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Pricing Test


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.

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:


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


Column Name Value Description
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)

Column Name Value Description
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
lat 42.89 Buffalo latitude
long -78.86 Buffalo longitude

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