Massive Mobile Data Analytics for
Retail and Real Estate
Gravity models and traffic counts only go so far. Partner with StreetLight to use real-world data to understand who visits, shops at, and drives by almost any location..
Real-World Analytics for Site Selection and Property Development
We transform location data from mobile devices into Metrics that describe the groups of people who visit specific locations and their travel patterns. Our analytics are derived from Massive Mobile Data, which means they're comprehensive and cost-effective. Even better, they're at your fingertips via our easy-to-use StreetLight InSight® web app. Our Metrics include:
- Visitors' home and work locations
- Visitors' Path to Purchase - that's where they come before visiting a site
- Visitors' travel distance - the number of miles they traveled to reach your site
- The home states and metro areas of the tourists that visit a site
- The relative volume of drive-by and parking activity at a site
- Demographic insights into visitors, including income level, race, education, and family status
You can use these Metrics to answer an array of questions for retail siting and real estate planning, including:
What is a site's true trade area?
How does the trade area change during different times of day?
Will this location cannibalize existing stores?
What is the demographic profile of visitors to the most my successful properties?
How do my retail sites stack up against competitors?
How does performance vary across a fleet of properties?
The StreetLight Data Advantage
Our data sets cover about 10% of the US adult population, and counting - that adds up to tens of millions of devices. We also use multiple location data sources to develop our Metrics.
Our GPS and Location-Based Services data has high spatial precison, and our algorithmic processing includes rigorous quality assurance. We won't tell you that the Path to Purchase is a parking lot.
As a leader in consumer privacy protection, we only work use de-identified data sets. Then we normalize, aggregate, and contextualize our data sets to further protect individual privacy.