Before we tackle whether personalization has replaced A/B testing, let’s slow down and take a minute to talk about the two most popular Conversion Rate Optimization (CRO) tools used by marketers today.

First, we have personalization, a CRO tool that has increased in popularity over the last 18 months. When you think about it, this is no major surprise. Your website should be your best sales asset, and the best way to do that is to cater a message/offer to the right person, at the right place, and at the right time.

Then there’s the reigning king of optimization tools, A/B testing. A/B testing, simply put, is the process of taking two different pages, splitting the traffic, and seeing which increased your desired conversion metric. This conceptually simple process that supercharges the bottom line has grown from a sparsely used tool to a cornerstone in a digital marketer’s tool belt.

The sixth annual Conversion Rate Optimization Report by RedEye & eConsultancy found that 67% of companies A/B test. This is the fourth year in a row that surveyed companies have ranked A/B testing as the #1 CRO method.

Key Takeaways

  • Complementary Tools: Personalization targets specific user segments, while A/B testing verifies changes. Both are essential for effective CRO.
  • Sample Size Matters: A/B testing needs a large sample size for valid results, which can be challenging with personalized segments.
  • Rising Trend: Personalization is increasingly popular, with many marketers using segmentation and dynamic content to boost conversions.
  • Strategic Implementation: Personalization should focus on key segments like new vs. return visitors, device type, and geolocation, using behavior metrics for refinement.
  • Balanced Approach: Use A/B testing for larger groups and apply insights to personalize smaller segments, ensuring continuous optimization.

Personalization vs. A/B Testing

Venn diagram showing the relationship between A/B testing and personalization in optimizing customer experience.

Both tools are extremely powerful and every marketer should already be using them. For those who are looking for instant gratification, here’s the answer to the question asked in the title: No, personalization has not replaced A/B testing, and here’s why:

The two tools are foundationally different! Personalization is meant to hyper-target a visitor with dynamic content based on advanced segments.

A/B testing is a verification tool – nothing more, nothing less. If we do not verify changes made to our site, marketing campaigns, or emails in a test environment we lose data & insights. Without data and insights, digital marketers are flying blind and aren’t making changes based on what works best, but what they think works best.

Personalization’s Popularity

Growing Trend in Personalization

Graph showing the relationship between personalization maturity and revenue, from single message mailing to predictive personalization.

Personalization is rapidly growing in popularity. A WhichTestWon study showed that 59% of marketers are doing some kind of segmentation work and 32% are using dynamic content. Personalization, as mentioned before, is a combination of the two practices, i.e., providing relevant content via dynamic insertion for a recognized segment.

Historical Context of Personalization

Comparison of A/B testing and contextual personalization showing audience preferences and visibility for discount offers and free shipping.

Personalization is not new; marketers have been using these tactics before having the ability to dynamically insert content based on advanced segments. Think about abandoned cart emails; these messages were sent to a specific subset of visitors and became one of the best retargeting strategies in an e-commerce marketer’s arsenal.

Best Practices for Personalization

Infographic showing various data types for personalization: location, technology, traffic source, 3rd party data, behavior, explicit data, time, and current page.

Personalization, like A/B testing, shouldn’t be done haphazardly. Identify your most important segments, and begin targeting them with personalized content. Here are some segments to initially target if you are new to personalization:

  • New vs. Return Visitors
  • The Visitor’s Device
  • The Visitor’s Geolocation

Analyzing Page Behavior for Better Segmentation

Dashboard displaying product metrics for riders, including rider activation, rides completed, new reward users, rides by mobile OS, riders by country, and retained riders.

I also recommend looking at page behavior to properly segment offers. Metrics to look at include page depth, time on site, scroll patterns, etc.

Identifying Opportunities for Personalization

Illustration of scaling-up personalization with people, charts, and a robot, highlighting data-driven strategies.

