More from our FAQ
Which is better – MMM, Last touch attribution, or incrementality?
What’s the Difference between Attribution vs Incrementality?
The three different methodologies are not “better” or “worse” than one another. It is not about you as a marketer making a choice “which attribution methodology should be used?”
All three attribution models may be relevant to your marketing activities.
Here is a comparison of the methodologies across several parameters, The Pros and Cons of Attribution and Incrementality, Outline of attribution models
Last Touch Attribution
Last Touch attribution works only for Digital marketing where the Advertiser is able to assign a dedicated URL for consumers to use to reach the conversion point.
Last touch attribution's biggest benefit is that it allows a digital marketer to associate the last ad the user engaged with before the conversion point - in real time.
There’s no need for any historical data , as the attribution happens based on a match between the click and the conversion.
For any advertiser spending a large sum of money for digital advertising - real time conversion data can provide a critical feedback loop for optimization. Last touch attribution can act as a great proxy to the performance of creatives or audience segments.
Over attribution is extremely common with Last Touch attribution. As a product becomes more known and popular, consumers search and interact with it more and more, leading to a point where consumers may unknowingly and worse off - involuntarily engage with an ad on their way to the conversion point, causing the attribution platform to credit a vendor for the conversion, while the user intent was to convert organically.
Last Touch attribution is susceptible to “attribution gaming” as fraudulent publishers discovered that if they trigger an ad engagement, with or without the user knowledge before the user reaches the conversion point - they can claim that the conversion was a result of their activity, thus, gaining budget or higher prices from the Advertiser.
Media Mix Modeling
Media Mix Modeling or Marketing Mix Modeling (or in short: MMM) is a statistical method to estimate the impact of various marketing tactics on sales in order to better forecast and come up with a better marketing strategy.
The method was developed in econometrics for the consumer packaged goods industry and has become common with brand cross platform Advertisers in the last years.
Media Mix Models require historical data to have any helpful outputs. Often, the data needs to include external influencing factors such as competitors activity, product launches, financial events, weather and any major event that may have influenced the performance of a product (i.e. during an Olympics year, more people buy sport goods).
Due to these requirements - Media Mix Models work best for refining a strategy, expecting influencing factors such as changes in the media mix and/or external factors to help understand what would be the best media mix to market a product over time.
Incrementality measurement a method to understand the true value of Advertising spend. It is more operational and tactical than Media Mix modeling , but is not as granular nor real time as Last Touch attribution.
Incrementality measurement does not assume to replace attribution. Incrementality measurement relies on last-touch attribution data to indicate if paid and/or attributable conversions had any value to the Advertiser or if the attributable conversions are cannibalizing the organic new user base or the user base arriving from other paid media sources.
Incrementality attribution can provide insights in granular levels over campaigns, demographics, vendors, geo location, contextual features. These insights can be used in an operational - tactical level.
As this form of measurement operates simultaneously to attribution, Advertisers can forward or stream conversions and ad spend data from any platform used to understand the true value of their spend, including non-digital mediums such as TV , Radio and obviously - Digital.