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Leveraging Geospatial Analysis for Investment Decisions: A Case Study of Jefferies
Technology Category
- Cybersecurity & Privacy - Intrusion Detection
- Sensors - GPS
Applicable Industries
- Equipment & Machinery
- Retail
Applicable Functions
- Logistics & Transportation
- Product Research & Development
Use Cases
- Behavior & Emotion Tracking
- Retail Store Automation
The Challenge
Jefferies, a global investment banking firm, was grappling with the challenge of making informed investment decisions and advising their clients effectively. Traditional data strategy and research were not formally integrated into most of their research, often being treated as an afterthought. The firm was also struggling with the time-consuming process of assembling information, which could take months or even years. Furthermore, the firm was trying to shift from making assumptions to making decisions based on grounded fundamental observations. The challenge was to find a key question that drives the key variable that a human is deciding is incredibly important, and shift it from being something that was previously assumed, to something that probably still has some assumption around it but can at least be based on a grounded fundamental observation.
About The Customer
The customer in this case study is Jefferies, a global investment banking firm. Jefferies provides insight, expertise, and execution to investors, companies, and governments. The firm provides a full range of investment banking, advisory, sales and trading, research, and wealth management services across all products in the Americas, Europe, and Asia. Jefferies' Leucadia Asset Management division is a growing alternative asset management platform. Jefferies is also among the largest and most successful investment banks that are not part of a larger commercial bank.
The Solution
Jefferies turned to geospatial analysis to make actual investment decisions and advise their clients. They used tools like CARTO and vendors like Safegraph to pivot the way they approached these problems. Instead of focusing on assembling the information, they could now focus on the analysis and the outcome. This approach significantly reduced the time taken for analysis from months or years to days and weeks. The firm also partnered with vendors to build tools that could be reapplied for their clients. They focused on answering key questions instead of owning the entire flow of it. They also combined geospatial data with traditional data sets within finance to perform analysis by looking at things like weather combined with foot traffic or census information to give demographic profiles. This helped them make investment decisions based on grounded fundamental observations.
Operational Impact
Quantitative Benefit
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