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

For small clients (as well as for clients with complex multi-channel analysis), I monitor pure CPC (clicks, CTR, cost-per-click, bounce).

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

To do this, I use Cost Per Useful Click (CUC) in analytics. This metric takes into account the cost per click and the bounce rate.

Formula : Cost / Clicks * ((100-BounseRate) / 100) I will
explain it in simple language:
We got 200 clicks for 2000 rubles, the rejection rate is 20%. So we really bought useful clicks 80pcs,
2000₽ / 80 = 25₽

Also, this metric helps to analyze statistics in small samples, where you can’t decide on conversions.

At the input, we should already have a finished DataFrame with statistics from the advertising system.

Enter a new column in the statistics.

Python doesn’t perform mathematical operations in the same way as in mathematics, therefore, we will 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 weaknesses in a few seconds.