How to measure and analyze the Effectiveness of Implemented Levers?
Once the growth levers have been implemented, we have to quantitatively assess their impact on targeted metrics over the mid and long term.
This process is crucial for understanding whether the lever has achieved its intended objectives.
It requires:
☞ collecting relevant data,
☞ applying statistical methods to analyze results, and
☞ interpreting these results to inform future strategies.
Example:
'HealthTrack', a health and wellness app in India, introduced a feature that allows users to set and track fitness goals.
To measure its effectiveness, they focus on 'User Retention Rate' and 'Average Daily Active Users'.
After three months, they analyze these metrics and find a 10% increase in user retention, a 15% increase in daily active users, and no second-order negative impact on downstream metrics, indicating the new feature's positive impact on user engagement.
Explanation:
The effectiveness analysis should be as rigorous as the initial experiment design.
HealthTrack's analysis involves comparing the pre- and post-implementation data while controlling for external factors like seasonal variations in app usage.
The increase in key metrics suggests that the new feature resonates with users, justifying further investment and development in similar features.
It also provides insights into user preferences, which can be crucial for future product development and marketing strategies.