Pricing Test
A more detailed description can be found in Data_Challenge_ConversionRate.pdf. Click on Predict_Conversion_Rate.ipynb to see the analysis.
Goal
Pricing optimization is, non surprisingly, another area where data science can provide huge value. The goal here is to evaluate whether a pricing test running on the site has been successful. As always, you should focus on user segmentation and provide insights about segments who behave differently as well as any other insights you might find.
Challenge Description
Company XYZ sells a software for $39. Since revenue has been flat for some time, the VP of Product has decided to run a test increasing the price. She hopes that this would increase revenue. In the experiment, 66% of the users have seen the old price ($39), while a random sample of 33% users a higher price ($59).The test has been running for some time and the VP of Product is interested in understanding how it went and whether it would make sense to increase the price for all the users. Especially he asked you the following questions: Should the company sell its software for $39 or $59?
The VP of Product is interested in having a holistic view into user behavior, especially focusing on actionable insights that might increase conversion rate. What are your main findings looking at the data?
[Bonus]
The VP of Product feels that the test has been running for too long and he should have been able to get statistically significant results in a shorter time. Do you agree with her intuition? After how many days you would have stopped the test? Please, explain why.