Foodspotting: Pretty Pictures and Good Data
Foodspotting lets users photograph, tag, and mini-review dishes at restaurants. Lately, they’ve put this data to use by building a recommendation engine that surfaces attractive food in your neighborhood that’s customized to your tastes.
Everyone working in a startup should analyze whether the company has sustainable competitive advantage. Startups that are just gimmicky twists on existing products typically lack sustainable advantage and will fail. In contrast, startups like Foodspotting own assets that would take other companies years to imitate.
In Foodspotting’s case, those assets are:
- Great photographs of specific dishes
- Accurate data associated with those photographs
- Sentiment (likes/dislikes/interest) associated with particular dishes
The crucial differentiator between Foodspotting and Yelp is #2: accurate data linked to food photographs. It might sound trivial, but the reality is that it would be extraordinarily expensive for Yelp to link people’s narratives with photographs of specific items at restaurants.
This linkage allows Foodspotting to serve recommendations that are more credible, more compelling, and more efficient than the rambling rants and arbitrary star ratings from Yelp’s slobbering masses.
How would you rather get your recommendations? These screenshots are both for Adrienne’s, a pizza restaurant in my neighborhood.
Yelp asks me to plow through people’s irrelevant narratives (sorry you had a long day at work, Michael D.) where Foodspotting just shows me what to order. It also tells me 16 people liked that particular dish, which is a high quality signal that the Old Fashioned Pizza is worthwhile.
What takes minutes on Yelp takes seconds on Foodspotting. That’s a pretty decisive defeat.
Public service announcement: my recommendation engine of choice is still foursquare Explore, another Yelp competitor. While I’m starting to enjoy Foodspotting’s service, it doesn’t know me as well as foursquare does. And yes, I used to work at foursquare.
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