What A/B testing methods can you use to validate your MVP?

Always set up a 'success criterion' for your A/B test ??. This involves deciding in advance what level of improvement (like a certain percentage increase in conversion rate) would validate your hypothesis ?? It's essential for your team to know exactly what success looks like, so you can make informed decisions on whether to pivot, persevere, or stop the experiment ???? This clarity will not only guide your MVP's development but also foster a culture of data-driven decision-making within your team ????

To build effective hypothesis statements, it's vital to identify a real user or business problem with supporting evidence. Hypotheses can take different forms, but it's crucial to include these elements: Solution and/or benefit User persona Expected result We believe that [Creating this experience] For [Persona] We will achieve [Expected result]

In my time at SmartYou, we frequently used this approach to validate new features on our platforms. For instance, we hypothesized that a simplified checkout process would boost sales. By focusing on specific metrics like cart abandonment rate and average checkout time, we could directly measure the impact of our changes. It's crucial to have a clear hypothesis and targeted metrics - they guide your testing and ensure that the results are meaningful and actionable. Remember, precision in your hypothesis sets the stage for impactful A/B testing!

Defining your hypothesis and metrics is the cornerstone of effective A/B testing to validate your MVP. Clarity in articulating what you expect to achieve and the metrics that matter most is pivotal. Whether it's increased user engagement, conversion rates, or other key performance indicators, a well-defined hypothesis sets the stage for meaningful insights. Choose your test type and sample size thoughtfully, ensuring statistical significance. Then, segment and randomize your users to minimize bias. As you run and analyze tests, not only trust the data but incorporate qualitative insights. It's a dynamic process, and through iteration and learning, you refine not just your MVP but your understanding of your users and product landscape.

Along with the success metric, guard rail metrics also need to be defined while designing an AB test. Often your success metric can improve but other metrics get worse resulting in an overall unfavorable scenario. For example often discounting related feature increases sales (Success metric) but overall profitability goes down.

First of all, not all tests can be randomized based on user. Especially in marketplaces, the features that affect the networks (demand/ supply combination) require a completely different methodology to randomize features or product distribution. One such example is let's say surge/peak price algorithm improvements. If only a certain group of people will receive the new version, the results can't be effectively extrapolated to the actual population. Instead, time sliced interleaved tests are employed for such scenarios, where feature is turned on for X amount of time for everyone and then turned off. This continues for a period (calculated using minimum detectable effect required) and then results are analyzed

To be more effective when segmenting the groups try to answer these questionsto be more effective when defining the sample: - Which user segments will be included in the test? -What are the criteria that should segment follow based on the purchases behavior, amount of money expended, and which cluster they are in (Active, Dormant, etc) - Do the characteristics of the control group match those of the test group? - What sample size is necessary to measure the expected impact? - Has any remaining bias been eliminated through the use of statistical analyses or other techniques?

Use tools like Launchdarkly to help you set enough feature flags and user segments so that you will have a clear view on their actions and map against the expected behaviour

Having enough power(in simpler terms traffic) to achieve statistical significance in a short period is an innate problem with AB testing. Establish Minimum detectable effect for your metrics and reverse engineer the amount of time required for the test to run for that MDE. Often I have witnessed that the AB test required a year to provide statistically confident results. In those scenarios, AB test might not be the best option.

For one of my project, we started with a broader segment during an AB experiment. Initially the number were small, so do not directly jump to conclusions. Once the numbers were sufficient enough, we analyze. If major drop then stop & check where the drop is coming from, is it from a particular segment? can the drop be reduced if the segment is excluded? Test result should answer questions like which segments have wins? which have drops? can the segment be further segregated to analyze the drop? What would be the impact if this 'drop' segment is excluded? Learning from the experiments is always important, & considering these learnings & modifying the experiment is further important. This goes in loop until you reach a stable winning state.

From my own experience, outlining WMBT (What Must Be True) elements for each hypothesis has proven beneficial. Say for example We are testing three hypotheses, it's essential to articulate the elements necessary for each of those hypothesis to succeed. This clarity facilitates the evaluation of outcomes, distinguishing what succeeded and what didn't. Moreover, it aids in refining subsequent iterations more effectively.

From what I've learned, distinguishing empirical from normative claims is beneficial when validating the Minimum Viable Product (MVP). To pinpoint empirical claims, having supportive data is important, obtainable through various methods like surveys and experiments, providing evidence for these claims. Among the essential tools for assessing empirical questions is the experiment, which involves controlling input variables, maintaining other variables constant, and measuring their impact on output variables. My approach to constructing an experiment and addressing empirical claims includes identifying hypotheses, determining input and output variables, designing and executing the experiment, and finally, analyzing results to conclude.

MVP validation: ? Headline changes: Test different headlines to see which grabs attention. ? Call to action: Experiment with different CTAs to boost conversions. ? Feature A vs. B: Compare two features to see which resonates better. ? Pricing tweaks: Test different price points to find the sweet spot. ? Homepage layout: Optimize your homepage for maximum engagement.