10 tips to avoid the most common pitfalls in experimentation

10 tips to avoid the most common pitfalls in experimentation

10 tips to avoid the most common pitfalls in experimentation :

  1. Define your hypothesis at the outset with specific parameters – This has some advantages. 1- It is very clear to everyone involved what measures you are trying to influence and the team is aligned with this objective. 2- Since it is a hypothesis, you have already inherently accepted that there is a risk of failure, so you avoid the reputational risk and pressure that many teams feel to deliver successful features. 3. You become less likely to be derailed by changes in metrics that are not primary to the experiment.
  2. Make sure there is sufficient tracking from the start – There is nothing worse than waiting a few weeks to realise that the metrics you specified are not being accurately tracked. This often requires your developer to have a good idea of the context of the experiment, what measures will be affected and how they will be affected.
  3. In order to properly engage stakeholders and ensure that other business changes do not impact on your results, you will need a rough timeline. To understand the duration of an experiment, you need to estimate the approximate change expected and how quickly you can reach the sample size (the rest is just statistics).
  4. Define the counting parameters you will monitor – Before you start your experiment, you should also define other business parameters that may be indirectly affected by your experiment. This is crucial if you are in a complex business and are experimenting with high-risk ideas. These countermeasures ensure that your experiments do not have negative effects on you and your partners.
  5. Experiment in phases, gradually increasing the risk and reward – New and innovative ideas are by definition high risk and high reward. To reduce exposure, start with smaller elements of your hypothesis or smaller changes in the experiment. If the basics work, you can gradually add more functionality and complexity, iterating as necessary.
  6. Design high-speed tests – To increase the impact of your tests, design high-speed experiments. If experiments are successful, redouble your efforts and optimise them further; if they fail, try something new.
  7. Triangulating results with qualitative user data – It is sometimes difficult to fully understand why certain experiments succeed or fail. This is where in-depth user understanding and qualitative research is the missing piece of the puzzle. By overlaying the results of the experiment with user research and sympathy, you can gain insights that won’t be revealed by the experiment alone.
  8. Tailor your experiences to different personas – Often, changes to the user experience across your entire user base are not statistically significant as a whole. Only some of your users will respond positively, and if they are a small proportion of your base, you won’t see a significant change. Instead, analyse your experiences for each segment separately and consider which segments will need their own backlog of experimentation ideas.
  9. Keep customers in the same treatment – To avoid inconsistency in the customer journey and contamination of your data, you can attach an experience ID to each of your users, ensuring that they don’t see multiple variations of the same experience. Most commercially available experimentation platforms have this functionality out of the box.
  10. Consider external changes – Often, the actions of your competitors or general market conditions can change the way your users react to a certain experience. Seasonal trends are a common example: users behave very differently when they sign up for self-improvement programmes in January than they do during the rest of the year. If you have an ongoing experimentation pipeline, keep in mind which experiences may be vulnerable to these changes and consider retesting them under different conditions.
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