Measure the Impact of YouTube Awareness Campaigns with MMM

2022-12-07 | Article | Insights

Marketers that use YouTube as a channel for awareness campaigns face the challenge of measuring the channel’s effectiveness. As YouTube is only one of many marketing channels in the marketing mix, it's difficult to assign credits to this channel. Customer journeys that are based on click paths are not helpful with YouTube, as the impact of video impressions is not measured in analytics tools such as Google Analytics.

Marketing mix modeling is a classic statistical method to measure the impact of different marketing channels on revenue or conversions. Regression analysis may sound old-fashioned compared to data driven attribution, but it's still a good tool for marketers. A regression analysis requires input variables and a target variable. The input can be different variables such as impressions or spend and it depends on the availability. The target variable is usually revenue or conversion data that can be exported from Google Analytics.

To calculate the effectiveness of YouTube awareness campaigns, the metric „video_views“ from the Google Ads API report data can be used, with the dimension „campaign“. It is recommended to use at least one year of data to create an accurate model.

As a result, we will receive a calculation of the effectiveness of our YouTube campaigns in relation to other marketing channels that are included in our model. In this case, 19% of revenue is assigned to YouTube and it has an ROAS of 162%.

With the marketing mix model, we can also calculate the carry-over and the saturation effects of YouTube. The carry-over value is 30% – that means 70% of the effect of the YouTube creative takes place on the day of exposure and the remaining 30% of total effect are carried-over to the following days. In this case, the advertising effect is gone 5 days after exposure. Conversely, the half-life is 1 day, which means that half of the effect is exhausted after one day.

If we take a closer look at the saturation of YouTube, it shows that there is still some potential for future campaign expansion, as the relationship between video views and attributed revenue is linear.

Please note that marketing mix modeling is only one solution to evaluate the effectiveness of marketing channels, although it provides a lot of valuable information on the historical impact of the channel on the target variable, the carry-over effect and the saturation effect. All of this provides a solid data-driven basis for making future budget decisions.

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