Ventures of Venture Capitalists: SF Bay Area
LotaData analyzes billions (with a “b”) of mobile location signals or geo-cookies each day to infer user profiles and behavioral segments for our customers.
Our machine intelligence processes vast amounts of anonymized location signals gathered from mobile phones, to understand why people are where they are and what is on their minds. We look for thousands of behavior patterns, from shopping habits to fitness trends to dining preferences to commute routes. Our algorithms often uncover new behaviors or segments. Just this week, we discovered a new segment: The Bay Area VC. So we went through looking at VC profiles to understand their behaviors in the real world.
Per CityLab and Martin Prosperity Institute, the San Francisco Bay Area including Silicon Valley is the largest cluster of venture capital firms and VCs. No surprise there! But what does a typical day look like for a VC in the San Francisco Bay Area? Where do they venture into the real world when they are not screening and funding startups?Where do they lunch and dine? Where do they hang out? What are their go-to legal firms? What cars do they drive?
Below infographic shows you the aggregated behaviors of Bay Area VCs based on their anonymized geo-profiles.
In alphabetic order, below is the list of behavioral segments that stood out for Bay Area VCs.
- Avid Sports Fan
- Cafe Daily
- Eats Out Daily
- Entertainment Enthusiast
- Financial Service Customer
- Frequent Healthcare
- Frequent Luxury Hotels
- Occasional Luxury Retail
- Gym Enthusiast
- Live Music Lover
- Luxury Car Owner
- Museum Goer
- Wellness Enthusiast
- Wine Connoisseur
Conspicuous by their absence were grocery stores, convenience stores, dry cleaners and laundry. We expect these services are personalized and delivered directly to homes or offices.
How did LotaData infer all of the above? Our “People Intelligence” platform ingests mobile location data or geo-cookies, in a privacy compliant manner, from mobile apps, programmatic exchanges and smart cities. We then correlate the location trails with our geo-temporal data: local places, businesses, brands, active deals, local events, experiences, environmental conditions.
It is relatively easy to infer the places of work like Sand Hill or South Park, based on the characteristics of repeating location signals.
The resulting profiles and behavioral segments are made available through LotaData’s cloud-based Geo-Dashboard, and also through searchable APIs.
LotaData transforms mobile signals into insightful intelligence about people. In a world gone digital, NPR has written about “the business of VCs that can’t happen online“. As VCs move from meeting to meeting, and as our location data continues to grow, our algorithms will eventually infer car makes, models, and doors that open “like this”.