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To understand which marketing channels have the largest impact on conversion, attribution is often used as the go-to approach. Whether a user has seen a display or paid search ad, or received an email newsletter, if it then converts, attribution determines how each touchpoint gets credited for the sale. This credit can be assigned using rules, such as giving the channel that was immediately before the purchase all the revenue credit (last touch attribution). It can be assigned using equal touch, where all get the same credit. It can be assigned using a time decay method, where the channels receive credit proportional to how close they were in time to the conversion. Or, as analytics maturity increases, it can be algorithmically calculated in the only ‘fair’ way using data driven attribution.–

However, in any of these attribution scenarios, one thing is always there: Attribution, whether rules based or data-driven, captures credit towards a conversion or purchase.

So what doesn’t get credit? Attribution models cannot measure things like brand awareness, recognition, and loyalty. Even if all advertising was turned off, some customers would still purchase with a brand because of this. These feelings can’t necessarily be measured in a hard and fast way using attribution pathing. Further, branding initiatives on offline channels such as billboards or TV ads may not get credit since attribution pathing can rarely connect a user in the offline world to the online world and all these pieces must come together.

This is where media mix modeling can complement your attribution. In media mix modeling, weekly data rather than user level data is analyzed to find out what weekly volumes were contributing the most to weekly sales. And one of the benefits of looking at this more aggregated data is that we can measure the impact of the base (what volume of revenue was not due to media effects). Further, seasonality, weather, offline spend, and other exogenous factors can be put into the model to get credit towards sales, provided the data exists.

This isn’t to say that an attribution model doesn’t provide insights that can help measure branding heavy campaign. If your attribution tool gives you path level data, you can investigate whether a particular branding heavy campaign is found frequently at the start of converting paths, being the introducer to a brand. This combined with the insights of a media mix model can begin to provide a measure of how branding and feelings impact revenue.

Whether you are considering a data driven attribution model or a media mix model, consider your base: users that are likely to purchase regardless of any advertising effects. Brand awareness, recognition, and loyalty are those harder to measure variables that help drive customer conversions.