Leveraging Geospatial Analysis for Strategic Investment Decisions in Private Equity
Technology Category
- Application Infrastructure & Middleware - Data Visualization
- Infrastructure as a Service (IaaS) - Private Cloud
Applicable Industries
- Cement
- Retail
Applicable Functions
- Procurement
Use Cases
- Retail Store Automation
- Time Sensitive Networking
About The Customer
The customer in this case study is American Securities, a private equity firm. The firm's business model involves buying private businesses, managing them for three to five years, and then selling them for a profit. The firm participates in competitive bidding processes to acquire businesses. In this case, the firm was considering the acquisition of a retail chain with several stores in a specific region in the United States. The firm needed to understand whether the retail chain could continue to be profitable by building more stores, and used spatial data analysis to gain insights into the potential profitability of the retail chain's expansion strategies.
The Challenge
The case study revolves around a private equity firm, American Securities, which was considering the acquisition of a retail chain. The primary question that needed to be answered before making the investment decision was whether the retail chain could continue to be profitable by building more stores. The firm was interested in two expansion strategies: whitespace, where the retail chain would expand into new markets where it didn't have a current brand presence, and In-Fill, where more stores would be created in the market that the retail chain was currently operating in. The challenge was to understand the potential profitability of these strategies in a short time frame, given the competitive and time-intensive nature of the bidding process. The firm also needed to understand the factors that could affect the performance of the stores, such as the impact of distance from the core market and other potential confounding factors.
The Solution
The firm used spatial data analysis to gain insights into the potential profitability of the retail chain's expansion strategies. They sourced around 500 spatial variables, including demographic, sociodemographics, and competitive presence data, and matched these variables to a 10 or 15 minute drive time radius around each one of the stores. They used isochrone technology to aggregate the data and identify correlations with store revenue. The firm also used Classification And Regression Tree (CART) analysis to understand the characteristics of new stores versus old stores and the characteristics of above average performing stores versus below average performing stores. The CART analysis revealed that newer stores were on average further away from the core and had no truck parking, and that stores with large lot sizes performed well even if they were far away from the core market. The firm also used CART to score census tracts around the existing market based on predicted revenue, which helped them identify potential locations for new stores.
Operational Impact
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