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Adtriba's Multi-Touch Attribution (EN)
Adtriba's Multi-Touch Attribution (EN)

Learn everything about our multitouch attribution algorithm

János Moldvay avatar
Written by János Moldvay
Updated over a week ago

What exactly is the Adtriba Multi-touch Attribution (MTA)?

Adtriba's onsite pixel captures all entries via marketing activities and organic channels on a user level, the entry marketing contact is called touchpoint. All of the users touchpoints will be aggregated to sequences. If a combination of touchpoints led to a conversion we are speaking of customer journeys, if the sequence was not followed by a conversion within 60 days (lookback window) we name them user journey. Based on the touchpoint sequences the Adtriba MTA algorithm dynamically calculates the proportional success of each touchpoint and respecting marketing activity across all digital actions and channels. Besides our default MTA Adtriba provides a CLV MTA which is calculated on a users lifetime value instead of a single conversion value.

Which method does the MTA use?

Adtriba uses a machine learning algorithm based on a deep learning framework (LSTM). The model is trained on user and customer journeys. By comparing converting and non-converting touchpoint sequences, the model calculates specific weights for each touchpoint and its effectiveness within the different sequences. The model is trained to estimate the incremental conversion probabilities based on a touchpoint and event. This estimation is the basis for the Adtriba attributed values that are used in the dynamic attribution.

Does the MTA include view contacts in the attribution?

In the Adtriba MTA, a view contact is included in the customer journey just like any other touchpoint. At Adtriba, however, we differentiate between social views (data coming from display views of social platforms, facebook, instagram...) and user level views. The data coming from social platforms views, for example, cannot be included in the regular customer journey because they are not available on a user level. This is where Adtriba uses its UMM modelling approach.

How do we handle APP-Traffic in Adtriba's MTA?

Adtriba has native integrations with AppsFlyer and Adjust. This integration enables Adtriba to get all the information needed to include App touchpoints in the MTA.

How can I actually work with the results?

  • Adtriba returns attributed values to GoogleAds based on the individual GoogleAd clicks - fully automated. Hence you can use the attributed credits in your Google Ads campaign management. There you can use the attributed results either for reporting or for bidding purposes.

  • Adtriba provides users with a streamlined and targeted dashboard to evaluate channel and campaign performance, including the comparison to common rule-based models. It focuses on performance metrics such as ROI, ROAS and KUR.

  • Adtribas raw data export is a preferred feature, which provides all relevant and important KPIs on touchpoint, cookie or conversion level. This allows you to use the Adtriba results in your internal systems and running advanced analysis.

  • The Adtriba results can also be used for the remuneration of your affiliate partners. As the Adtriba MTA algorithm, also incorporates qualitative on-site events to ensures that affiliate touchpoints are not overrated.

What else must be considered when dealing with the MTA?

The MTA figures can change within 3 days. This is caused by the data delivery via the GoogleAds API by a max. of 3 days.

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