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Every visitor is assigned to either a test group or a control group. Members of the control group do not get incentivised. Based on the revenue and impressions of each group, the additional revenue is then calculated as follows:

Additional Revenue = Revenue Test Group - (Revenue Control Group * 3)
The term significance does not imply importance. It describes the likelihood of something happening for a reason instead of just randomly. Statistically significant results can be used for further analysis.
The more data we have (meaning more impressions, more conversions), the greater the chance of significant results. Therefor longer time frames are often better to look at for analysis.
We measure the difference between test and control group in order to determine whether there is a positive difference thanks to our campaigns. Or are the results just random? Then we need to keep on working on our campaigns!
Significantly Equal:
There is no significant difference between test- and control group, it is very likely that this value is random.
Meaning: the campaign is not making as much of a difference as wanted! We are then working together with our Data Science team to find optimization potential.
Significantly Different:
There is a significant difference between test- and control group, it is very unlikely that this is random.

The test group performs significantly better than the control group:
Here we can analyze what makes this campaign different from less well performing ones? Can we learn something, that we can apply to other campaigns?
The test group performs significantly worse than the control group:
Your Account Manager and the Data Science team colleagues are already on it and looking for indicators to improve the performance.

Please contact your Account Manager in case of questions or uncertainty!