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Discover the most significant behaviors around your store now.
The missing voice for retail location intelligence.
Dynamic location-based behavioral insights from social media for retailers and restaurants
Social media is pouring out of cities every day. Unlike census data which gives you a snapshot, Spatial data can track the trajectory of a location quarter to quarter.
Unlike survey data which can be biased, our psychographic dataset is built on organic media from locations, uncovering how humans naturally interact with their environment.
Our highly dimensional data has been found to be predictive of site success in data science models.
“Retail and restaurant operators often rely on proxies for human behavior and personality as they make real estate decisions. Spatial.ai’s socially-driven data provides a new way for organizations to directly incorporate the voice of consumers within trade areas of interest.”
Dave HuntoonPresident of Intalytics
“Typically, data driven decisions and qualitative insights are very separate. Spatial brings both together - quantifying the personalities of people in our restaurant communities and their impact on our business success.”
Richard ZimmerDirector of Franchising and Real Estate at Zoup!
Unparalleled consumer insights
Location is the biggest factor that determines success or failure. Why can’t companies measure the experiences, behaviors, and personalities of the people in those communities?
Spatial.ai turns billions of conversations happening in communities (on social media) into location-based, quantifiable features. When compared to your store by store performance, we can deliver “Insights” around the social behaviors that positively - and negatively - affect your success. These insights can then be used to inform marketing decisions, store by store optimization, and - most importantly - where to place your next store.
Custom Social Indicators proven to be predictive of site success
We filter out the noise, providing data that can be easily added to predictive revenue models. Categories are optimized for predictive qualities. Our partners have seen reduction in error in their models and more accurate revenue forecasts and network analysis.
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