Sense Networks: Interesting but Is the Market Ready?

Last June we wrote briefly about CitySense (from Sense Networks) and its effort to use real-time, aggregated urban heatmap data to profile and make recommendations to people looking for things to do. VentureBeat reported yesterday there was a bit of a bidding war by two VC firms to provide the second round of funding ($6 million) to the company:

Semiconductor giant Intel led the deal, after beating out Sequoia Capital, the well-known Silicon Valley venture capital firm, we’ve learned from a source very close to the deal. The negotiations got tense after Intel made its offer for investment. The following day, after hearing about Intel’s offer, Sequoia tried to make the company’s CEO take money exclusively from Sequoia. The company’s chief executive Greg Skibiski, we’re told, wasn’t impressed, and decided to take Intel’s money. One point we aren’t able to confirm is whether Intel offered better deal terms. Neither Sense Networks nor Intel nor Sequoia responded to requests for comment. An announcement about the deal is expected in about a week.

The idea behind CitySense is to provide recommendations to end users about things to do and "eliminate the need to search" by using aggregated profiling data. Whrrl originally was pursuing a similar idea and, conceptually, so is Geodelic but without group profiling.

The so-called alpha version of CitySense offers a pretty poor user experience. However, the company is reportedly just demonstrating its technology with the app and is ultimately not interested in competing in the consumer market directly. It's apparently planning to aggregate and provide data to other consumer-facing companies and mobile ad networks, perhaps even brands and marketers directly. Hence the $6 million second round. 

Sense Networks' techology uses machine learning to identify patterns and trends that third parties could then use for recommendation or advertising purposes:

Sense Networks applies advanced statistical algorithms to normalize activity based on years of historical data combined with demographic, weather, and other variables. Once a broad understanding of the spatial behaviors in a city is available, companies and investors can leverage the continuously updating framework to better understand their own customers from sparse location data, discover trends in aggregate consumer behavior for correlation with financial indicators, and predict demand for services and places.

Here's how the company describes its process for identifying the top nightlife destinations in San Francisco:

  1. Identify and isolate the top 200 nightlife destinations
  2. Create a network of movement between these locations
  3. Machine learning algorithms analyze each location in the context of the overall movement and categorize it (colored dots added) by examining everyone's point of origin, and where everyone goes afterwards
  4. The categorized places are now grouped by behavioral similarity, not proximity (convergent dots represent places with the most similar "type" of people present, in real-time, versus geographical proximity)
  5. The spatial behavioral map is overlaid onto a spatial geographical map and the system continuously learns as new live data is received

 Picture 4

Clearly there's value in this data to third party publishers and apps, as well as to ad networks. But like the promise of LBS and the notion of the "right ad, right time, right place," marketers and ad networks may not be prepared or able to truly to take full advantage of such data for some time.