Retailers have already recognized the value of geolocation data, for everything from targeting promotional messages to determining the optimal location for a new brick-and-mortar store. But to truly maximize the value of geolocation data, retailers need solutions that can not only comb through extremely large data sets, but that can identify which specific factors within those data sets are important and relevant to the retailer’s business. Accomplishing both of these goals requires the application of machine learning to geolocation data.

This Connected Consumer Series webinar will explain how machine-learning enhanced geolocation data can:

• Predict, with a high degree of accuracy, the incremental sales increases from both digital and physical sales channels that will be generated by a proposed new brick-and-mortar store in a given area;
• Use expanded data sets to identify, and quantify, new market opportunities on a wider geographic scale than has been previously possible; and
• Quantify geographic data to determine the relevance of specific data points to the query posed by the retailer.

Presenters Gary Sankary, Industry Marketing Strategy – Retail, and Joel McCune, Spatial Data Scientist – GeoAI Business Development, from Esri will discuss how machine learning multiplies the value of location data, and also provide several examples and retail use cases showing the benefits of this supercharged data analysis.