What are the most effective A/B testing practices for product managers in product engineering?

It's not just defining the hypothesis and metrics at this stage but also creating a test plan. Here's how I have done it in the past: - Objective (What you aim to achieve) - Insight (Context to what led to the creation of the test. Data trends, market insights, user behaviour, etc). - Description (Detailed description of what the A/B test entails) - Hypothesis (Expected outcome or educated guess of how the changes will impact the metrics) - KPIs (Metrics used to measure the success of the test) - Reporting Requirements (How results will be documented, reported, frequency, format, which stakeholder will receive the report). - Timings & Dependencies (Timeline of the test, start date, dependencies on other teams and project)

?? A/B testing is a linchpin in product decision-making. I'd methodically define hypotheses and pertinent metrics to gauge the test's success. Choosing an apt test design and determining a robust sample size are vital for credible results. Diligent monitoring during testing ensures accurate execution. Analyzing outcomes provides actionable insights, guiding informed product modifications. Transparently communicating these insights with stakeholders cements data-driven decision-making. This structured approach to A/B testing empowers product managers to refine products with precision and confidence. ??????

I agree, clear hypothesis and metrics definition is a key. And make sure you clearly define each before you move to next step and be sure that all the metrics are really aligned with the goals and objectives of the product.

Effective A/B testing involves setting clear objectives, forming hypotheses, using randomized sampling, considering segmentation, ensuring sufficient sample sizes, focusing on key metrics, monitoring secondary metrics, emphasizing statistical significance, documenting results, fostering cross-functional collaboration, and adopting an iterative approach for continuous optimization.

In simple language, before starting an A/B test, clearly state what you think will happen and how you'll measure it. This is your hypothesis and metrics. For example, if you believe changing the color of a button will increase clicks, your hypothesis is "changing the button color will increase clicks," and the metric is the number of clicks. This helps you focus on what you want to achieve and measure the impact accurately.

When conducting A/B tests, it's important to carefully choose how you'll run the test and the number of users involved. This is called test design and sample size. Imagine you're comparing two versions of a webpage. Ensure that the design is fair and the sample size is large enough to draw reliable conclusions. It's like making sure your experiment has enough participants to give meaningful results.

A key design element that people get tripped up on is "when should you stop running the test?" PMs often assume that you can stop the test as soon as you see a stat sig result, but this is can lead to poor outcomes. Your initial few test results can be biased, and this can give you false signals very early on. Ex: you are working with a product that has weekend/weekday differences in demand patterns. If you launch on a Friday, you will see results by Saturday. Ideally, you should wait atleast a full week before generalizing results. When deciding test stop criteria, instead of stopping as soon as test is stat sig, the better approach is to set a sample size goal or duration goal. This can smartly account for biases in your data.

After setting up your A/B test, it's time to run the experiment and closely monitor the results. Think of it as launching your test and keeping a watchful eye on how users interact with each version. This allows you to collect valuable data and observe any significant differences between the variants, helping you draw meaningful conclusions.

Once your A/B test has run its course, it's crucial to thoroughly analyze the results. Dive into the data, compare the performance metrics of each variant, and draw conclusions based on statistical significance. This phase is about understanding the impact of the changes you introduced and determining which version performed better in achieving your goals. Accurate analysis is key to making informed decisions for future iterations and improvements.

Findings need to be reported and communicated in detail tailored to stakeholders level of understanding.? The report could include test overview, test insights, overall conversion rate if this is what was getting measured, device types, conversion rate by device, visitor type, revenue forecasting, recommendation, next steps and anything else that would make it easier for the person going through the report to understand.? Any screenshots in the report can be immensely helpful too for stakeholders to visualise the changes.

After completing the analysis of your A/B test, the next crucial step is to communicate your findings effectively. Share the results with relevant stakeholders, including team members, decision-makers, and anyone impacted by the changes. Clearly present the data, insights, and the implications for the product. Based on these findings, collaborate with the team to implement changes or make informed decisions about the next steps. Clear communication ensures that everyone is on the same page and can contribute to the product's continuous improvement.

A/B Testing, while a valuable tool, inherently involves uncertainties and variables; it is not a perfect science, and getting to a Stat Sig win does not mean you should immediately push your feature to production. A/B Testing is a tool that should be used to improve your confidence in the solution you plan to build and eventually productionalize. As a Product Manager, it is your responsibility to use your best judgment to make the best decision on a path forward for your product using imperfect information. A/B testing provides valuable insights, but it's crucial to recognize its limitations and contextualize findings within the broader product strategy.