Tom Webster, writing and speaking

Foursquare, Loyalty Cards And Market Baskets

Added on by Tom Webster.

There's a fantastic interview on O'Reilly Radar today with Dennis Crowley, co-founder of Foursquare. Of course, the topic of revenue models came up, and one of Crowley's ideas was to create "scrappy promotions" for local businesses--check in five times at the same coffee shop and get a free cup, for instance. The "loyalty scheme" is a pretty promising angle for location-based apps like Foursquare and Gowalla (and exciting, hyperlocal variants like North Carolina's own TriOut.) Certainly they provide the "scrappy" promotional angle that Crowley speaks of, but they also provide local businesses with a loyalty scheme that makes sense and doesn't requires consumers to carry around a rolodex full of punch/stamp cards or other pocket detritus. As consumers, we should be rooting for the success of these ventures, and for this revenue model in particular. Not only can local businesses profit from location-based apps and services, but their bigger-box brethren might potentially profit from incorporating similar check-in loyalty schemes. The "loyalty card" system, as practiced by national chains like Borders or Barnes and Noble, or by national grocers like Albertsons or Kroger, has two purposes. The first, to incentivize frequent purchases, is certainly well-understood by consumers. The second, information-gathering purpose is not so well-understood, and slightly more sinister. In effect, when you use your VIP card at your local Stop N Shop, you are providing the company a detailed record of purchases tied to your account, so that they can research the buying habits of consumers. Though the data isn't tied to you personally, it still represents more of your personal data spilling around the Interwebs, out of your control and owned by corporate entities. For most Americans, the trade-off seems innocuous enough--after all, if your frequent customer card saves you 10 bucks a trip at your local supermarket, who cares that they know you bought 6 cases of Rolling Rock and a case of diapers?

Still, a little part of me cringes every time I am asked for my card at the grocery store, or while buying books. Maybe I don't want my buying habits analyzed. What the Foursquares and TriOuts of the world enable is the promise of a world where I get to choose when--and where--I release my purchase data. "Checking in" at my local coffee shop (the outstanding Jessee's Coffee, in Carrboro) may not provide my barista with purchase data tied to a customer, but it does provide them a way to incentivize loyalty without asking for my personal data (or requiring space in my crowded wallet), and gets them a little social proof/free advertising to boot in the process through my public advocacy of their coffee. They win, and I win by keeping my personal purchase habits private. Of course, I am still tweeting my location everywhere, but I am not going to do this unless I am comfortable doing so at the business in question, so the check-in doesn't just instill loyalty, it reflects it.

This leads to another potentially fascinating angle for location-based services to pursue--a true market basket analysis of consumer habits. Current loyalty programs enable businesses to conduct what we market researchers call market basket analyses of purchase data--by determining what items frequently get purchased together or at least by the same types of consumer, companies can optimize promotions (the coupons you get printed out with your receipts) and even optimize the physical layout of their stores. There is a reason why Godiva chocolates are sold near the jewelry counters, and why (to use my example above) beer and diapers are often near each other in convenience stores.

What location-app data could enable is something far more profound--a kind of meta market basket analysis. Individual companies would not get the individual purchase data specific to each visit, but the Foursquares of the world would be throwing off heaps of data about what types of businesses are typically frequented on the same day, or even in succession. The obvious conclusions are the types of restaurants people go to after the game, or the clubs people visit in succession, but consider this--if Home Depot customers were also often checking in at a car parts store on the same day, or visitors to a certain salon also frequented a certain cafe shortly after, the cross-promotional and business optimization opportunities are truly endless. The more complex that data is, the richer the insights gained. At the national level, this data would yield incredible insights, but at the local level, that's scrappy promotion gold. Listening, TriOut?