When it comes to conversion rate optimization, many people approach A/B testing as if they are throwing darts at a dartboard, hoping that one of their ideas will stick. But the real winners in CRO are those who approach A/B testing like calculated moves on a checkerboard-each action informed by data and each hypothesis rooted in a deep understanding of user behavior. Just like in checkers, not every move will be correct, but when it is, the payoff can be immense.
A truly effective A/B test starts well before the test itself. It begins with a deep dive into the data and a clear understanding of the problems that need to be solved. The difference between success and failure lies in the preparation. Here are five key steps to follow for a calculated approach to CRO testing:
1. Start with Data, Not Assumptions
The wrong approach is to jump straight into testing without understanding the underlying issues. Many agencies fall into the trap of relying on assumptions about what might improve conversions, like changing a button color or rewriting a headline. However, these assumptions are just guesses without a data-driven foundation.
The right approach is to use a combination of quantitative and qualitative data to identify problems first. Start by analyzing heatmaps, click maps, session recordings, and site analytics to understand where users are dropping off and why. Combine these insights with qualitative data from user surveys, interviews, and usability tests to understand the “why” behind user behavior.
For example, Amazon uses deep data analytics to understand its customers’ buying habits. Amazon’s CRO efforts are not just about superficial tweaks; they are rooted in understanding the customer journey, which allows the ecommerce platform to recommend products and improve the user experience in a way that significantly increases conversionsÂ
2. Create a Hypothesis Based on Insights
Once you’ve gathered your data, the next step is to formulate a hypothesis. This is where many CRO efforts go wrong. Testing random elements without a clear hypothesis is like playing roulette. Instead, every A/B test should have a well-thought-out hypothesis that is directly linked to a specific user pain point or behavioral insight.
A hypothesis should look something like this:
We believe that simplifying the checkout process will reduce cart abandonment rates because users have indicated frustration with the number of steps involved.
This kind of hypothesis is grounded in data and addresses a specific issue that users are experiencing.
Netflix is a prime example of a company that formulates strong, data-driven hypotheses. When the streaming platform tested different signup flows, it didn’t just randomly pick changes. Netflix based their tests on user behavior insights, such as understanding at which step users dropped off and why. By simplifying the signup process based on these insights, Netflix was able to significantly increase its conversion rates.
Also, have you ever noticed that Netflix’s movie artwork differs greatly from what the original movie posters may have looked like? This is because Netflix is continuously testing what art resonates with its visitors. The conversion goal is to have the visitor actually click on the movie and then watch it.
Hypothesis: The default artwork did not effectively convey the movie’s story. A more effective artwork would broaden the audience and increase engagement.
Source: Convert.com
Result:
3. Segment Your Audience
Not all users are the same, and treating them as such is a mistake. Before conducting an A/B test, it’s crucial to segment your audience. Different user groups may have different motivations, pain points, and behaviors. By segmenting your audience, you can create more personalized tests that cater to the specific needs of each segment.
For instance, Spotify segments users based on their interaction with the platform. They run tests targeting different segments, such as new users versus long-term subscribers, because they understand that these groups have different needs and behaviors. This level of segmentation allows Spotify to create highly targeted tests that yield more meaningful results.
4. Avoid Common Pitfalls
One of the most common mistakes in CRO is stopping tests too early. Statistical significance is crucial, and ending a test before it reaches significance can lead to misleading results. Another pitfall is focusing on vanity metrics, such as clicks, rather than metrics that directly impact the bottom line, like purchases or signups.
A well-known failure to avoid is what happened with Google+. Google launched Google+ without thoroughly understanding user needs and motivations. Google focused on features it thought users would like, rather than testing and iterating based on actual user feedback and behavior. As a result, Google+ failed to gain traction and was eventually shut down.
5. Iterate and Learn
Even when a test “fails” to produce positive results, it provides valuable insights. CRO is not a one-time effort; it’s an ongoing process of iteration. Each test, whether successful or not, should teach you something new about your users.
Airbnb is a great example of a company that understands the importance of iteration. The company continuously tests and refines its platform, using insights from previous tests to inform future experiments. This iterative approach has enabled Airbnb to create a seamless booking experience that caters to both hosts and guests, resulting in significant growth in conversions.
Real-World Success Stories
To see the power of a calculated approach to CRO in action, let’s look at Booking.com. This company has a culture of relentless A/B testing, running thousands of tests each year. What makes its approach successful is that every test is rooted in deep data analysis. Booking.com uses behavioral data to identify friction points and formulate hypotheses aimed at solving specific issues. This data-driven, calculated approach has helped them achieve one of the highest conversion rates in the travel industry.
Another example is Shopify, which used a strategic approach to improve its onboarding process. By analyzing user data, the company realized that many potential customers were dropping off during the setup phase. Shopify formulated a hypothesis that simplifying the onboarding steps would reduce friction. After testing this hypothesis and making adjustments, Shopify saw a significant increase in completed signups and customer satisfaction.
Mastering CRO: The Strategic Balance of Data, Discipline, and Precision
Conversion rate optimization is both an art and a science. The best CRO practitioners understand that it’s not just about making random changes and hoping for the best. It’s about making calculated moves based on deep data analysis, much like a pool shark lining up the perfect shot.
By avoiding common pitfalls, formulating data-driven hypotheses, and continuously iterating, you can achieve significant wins in CRO—the kind that makes all the preparation and analysis worth it.
