Location data is commonly used in many industries to help inform investment decisions and to both detect and measure change. Those use cases are clear for Commercial Real Estate, Retail and the Public Sector.
What’s less commonly understood are the use cases for location data in the Financial Services industry.
What does a hedge fund manager care about migration patterns? Why would a bank targeting business customers spend time and money to study foot traffic on Main Street? Better yet, how can a financial services consumer - aka all of us - actually benefit from the industry’s use of location data?
The 3 most common use cases for location data in financial services that we encounter here at Unacast typically fall into two categories: investment risk assessment and marketing to both business and consumer segments.
We’ll start with using location data to inform business and credit risk assessment.
Informing Business and credit/lending risk assessment
In another post, our Chief Technology Officer, Frode Bjerke, points out that there are really only three overarching use cases for location data across all industries: Informing decision making, Detecting change and Measuring change.
The first use case in the financial services industry is squarely in the camp of informing decision-making, specifically around different types of investments. One technique that is often applied is to use visitors to a given location as a proxy for how a business is performing -- not just in a specific location but across a franchise and in relation to the competition.
For example, a hedge fund could use location data to examine migration patterns to discover emerging areas, or investment opportunity zones, with indicators such as net positive population growth and upwards mobility in terms of average income.
In another application, the investor wants to look at foot traffic data over a given period of time around a series of custom points of interest, let’s say publicly-traded retail brand locations. In this scenario, foot traffic volume and trends versus other locations and brands help to inform the decision whether to buy, sell or hold that retailer’s stock.
In a final example for this use case, a lender wants to inform their credit risk assessment for a privately-held restaurant chain with 200 locations across a dozen states. With fewer public instruments available to reference, location data can add a valuable dimension to risk assessment models.
To begin with, the lender could examine the population flow in the areas around each store to understand if the market was growing. The lender could also assess risk by using foot traffic data relative to specific venues, addresses, or brands to detect variances indicating a rise or decline in opportunity.
If the restaurant chain operates some of its own warehouses, no problem. We can pan over to those venues and study foot traffic patterns that will indicate peaks and valleys in human activity. For example, a sustained decrease of typical mobility in a light industrial/warehousing area may be indicative of a slow-down in operations due to decreased demand in individual restaurants, etc.
In the first two of these examples, the purpose of location data is to augment and inform other data to support investment decision-making. In the final example, the purpose of location data is both to detect market variations, and to inform a wise lending decision.
In the next two use cases, the purpose is to extend some of these capabilities into a financial service organization’s marketing efforts.
Knowing your customers’ customers is key to marketing. We can’t begin to position our own stuff for success until we understand what it is that makes our customers successful, right?
There are plenty of marketing and advertising technologies in use by financial services providers that help at a demographic level. Plugging location data into the mix adds a great deal of value.
For example, let’s say a major bank wanted to get better at targeting business customers. The simple question location data can help answer is: Based on their visitors, on which businesses should we target our marketing efforts?
In this scenario, location data, which blends beautifully with demographics, can be used to identify individual venues, locations and businesses with traffic patterns that reflect the core characteristics of the bank’s target audience.
So, even though this looks like a very different type of decision than credit risk assessment, in both cases location data is being used to inform an investment decision, albeit different types.
The third common use case for location data in financial services extends on the idea of knowing the customer’s customer by examining not just foot traffic, but area of origin and broader movement patterns.
Consumer-based origin destination marketing
Even in the COVID age and perhaps now more than ever, studying how people move through the physical world is vital to understanding their path as consumers. In particular, understanding where people come from is vital to understanding where they go and what they do.
If I am in County A and the nearest Home Depot is in County B next door, am I willing to make that trip, or will I choose to stay closer to home? How does COVID affect that here and now?
If so, by understanding more about these people - where exactly they are from, the average income of that place, the brands and services that do well there - you can do a better job of marketing to them, either at-home or while they are visiting. And that is ultimately good for the consumer.
For financial services providers, this can be applied either for targeting their own consumer audience, or once again as an assessment tool when devising strategies for pursuing and enabling the banks’ own clientele to service their customers.
Many use cases for location data boil down to informing investment decisions and the financial services industry is certainly no different. Though the manner of the investment varies, the need in financial services that location data meets is to intelligently augment broader decision-making processes.
Would you like to discuss your own use case for location data in financial services? email@example.com