CASE STUDY - Innovative Analytics for Improved Performance and Consistent Decision Processes
- By Steve Trammell
- October 18, 2007
Commentary by Steve Trammell, Corporate Alliances, ESRI
A major food and beverage provider was experiencing inconsistent performance from its new and existing outlets throughout the U.S. Furthermore, the more competitive environment was requiring a more agile approach to managing real estate assets.
Management suspected that existing BI and geographic information systems (GIS) applications could help solve the problems, but was unsure how to leverage these assets. The challenge was to get these disparate systems working together either to arrive at a solution or to determine if additional applications were needed.
A consulting firm was engaged to evaluate the existing applications and suggest possible courses of action. It was determined that updated versions of the BI and GIS applications were needed, as well as new GIS functionality and current GIS data. The consultant brought in the existing BI and GIS providers to discuss their current capabilities and to lay out a high-level vision of how the client’s challenges could be addressed.
The BI customer analytics application was augmented with data from a new customer survey, resulting in a more accurate customer profile. This customer profile was then matched to ESRI’s Community Tapestry data (demographics and purchasing potential), and a nationwide map was generated showing the distribution of people matching the profile.
The GIS could then be used to create new trade areas, and thus revenue potential, for each outlet across the nation. Trade areas for each outlet were created based on actual travel time over the street network as opposed to the more traditional ring analysis. A further refinement of the trade-area determination model accounted for the proximity of the company’s competitors and target customers. This method of modeling trade areas is a much more accurate measure of spending potential than a purely drive-time model.
Utilizing nationwide data and a more sophisticated trade-area model addressed many of the site performance inconsistencies the company experienced using the previous analysis method. These same improvements to existing systems could now be leveraged more proactively to manage the company’s entire real estate portfolio using “what if” scenarios.
Figure 1. If the market area will support another outlet, the user can display a map showingprojected revenue potential for various locations.
For example, if a market area is found to be underperforming, a new trade-area analysis for each outlet is conducted to check individual site performance. Depending on the outcome of the analysis, the user is presented with options for improving the area’s performance, such as closing, moving, remodeling, or adding outlets. If the market area will support another outlet, the user can display a map showing projected revenue potential for various locations. (See Figure 1.) If an underserved location is identified, the user can then add an outlet and conduct a trade-area analysis for the new location. The revenue potential for the new trade area is then passed back to the BI analysis package, where factors such as acquisition, construction, and operations are considered and annual net revenue is projected. If the projection is favorable, the real estate acquisition group is notified and tasked with obtaining a site in the newly identified area. Available sites are run through the trade-area analysis application to validate potential performance.
A more rigorous and repeatable market analysis methodology gave the company greater confidence in sales forecasts. The flexible and simple-to-use, scenario-based market analysis tool greatly accelerated the site selection process, resulting in quicker returns on investment for expansion projects.
The company is now able to react to changes in the marketplace much more effectively. Developing problems can be mitigated early to minimize or eliminate losses, and newly developing markets can be served more quickly.
This article originally appeared in the issue of .