Most retailers select sites using a combination of competitor insight, market data, location planning, and strategic fit. It’s a data-driven decision because investment in a retail location involves the upside of increased market share with the downside of real estate risk in a given area. Even though most retailers have abundant data available, that data often resides in spreadsheets or silos around the organization, or in limited mapping and reporting tools. Pulling together a risk profile of a particular location is a labor-intensive process fraught with error.
Spatial analysis enables companies to blend geographic and descriptive data together from a variety of sources and use that data in geographic models and visualizations. In a form of geographic business intelligence, companies build models from geospatial data on locations, relationships, and attributes. Spatial analysis goes beyond the traditional Geographic Information Systems (GIS) framework of displaying descriptive information on a map, now reaching into data science and machine learning. The combination of cloud computing, geospatial data, on-demand analytics, and rich reporting pushes BI-based insights down to the level of individual stores, departments, and product categories. For retail site selection that means that property managers can incorporate point-of-sale data into their selection and forecasting models. They can include previously impractical data on urban storefronts, shopping centers, endcaps or pads, parking, view corridors, and zoning.
With Alteryx, you can:
- Download the Spatial Analytics Starter Kit to combine customer value data, customer location, and new store location data to analyze customer behavior and increase top-line revenue.
- Create store trade areas based on customer distance to stores to put products in the hands of more customers.
- Blend spatial data to calculate ad area distribution, increase sales, and reach more customers.