Traffic Structure and Dynamics

Dataplace.ai helps businesses understand where their customers are in the offline world. Whether it’s about mapping customers’ journey, defining location for a new store, targeting the right customers with marketing efforts and driving offline to online communication or identifying points of sale with the highest sales potential – we make everyday business decisions in the offline world easier.

Our data, products and services help business to better understand the power of ‘where’. Thanks to our technology and data science we’re able to build precise prediction models, extrapolate raw mobile location data into actual traffic insights with over 90% accuracy and provide complex traffic analysis at any location level – from a zip code level, through a geo-hash, to a particular address.

Sample Tables:
- Location traffic analysis
- TLM Traffic locality metric
- Shared traffic between locations

Fields included:
- Type of spatial unit (geohash, zipcode, selected catchment area)
- average number of people moving through a selected area daily
- average number of people moving through a selected area daily, with hourly division
- car and pedestrian traffic division in a selected timeframe
- footfall intensity per month in a selected address
- average traffic division between residents (nighttime population) and visitors (daytime population) in a selected area and timeframe
- average time spent by people in a selected area
- recurrence of people visiting a selected area
- shared traffic between 2 locations
- cannibalization between 2 locations
- traffic locality metric (% of people who commute to a given location not longer than 5 min.)
- customer journey (top 5 areas from which traffic to a selected location originates)
- 11 additional fields