How do you improve your MVP with data?

Absolutely crucial to enhancing your MVP is the palpable value it brings to customers. This value might take the form of cost savings, revenue amplification, or the shielding of potential risks. When these metrics are meticulously honed, you're unmistakably steering your project in the right direction.

If you're new product/service is 1. Exponentially better than what is currently available, it's easy to prove. persuade. with data 2. But can new entrants that you sell to benefit greatly? If they can then, they are more likely to purchase your product or service.. immediately!

Collecting data to improve your MVP is an iterative process Refining your data collection processes is the key. First collect data based on your objectives. Use tracking tools like surveys and web tools to observe and gathering other relevant data. Analyze the data and look for patterns, trends, anomalies, and insights so you can understand user interaction with the MVP. See if the desired outcome is happening. Based on this you can determine what's working well and what's not. Next, consider changes that may enhance the user experience or fix issues. Next make targeted changes to your MVP. Things like modifying features, user interfaces, workflows and more. Next, after the adj's are implemented, collect more data and repeat.

When discussing data analysis, it's crucial to consider the following factors: Properly planning the experiment - determining: traffic sources, sample size, key indicators, primary and dependent metrics, seasonality and segmentation. Metrics may exhibit different sensitivities; understanding how much you can enhance them is important. Recognizing that certain experiments might have cumulative effects. Your task is to correctly interpret the experiment results as they impact your decisions. Incorrect conclusions could set a misguided product development direction. It's also essential to gauge the quality of your experiments and the usefulness of the data you obtain for your next experiments.

Wish I knew this trick earlier. A/B testing MVPs faces challenges in achieving statistical significance with a small audience. A technique can amplify significance with the same audience size. Consider a game where two offer variations appear at level 10, differing in visuals. Imagine 20k players split evenly into A/B groups. Until level 10, 2k players reach this for each variation. In an optimistic scenario, 1.5k from one and 1.6k from the other convert. Calculating based on total game starters (10k per variation) yields 15% and 16% conversion — insignificant at 95% confidence. Yet, calculating from the 2k users who reached this point yields 75% and 80% conversion — statistically significant. This approach enhances testing efficiency.