Analysis of statistics on advertising campaigns - create a new metric in the DataFrame (python)

For small clients (as well as for clients whose multi-channel is difficult to analyze), I follow the net CPC (clicks, CTR, cost per click, refusals).



Task : to understand which pk works more efficiently and, based on this, edit the rates.



To do this, I use the cost per useful click (CUC) in analytics. This indicator takes into account the cost per click, and the bounce rate.

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Formula : Cost / Clicks * ((100-BounseRate) / 100)

I will explain in simple language:

We received 200 clicks for 2000₽, the percentage of failures is 20%. So really useful clicks we bought 80pcs,

$ 2000/80 = $ 25



Also, this metric helps to analyze statistics in small samples, where conversion cannot be decided.



At the entrance we should already have a ready-made DataFrame with statistics from the advertising system.



Enter a new column in the statistics.



Python doesn’t do math in the same way as in mathematics, so we’ll do each action on a separate line:

#f['CUC'] = f['Cost']/f['Clicks']*((100-f['BounceRate'])/100) f['CUC'] = 100-f['BounceRate'] f['CUC'] = f['CUC']/100 f['CUC'] = f['Clicks']*f['CUC'] f['CUC'] = f['Cost']/f['CUC'] 

We get the following:







Looking at this indicator, we can see weak points in a few seconds.