Our latest announcements, resources, and case studies.
Together, we’re making it easier to keep up with rapidly changing consumer movement patterns and interests.
Sonic jumped from the 22nd most popular restaurant chain to #5 in a matter of weeks. How can Sonic understand who their new customers are and retain that audience as the economy moves back to a new normal?
In this case study, we identify Better Buzz Coffee's most profitable loyalty customers to aid in site expansion, geomarketing, and tailored product offerings.
How homebuyers and agents can find the perfect home and understand any neighborhood like a local.
Analyzing the relationship between social media & credit card data allows retailers to gain key market insights, optimize their footprint, & boost sales.
Using its behavioral segments along with Esri technology, Spatial.ai helped an upscale thrift store pick its first location in a new city.
In partnership with Spatial.ai and CurisData, eSite is utilizing geosocial sentiment data with healthcare demand data to deepen the understanding of patients and their utilization of healthcare.
SitesUSA has created an excellent data visualization tool to help analyze unemployment and sentiment data during COVID-19. Here's a breakdown of the datasets.
Trade Area Systems (TAS) and Spatial.ai today announce a partnership integrating Geosocial data into TAS’s suite of applications.
PiinPoint partners with Spatial.ai, the leading Geosocial data provider, allowing retailers and landlords to get a deeper understanding of each community’s multifaceted interests.
A look at the relationship between the Smart Chic segment and Nordstrom Rack store closures.
Tetrad and Spatial.ai today announce a new partnership integrating Geosocial data into Tetrad’s Sitewise platform.
How can you use Geosocial data? How have other companies, like Subway, leveraged the data to make better location decisions?
Select the relevant Geosocial segments for your brand.
Determine new commerce opportunities best suited for a developing town or city.
Data partnership between data exchange adsquare and Spatial.ai allows Geosocial data usage in real-time programmatic advertising
Profile consumer interests and attitudes, and customize in-store experience (hours of operation, store concept, design, etc).
Ranking locations for site selection using Geosocial data
PECO, one of the nation’s largest owners and operators of grocery-anchored shopping centers, will utilize Spatial.AI’s Geosocial data to help inform business decisions.
Curate the best potential locations for your client based on the attitudes and interests of the community.
TSCG and Spatial.ai have announced the integration of Geosocial data into TSCG’s location analytics toolset.
Merchandise your center with shopping and experiences that align with consumer interests and attitudes
Surgically select your next store or optimize your portfolio based on real customer behaviors.
Learn the best practices when visualizing Geosocial data for data analysis or in your own platform.
Griffin Morris, VP of Product, had the opportunity to sit down with Amy Stulick at the San Fernando Valley Business Journal to talk about Toys "R" Us's new real estate strategy.
SiteZeus introduces a brand new solution for retailers powered by geosocial and mobile location data.
Cincinnati startup Spatial.ai has hired a critical player in driving innovation and growth for some of the world's largest companies through the use of location analytics.
We are excited to announce that we now provide full Geosocial data coverage for the Mexico market.
Geosocial segments are now available within the Esri ArcGIS Marketplace.
Lyden Foust, CEO of Spatial.ai, speaks with Mariel Ebrahimi, Cofounder and CEO of Disrupt CRE.
Learn how to quantify the 'vibe' or 'feel' of an area that fits your brand, and then find more of these ideal locations.
We are proud to have received the "Audience Favorite" and "Judges' Favorite" awards.
Spatial.ai was featured in a Commercial Observer article on how shopping centers are using Geosocial data and location intelligence to improve customer experience and make leasing decisions.
We analyzed every Opportunity Zone across all 50 states to identify low-risk investment opportunities. Here's our final ranking.
Breaking down the (often confusing) data landscape of commercial real estate.
Spatial.ai was featured in a Bloomberg article on how retailers are using location data sources to improve customer experience and make real estate decisions.
A comparison between standard psychographic segmentation and geosocial segmentation.
A look at how banks can leverage social media date to improve site selection decisions.
Through their partnership with SiteZeus, Subway found an insight from Geosocial data that improved sales by 10%+.
We are proud to join the NVIDIA Inception program as we continue to push the limits of human understanding in the location intelligence space.
Keenan Baldwin, Cofounder of SiteZeus, shares his thoughts on geosocial data.
Today we announce the launch of our new geosocial dataset. Here's a first look.
A brief introduction to our dataset and the approach for building out the segmentation.
You can now access the power of geosocial within SiteZeus' platform. Here's what that means.
Learn more about our new partnership with SiteZeus.
Using Lululemon as a case study, we walk you through how you can incorporate geosocial data into your site selection and marketing decisions.
The right product, the right ad, the right people, the perfect location — using social media data.
Everything you need to know to get started with our dataset.
Find tenants that match the community's social interests.
Knowing what kind of data to use in your analysis is half the battle. Here's a brief overview of 5 major types of location data sources.
Intalytics incorporates Spatial.ai's social media data into their predictive modeling processes.
Going beyond census data to inform retail location decisions.
Hint: it's more than demographics.
How a dry cleaning franchise used geosocial data in their sales projections.
A more organic approach using geotagged social media data.
Using word vectors and natural language processing to find commonalities amongst cities and neighborhoods.
How new data sources can build upon our understanding of cities and help them thrive.
