Use case #1: Identifying customer spending potential to boost growth for a retail chain
Objective
A large retail chain wanted to understand the spending potential of each customer in their stores. Their goal was to uncover and maximize untapped spending potential.
Solution
The large retail chain licensed Marketing Attributes to identify the top demographic factors that drove spending in the retail store the previous year. The four key drivers were: Age, Income, Family structure (household composition), Location/region.
Results
By combining these attributes to create custom segments, we uncovered two valuable annual estimates:
- Potential spend: A conservative estimate of how much a customer could spend if they reached the top 20% of spenders within their specific demographic segment (based on data from the highest spenders).
- Unrealized spend: The difference between a customer’s annual potential spend and their current spend. An estimate of how much more they could be spending each year.
The solution
By syncing its cookies with the Digital Graph, the DSP gained access to related identifiers, including:
- MAIDs
- CTV Ids
- IP addresses
- Experian cookies
This expanded identity universe gave the DSP a unified view of individuals and households, making it possible to connect impressions to conversions across devices and channels. With each weekly refresh, attribution models stayed accurate and up to date, turning fragmented signals into proof of performance.
Results
Within weeks, the DSP saw measurable improvements
- 84% of IDs synced
- 9% increase in match rates