What is Multi-Touch Attribution (MTA)?

Multi touch attribution (MTA) collects individual, user-level data for addressable (trackable) media and conversion events to determine the impact each media event has on a customers’ path to conversion. Because MTA requires tracking and connecting all media at the user level, it does not account for non-addressable media, like print, radio, and traditional (linear) TV, which cannot be tracked to individuals.

Implementing an effective multi touch attribution model is a complex and difficult process, but can deliver results far superior to first or last click reporting, especially if the media mix is largely made up of addressable media. 

How is multi touch attribution implemented?

User-level tracking is typically performed using Google Analytics, tools from data tracking vendors, or one of the many open-source tracking pixels available. Theoretically, the tracking data is then used to create detailed user click paths that map out the media touchpoints a customer encountered leading up to a conversion. 

Capturing impression-level data and piping it into attribution models can be a challenge because more and more publishers and platforms have become walled gardens and refuse to share user data. Impression views are an important part of the overall picture and this lack of visibility has been the biggest detractor to using MTA. Access to this critical data will become even more restricted with Google’s recent decision to disable cookies and new privacy-driven policies associated with Apple iOS 14 and Facebook attribution.

what is the difference between a wholesome attribution model and a fractional attribution model?

A wholesome attribution model assigns all the credit to the first touch or the last touch. A fractional attribution model spreads credit across all marketing touchpoints in the consumer journey leading to a conversion event.

What types of attribution models are there?

The most common multi touch attribution models are:

• Rules-Based Weighted Distribution – Assigns weight percentages to first-touch and last-touch, then the third percentage to all the touchpoints in between. (Ex: 60% first-touch, 30% last-touch, 10% other) This model requires diligence, ongoing review, and frequent revisions to the weights to keep it close to a version of the truth.

• Algorithmic – Uses machine learning to objectively determine the impact of marketing events along the path to conversion. Building this type of model is extremely time-consuming and labor-intensive. It is also fraught with data breakage and lack of impression visibility in many major marketing channels.

• Rules-Based Even Distribution – Divides credit up equally across all touchpoints in the path to a conversion. While much simpler to calculate, this model is less common and less accurate than weighted or algorithmic models.
• Last Touch Attribution Model – In the last-touch attribution model, the last touchpoint receives 100% of the credit for the sales conversion. 

• First Touch Attribution Model – In the first-touch attribution model, the first touchpoint receives 100% of the credit for the sales conversion.

• Time Decay Attribution Model – In the time-decay attribution model, the touchpoints closest in time to the sales conversion get the most credit. In this case, the last four touch points before the sales conversion receive the most credit, whereas the others receive significantly less.

Can MTA be used for forecasting?

MTA models estimate propensity to convert rather than demand and are therefore not directly applicable to forecasting. While demand curves can be inferred from MTA models, they typically do not have much validity at the campaign level and only inform sub-channel tactical decisions without forecasting or strategic decision-making support.

What is an attribution platform or attribution solution provider?

Rather than take on the enormous task of building an MTA system in-house, many brands choose to implement an attribution platform, marketing technology software that captures user-level events across marketing channels and applies an algorithmic model to assign appropriate credit to the media touchpoints. There are also “full-service” MTA providers that instrument the tracking of the user-level events across media publishers and platforms, apply their own proprietary attribution models, and deliver a bespoke reporting tool. 

Is multi touch attribution right for me?

Whether the system is built in-house or an attribution provider is brought in, MTA is an extremely difficult exercise to land. With each new channel added to the digital marketing mix comes another level of added complexity. MTA can take months to implement. It’s expensive. It’s complicated. And now, without user-level tracking, it’s not likely to survive.

Anticipating the degradation of ID-tracking, Measured bet on incrementality testing and cohort-analytics as the future of measurement. It’s an effective solution to the growing conflict between performance measurement and privacy because it is not plagued by user-level data challenges encountered by MTA.  

Deployed within the publisher platforms themselves, Measured experiments provide marketers with a true understanding of the incremental contribution of each marketing channel down to the most granular level. In addition, incrementality measurement is quicker than MTA to set up, can be used for scale testing and forecasting, and measures the impact of both addressable and non-addressable media. Read the guide to learn more about incrementality testing and experiments.

Compare Measured to platform reporting, MTA & MMM