The monetization of data to influence customer purchases entails granular personalization, said Steve Rubinow, Ph.D., the Chief Technical Officer at Catalina Media Lab, while speaking at the DATAVERSITY® CDOVision 2015 Conference. Mr. Rubinow says that the personalization of data provides an incomparable opportunity that evolves shopper by shopper, device by device, year after year. This sort of data monetization involves a process that entails capturing customer spending data, refining that data, and turning it into insights, which can then be used as a distinct competitive advantage.

“We’d like to understand every step in terms of utilization of packaged goods in the home,” says Rubinow. He presented a short video outlining the challenges of influencing customer purchasing behavior. If the average consumer sees over 5000 marketing messages a day, how can retail marketers get through the “tune-out process” when consumers ignore 99 percent of what they see while shopping? In a crowded marketing technology space, Rubinow asserts that Catalina Media Lab has a distinct advantage. With point-of-sale (POS) printers gathering each grocery transaction in 425,000 checkout lanes throughout 48,000 stores in the US, Europe, and Japan, “no one has a database like we do when it comes to consumer packaged goods,” he says, yet data is meaningless without relevant insight into shopper behavior.

Understanding Jill and Jane

In the video, Rubinow showed how useful personalized data can be for shoppers with identical demographics – twin sisters called Jill and Jane. Jill is a new mother; she cooks vegan cuisine from scratch at home, and enjoys red wine. Jill carefully plans her trips to the store in advance. Her sister Jane has a new puppy, eats junk food, and drinks white wine. She shops when she can, without a lot of planning ahead. Using only their ages, marital status, and zip code, both women would get identical coupon offers; but, based on each sister’s purchase history, it’s possible to know what she’s likely to buy and therefore her choices can be influenced with personalized ads and offers.

Opportunities to influence decisions on Jill’s always-connected shopping “journey” start at home. Personalized web coupons for baby food and rice arrive in Jill’s email box from her local store and she prepares her list online using those coupons. She pre-orders her groceries, putting them in a virtual cart, which triggers a POS coupon for another one of the items in her cart. She pays online and in the store during pickup, scans her pre-paid groceries with her phone, skipping the traditional checkout line.

Her sister Jane drives to the store to shop and a geo-fenced alert reminds her to use her mobile app, showing her how much she can save today. As she logs in, a targeted offer for puppy food pops up on her phone. Additional targeted offers arrive as she navigates the store. As she shops, Jane not only gets offers relevant to her past needs, but also her future needs, brand preferences, and location in the store. For Jill, the offers will be different. Both sisters receive personalized thank you messages with their loyalty balances, additional relevant coupons, and targeted newsletters on the way home.

The sisters are what he called “omnichannel” or “always connected” shoppers, with smart phones, home computers, and other smart devices they use throughout the day. He pointed out that omnichannel shoppers differ from multichannel shoppers in that omnichannel devices talk to each other, allowing for even greater personalization. For example, Jill’s phone would not offer her a duplicate of the coupons she received in her email box, because it “knows” that she has already seen those coupons.

Rubinow says it’s important to treat each customer’s shopping experience as part of a progression of events on a “shopping journey” across all channels. Each interaction declares intent (or progression), and when marketers understand the shopping process as a continuum of points, “you can see how rich the data can become.” So when Jane clicks on an ad at home or purchases an item based on an offer, this information can be used to “evolve” her next ad and offer accordingly.

Data Monetization Drivers

“We think about new ways for getting value from what we’ve always done, and then we’re talking about data monetization,” he says. As data volumes are getting bigger and the variety of data available for capture is getting richer due to enhanced ability to connect with consumers electronically, companies are seeing data as a potential revenue source. Retail businesses see the importance of personalization for influencing shopper behavior, and an increasing number are willing to pay for relevant data.

How much value can be added to data? He says it’s important to find new value beyond conventional data sources, yet cautions that some channels are interesting, but not worth a lot at this point in time. For example, social media has tremendous potential, but it’s not yet clear how useful it is for influencing grocery purchases. Making data relevant is sometimes a backwards process. Data becomes more valuable as emerging technologies allow for new insights that didn’t exist when the data was first collected. Some are interesting, but not worth a lot of money.

Raw data becomes more useful as it “moves through a distillation process, each step adding value,” says Rubinow. “We ask ourselves what can we do with data that we haven’t done before, in addition to the value that we’ve already added.” Using models, data can be normalized, aggregated, cleaned, or combined with other data to provide “saleable” insights into behavior. There can be value in the data at any step of the distillation process, he says. Gathered information provides insight that can be shared or sold, allowing businesses to take action to influence consumer behavior. He also says it’s useful to analyze why a particular purchase happened, to simulate that process, predict future behavior and try to affect that behavior. Rubinow says that with the tools available now and an evolving model, it’s possible to get much closer, but also cautioned that the only sure thing we know is that the model will be different in the future.

Launching a Data Monetization Business

Rubinow presented a slide with five steps to get monetization started, each step with activities and expected outcomes. He briefly highlighted activities under the following steps:

• Strategy Activities
  • Legal considerations
  • Pricing

• Planning Activities
  • Legal considerations
  • Pricing

• Mobilization Activities
  • Anonymization and privacy
  • Operating model
  • Sales operations

  Innovation and product roadmap

• Deliver Outcomes
  • Product socialized with customers

• Launch Activities
  • Customer service and technical support

He noted that not everyone starts at the same point. “Many of us did not begin collecting data hoping to go into the monetization business,” he says, so data use may be constrained by a variety of factors. The contract to acquire the data may have restrictions on how it can be used. PII issues, anonymization, and international boundaries in the Cloud are requiring more care than in the past due to increased awareness about safety. Customer expectations can have an effect on how captured data can be used. As a result, a company may be starting anywhere on the continuum and looking backward and forward at the same time.

Connectedness Creates Opportunity

Rubinow believes taking full advantage of the Internet of Things will only increase the potential opportunities for data monetization as the industry moves from focusing marketing on a brand model to a smart object model. With sensors that can be stationed throughout a grocery store communicating with smart objects and each other, he asks, “Will all that data have value? I’m not really sure, but you can be sure that lots of people are going to put it together so that we can come up with new data sources, monetize it, refine it, add insight to it,” creating “lots of opportunity for monetization” which Rubinow says (with a chuckle) will finally put us in the “Golden Age of Marketing.”

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Here is the video of the CDOVision 2015 Presentation: