.

With the advent of multiple devices using the Internet, Google released Universal Tracker in 2012. With more profound data at its fingertips, demographics, behavior, and lifestyle began to be segmented more accurately, further categorizing customers for more predictable results. App personalization becomes more and more important as phone use begins to outstrip laptops/desktops for online use. Machine learning on mobile soon improves.

Soon after, personalization magnifies the effectiveness of CRM (Customer Relation Management), which focuses on user experience and customer retention. This only becomes possible as big data is collected at ever higher rates, allowing companies to truly understand their customer’s needs.

With the use of A/B testing, the future of personalization is now highly managed from beginning to end. No longer is trial and error at the forefront of designers of websites or marketing campaigns. Behavior on site is monitored to a level thought unimaginable just a few years ago and personalization is becoming truly individualized. User experience is now at the heart of personalization, and with the likelihood of more powerful algorithms and customer understanding to come, personalization seems to be very much in its infancy.

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How to start your personalization strategy

Website personalization shouldn’t be a complicated undertaking. In fact, it is becoming easier, and therefore more widespread, every day. With several tools at a business’s disposal, there are many ways to go about creating personalization that works for each business. In other words, personalization should be personal for each business using it.

The first place to begin is getting to know an audience. Too many start with the concept that the product is key and then try to persuade an audience that they are right. This is similar to a waiter insisting that the customer has made the wrong order when they chose the duck and bringing them beef instead. As should be clear now, customer experience has become one of the most important aspects of personalization, and that cannot be achieved without getting to know exactly what it is your customer expects from the interaction with your website.

Want more on personalization? Read our E-book: Your Guide to Personalization

What’s Better: Personalization or Customization?

You want your customers to feel unique and have a positive experience with your brand. For you, this could mean implementing personalization tactics or dabbling into the world of product customization. Bettering your customers’ experience could mean focusing on customization, personalization, or a mixture of both. If you design your roadmap with your customers in mind, you’ll find customer loyalty and satisfaction along the way.

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

What is one-to-one personalization in marketing?

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

How do you find user data?

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

How to personalize interactions with customers:

What messages should you personalize?

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

What is omnichannel personalization?

What are the advantages of omnichannel personalization?

8 Examples of 1-1 Personalization strategies from retail brands

1. ASOS’s Social Connection

2. Nordstrom Remembers Your Size

3. Clarins personalization and gamification

5. Nike and Their Customized Approach

6. Net-A-Porter’s Personalized Touch

7. Coca-Cola’s Name Campaign

8. Target’s Guest ID

Everyone wins with one-to-one personalization

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Who Can Benefit From A Customer Journey Map?

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Current state template

2. Future state template

3. Day in the Life Template

4. Service Blueprint Template

How to Create a Customer Journey Map (7 Steps)

Step 1: Create Buyer Personas

Step 2: Select Your Target Customer

Step 3: List Customer Touchpoints

Step 4: Identify Customer Actions

Step 5: Understand your available resources

Step 6: Analyzing the Customer Journey

Step 7: Take Business Action

Customer Journey Map Examples

The Truth about Customer Journeys

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What’s the difference between customization and personalization?

Customization Definition: Giving the power to the users

Customization examples: