Simple Trick to See If Your New Feature Helps Your Product Grow
Have you ever been in a situation where you’re launching a new product, and transforming numerous user stories into features? In startup mode, this process becomes a daily task.
However, the real question is — Are these features contributing to the product’s growth?
You observe an increase in registrations, visitors, and logins. When you question your team about the effectiveness of these features, they respond positively, claiming they are delivering what users requested.
You then evaluate growth by the daily influx of users to your website, often noticing a sharp increase.
But consider this scenario — someone asks you to detail how a specific feature, let’s call it feature X, has aided growth.
How do you assess its impact? Numerous factors come into play, such as timing, seasonal trends, or the customer’s mindset at launch. Pinpointing the exact contribution becomes nearly impossible.
So, what’s the solution?
Cohorts and Split Testing (also known as A/B testing).
By deploying at least two versions of your features or targeting a specific group of users, you create a controlled environment for evaluation.
Although commonly used by marketing teams, this strategy isn’t exclusive to them.
Applying features to specific cohorts facilitates easier comparison and evaluation of a feature’s success.
Instead of being misled by the overall increase in user numbers (a vanity metric), cohort-based testing, though more complex due to the need to track multiple statistics, assists in identifying features that seem beneficial but are not.
