Sense networks
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Sense Networks uses proprietary technology and location analytics expertise to analyze location history and behavior patterns to deliver unique insights and intelligence. They have built a high-capacity platform called MacroSense that can extract information from tens of millions of user locations and points of interest, segmenting users and predicting future behaviors. Their location-based segments have been shown to drive user responses and actions.
Sense networks
- 2. Sense Networks Overview • Founded in 2006 by world-class MIT and Columbia Computer Scientists interested in understanding human behavior through location information • Proprietary technology and deep expertise in geospatial and temporal analysis to deliver unique insights, trends and intelligence based on behavior patterns • Team of 16, based in New York and San Francisco, funded by Intel Capital and Javelin Ventures World’s Most Intriguing 2009 Excellence Award 2009 Cool Vendor 2009 Company to 2009 AlwaysOn Startups Watch Award Award 2
- 3. Location-Based Services and Mobile Advertising: For Years, Focused on Simple Proximity-based Coupons • Maybe some context like the weather or day of week • No personalization – ads based either on opt-in lists from retailers or mass marketing => 3 Proprietary & Confidential
- 4. A Better Way: Use The Best Context Available – Current and Historical Location Information • Existing targeting: context from mobile content consumption • What about location history? Best predictor of behavior and how we interact with the real world 4 Proprietary & Confidential
- 5. Location History Can Drive Much Better Recommendations and Mobile Advertisements Segment: Health & Fitness => Young Adult Outdoorsy . . . A Different Ad Location History: Parks and Recreational Areas + Current Context: Location, Weather, Time-of-Day, User “Mode” (e.g. shopping or commuting) = 5 Proprietary & Confidential
- 6. Sense Networks Has Built a High-Capacity Platform for Extracting Intelligence From Location Data and Summarizing It MacroSense Software Platform Information Output Data Reduction Input Locations Segmentation Extract Info Normalize Prediction Clean and We can extract Proprietary thousands of location algorithms to “features” from tens of summarize all millions of users and this information tens of millions of more efficiently points-of-interest (“jpeg for data”) 6 Proprietary & Confidential
- 7. Example: A Mobile User’s Location Data and Call Activity Can Be Abstracted to Commercial, Advertising Exposure . . . Flow Call Activity Demographics, Commercial, Ad Exposure Week FLO FLO … FLO2 SIC SIC … SIC DEM DEM … DEM Hour 1 2 0 1 2 97 1 2 78 1 .03 .31 .14 .03 .05 .41 .11 .04 .01 2 .14 .34 .02 .04 .05 .52 .01 .01 .00 … 168 .07 .34 .51 .02 .06 .48 .02 .01 .00 7 Proprietary & Confidential
- 8. . . . And Compacted Into A “Location DNA” for Each User . . . DNA User 1 DNA User 2 Time of Day, Day of Week Category of Commercial Exposure Category of Commercial Exposure (i.e. restaurants, schools, golf (i.e. restaurants, schools, golf courses) courses) 8 Proprietary & Confidential
- 9. . . . To Create Segments – All From Anonymous Location Data Nightlife Profiles, Primary Clusters How often do they go out each day of the week? Where do they hang out? What is the avg age of most people in the neighborhoods they spend time in? How racially diverse are the neighborhoods they spend time in? Are the places they spend time in rich neighborhoods or poor neighborhoods? “Young & Edgy” •Out every night in young, racially diverse, low income neighborhoods “Weekend Mole” “Mature Homebody” •Out occasionally on •Rarely goes out, typically weeknights, typically middle- spends nights in mature, aged, Latino, middle-income white, higher income neighborhoods neighborhoods 9 9 Proprietary & Confidential
- 10. Location-Based Segments Proven to Drive User Behavior • Example: Using data from a mobile location app, we predicted new places that users would go based on their “tribe” • If we gave users 500 recommendations, 20% were acted on User Response To Sense’s Top Recommendations 20% 15% 10% 20% Sense Networks Baseline 5% 8% 5% 0% 1% 4% 0% 50 100 500 We examined 30k users and 5k points of interest. If users were presented with 50, 100, or 500 place recommendations they had not previously visited, what % of those would they visit and check in? For 100 Sense recommendations, 8% were acted upon by users. For 500 Sense recommendations, 20% were acted upon. 10 Proprietary & Confidential
- 11. Contact Info 1123 Broadway (between 25th and 26th Streets) Suite 817 New York, NY 10010 +1 646 758 6227 Mikki Nasch, EVP Business Development: mikki@sensenetworks.com, +1 646-845-9859 Christine Lemke, COO: christine@sensenworks.com, +1 917-284-8384 David Petersen, CEO: david@sensenetworks.com, +1 415-336-3948 11 Proprietary & Confidential