A few weeks ago while between meetings at AdTech NY, I stepped into a session just in time for Retailigence CEO Jeremy Geiger to trigger an a-ha moment about local product pricing. Then the next day at Qualcomm’s LTE Direct Summit, it surfaced again.
The idea stems from the shifting attitude in local to use proximity-based data beyond just ad targeting, toward better ad creative. This takes in variables like weather, time of day, and product availability. What’s nearby, on the shelf, right now? And what does it cost?
Retailigence has done this for years with an API for app developers and ad networks to bake in SKU-level product data. But developing more recently is combining this with increasingly robust mobile signals and sometimes obscure data that capture situational relevance.
Geiger provided the example of measuring something as esoteric as the airborne pollen count. In concert with inventory data of nearby pharmacies, you can imagine how ads can be targeted and crafted in ways to reach allergy sufferers in time-sensitive ways.
This isn’t totally new, but it gets interesting when the data are used to not only to create relevant ads, but also better yield management. In other words, variable pricing to generate or capture demand in more economically strategic ways. Think airfare pricing.
It boils down to segmenting consumers by willingness to pay for something — a function of location-oriented factors. It’s a jacked up version of the airline model in that it that maximizes revenue with demand-driven pricing. But is there a local commerce version?
We’re getting closer: all these proximity-related factors enable predictive modeling around transaction probability. That can then be plugged into an equation to determine price sensitivity or elasticity on an individual level. What discount will get your attention?
From there, it’s a matter of variable pricing to customers that are new, repeat, faraway, nearby on foot versus driving by at 60 mph, and so on. And that’s the key: though we have time-based variable pricing (a la airlines), proximity-based personalized pricing is the next phase.
It’s especially relevant for perishable inventory (think empty movie theaters). This also isn’t new, and gets to the yield management endgame of the local daily deals craze of 2011. But adding layers of relevance like location and individual behavior gives it much more dimension.
Groupon is moving this way with mobile and loyalty/CRM efforts. Uber and Lyft apply “Surge Pricing.” These are really just another flavor of demand-driven dynamic pricing, but more transparent and populated marketplaces, via mobile, will boost network effect and liquidity.
The beginnings for mobile ad models along these lines can be seen from Placecast, Thinknear (
Of course this all introduces complexity in advertisers’ operations and POS systems. That’s especially a challenge with SMBs, where ad sales are hard enough already. On the bright side, it works towards the “OS for SMBs” concept of deeper operational relationships and higher lock-in.
The elusive local full-service ad agency that everyone loves to talk about has a better chance if it hooks in at these operational levels (think CRM, inventory,etc.). That ties naturally to marketing, product planning and, eventually, variable product pricing.
That’s when we’ll really see a local deals craze.
Mike Boland is senior analyst at BIA/Kelsey, where he heads up the firm’s mobile local coverage. Previously, he was a tech journalist for Forbes, Red Herring, Business 2.0, and other outlets.