“Data-Driven Thinking” is written by members of the media community and contains fresh ideas on the digital revolution in media.
Today’s column is written by Allen Mason, vice president of strategy at 84.51°.
On a recent drive home, I wondered how data would define my family vs. the roughly 50 families that live in my neighborhood. Like me, most are professionals with children, while a few are nearing or in retirement and are empty nesters. My neighbors likely look very similar to my family in the eyes of an advertiser, based on number of kids and cars or the value of our homes. I’ll call this definition of my household the one-dimensional profile.
The real picture of my household becomes quite different when digging deeper. I, for instance, enjoy gardening and working around the house. Any chance to buy a new “toy” is justification for taking on a new project. My neighbor, on the other hand, seems overwhelmed with even the most mundane tasks and chooses to hire out everything from lawn care to home maintenance. My family is very active with running, tennis and outdoor sports. My neighbors are rarely outside and make dozens of trips to and from home on a given day. The point is when you look at a multidimensional profile of my household, we appear very different from my neighbors.
This idea of a multidimensional definition of an individual is a very important factor in advertising effectiveness, and the ability to leverage it is becoming more of a reality. It wasn’t long ago that I relied on poorly defined segments using demographic information and generic consumer definitions, such as “women aged 18-55 who like cleaning.” I still find it amazing that my media agencies found those women.
First-party data is transforming the precision at which a marketer can find and connect their content with their desired consumer target. Marketers have a few choices for accessing and leveraging first-party data. First, major platforms, such as Google and Facebook, are amassing huge amounts of first-party data in closed ecosystems and creating solutions for advertisers. Second, marketers can build their own databases by combining personally identifiable information (PII) from CRM programs or email lists with online behavior. This can be costly and time consuming before it delivers value, and requires partners for data management and on-ramping PII for matching consumers with cookie data.
A third option is to leverage purchase data, an option with significant, untapped potential. The idea of using purchase data is not new, and companies like Amazon have been offering media solutions using sales data for a few years. Electronic retailers have some advantages since they likely have email addresses, UPC-level purchase history and the ability to perform closed-loop measurement. Those advantages are offset by the penetration of electronic retail vs. offline sales, especially in certain categories. Brick-and-mortar retailers, however, are still the dominant sales channel and can serve as powerful media partners to advertisers.
This is a cultural shift that both advertisers and retailers will likely struggle to embrace. Evolving this relationship, however, is good for both parties and the consumer. By using purchase data to create a multidimensional consumer profile, marketers can more precisely connect the right messaging with the right consumers. Further, retailers with robust customer data systems can help measure the impact of communications and media since they have a closed-loop system. This takes the mystery out of media attribution and gives retailers a competitive advantage over other media solutions. Retailers with existing loyalty programs are well positioned to activate such an offering, provided they retain the longitudinal purchase history. Those retailers who have resisted implementing a loyalty card or similar affinity program should re-evaluate the potential value of the household-level data to be captured through a more strategic view.
There is one final reason why I believe a multidimensional targeting approach, based on purchase data, should be coveted by brand marketers. For new trial, purchase data can be used to model highly effective lookalike segments. This makes sense when you think about things you buy vs. what your friends and family buy. My close friends and I tend to be interested in and buy similar things.
Purchase data also gives brand marketers an opportunity to expand their marketing strategy beyond new trial alone into the often taboo realm of loyalty marketing. During my time as a CPG brand marketer, driving new trial was the top priority despite knowing that customers were leaving the brand nearly as fast as we could gain new ones. Securing investment in anything characterized as a loyalty tactic would take working dollars away from my trial efforts. Every brand needs a trial strategy but marketing based on purchase data provides an extremely efficient and highly effective way to keep and likely increase engagement among existing customers. That increased engagement builds loyalty, and building customer loyalty should be the ultimate goal of every brand marketer.
Follow 84.51° (@8451group) and AdExchanger (@adexchanger) on Twitter.
