Daily Dose of iQ: Neighborhood Mapping Based on Foursquare Check-ins

Apr 27, 2012 — Faai Steuer

Last week (April 19), Fast Company's Infographic of the Day featured the work of Livehoods, a research project based out of Carnegie Mellon's Mobile Commerce Lab. Livehoods analyzed 18 million Foursquare check-ins to identify "algorithmic relationships between the spots people frequent," wrote Fast Company's Mark Wilson.

"As more and more people and places are analyzed, Livehoods clusters this data into what becomes a collection distinctive neighborhoods -- places filled with people who enjoy going to the same restaurants, coffee shops, and music venues."

Naturally, this type of data could be a boon for advertisers and retailers alike.

Some clear benefits to consider:

  • Higher data quality: Livehoods offers more in-depth and granular data than market research surveys can typically identify. Consumer answers are based on what they want to buy or where they want to go -- they don't necessarily reflect their actual behavior. Livehoods shows where they're really going and when.
  • Improved geographic analysis: Conducting cluster analysis using a location-based social network like Foursquare allows marketers to create a physical map that truly reflects the mental map of consumers. We not only know where and what time they visit particular places, we can also see who they hang out with.

And for retailers, this means:

  • Opening new locations: Retailers can use this info to strategically open new store locations in the neighborhoods that fit their brand personality. They can also plan their expansion to the neighborhood that have similar characteristics to ensure the brand fit and acceptance.
  • Better product assortment: Plan in-store merchandising based on which products should be offered in specific neighborhoods. Manage your inventory better, increase inventory turnover and increase profit per square foot.
  • Cross promotion: Knowing where consumers go and what they do, retailers can pursue specific cross-promotional opportunities. If you're a wireless retailer in a healthy, active neighborhood, your target groups like to go to coffee shops or sandwich bars after running. You could create a co-promotion with those businesses and promote mobile/fitness accessories like running armbands or earbuds. At the same time, you can return the favor by recommending customers to that coffee shop.

Sample size is Livehoods' biggest weakness.

Unfortunately, because the Livehoods' data is limited to people that use Foursquare (younger, smartphone carrying, early adopters), it only covers so much of the purchasing public.

The data is also biased on check-in behavior. Since they are voluntary, Foursquare check-ins is not mobile phone tracking. Most users do not check in EVERYWHERE they go, so the data is actually quite spotty. They are preferential -- they check in where they want to be seen. They are also less likely to check into banal, everyday places (e.g. grocery stores or pharmacies), not to mention places they prefer to keep hush-hush (like strip clubs, for example).

Take-home message: Livehoods' analysis is great and can definitely help retailers and advertisers get to know their consumers and audience better. It can be used to improve brand positioning and plan the marketing mix, especially if your retail store targets consumers who match Foursquare's user profile. Once they get less biased sources of data, they should be able to produce more accurate views. Of course, this depends on broader Foursquare adoption overall (or a different tracking method to begin with).

Topics: Retail Operations, Mobile Industry, Customer Experience, Business Intelligence, Retail Marketing

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