How do you use A/B testing to leverage behavioral economics or psychology principles?

Using psychology to inspire A/B tests is great, but not as easy as it seems. For every bias, there is another bias that goes against it. Most start with Cialidini's principles. Then you can start reading about biases. A great online page to follow up later is '250 Best A/B Testing Ideas Based On Neuromarketing' (go google!) Additionally you can read the work of Dan Ariely (Predictably Irrational) or Kahneman (Thinking Fast and Slow). Have fun!

At one point in our lives, we all learned about homo economicus, modelled with the assumption of the perfectly rational human. Turns out, we humans are not that rational after all. We buy products only because others have them (social proof) or because nobody has them (anti-conformity). We hold losing stocks way too long (loss aversion), while we sell winning investments way too early (disposition effect). We even make different decisions based on how information is presented although the meaning is exactly the same (framing). Be aware of biases that might occur with your AB test. Sometimes, you may want to test for different biases or completely rule them out. Be intentional about it, otherwise you might distort your test results.

Consider the way you frame messages in a gain-framed (positive) or loss-framed (negative) context. According to behavioral economic researchers, Tversky and Kahneman, in their assertion of prospect theory, people perceive gains and losses differently, and typically act to advert losses in favor of gains. People may be more motivated to take action and convert when the benefits associated with doing so are explained. Conversely, users may be more motivated to take protective action when the risks of failing to do so are outlined. Framing your messages in a gain-framed, positive, benefit-based context or in a loss-framed, risk-based context can motivate certain behaviors. Test to see which approach works best for your site and audience.

Tapping into behavioral economics and psychology gives us a peek into how people think. By using these insights in A/B testing, we can create products that really vibe with users, making them more engaging and accurate with what they want.

Some famous books in the field of behavioral economics: - Predictably Irrational - Thinking, Fast and Slow - Nudge - Freakonomics read them all, and then start experimenting

Before you start your A/B testing, you need to identify the behavioral insights that are relevant for your product and your users. You can use various sources of data and research, such as user feedback, surveys, interviews, analytics, competitors, best practices, and academic literature. You can also use frameworks and tools, such as the Fogg Behavior Model, the Hook Model, or the BJ Fogg Tiny Habits Method, to map out the factors that influence user behavior. The goal is to find the key pain points, goals, motivations, and triggers that you can address with your A/B testing.

Before starting A/B testing, identifying relevant behavioral insights is crucial for targeting the right aspects of user behavior. Gather data from user feedback, surveys, interviews, and analytics to understand underlying motivations and pain points. Utilize frameworks like the Fogg Behavior Model or the Hook Model to map out user behavior drivers, such as triggers and motivations. The goal is to pinpoint key factors that influence user actions, which can then be addressed through your testing strategies.

When creating user journeys, whether for A/B tests or not, it always helps map out the positive and negative psychological aspects involved with each step. I use the Psych Framework from Darius Contractor. It revolves around 1. Psych units - basically points (positive or negative) that you attribute to each step and; 2. Psych Elements - The elements on the journey you're analyzing. For example, an extended formulary with many fields (Element) will have a Negative Psych (negative points). 3. Use Miro or Google Sheets to design your User Journey and assign values to each step. You can A/B test different flows and pages to validate your hypothesis. It's worth it to read the original content on Andrew Chen's blog.

Let's face it this way, product managers, are like treasure hunters seeking hidden riches, embarking on a journey to uncover the behavioral insights that hold the key to successful A/B testing. Their tools are user feedback, surveys, interviews, and analytics, providing a map of the hidden treasures of user pain points, motivations, and triggers. The ultimate goal is to identify the behavioral gems that drive user engagement, conversion, and satisfaction. By understanding these behavioral patterns, product managers can design A/B tests that unlock these treasures, leading to a more optimized and user-centric product experience.

Identify behavioral insights by studying human behavior, observing user interactions, and analyzing customer feedback. Use existing research and data to identify patterns and biases that can be leveraged in your A/B testing. Consider factors like cognitive biases, emotional triggers, and social influences that can impact decision-making. Look for opportunities to apply behavioral economics and psychology principles to your A/B testing.

Cooking up hypotheses based on behavioral insights is like making a recipe. First, look at the weird stuff people are doing, then make an educated guess about what might change if you tweak things a bit. That's your hypothesis, the secret sauce for your A/B test.

Firstly, identify key user behaviours from available data and research. Try to understand underlying motivations, triggers, and pain points. Then, predict how specific changes might influence these behaviours. Frame it as: "If we make [specific change], then [expected behaviour] will occur due to [underlying insight].

Hypothesis is a proposed explanation for what you observed. It's basically an educated guess → Example: I believe that users abandon the PDP, because they believe that the offer is going to be there tomorrow and they take their time to make a decision Prediction is about what will happen under specific conditions if the hypothesis is correct (these are commonly called hypotheses, although they are NOT!) → Examples: 1. If I add a countdown, then the CR will increase 2. If I add how many items are left, then ? Failed tests ≠ wrong hypothesis If you've tested only one prediction, you should continuously test more Use hypotheses carefully to get small scoped, simple solutions that help you understand why you're doing what you're doing

Identify key behavioral insights from user data or research, focusing on specific user triggers or motivations. For example, if evidence suggests users are influenced by social proof, your hypothesis could be: "Adding user reviews on product pages will increase conversions by enhancing trust." Define each hypothesis clearly and ensure it is measurable, stating exactly how the change will be tested and what metric will improve. This method provides a direct, testable path for applying behavioral insights to A/B testing.

