What are the best A/B testing practices for optimizing onboarding?

Defining hypothesis and metrics is vital in A/B testing for onboarding: 1. Clarity:Provides clear objectives for onboarding changes. 2. Measurability:Offers measurable outcomes to assess success. 3. Focus:Identifies key variables, focusing the A/B test. 4. Data-Driven:Facilitates data-driven decision-making. 5. Alignment:Ensures alignment with broader business goals. 6. Iterative:Contributes to an iterative improvement process. 7. Avoids Ambiguity:Helps avoid ambiguity in interpreting results. 8. Efficiency:Guides efficient resource allocation for impactful onboarding enhancements.

A/B test is conducted to validate the efficacy of any blue print which is designed to make the service/product better (efficient, engaging etc). The intent is to test the waters before implementing the change on a larger set of users. I prefer to begin with a user set which constitutes the largest % of the total active users (let's keep the subsets aside for the sake of discussion) as it is easier to analyse the current trends, patterns and derive insights. This helps in setting informed hypothesis, projections and achievable metrics. It is recommended not to fall in trap of standardising metrics basis similar products/services/benchmarks as the user journey of your product may not be similar

Para fazer uma boa hipótese, sempre tente quebrar a solução em pequenas partes, eu busco sempre pensar em uma mudança por vez, assim, sempre que vou testar consigo ter diversas possibilidades e consigo quantificar cada uma delas. O maior erro é querer fazer um teste AB, sem um controle do que foi mudado ou fazer mais de uma mudança de uma vez e depois não saber o que afetou o resultado.

Beware of vanity metrics as key indicators! It's difficult to achieve but try tp define expected behaviors. For example, as a retailer, set key onboarding metrics as purchases within 30 days or items added to favorites, not just account creation conversions. This indicates a longer-term shopping habits experience.

Hypothesis and Research Questions should never be formulated to have a Yes/No answer, but rather allow for a more complex answer. That's why they should begin "How much..." or "to what extent does..."

To help you with this challenge, you can use online tools that calculate the optimal sample size and duration for your A/B test based on your inputs and assumptions. Some examples of these tools are Unbounce and AB Tasty. These tools can help you estimate how many users and how long you need to run your test to achieve a certain level of statistical significance and power. However, you should also be aware of the limitations and assumptions of these tools, and use them as a guide rather than a rule. You should also monitor your test results and adjust your sample size and duration accordingly if needed.

Save time, use a tool to A/B test. And if you really don't want to use one, make sure to actually give the task to someone who knows what they're doing. Don't try to do "guess work" with sample size or duration, just do the maths and execute.

When doing A/B testing is crucial that you have enough data. Otherwise you run into the issue of doing a test that wouldn't be statistically significant and then go into your decision with confirmation bias that's not really a true representation of the reality.

Para definir duração do teste, é importante ter confiabilidade na solução proposta e também metas bem definidas. E o tamanho da amostra eu sempre tento seguir uma base de 1 a 5 porcento do total, mas sempre estatisticamente proporcional.

To determine the right sample size and duration for their A/B test, EcoWear considered their baseline conversion rate (the existing rate of purchases through the mobile app) and the expected effect size of the new checkout process. They chose a 95% confidence level to ensure result reliability while maintaining practicality, a standard in such tests. Lastly, they evaluated their traffic volume to ensure they had enough users to participate in the test without affecting the regular business operations. Based on these factors, EcoWear calculated an appropriate sample size and test duration to ensure their results would be both reliable and actionable, avoiding unnecessary resource expenditure.

This part of the text is unclear and even incorrect: “…you can tailor your onboarding to different user needs and expectations, and increase the relevance and value of your product or service” What “onboarding” we are talking of there?

Continuing with the EcoWear example, the company realized the importance of segmenting its user base for more targeted A/B testing. They identified distinct user groups, such as new and returning users and those who signed up via email versus social media. EcoWear hypothesized that these segments might respond differently to the streamlined checkout process. By creating personalized tests for each segment, EcoWear aimed to tailor the checkout experience to meet diverse user needs and preferences. This approach provided more granular insights into user behaviour. It also helped enhance the relevance and effectiveness of their mobile app for different user groups.

