Originally published in MediaPost's Social Media Insider
The famous chicken and waffle brunch at Birch & Barley
It's one of those rules parents try to instill at an early age, right up there with eating vegetables and not watching TV before finishing homework. In my adult life, I've fallen short in all regards. Anyone who's seen my photos on Foodspotting knows I don't eat nearly enough produce, I'm prone to playing Angry Birds when there's work to do, and it's part of the job to talk to strangers all the time. Beyond that, though, I'm realizing that I've been benefiting regularly by listening to strangers.
Consider an example from my recent travels to Washington D.C. Thursday night, after arriving in the nation's capital, my wife and I drove straight to dinner at Birch & Barley, a trendy restaurant that boasts over 500 beers on the menu. My wife had heard of the spot from various sources, and gastronomes tend to vouch for it.
While waiting for our table, I checked in on Foursquare and then checked out the tips, a couple of which referenced brunch. Then I checked in on Yelp's app, which has the useful feature of highlighting the most frequently cited phrases in a location's reviews. Yelpers apparently are crazy about Birch's chicken and waffles, a dish that wasn't on the dinner menu. Well into what was proving to be one of the best meals either of us had eaten, I asked the waiter if brunch was a different experience, and he started telling us about the new menu. Before dessert arrived, I had booked a Sunday brunch reservation with the OpenTable app.
The whole process traversed four stages that make recommendations effective: discovery, validation, confirmation, and actualization. We'll look at all four, specifically in how they're used in mobile situations.
1) Discovery: Recommendations need to be readily accessible. Right now, more technologically savvy consumers can find location-based recommendations easily through check-in services, Twitter, barcode scanning, and other means. To gain wider adoption, they'll have to gain even wider distribution, especially through default mapping and local search offerings on both feature phones and smartphones.
2) Relevance: The recommendations need to resonate in some way with their audience. At Birch & Barley, there were more recommendations for the Brussels sprouts than brunch, but I quickly ignored them and forgot about the vegetables. Food's a salient example, but this could relate to anything. When I shop at J. Crew, it won't help if I only see mentions of women's clothing. When I'm at a hotel, I'll care more about the WiFi than the spa. Venues and location-based marketers will need to know their audience.
3) Validation: Consumers must make sure there's some credible reason to listen to the recommendation. If there's one reviewer saying something that strikes a deeply personal chord, it may not matter at all who that reviewer is. In my case, there could be one tourist from Kazakhstan raving about fried chicken, and I'm fine taking a chance. Most of the time, other cues are needed. These factors include: quantity -- the sheer number of recommendations listed; convergence -- several reviews echoing similar notes; and proximity -- how closely you identify with the reviewers.
There are degrees of proximity. Complete strangers are the least credible, unless it's a relatively small and like-minded community (this can even apply to Foursquare today). Brands familiar to a consumer will provide a useful filter; around D.C., I consistently found sound, reliable information from AskMen, C-Span, Epicurious, and The History Channel. Then there are the spheres of known acquaintances and close contacts. Other cues can come from local subject matter experts, such as when I needed to ask the waiter about brunch and gauge his reaction before making a reservation.
4) Actualization: How can a consumer take action? Can someone act on it right there? Is it close by? Is there a long wait? Is there any compelling reason to do something about it sooner rather than later? These forces can determine if the payoff will happen and what kind of latency is involved.
Reading it like that, recommendations look like a lot of work. Satisfied customers don't think much about it, though, as for them it's all about the reward.