Pricing optimization is, not surprisingly, another area where data science can provide huge value.
The goal of this challenge 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.
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.
We have two tables downloadable by clicking here.
The two tables are:
test_results - data about the test
user_table - Information about the user
Let's check the first user
|user_id||604839||The Id of the user|
|timestamp||2015-05-08 03:38:34||The user landed on the site on May, 8 at 3 and 38AM (and 34 seconds).|
|source||ads_facebook||User came via Facebook ads|
|device||mobile||User was using mobile|
|operative_system||iOS||Was using iOS|
|test||0||Was in the control group, i.e. seeing the old price|
|price||39||Indeed, the price she saw was just $39|
|converted||0||Alas, left the site without purchasing the software|
Let's check location info for that user
subset (user_table,user_id == 604839)
|user_id||604839||User id. Same user as in the previous table|
|city||Buffalo||She was based in Buffalo when she hit the site|
|country||USA||The country where Buffalo is|