How to Use Bad A/B Testing Results to Unlock Big Digital Wins

Maria Domenica Baquilod

Author & Editor

Social Media Team Lead

Published on: Oct 12, 2023 Updated on: May 17, 2024

Data-driven A/B testing results are some of the best sources of insight for any brand or expert digital marketing agency. You can unlock a lot of growth from these split analysis findings, especially when you know how to interpret and leverage your data properly.

But what happens when your experiment goes awry? If your findings for your pay-per-click (PPC) ad turn out to be negative, or your p-value calculator returns results you don’t expect, how can you expect to bounce back from this unexpected “failure”?

If bad test results are your worst nightmare, then this guide is for you. By unlocking various data interpretation tips, tools, and digital marketing services, you can easily leverage your findings and turn them into something productive for your brand. No matter the insights, you can stand to gain from your split analysis – as long as you know how to utilize them the right way today.

Make data-driven decisions with A/B experiment design and turn big failures into the biggest testing wins. Check out this extensive guide from the experts at Propelrr to find out how right now.

How to interpret A/B testing results

To garner helpful information from a split experiment, marketers like you need to know how to analyze and interpret data properly. Interpreting the information from any marketing experiment, however, can be difficult without the right guidance. Here are some tips to interpret your A/B analysis effectively, along with key learnings you can gain from “bad” test findings:

Statistical analysis and its significance to your testing

Statistical analysis is a technique used to analyze, interpret, and understand data collected during A/B tests. In this process, you utilize statistics to see if the results you garnered are significant enough to be considered as valuable; this allows you to accept or reject your collected information in order to form a reliable conclusion from your experimentation.

Accurately interpreting results

In order to interpret your A/B experiment findings accurately, you need to ensure their statistical significance first. To do so, you may utilize statistical methods such as a t-test or a chi-square test. You can even use a p-value calculator to see if you have enough statistical power to reject your null hypothesis and declare your insights as sound.

At the same time, you have to be able to consider the context of your experiment’s findings, and the external factors which may have influenced your collected insights. If you’re testing out a Facebook ad, for example, then you need to account for things like the popularity of the platform among audiences, or the time at which you ran your comparison.

By considering these external and contextual factors, you can get a fairer interpretation of the information you collected from your split experiment, thus providing you with better insights for your brand’s page optimization needs.

Common interpretation mistakes

Interpretation mistakes still abound, even among the most expert of marketers. Some common interpretation mistakes include:

  • Non-specific hypotheses;
  • Miscalculated sample sizes;
  • Confirmation and selection biases;
  • Forgetting to account for external seasonality factors, and many more.

Avoid these common pitfalls to keep your experimentation findings as clean as possible. But if you still get “bad” results from a clean test, you can still utilize them for the benefit of your brand’s PPC campaigns, content marketing, social media, and more.

With all these interpretation tips in mind, how else might you unlock the benefits of a “negative” finding? Let’s move on to some more explicit ways you can unlock the power of your tests, even if they return “negative” results for your brand.

Unlocking the power of negative A/B testing interpretation results

An experiment that returns a negative result can be seen as “bad” or a “failure,” but if you look at your split comparison in the right light, then you might find it to be even more valuable than a comparison with positive or “good” findings. Here are some ways you can shift your mindset in order to unlock the power of these “failed” A/B runs:

Identify critical issues in your design

With negative findings, you can immediately spot if there are critical issues in your experiment’s design. Did you accidentally commit any of the common pitfalls mentioned in the previous section? If you did, then you can identify and rectify your mistakes right away in order to avoid them in future runs.

Observe underlying causes and external factors

You can also take this as an opportunity to conduct and interpret a root cause analysis, beyond the current scope of your data. Were there underlying causes or factors that could have affected your work, like seasonal influences, customer trends, or other external factors?

By interpreting your test in this way and observing these root causes, you can either take steps to better isolate your next run, or you can use it as an opportunity to learn more about your external environment. You never know, you might end up discovering new things about your audiences or brand’s market through these “bad” or “failed” runs.

