Como você usa dados e insights para gerar novas ideias de hipóteses para testes A/B?

🔍Collect data from diverse sources like Google Analytics, surveys, and competitor analysis tools to identify trends and user behavior. 📊Analyze this data to uncover patterns, pain points, and opportunities for improvement. 🎯Use insights to generate hypothesis ideas focused on key metrics, such as conversion rates or user engagement. 🗣Incorporate feedback from user research, interviews, and focus groups to refine hypotheses. 🔄Benchmark your current performance against industry standards to set realistic and impactful A/B testing goals. 🚀Leverage data-driven insights to prioritize hypotheses that address the most significant business needs.

Il est également possible de collecter de la donnée directement personnalisée avec l'outil d'ab testing. Je pense à Kamaleoon par exemple. vous pouvez y ajouter des objectifs personnalisées afin d'en garantir l'exactitude de data.

For me, the best starting point has always been to look at overall funnel performance, see WHERE people seem to get stuck and then do interviews to get hints as to WHY people might get stuck there. Then generate A/B test ideas to validate any hypothesis that come from that process.

Data for generating hypothesis ideas in A/B testing can be derived from various sources. Analyzing website or app analytics provides quantitative insights into user behavior and interaction patterns. This includes metrics like click-through rates, bounce rates, session durations, and conversion funnels. Additionally, gathering data from user feedback channels such as surveys, interviews, or customer support communications offers qualitative insights into user preferences and pain points. Social media analytics, heatmaps, and usability tests also contribute valuable data for hypothesis generation by revealing how users engage with content and features.

The best testing programmes I've seen leverage experiments being run as a vehicle for capturing even more data. Close clicks, something downstream - there's never enough when it comes to data and insight of user behaviour, and it's an excellent opportunity if you're already running an experiment and so going through the cycle of dev/QA to push some additional metrics to capture into the loop, so you can feed the roadmap of future experiments. For example: We're testing on a basket page - go and track errors that people are seeing on the next step, so we can then work out if it's a big problem and what to do about it.

Data can manifest as qualitative, or qualitative. Don't ignore one for the other. Live chat logs, customer service feedback, user testing...these are all fantastic sources of data for you to inform in your experimentation program. The more data sources corroborating a problem, the higher likelihood a solution testing for solving that problem will generate revenue / improve UX.

Funnel analysis allows you to track the steps that users take to complete a desired action, such as registering or subscribing. This can help you to identify areas where you can improve the user experience and increase conversions. Heatmap analysis allows you to visualize how users interact with your website or app. This can help you to identify areas where users are having difficulty or where they are spending the most time. Finally, hypothesis testing allows you to test a specific hypothesis or claim about your data using statistical methods. This can help you to validate or invalidate your hypotheses and make more informed decisions about your website or app.

In your data analysis you have to keep in mind that correlation does not imply causation. Just because two data points are correlated it does not necessarily mean thats the cause.

Le modèle tel que Si ... est une bonne manière de rédiger une hypothèse. Il est également important de mentionner les conditions du succès de l'hypothèse (Chiffer l'objectif à atteindre) afin d'éviter tout débat sur la validation d'une hypothèse. Vous pouvez également déjà préparer plusieurs petit test A/B pour valider une Même hypothèse, ce qui permet d'être plus rapide et agile sur les itérations après analyse.

The biggest problem I've observed with hypothesis generation is a fundamental lack of connecting research TO the hypothesis. And how to accomplish that is by generating problem statements from research. Instead of spaghetti testing (e.g. throwing sh*t at the wall and seeing what sticks) - follow a structured, iterative approach by using research to generate a problem statement, THEN hypothesize on how to solve for that problem statement.

Start with your goals. What do you hope to achieve with your A/B test? Are you looking to increase conversion rates, improve user engagement, or something else? Understand your users. What are their needs, wants, and pain points? What are they trying to achieve when they use your website or app? Gather data. Collect data from a variety of sources, including analytics, surveys, and user interviews. Use a framework. There are a number of frameworks that can help you to generate and prioritize A/B testing hypotheses. Be specific. Your hypotheses should be specific, measurable, achievable, relevant, and time-bound (SMART). Be creative. Don't be afraid to think outside the box when generating A/B testing hypotheses.

The Hypothesis generation should only ever be done in two ways. You start with your app/websites status quo. Is something missing? Can you change or remove something? You then define valid reasons (backed by research) as to why this would benefit the user. The other way to do it is you start with the research. And then compare those findings to your status quo. If something differs, you have found your hypothesis in the reasoning of the research you have already done. Anything else is guesswork. Don't do it! ;D

A lot of teams reverse the process and start with the hypothesis. Only after the fact looking for "some" data to back up their initial starting point (the hypothesis). This can create a dangerous culture and more importantly unreliable results from your A/B testing.

People tend to spend too much time on quantitative data when looking for optimization points, but don't forget to take a step back and actually observe / interview a couple of real customers going through your funnel or using your product. It'll shorten your research period and you'll end up with higher quality hypothesis.

In addition to data sources and data analysis, when generating hypothesis ideas for A/B testing, there are a few other factors to consider: 1. Industry trends: Stay updated on the latest trends and best practices in your industry. This can help you identify potential areas of improvement or new strategies to test. 2. Customer feedback: Gather insights from customer feedback channels such as surveys, reviews, and customer support interactions. This can provide valuable information about pain points or areas where your product or service can be enhanced. 3. Competitive analysis: Analyze your competitors' websites, marketing strategies, and user experiences. Identify potential gaps or opportunities that you can test and improve upon.

Ideas: Utilice datos de análisis para identificar las áreas de su sitio web o aplicación que necesitan mejoras. Utilice datos de encuestas para comprender mejor las necesidades y preferencias de sus usuarios. Utilice datos de pruebas de usuario para identificar errores y problemas de usabilidad en su sitio web o aplicación. Utilice la información de sus competidores para generar nuevas ideas para pruebas A/B. Manténgase actualizado sobre las últimas tendencias en pruebas A/B.

Make sure you have a firm handle on statistics before starting your A/B testing and experimentation journey. More often than not, you don't have enough traffic to justify A/B testing BUT you can do other forms of experimentation and optimization like message testing with your ICPs (ideal customer profiles). A small-ish group of people (15-20) can help you fix 80% of your messaging and positioning IF they are exactly who you target.