What can you learn from A/B testing for Product Management?

A/B testing is a simple and easy way to validate your ideas, feedbacks by forming a hypothesis. I have often used A/B testing for optimizations that are targeted towards sign-ups, lead generation and user engagement. A/B testing - Allows you to validate versions of a product element and test which works better for users. - It enables you to test these changes without any dev release. - Helps you to study user behavior that helps in product optimization. - Provides the flexibility of testing from simple color change to revamped user flow. - Allows you to test your new product launch version against the existing one. This definitely helps you to switch back when things don't go your way.

If you're testing a "Buy Now" button that has two versions (Version A which is green and Version B which is red), we can track which colour makes more people click and buy. This is called A/B testing. It's a smart way to make data-driven decisions, refine products, and improve user experience.

A/B testing is the confrontation of two scenarios, A and B, with (ideally) only one key parameter that differs between A and B. By confronting users, randomly, in a 50/50 ratio to one of the 2 scenarios, you can grasp a trend of what scenario is better for you. This necessitates to have a statistically relevant number of users going through A or B, and to be able to monitor the KPI you want to use to choose a winning scenario.

A/B test helps test your hypothesis- By making “X” change I’m expecting “Y” result. For PM’s working especially in B2C setup it's a great way to build a culture of incremental updates based on measurable outcomes. For e.g. Let’s think about the home page of an e-commerce site. There are so many things happening there. Now how do you measure what change on this page can have a positive impact on checkout completion. The easy thing would be to say let's revamp the entire homepage:), spend months doing it and then coming up short . A/B test will help the PM do this the right way. Come up with all the hypotheses in consultation with stakeholders, prioritize and then test them. You will get data which will then drive your decision making.

One of the biggest challenges in market research is that people don’t give you the honest answer. I don’t mean they deliberately lie. I mean they say what they think they will do but, in practice, not. A/B testing is a good way to test what really happens. Do they really buy the more sustainable option, or do they still buy on price? When Sainsbury’s set out their 20 for 20 sustainability plan it was based on market research saying over 2/3 of consumers would buy the more sustainable options. They had to re-write / issue the plan when it turned out less than 1/3 was willing to pay more for sustainability. Maybe their market research questions were wrong, but A/B testing would have proven one was or the other.

In most cases, you will use A/B testing to improve conversion: - What wording works best on a CTA? - What onboarding flow has the best completion rate? - What component works best? Taken on a small scale, A/B tests will generally improve a specific action by a few points if you are lucky. However, if you use it to reduce friction on an application level scale, by chasing these few points in several areas, your conversion will significantly improve overall.

I find A/B testing a quick and relatively easy way to get feedback for rapid iteration. But I keep the ideas/changes being evaluated to a max of 2 otherwise the results get too complicated. If you are comparing more ideas/changes at once, use multi-variant testing.

A/B testing in product management should be seen a validation process. Its should be mechanism to back your decisions with data, if there is a need to do that. 1. Good Products are developed iteratively, and A/B testing should be an integral part of these iterative improvements. 2. A/B testing should be contextual and aligned with the overall product strategy. It's not about making random changes but rather strategically testing variations that align with the product roadmap and user needs. The most important thing to first understand why you want to do the A/B test. 3. Finalise the KPIs or parameters that need will be judged to analyse this decision.

A/B testing in product management indeed has its challenges. Based on my experience: Segmentation Analysis: Adding a layer of segmentation analysis to A/B test data is invaluable. It helps product managers understand the underlying factors driving the results, leading to deeper insights and more targeted strategies. Avoiding Local Optimization: To ensure that A/B tests contribute to broader business objectives, it's crucial to establish clear guardrail metrics from the start. Tests should be prioritized based on their alignment with overarching business goals, avoiding the trap of local optimization.

A/B testing discovers what resonates with your unique users - not theoretical "best practices." Outcomes depend heavily on audience specifics. A winner for Company A could flop with Company B's customers. ??♂? So while best practices provide sound starting points, the real power lies in experimentation. Testing with your users uncovers optimal engagement triggers and messaging that generic advice could never reveal. This is why testing at scale matters - to build customer intelligence customized to your business. ???♂?

To be efficient, A/B testing needs to be done with: - a significant user base (at least a thousand users per scenario) - a preset KPI you are able to monitor and whose variation will actually be triggered by the tested scenarios - a single parameter varying between A and B

A. Use the real life personas. B.. Explain the rationale of changes. C. Keep change being tested to max 2. Best practice is 1. D. Note down the frustrations or excitements as the test is on-going. F. Define the metric of testing: time to click, time to find etc.

A/B testing in Product Management provides invaluable insights into user preferences and behavior. It helps identify which features or changes positively impact user engagement, retention, and conversion rates. Through controlled testing, you learn what resonates with your audience, enabling data-driven decision-making. This approach minimizes assumptions and biases in product development, ensuring that resources are invested in enhancements that genuinely add value. Additionally, A/B testing fosters a culture of experimentation and continuous improvement, essential for evolving products in dynamic markets.

A\B provides a view. Include other appropriate test methods to garner feedback before concluding. Check the results of the other tests for trends, alignment.

-Conducting a comparative analysis allows for early identification of impacts before the final release. This approach will help the anticipation of effects, providing valuable insights into the potential outcomes prior to the product's full launch. -A/B testing will enable us: User Behavior understanding, How User Interact, Risk mitigation