Key learnings from “bad” test results

You can learn so much from a “bad” experiment. Here are some of the most relevant key learnings that we have found over the years, to extract benefits from findings that don’t necessarily match with your initial marketing assumptions or hypotheses:

  • Keep going, even if your results don’t meet your KPIs. If your findings don’t meet your established KPIs, this should at least signal to you that you need to take a step back in order to analyze why your experiment “failed.” By doing so, you can derive a ton of value to guide subsequent content marketing experiments.
  • Identify the test’s “why”s. Don’t take your collected information at face value; take time to dig deeper and understand the “why”s of your “failed” experiment to see how you got to the insights you received in the first place.
  • Make sure you’re using the best combination of tools. With the right combination of A/B tools, you can have confidence in the insights you extract from the data you collect, even if they don’t meet your expectations. A system with analytics and tag management tools, for example, can help you with future analyses and still bring forth helpful insights.
  • Be confident in your findings. When you present results to the leaders of your brand, show confidence in your insights. Talk about your findings versus the KPIs, and the key ad optimization insights you derived all the same. Be prepared with next steps too, so that they can understand your plan and be happy with your insights – even if they come from a “failed” run.

By shifting your mindset and refocusing your perspectives, you can make the most out of every run – even the ones that “fail” or don’t meet your expectations today. Just always remember to improve on every iteration of your data collection process, to ensure you aren’t making the same mistakes again and again for your brand’s marketing.

How to improve A/B testing results with tools and technologies

To be able to constantly improve and leverage any form of data collection with ease, you’ll need tools and technologies for streamlining your information interpretation process. Here are some examples of software you can use to improve how you analyze your findings today:

  • AB Tasty – A classic piece of software for A/B experimentation, AB Tasty runs and automates your A/B, A/B/n, multivariate, and other experiments, allowing you to test out your digital marketing executions with little to no risk.
  • Optimizely – Aside from running an effective experimentation system for marketers, Optimizely also offers a content management system, a customized commerce suite, and many other services on its advanced platform.
  • VWO – An especially helpful platform for data interpretation, VWO helps marketers to maximize their marketing executions by offering behavior analytics and other automated tools for information analysis.
  • Adobe Target – Last but not least is Adobe Target. With its AI-powered experimentation, omnichannel personalization, and at-scale automation, you can easily gather and dissect collected data for better interpretation and utilization today.

With the help of these tools and technologies, you’ll be able to run comparisons and collect information with ease. You’ll also have an easier time breaking down your findings and formulating sound conclusions – thus enabling you to make effective more data-driven decisions for your brand in the long run.

Small changes for A/B testing that yields bigger results

With data-driven decision-making supporting your executions, you can ensure bigger, better results for your business over time. Of course, “bad” findings can seem like a bump in the road of this process, but as mentioned before, they can also contribute to better, more efficient split analyses for your brand in the long run.

Learning from your mistakes can enable you to reduce redundancy and optimize strategies for better executions. This will help you save further on resources and accelerate your development cycle all throughout your brand’s digital marketing. By making these small improvements and changes all the time, you can innovate your experimentation process and yield bigger results for your brand today.

Key takeaways

Learn from your mistakes and win big with your A/B analyses. Here are some final reminders for your journey as you take small steps towards big wins for your brand right now:

  • Base your analysis on both statistics and context. To get a holistic understanding of your collected information, you need to use both statistical and contextual analysis to paint a better, fuller, more informed picture.
  • Shift your mindset on “failed” runs. Take each failure as an opportunity to ask more questions and discover more underlying causes, as these can lead to innovative solutions in the long run.
  • Get expert support for difficult analyses. Having a hard time understanding your collected data? Don’t be afraid to utilize tools or ask for help from the experts at Propelrr to make the most of your data today.

If you have any other questions, send us a message via our Facebook, X, and LinkedIn accounts. Let’s chat!

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