CLV Attribution

Put your dollars where they matter

Learn which (combinations of) touchpoints are driving conversions most effectively with unbiased attribution reports. And re-focus back to the future of your brand growth with Predictive CLV. 


Multi-touch attribution reports with CLV Predictions

First-party tagging and identity resolution gives us the foundation for more accurate multi-touch attribution. Your customer data platform is already connecting customer touchpoints across devices and channels. Machine learning helps decode which touchpoints are separate attributable events, and which can be identified as one multi-screen session.

Your CDP’s attribution reports use multi-touch attribution modeling to decide which of those touchpoints have the biggest impact on revenue, now and in the future.



Know which sources, mediums, and campaigns are responsible for specific conversions and how much revenue they drove.



Clear and reliable information based on your own data from each marketing source. 



Get a long-term perspective on revenue tracking per channel powered by machine learning.

custom audiences for fashion brands


Audience Segmentation with Predictive CLV

Our machine learning model is trained to follow the same basic RFM (recency, frequency, monetary value) formula as a standard CLV calculation. The difference is it doesn’t need a year’s worth of historical data, and it updates each customer’s CLV prediction with every new interaction.

When creating audience segments with predictive CLV, you can choose a defined time frame such as “customers who will spend $XX within the next 3 months” to isolate customers with high value in the near term. Or you can target customers with low CLV predictions to give them a better experience and nurture the relationship.

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CLV Attribution


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