Rokt and mParticle Highlight Connected Customer History's Value

Performance marketers possess extensive conversion data, including product choices, basket value, discounts, channel, and location. However, paid media decisions often overlook the nuanced differences between customer relationships, treating identical transaction values as equivalent regardless of customer history. A $100 order from a new customer and a $100 order from a repeat purchaser may appear identical in a conversion feed, masking significant disparities in customer lifetime value and engagement. Connecting these transactions to a persistent, unified customer profile reveals their distinct natures: one represents a first purchase, while the other signifies a regular, bi-weekly transaction. The true differentiator is not the transaction itself, but the underlying customer history.

Achieving connected history begins with robust identity coverage, which involves recognizing customers across their various interactions with a brand. The method for establishing this coverage varies by business model. E-commerce businesses often utilize authenticated user accounts, while subscription services inherently possess identity by design. Brick-and-mortar retail, including grocery and quick-service restaurants (QSR), faces a more complex challenge, as transactions can occur without explicit customer identification. Loyalty programs are instrumental in bridging this gap, attaching in-store or drive-through purchases to known customer profiles. The effectiveness of a loyalty program is measured not by the sheer number of members, but by the proportion of transactions that can be linked to an identified customer. This high coverage rate is crucial for unlocking valuable customer data.

This comprehensive identity coverage supports three primary categories of customer data. Firstly, identity and governance data encompasses identifiers, consent preferences, permitted data usage policies, and the relationships between different customer profiles. This foundational layer is essential for enabling all subsequent data utilization. Secondly, loyalty program state data provides insights into a customer's tier, points balance, reward eligibility, redemption history, and tenure within the program. This information is exclusively available for businesses with active loyalty programs. Thirdly, derived attributes are computed from linked events rather than being directly recorded in source systems. These include metrics such as purchase cadence, time between orders, category affinities, preferred times of day and locations for purchases, channel mix, and response rates to specific offers. These derived attributes require identified customer history to be accurately calculated and leveraged for strategic decision-making.