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Customer Data Platforms: A New Tool to Unify Shopper Data

By Ron Menich, VP, Advanced Data & Analytics, Catalina Marketing

Ron Menich, VP, Advanced Data & Analytics, Catalina MarketingRon Menich, VP, Advanced Data & Analytics, Catalina Marketing

Back in the 20th century, marketers perfected mass-marketing tools such as weekly circulars and TV commercials to reach vast audiences. But now, thanks to the proliferation of digital channels, marketers can more easily target micro-markets and even individual shoppers within that broader audience.

Each micro-market represents different shopper likes, wants and desires, with each segment likely responding differently to messages or offers. Consumer data platforms (CDPs) give you a way to organize this customer-specific data so your marketing dollars work harder than the traditional “spray and pray” messaging to an undifferentiated audience.  The focus of this article is on well-built CDPs which do not hold Personal Identification Information (PII) data, thereby preventing anonymous shopper ID purchase data from being re-identifiable.

Many shoppers are addressable because they regularly use a frequent buyer card when shopping.  A CDP can compute recency/frequency/monetary (RFM) measures for each of them. A retailer can, for example, identify an audience of shoppers who have never purchased a subcategory.  Data science models can then rank this subcategory by that audience’s projected propensity to purchase related subcategories within the same category. Each shopper receives their own score, creating a prioritized list of those likely to try that subcategory.

"Personalization, micro-marketing, and digital media are here to stay, and we can expect the scope to continue to broaden and deepen in the months and years ahead"

A brand selling a new type of yogurt might wish to find a micro-market across several retailers of who might potentially try it. In the past, it could be difficult to zero in on a single shopper because they may have frequent shopper cards across several retailers. When looking at data from retailer A, that customer may appear to never buy this new yogurt from them, yet has made several purchases of it at retailer B.  Modern CDPs integrate frequent shopper behavior across multiple retailers, supplementing point-of-sale data with household demographics.

As today’s shoppers often have multiple digital devices, marketers can reach shoppers with media on a combination of them as well as in-store. While maintaining an individual’s anonymity, an advanced CDP associates digital devices to frequent shopper cards, enabling marketers to understand that shopper’s browsing and purchase behavior and push relevant ads to those digital devices. 

With the ability to reach shoppers in-store, on desktop computers, mobile phones, smart TVs, and other ways, a natural question arises: Which channels are really driving in-store purchase results? Advanced CDPs provide a foundation for machine learning-based multi-touch attribution algorithms to help answer these questions.

Not everybody has or wants a frequent shopper card.  Marketers can still reach such shoppers in-store and provide different customers different messages or offers based on the contents of their current baskets.  For example, a shopper buying yogurt could receive an offer for a new type or brand at checkout.

Personalization, micro-marketing, and digital media are here to stay, and we can expect the scope to continue to broaden and deepen in the months and years ahead.  A well-constructed CDP creates the foundation for serving the right media to the right shopper at the right time through the right channel for maximum marketing effectiveness.

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