Can Beacons And Predictive Analytics Lift Packaged Goods Sales In-Store?

One advertiser used beacon platform inMarket's 'Quantum Receptivity' tools to generate 3.5x higher sales.

Predictive analytics is increasingly in demand by marketers of all stripes, but skepticism about the efficacy of such forecasting tools remains, particularly when it comes to the science of consumers’ decisions at the in-store shelf level.

Since beacon platform inMarket unveiled its predictive analytics program, dubbed Quantum Receptivity, in November, the company has been winning over doubters in the consumer packaged goods space.

A recent campaign by Bona Hardwood Floor Cleaner, a 90-year-old CPG marketer that sells a range of polishes and mop oils, made that company a believer.

In an effort that was launched in Q1 with Bona’s digital agency, Crescendo Collective, the brand aimed the Quantum Receptivity predictive tools at inMarket’s audience of 46.2MM monthly active app users across the U.S. As a result, Bona reported a 3.2x ROI for the campaign, with a 25.3 percent increase in post-engagement purchase intent and 55 percent increase in average brand awareness.

The Right Mindset

“Quantum Receptivity has beaten the paid search, promoted social, and display ads that we have used (successfully) in the past,” said Bernard Gomez, partner, Crescendo Collective, in a statement. “Sporting possibly the smartest targeting that we’ve seen, Quantum Receptivity delivers a more engaged audience than older forms of digital marketing.  It delivers a lot more bang for our clients’ buck in both impressions and engagement. Quantum Receptivity allows us to deliver context-sensitive messaging to an engaged audience, which then pays off with a better conversion rate for our clients.”

Quantum Receptivity is all about mindset targeting — sending consumers a messages at natural points of the day or purchase cycle. In inMarket’s hands, the technique uses “beacon-level” brick-and-mortar store visit data to reach consumers across mobile, digital, and video channels.

Quantum Receptivity ingests that data and then measures the shopping cycles for “tens of millions of opted-in shoppers” across categories such as grocery, entertainment, discount, and auto.

Knowing When Not To Target

After averaging out the time a retail item is used by consumers, the platform calculates when shoppers are most likely to respond to an offer —i.e., when a shopper might want to buy more breakfast cereal or floor polish.

Knowing when not to send a message to consumers is the other part of the equation, solving the issue of wasted ad spending. Quantum Receptivity works by avoiding messaging shoppers when they’re least likely to want to buy something, such as right after they’ve left a store.

A separate Q1 Quantum Receptivity program that was also employed by Crescendo Collective on behalf of a “packaged meat” marketer targeted a subset of 17 million shoppers across five top shopping regions produced a “verified sales lift of 3.5 percent in those markets” according to Nielsen measurements.

“Receptivity increases the closer you get to your next store visit, and peaks during your shopping trip,” said inMarket CEO Todd Dipaola. “Since 2010, we’ve specialized in connecting brands with shoppers in-store. Now, we’re using billions of our first-party data points to form a one-to-one model for each shopper and influence them when they are most receptive in the purchase cycle. We’re helping mobile ads get smarter and more efficient, and brands leverage mobile to drive offline sales.”

About The Author
David Kaplan David Kaplan @davidakaplan

A New York City-based journalist for over 20 years, David Kaplan is managing editor of A former editor and reporter at AdExchanger, paidContent, Adweek and MediaPost.