There are a lot of testing methods available to marketers seeking to increase their website conversion rates. Two of the most popular methods are through A/B testing and personalization.
Both testing methods can be extremely effective when used on their own, but can also be used together to optimize your web experience in different ways.
But at the end of the day, the purpose of these testing methods is to improve the experience for prospective buyers and generate more conversions.
There are 5 key areas where A/B testing and personalization differ:
Audience approach: Who is involved in the test?
Experiment parameters: What is being tested?
Experiment velocity: How quickly are tests run?
Technical resources required: What setup is required to run the test?
End goal: How will the results of the test be used?
This post will also discuss what conversion results can be expected following running an A/B test or personalization.
As we answer each of these key questions, you'll see the differences between A/B testing and begin to see how each method can be used in your work.
A/B tests are typically run to all the traffic being sent to a particular page. Some visitors are shown the original control page, while others are shown the modified testing page. There's no differentiation between visitors, and which version they see is randomized.
With personalization, there's a focus on a particular segment visiting the page. Visitors who match the parameters for that segment are diverted and shown a particular version of a page. For example, you might want to run a test only for website visitors who work in the financial industry, or at companies larger than 1,000 employees, or maybe both!
A/B tests typically constitutes making structural changes to the website to see how it influences user behavior. Edits like moving around a section or bringing the social proof and logo section to the top of the page. A single variable will be modified at a time. This happens so you can prove or disprove whether the change in structure led to an increase in conversion.
Personalization focuses more on conversion and content, less about the structure. So the scope of a personalization experiment might be to change the H1 header, the image, and the copy of the call to action to make it more relevant to the segment being shown the webpage. The effects of these changes will be measured against an increase or decrease in conversion rate.
A/B tests are run sequentially. Once a test reaches statistical significance, the modification becomes part of the control test and a new test begins. Because A/B tests are looking to see which changes affect the behavior on the page, only one test can be run at a time as not to influence the results of any other experiment.
Personalization experiments can be run in parallel across different segments. Personalization is additive, meaning you’ll learn something new about the segment whether the conversion rate goes up or down.
Those buyer insights can then be used to influence the next experiment, or dovetail off into a new experimental thesis. This allows you to get compounding results to increase your overall conversion rate and increase your experimentation velocity by launching more experiments at a faster rate.
A/B testing requires structural modification to the website, which often requires additional engineering resources above what most marketers are able to do alone. This is to ensure that the website doesn’t break, and also so that the testing parameters are consistent.
Personalization doesn’t require as much heavy modification. This means that the content of the website can be edited, the experiment can be launched, and results can begin being collected in a matter of minutes using Mutiny – no engineering resources required.
The goal of A/B testing is to isolate individual variables on the website and find how modifying them leads to a change in user behavior.
The goal of personalization is to generate quick learnings about a specific buyer persona and apply those learnings quickly so they compound over time.
Now that we understand the differences between A/B testing and personalization, let’s explore the results we get from running these two experiments.
The two metrics to consider when understanding how A/B testing and personalization are experiment win rate, and lift in conversions.
The experiment win rate tells us how often an experiment leads to a statistically significant winning result. This is what we want. The more often an experiment leads to a winning result, the closer you get to a fully optimized web experience.
With traditional A/B testing, the win rate is about 25% of the time.
With personalization, the win rate is about 75%
Why? Because with personalization, you have access to many more data points and integrations to tailor your message exactly to the audience. Data enrichment tools like Clearbit, 6sense can be used to get data on the target audience. And connecting to your CRM like Hubspot or Salesforce allows you to personalize down to the contact level.
A/B testing is an iterative process where you prove or disprove the hypothesis you have about how a change will influence the behavior of the user. The test parameters are often built based on previous experience, best practices, or from insights gleaned from previous tests.
At the end of it all, the only thing that really matters is how many more conversions resulted from running that test.
With traditional A/B testing, the conversion lift is about 3-5%.
With personalization, the conversion lift is usually around 30-50%.
Why? Because A/B testing is constantly modifying the webpage to find the most average experience possible. This adjustment over time will influence the average conversion rate of the page once a statistically significant number of visitors have seen it.
The conversion lift from personalization can be quite high because there are a number of improvements being made in parallel, all improving the conversion rate in their own way. And because the audience is pre-defined, a change in headline or content can significantly improve the landing page experience for the user.
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