This is only the tip of the iceberg when it comes to personalization. The real trick comes in identifying pages that can benefit from personalization. Dig through your analytics to identify problem pages and ask yourself, ‘Will adding personalization help this page?’ If you answered ‘Yes’ then build your hypothesis and test it out!

A/B Testing As A Verification Tool

A/B testing dashboard with a highlighted button to create a new A/B test.

As I said above, A/B testing is a verification tool, nothing more. After you’ve developed a test hypothesis and launched your test, your A/B test will only tell you if the control or variation won. Again, that’s it!

The power of A/B testing comes in how a marketer develops the test and analyzes the results. This is why no other optimization tool we add to our testing tool belt will usurp the king of CRO. Personalization is a tool to increase conversions, whereas A/B testing is the tool that verifies if it is working.

Where The Two Butt Heads

Sample Size and Representation Issues

Comparison of success rates between two samples showing no significant difference with a 95% confidence level.

For an A/B test to be valid, you need both a large and representative sample size. This is where tension occurs between the two methods. Personalization is only triggered on a subset of traffic, so it reduces the sample size and is not a true representation of the whole. That said, there are times when you simply cannot test your personalization efforts because you don’t have a big enough sample size. 

Furthermore, the learnings gathered from small-scale personalization efforts are not scalable to other aspects of your site. That said, you can still use personalization on these pages! Just because you can’t test, doesn’t mean you can’t optimize – and personalization is one of the best ways to optimize in 2014-2015.

Complementary Use of Personalization and A/B Testing

Comparison of two website variations for A/B testing: Variation A with a single product image and Variation B with multiple product images.

Just Use Both! Repeat after me: “Not all pages are test-worthy.” Once you understand this important CRO maxim, it is easy to see how personalization and AB testing complement one another. Personalization has a proven track record of increasing conversions, but due to highly targeted segments may not be testable. 

However, you can take the learnings you get from testing larger subsets of traffic and apply these learnings to these ‘un-testable’ segments. Just like any good CRO campaign, you have to pick your battles. Know when and where it is appropriate to test and focus your A/B testing efforts there. As for the pages that are not test-worthy, use tangential data and other CRO tools, e.g., personalization, to lift your conversion rate!

Frequently Asked Questions

What are the two types of personalization?

The two main types of personalization are explicit and implicit personalization. Explicit personalization is based on data that users actively provide, such as preferences, interests, and demographics. Implicit personalization relies on data collected from user behavior, such as browsing history, purchase patterns, and interaction with content. Both types aim to deliver a more relevant and engaging user experience by tailoring content to individual needs and preferences.

Why do people want personalization?

People want personalization because it makes their interactions with brands more relevant and enjoyable. Personalized experiences can save time by showing users content that matches their interests and needs, increase satisfaction by addressing specific preferences, and build a stronger connection with the brand. In a world with abundant choices and information, personalization helps cut through the noise and delivers value directly to the user.

What is an example of personalization?

An example of personalization is an e-commerce website that displays product recommendations based on a user’s previous browsing history and purchase behavior. For instance, if a user frequently browses for running shoes, the website might highlight new arrivals or special offers on running gear on the homepage. This targeted approach increases the likelihood of engaging the user and driving conversions.

What is the goal of personalization?

The goal of personalization is to enhance user engagement and satisfaction by delivering content and experiences tailored to individual preferences and behaviors. By understanding and anticipating user needs, businesses can create more meaningful interactions, foster loyalty, and ultimately drive better business outcomes such as increased conversion rates, higher customer retention, and greater overall revenue.

What are the three levels of personalization?

The three levels of personalization are:

  • Basic Personalization: This includes simple changes like using a user’s name in emails or displaying recently viewed items on a website.
  • Segment-based Personalization: This level targets broader user segments based on shared characteristics or behaviors, such as showing different homepage content to new visitors versus returning customers.
  • Individual Personalization: The most advanced level, where content and experiences are tailored to the specific needs and preferences of each user, often leveraging machine learning and AI to predict and respond to user behavior in real time.