It has happened to everyone. And it will keep happening. Many people, when taking their first steps in CRO and optimizing their digital business, fall into the same trap: trying something new and validating it quickly with an A/B test. What at first seems like a great idea ends up becoming a problem.
The problem we face is none other than the lack of a coherent and solid testing strategy. That is: a good A/B test planning that meets the right criteria and endures over time. And this is a very common problem among digital businesses.
In this article we will explain why in almost 100% of cases isolated A/B tests end up failing. But above all we will explain the importance of properly planning your testing strategy within your CRO efforts. Trust us: it will be worth it.
It is very easy to fall into the trap — or the temptation. Many companies think they see it clearly: it is as simple as testing a green button against a red button. And within hours the result should be firm and conclusive. Spoiler: that is not how it works.
In fact, between 70% and 80% of A/B tests are inconclusive. Or in other words: three quarters of the tests run by digital businesses do not yield correct data. And that translates into poor business decisions — or at least not the most appropriate ones.
To be able to trust your test results, you need a solid strategy. No matter how simple and straightforward an isolated test may seem, the conditions in which it runs can very negatively affect the results. Here is why:
Let us be clear about something: A/B testing on its own does not generate results. To use it as a CRO tool, it needs a good method. A methodology that extends to before, during, and after the test itself.
Isolated tests face many challenges that make results weak, barely significant, and unable to support good business decisions. While every test is different and the variables that affect it are too, here are the most common mistakes they face.
For a test to yield useful information, the problem must first be correctly identified. Otherwise, any change or improvement we make will be based exclusively on our intuition.
That is precisely why a good A/B test starts with an analysis of the prior situation: what does the data tell us? What needs to improve on our website? Is there any unusual behavior or metric? Web analytics is the only source of information you can trust.
With the problem clear, the next step is to form a hypothesis. A good A/B test has only one objective: to confirm or disprove that hypothesis. That is why it is important to commit to a single variable we want to confirm. If we try to cover everything, we will not know what is driving the results.
A/B tests are not a matter of a couple of users. Unless your digital business has enormous traffic, you will likely need quite a bit of time for the test to be significant. That is why being patient matters so much.
Running a "quick" test to draw conclusions as fast as possible is pointless. Your test must have significant traffic for you to conclude which variant is better. That traffic must be comparable between variants and, without a doubt, representative of your user base.
It is also important to take into account the characteristics of the traffic itself. If, for example, the majority of your conversions come from mobile devices, do not forget this characteristic when setting up your test: run it directly in that format so the conclusions are more solid for your business model.
In addition to traffic, you must also consider the duration of the test. Regardless of the traffic you have, quality A/B tests need a certain amount of time to be conclusive. Otherwise, they can be skewed by an endless number of things.
Server outages, specific high-importance dates, issues with your business operations... There are countless external factors that directly affect test results and that is precisely why it is important to have a sufficient time window so that the results "reflect" a normal situation.
Learning is cumulative, not isolated. Or in other words: everything you learn from one A/B test can help you with the next one. And that is precisely the reason why good planning and a testing method are so important for CRO.
Optimizing your website does not depend exclusively on a single test. Many variables affect your results and that requires a plan of different tests that support each other and help you find the definitive version for your business. And that plan must be comprehensive.
Your A/B tests must be well organized and planned, with different priorities and a clear calendar. This way you can draw independent conclusions and, as your plan progresses, arrive at that version that will ultimately drive your results.
The main difference between a completely isolated A/B test and a quality CRO strategy is having a good plan. A plan from start to finish that considers thorough prior analysis, a clear organization of priorities, orderly execution, and constant learning:
Digital analytics from start to finish — To obtain real results for your business, you need to base all your decisions on the data you have. Before, during, and after every test. Data is the only source of truth you can trust — not your intuition.
Identification of insights and hypotheses — A good CRO strategy is always based on a series of hypotheses that respond to specific business problems and are grounded in verified, reliable information (e.g. heat maps, user interviews, or key metrics).
Test prioritization and clear roadmap — CRO can often feel overwhelming. Many improvements, many hypotheses, and many actions require an order that is carried out in a controlled and gradual way. There is no point in starting to test without any direction.
Constant learning and iteration — CRO is not something you do once and then forget. Every test must be part of an ongoing improvement effort, with constant iterations and new hypotheses that allow conversion to be maximized as much as possible.
In short, what you need is a good plan. A CRO strategy that properly plans the before, during, and after of your A/B tests and allows you to make business decisions that benefit you in the long run.
Define clear hypotheses based on your data — Start by identifying the main problems affecting your conversion and generate hypotheses focused on a single variable so you can validate them one by one.
Make sure you have adequate traffic and time — Analyze your website traffic thoroughly and the characteristics of your audience and test to define the ideal conditions and setup. Take the time needed to ensure results are reliable.
Prioritize and schedule different tests — Organize your priorities well according to the hypotheses you believe could most significantly impact your business results. Then plan all your tests on a calendar, making sure they do not overlap.
Measure the real impact on clear KPIs — Do not be fooled by the direct results of a test alone — you need to understand the complete picture. Your business has clear priorities and you must also know how to measure other important metrics for your future.
To truly understand the results your A/B tests yield, you need to understand their limitations. If you do not want to jump to hasty conclusions and make decisions that do not reflect the reality of your business, you need to move away from isolation and toward a good CRO plan.
Choosing your priorities wisely, defining good hypotheses, and taking the time needed to draw conclusions is part of a worthwhile process that ultimately results in business growth through more strategic decisions.
If you want to start designing a solid, reliable CRO strategy that stands the test of time, contact us so we can get to work — guided by your data, of course.
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