Context: In the realm of Paid Search Marketing. Current reporting does not provide event level data only aggregate totals with different segments. Want to compare distributions/test statistical significance of A/B test results. Did not want to assume that data followed normal distribution or know STDEV for data so came with this approach.
My Question: I am going to use the average "CPA" or "CTR" for a date range, and generate an observation for each conversion based off the average for a time range. Is this statistically sound way if I want to generate raw data? Would I have wonky distributions because of the multiple averages? Just want a gutcheck if I'm completely off base.
My Aggregate data looks like below:
| Day |
Cost |
Acquisition |
CPA or CTR |
| 1 |
40 |
2 |
$20 |
| 2 |
75 |
3 |
$25 |
Observation data I generate looks like below:
| Day |
Acquisition |
| 1 |
$20 |
| 1 |
$20 |
| 2 |
$25 |
| 2 |
$25 |
| 2 |
$25 |
I really appreciate your help with this question! An important project to me at work.