Apply a cinema ticket analogy—testing different entrance styles for moviegoers based on preferences. Tailoring experiences increases onboarding effectiveness, enhancing user satisfaction and engagement by aligning with individual preferences and behaviors.

Leveraging user data and behavior to create personalized experiences, and optimizing engagement and conversion rates for different user profiles can be a key parameter.

Sometimes you have to resist segmentation stereotypes. I remember a time at Starbucks when we personalized prompts for mobile order onboarding by region. E.g: displaying store locations first to drive adoption for less saturated cities. Get creative based on your business.

After conducting their segmented A/B tests, EcoWear analyzed the results using statistical methods to compare the performance of the new checkout process across different user segments. They focused on whether the observed differences in conversion rates and user engagement were statistically significant and not due to random chance. They used these insights to refine their hypothesis and adjust the design, targeting specific user segments more effectively. This iterative process helped EcoWear continuously improve its app's user experience.

Emulate a game show format for user onboarding options, testing contestant-like paths. Iterating based on engagement metrics ensures a tailored onboarding experience, enhancing user satisfaction and product adoption.

From my experience, time is the real enemy. I was forced to get together the test without having proper time to prepare, which eventually led to collecting useless data and (ironically) wasting time.

A/B testing should be one of many tools in designer’s arsenal. If overused, it could lead to local maximum optimization of the product and its values conveyed to user. From time to time, a product design needs a paradigm shift, rader than incremental optimization.

In conclusion, EcoWear's experience with A/B testing highlights the importance of avoiding common pitfalls. They focused on testing with clear goals and hypotheses, ensuring sufficient data and appropriate testing duration. They avoided testing too many variables at once and segmented their users to tailor the experience. This approach helped them avoid biases and irrelevant changes, demonstrating the necessity of structured testing and personalization in understanding and catering to the diverse needs of users. Such meticulousness in A/B testing is crucial for effectively optimizing onboarding experiences.

In my experience, A/B testing mastery extends beyond the superficial allure of click-centric evaluations. It's pivotal to elevate our analysis, delving into nuanced metrics encapsulating the entire user journey.

Focusing on what we actually want to find out by A/B testing and designing our test and metrics accordingly is crucial to what we actually can measure and conclude. This is a pivotal point regarding the quality of our test - much more than a potential pitfall.

I think that alot of times people think of A/B testing as something too complex. It needs not be. Even a simple data sheet can help you achieve the results you need. You just need to make sure that your have enough data points to make your case, and then base your decisions on those coupled with your empathic decision making. UX is all about how well you process empathy; whether it's a door handle you are trying to design, or an elevator interface, or an online platform on boarding screen.

The best way would be to have good social following. It might seem indirect, but that's how you connect with people directly and ask them whatever you want information about. Once you're connected with your audience, you have option to ask them what they like or not. That's how simple this is!

In a nutshell, A/B testing is a method where we compare two versions of a webpage/app to see which one works better. It's a step-by-step process. 1st, we decide what we want to achieve, like getting more clicks. Then, we come up with ideas for changes and pick things to test, like different colors or headlines. Next, we randomly show one version to some users(A) and the other version to different users(B). We check how each version performs by collecting data on things like clicks. After that, we use math to figure out the correct one. For ex, we might test two colors of a button on a website, blue(A) and green(B). If the green button gets more clicks, and the math says it's not just luck, we might decide to use the green button everywhere.

This is like the extra slice of pizza you didn't know you needed. Let's throw in the real talk, the war stories, the unexpected wins, and the 'aha' moments that didn't fit into the playbook. What's your secret sauce for A/B testing onboarding? Any quirky anecdotes or surprising lessons you've learned along the way? Share the good stuff!

Segment users, optimize CTAs, simplify steps, personalize experiences, test visual design, vary messaging tone, experiment with progress indicators, gather user feedback, incorporate gamification, and prioritize mobile optimization for effective onboarding through A/B testing.