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Campains Recommendations

Please analyse the sheet

Description:

The file contains 7 dimensions and 4 metrics (Total Conversion = Post Click+ Post View). All dimensions are categorical values, and I’ve purposely masked few dimensions.

The metrics are:

1. Impressions – The number of times the ad was shown.

2. Clicks – The number of clicks driven

3. Sale – The total sales driven

4. Cost – The cost of buying the impressions.

You would see some sale occuring even when clicks are 0. We call these Post-view sales where the sale happened after user viewed the ad. So 'Sale' contains all the post-click and post-view sales.

The goal is to minimize the Cost per sale, without affecting the overall delivery (impressions) too much.

Assume that your analysis would be used by campaign managers who have to implement your recommendations into actual campaigns. These people are not well versed with the algorithms or methods used to arrive at the conclusion. For campaign managers, an output in the following format would make more sense (all values are indicative and do not represent any actual solution):

* Values creative_concept A, B & C combined with Values 4, 5, 6, 7, 8 from City & on Audience xyz would give the CPS of Rs.200 and drive 70% of the sales.

List down the best strategies to keep or where the money is wasted

Or you can give the summary in an excel format explaining each result

Skills: Data Analytics, Data Science, Python

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