Formulate hypotheses by asking questions that test specific behavioral economics and psychology principles. For example, "Will people respond better to a loss aversion message or a gain framing message?" or "Will people be more likely to complete a task if it's gamified or if there's a social component?" Use these questions to create specific, testable hypotheses that can be validated through A/B testing.

Designing variants with behavioral insights is like giving your experiment a snazzy makeover. Take what you've learned about how people behave and tweak your website, app, or whatever you're testing to see if you can make it more user-friendly or enticing. So that way, you can see if your changes really make a difference in how people act.

To design variants using behavioural insights, pinpoint key user motivations and pain points. Create alternative versions of your product or feature that address these insights directly. Ensure each variant reflects a potential solution or appeal to the identified behaviour, then test to validate its effectiveness.

Designing variants based on behavioral insights is crucial for creating impactful A/B tests, and PickFu simplifies this process. To test your hypotheses effectively, you need to create a control variant (the original version) and at least one treatment variant (the modified version) that incorporates changes based on the identified behavioral insights. PickFu's user-friendly platform helps you ensure that your variants are consistent, clear, and relevant to your target audience. For example, when testing the social proof principle, you can use PickFu to create variants showcasing different types of testimonials, ratings, or reviews on your product page.

Designing variants for A/B testing based on behavioral insights involves incorporating changes that reflect an understanding of human psychology to influence user behavior effectively. Start by ensuring you have a clear control variant, your baseline or original product version. Then, develop one or more treatment variants that apply behavioral principles. For instance, if leveraging the social proof principle, introduce variations in how testimonials, ratings, or reviews are displayed on your product page. Make sure each variant is consistent with the overall design and clear in its intent, allowing users to react naturally to the changes. This clarity ensures that the data you collect is a true reflection of the changes’ impact.

Design variants that incorporate the behavioral insights you've identified. For example, if you're testing the effect of loss aversion, create two versions of a message: one that highlights the potential loss and one that highlights the potential gain. Use these variants to test your hypotheses and measure the impact on user behavior.

Running and analyzing A/B tests with behavioral insights is like being a digital scientist. You set up two versions, see how people react, and then do some number-crunching to figure out which one's the winner – like a tasty bake-off for websites.

For A/B tests rooted in behavioural insights, first ensure clear hypotheses. Run tests, then analyse results focusing on user behaviours you aimed to influence. Compare variants' performance, and delve into why certain designs resonated based on the behavioural principles applied. Then adjust and iterate based on your findings.

Running and analyzing A/B tests based on behavioral insights is essential for optimizing your product, and PickFu streamlines the entire process. To ensure reliable results, follow best practices such as defining your sample size, duration, and success metrics, randomizing and segmenting your users, and using a trustworthy platform like PickFu. The tool's built-in features help you monitor and evaluate your results using statistical methods, including confidence intervals, significance levels, and effect sizes. By comparing the performance of your variants, you can determine whether your hypotheses are confirmed or rejected. PickFu's comprehensive reporting also allows you to identify findings that may reveal new behavioral insights.

Running and analyzing A/B tests based on behavioral insights requires precise setup and evaluation. Define testing parameters like sample size, duration, and success metrics to ensure robust data collection. Randomize and segment your users to capture diverse behaviors and preferences. Utilize reliable A/B testing tools for accurate impact measurement. Apply statistical methods like confidence intervals and significance levels to assess results. Compare variant performances to verify or refute your hypotheses. Watch for unexpected results that could reveal new behavioral insights.

Run and analyze your A/B tests using statistical methods like t-tests or ANOVA to measure the significance of the results. Look for statistically significant differences between the control and treatment groups, and use those results to inform future design decisions. Use data visualization tools to help illustrate the findings and make it easier to communicate with stakeholders.

Iterate and optimize your A/B testing by refining your hypotheses based on the results of previous tests. Use feedback from stakeholders and customers to identify new areas for improvement, and continue testing new variants to validate or refute your findings. Continuously refine your approach based on new insights and data-driven results.

It is important to have a clear hypothesis for every AB test, which will help you to achieve your research goal. I find it helpful to approach AB tests as a conversation with the customers. With each test, I am implicitly asking a question, my research question for the specific test, to the customer. The customers reply with their individual actions. Through this, I am gaining a better understanding of potential customers' behavioural patterns, allowing me to deepen the conversation. Thus, I narrow down the research question per test, eventually answering the overarching research goal.

Example: If we know that users repeatedly ask if the product is original, incorporating elements that appeal to authority bias (as Ciadini expresses it) can generate a huge effect!

Focus on the novelty, creativity, and learning potential of each test. This encourages exploring new ideas, testing hypotheses that challenge existing assumptions, and learning from each test’s outcomes, irrespective of it being a success or failure. Measurement: Scored from 1-10, where 1 represents tests that are safe, traditional, and closely follow existing benchmarks or practices with little new insights gained. In contrast, a score of 10 would represent tests that are highly innovative, challenging the status quo, potentially leading to significant breakthroughs or new understandings, even if they carry a higher risk of failure.