Geo Data is the new Yelp
At the end of his early 2014 article in ‘StreetFight’, which focuses on the business of ‘Hyperlocal’, Steven Jacobs made this point:
Social recommendation products from Facebook and Twitter could threaten Yelp’s position in the market, but the longer-term threat to Yelp comes from a more data-driven approach. As mobile positioning and battery-life improves, and devices can persistently track consumer movements, there’s an opportunity over the next decade for companies to use those data to facilitate discovery similar to Amazon recommendations. Content isn’t going anywhere soon; but data will have its day.
The larger premise was a discussion of ‘Perfect Information’, which in economics is ideal for the educated decision-making of consumers. In business, it can lead to commodization, as brands struggle to differentiate and often mimick the services of competitors. Take, for example, the airline industry. Near-perfect information about the features customers cared about, led to lots of price-comparing platforms, and the eventual commodiziation of the travel offering. But in the local retail market, better information will drive more commerce and enable upstarts to thrive, which is a step in the right direction.
Jacobs’ point really speaks to an evolution in information the mobile web will enable, as non-social data begins to match social data in terms of relevancy, accuracy and supply. Social and publisher content has been essential in filling the gaps that business and location data leave because without updated data points in local such as store hours, offerings, sentiment, location and more, social content is a useful substitute.
Before the internet, and throughout the early internet, the Yellow Pages were the resource for such data. Whether in the book, on their website, or via phone, this old-fashioned approach sought to provide consumers with the information they needed to make decisions. It was updated once or twice a year, and unless they bought nice advertisement, did little to drive demand. As Jacobs pointed out, this culture of information led consumers to trust primarily large brands, such as “Hilton” for their known quality – the information to advocate for smaller brands just didn’t exist.
Enter social. Social content and sites such as Yelp, Trip Advisor and many more empowered people to curate the information, data and content of businesses in their locale and those they visit. With a social sense of community, and hopefully enough iterations, these platforms could produce a reliable proxy for verified data, on all things important to retail. And not only does this content illuminate where the business is, when they are open and what’s on the menu, but it goes a step further, to depict the kind of traffic a business or commercial center was seeing. With high review volume a reader can be more confident that the business in question is worthwhile.
Non-social, Geo-Behavioral data has the ability to do the same thing, at scale, with less reliance on the nuances of social authorship. By tracking non-PII consumer movements locally, including time spent, and affinity with other known data points like sporting events, concerts and more, geo-profiling at scale will fill our local information sources with automatically curated data – which in many cases will prove to be more reliable.
The reliability challenge with social content is that it often requires a polarizing subject to receive volume. Therefore, people must be passionate about it – whether or not it’s role in the community is truly superior. For example, a fun Frozen Yogurt shop will receive more content than a reliable breakfast joint that serves an older demographic and certainly more than a local oil-change garage which has served the community for decades. With improved data automation through geo-locational aggregation, non-polarizing yet high quality businesses will regain equal footing with the trendy places. Social content does a great job of communicating what we love and what we hate, but a poor job of communicating what we need.
With more iterations, driven by the world-wide proliferation of mobile devices, more instances of mobile ‘check-in’ and interaction and better data capture from both structured and unstructured sources, real-life ‘data’ will start to communicate as much about local businesses as real-life sentiment through social. And through automation, local behavioral data will paint a very accurate picture of both essential business information and the more nuanced as algorithmic modeling depicts interesting facts about a place such as ‘Best for Sporting Events’ driven by unbiased, non-polarized real-life data. Many small businesses post the sticker “Think Global, Act Local” on their windows. The data ecosystem is enabling just that as local actions will drive more data and insights globally for a new level of near-perfect information for retail.
