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In technical terms, Experimentation is a systematic approach to testing hypotheses and making discoveries through controlled investigation. The goal of experimentation is to explore cause-and-effect relationships by manipulating one or more variables (independent variables) and observing the effect on some outcome (dependent variables).
In simple terms, it is trying different things out to see what works best. It's like asking a question and then finding the answer through a hands-on data-driven approach.
Imagine you're baking a cake. You have a recipe to follow, but you're curious about what would happen if you changed some things. Experimentation is like trying out different ideas to see what works best! Here's a simple way to explain it:
Experimentation is like a science project for everyday things.
The Question: You start with something you wonder about. Like, "Does adding more sprinkles make the cake taste better?"
The Guess (Hypothesis): You think about what you believe will happen. You might guess, "Yes! More sprinkles will make a super yummy cake!"
The Test: This is the fun part! You try out your idea. You bake one cake with the usual amount of sprinkles, and another with EXTRA sprinkles.
The Results: You taste both cakes and see which one you like best. This helps you figure out if your guess was right.
Learning: Even if your guess was wrong (maybe too many sprinkles aren't so great!), you learned something new about baking a cake!
The real question for today is: How can you conduct a structured experimentation to solve real business problems?
As someone that leads experimentation for a global subscription based product, here's a structured approach to conducting an experimentation;
In this example, let’s assume the goal of the experimentation is to optimize conversion rate for a newly launched AI SAAS product. Designing and executing such an experiment, starting with hypothesis formulation and moving on to experiment sizing for sample size determination.
Begin with a clear, specific, measurable & testable hypothesis that is based on insights from data analysis, user feedback, or industry best practices. Your hypothesis should propose a specific change that you believe will increase the conversion rate.
Example: Changing the signup form from multi-step to a single-page design will increase signup rate by 10% within a 2-week test period or Redesigning the signup page to include testimonials from current users will increase the conversion rate by 10%."
Identify the primary metric that will indicate the success of the experiment. In this case, the primary metric is the conversion rate of users completing the signup process. Additionally, decide on secondary metrics that might be impacted, such as time spent on the CTR or dropout rate at each step of the signup process.
To determine the sample size needed for statistical significance, you need to conduct experiment sizing. This involves specifying the desired statistical power (usually 80% or 90%), the significance level (commonly set at 5%), the expected effect size (e.g., a 10% increase in conversion rate), and the baseline conversion rate.
Determine the desired level of statistical significance: Typically, aim for a 95% confidence level to be confident your results are not due to chance.
Estimate the expected effect size: This is the anticipated difference in conversion rate between the control and treatment groups. A larger effect size allows for a smaller sample size.
Choose a desired statistical power: This represents the probability of detecting a true effect, if it exists. Aim for at least 80% power.
Use a sample size calculator: Use an online sample size calculator or conduct a power analysis with statistical software. You will input the baseline conversion rate, the minimum detectable effect (the smallest change in conversion rate you wish to detect, based on your hypothesis), the desired power level, and the alpha level.
Here's an example:
Significance level: 95%
Expected effect size: 10% increase
Desired power: 80%
After using a sample size calculator, you might find you need approximately 384 total users (192 per group) to achieve the desired level of statistical power.
Develop the treatment version: This could involve a new webpage layout, revised form, or modified call to action. Ensure consistency in brand and functionality across control and treatment groups.
Randomly assign users: Employ a random selection process to ensure both groups represent a fair sample of your target audience.
Track key metrics: Monitor the conversion rate and other relevant metrics (e.g., time spent on signup page, form abandonment rate) for both groups throughout the experiment duration.
By following these steps and continuously analyzing your data, you can conduct effective experiments, identify the best path forward, and optimize your SaaS product's signup process for a higher conversion rate.
Run the experiment for a sufficient duration to collect the required sample size. Monitor the experiment closely to ensure data integrity and to watch for any unexpected issues.
Once the experiment concludes, statistically analyze the collected data. After collecting enough data, analyze the results. Compare the conversion rates between the control and treatment groups using a statistical significance test (e.g., a t-test or chi-squared test). Check if the observed difference in conversion rates is statistically significant and if the results support your hypothesis.
Interpret the results: If the change led to a statistically significant increase in conversion, consider implementing it across your platform. If not, refine your hypothesis and iterate further.
If the experiment shows a significant increase in conversion rate for the treatment group, consider implementing the change across the site. If the results are inconclusive or negative, analyze secondary metrics for insights and consider developing a new hypothesis to test.
Document and share the experiment's outcomes, insights, and next steps with your team. This practice fosters a culture of data-driven decision-making and continuous improvement.
The key is to start with a solid hypothesis, ensure your experiment is well-sized for statistical significance, and proceed methodically through execution, analysis, and beyond. Continuous testing and learning are fundamental to improving conversion rates and achieving business growth.
