Using State-of-the-Art machine learning models we predict sales, foot-traffic, and visitor demographics in real-time to enhance:

Site Selection Strategies:

De-risk and protect millions of investment dollars by selecting the right site with the highest future revenue and lowest costs. Sites are selected based on the distance target customers historically drive. Target customers are identified based on the client’s own customer data and additional insights generated from foot traffic both in client locations and the client’s competitors. Customers can be segmented into purchasing behaviour, ethnicity, income, education, and other defining characteristics. Segmenting customers has the added benefit of informing future marketing strategies as well. By analyzing how far a customer will travel to a client’s location, a client can learn if they have saturated a market or if more market penetration is possible.

Portfolio Optimization:

Cull low performing locations and discover efficiency gains and cross-marketing opportunities from other sites. Using foot-traffic data a client can see where customers are spending time when they are not at the client’s locations. This can identify partnership opportunities with other brands that share customers. With location data sets, the client can learn how their engagement with their customers compares to their competitors. If the client’s customer engagement is higher then there could be possibilities to expand into a competitor’s area and capture market share. If a client’s customer engagement is lower than expected at some locations then they should be examined for improvements or closed

Customer Acquisition and Retention:

Target households with the right message and the right media at the right time. After customer segments are identified, a client can cross-reference against viewership data sets to discover what media the customer consumes and when.