Why data matters

[Graybeard]

This is a example of **from over 130,000 'events'
55,641 contained within this chart's time line
--in this case deaths in the USA from the pandemic
The same logic could be applied to a CTR >>LP Response Ratio >>>CR (Conversion Ratio)
But you need to understand the underlying factors of the traffic, the event actions (by response and perhaps heat mapping) --> the conversion factors ...

You need a large sample to get an r2 factor the makes some sense to reach any conclusion ... not 'the traffic was bad', 'the offer doesn't convert' when your sample size is too small ...

How high an R-squared value needs to be depends on how precise you need to be. For example, in scientific studies, the R-squared may need to be above 0.95 for a regression model to be considered reliable. In other domains, an R-squared of just 0.3 may be sufficient if there is extreme variability in the dataset.

What is a Good R-squared Value? - Statology

• This chart shows a downward trend with a recent uptick in deaths.
  
• The "IFR" or the Infection rates are up 50% (this is not on the chart for reason of scale).
  
• The lag time of the 12% that will be serious and critical (hospitalized) is about 15-40 days from the infection date.

So, although the death trend is currently declining --it will rise again.

If this chart's 'deaths' were CR Sales and the IFR was CTR's from ads --then this campaign 'should be continued' even though there there is some current decline
--there is hope in the next 30 day window