Last updated on May 5, 2020
Carryover effects in marketing are a tricky beast. On one hand, you don’t want to prematurely judge a campaign because the effect of advertising may be delayed. On the other hand, you don’t want bad campaigns to be defended with this same argument.
What’s the solution then? They need to be quantified, or didn’t exist. Some ways to quantify are available in Google Analytics:
- first, you have the time lag report of conversions – this shows how long it has taken for customers to convert
- second, you have the possibility to increase the inspection window – by looking at a longer period, you can capture more carryover effects (e.g., you ran a major display campaign on July; looking back on December you might still see effects) [Notice that cookie duration limits the tracking, and also remember to use UTM parameters for tracking.]
- third, you can look at assisted conversions to see the carryover effect in conversion paths – many campaigns may not directly convert, but are a part of the conversion path.
All these methods, however, are retrospective in nature. Predicting carryover effects is notoriously hard, and I’m not sure it would even be possible with such accuracy that it should be pursued.
In conclusion, I’d advise against being too hasty in drawing conclusion about campaign performance. This way you avoid the problem of premature judgment. The problem of shielding inferior campaigns can be tackled by using other proxy metrics of performance, such as the bounce rate. This would effectively tell you whether a campaign has even a theoretical chance of providing positive carryover effects. Indeed, regarding the prediction problem, proving the association between high bounce rate and low carryover effects would enforce this “rule of thumb” even further.
Dr. Joni Salminen holds a PhD in marketing from the Turku School of Economics. His research interests relate to startups, platforms, and digital marketing.
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