How to design Experiments to test Growth Levers?
Once growth levers have been identified, mapped to relevant growth metrics, and prioritised based on their potential impact, they need to be tested through experiments.
Designing experiments to test growth levers involves creating structured tests to evaluate the effectiveness of specific actions or changes in a controlled manner.
Isolating impact of the changes could be done by, for example, conducting A/B tests to gauge user response to a new feature, or measuring the impact of a targeted marketing campaign in a specific region.
Example:
'FashionFiesta', an online apparel store in India, decides to test a new chatbot feature to improve customer service.
They design an experiment where 50% of users are directed to the chatbot, while the other 50% continue to receive traditional customer service.
The key metric for comparison is the 'Customer Satisfaction Score'. By comparing these scores between the two groups over a month, FashionFiesta can quantitatively assess the impact of the chatbot on customer satisfaction.
Explanation:
The experiment should be designed to isolate the effect of the lever being tested.
This involves setting clear objectives, choosing the right metrics to measure, and ensuring a control group for accurate comparison.
In FashionFiesta's case, the experiment is structured to specifically evaluate the chatbot's impact, independent of other variables.
The results from such experiments can provide actionable insights and inform decisions on whether to implement the lever across the board.