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The Demyst Team

The Demyst Team

Emerging Small Business Use-Cases with Geolocation Data

As financial services and insurance companies attempt to find new types of data to assess small businesses, geolocation data will add lift as it is mapped to specific businesses. Examples of derived attributes include:

  • Business open hours based on visitor counts
  • Historical vs. present analysis of visitor counts to specific areas
  • Historical vs. present analysis of visitor counts to specific businesses and brands
  • Analysis of which types of customers are visiting certain businesses (based on geographic characteristics)

This article will summarize how these attributes may be mapped to specific use-cases in the financial services and insurance industries.

Typical Use-Cases

The typical use-cases for geolocation data (tracking anonymized device IDs) are business strategy and marketing. Some examples of questions that can be answered are below:

  • A corporate franchise (e.g., McDonald’s) would like to understand which of its franchises in a given postcode are receiving the most/least foot traffic, so it can make decisions about marketing spend.
  • A real estate developer would like to understand which types of shoppers are coming to their shopping complex today, so that they can rent vacant spaces to complementary stores.
  • A luxury hotel chain seeks to understand which types visitors are visiting a certain area to identify if their brand is a good fit for the local market.
  • A local government would like to understand the extent to which social distance guidelines are being followed during the Covid-19 pandemic.
  • A bank would like to understand if it makes sense to place a new ATM or branch in a certain area.

While the above use-cases are commonplace, and Demyst has served clients that seek data for them through our geolocation partners, we’re seeing demand for new types of use-cases that focus on using footfall data across business lending and insurance verticals as well. Provided below is a brief understanding of use-cases we’ve recently analyzed using geolocation data across these verticals.

Commercial Lending Use-Cases

    • Local Growth: Retail and restaurant business lending is vitally dependent on local dynamics. Is a certain locality growing or contracting? Foot traffic data provides an opportunity to aggregate visitor counts by brand, industry, and location. Lenders may also develop active monitoring dashboards to identify which localities may be growing faster than others, thus justifying greater capital investments.
    • Business Growth: Comparing visitor counts across time for a given business or across all locations of that business provides an opportunity to move away from traditional indicators of business credit such as number of employees and sales revenue. Furthermore, while the latter tend to be inaccurate in most databases, visitor count is an actual metric that may be aggregated in real-time. An increase in visitor counts is likely to be an early indicator of business growth and a credit positive.
    • Capital Allocation: A large bank that is examining loans in a particular area seeks to understand the demographics of who is visiting businesses in that area. More specifically, it can identify visitors based on visiting census block groups. Based on this analysis, it can allocate capital not only based on visitor volume, but also characteristics such as income and employment.

Commercial Insurance Use-Cases

    • Occupancy Limits: Is a certain store, restaurant, or office, receiving more visitors than expected, and given this, should their premiums increase? This question is easy to answer based on a historical look back of daily visit counts. Furthermore, insurers can also actively monitor the daily visit counts as this data is aggregated on a weekly basis. It’s also possible to consider dynamic pricing mechanisms based on visitor counts.
    • Vacant Commercial Space: During the Covid-19 pandemic, a new phenomenon emerged in which businesses continued to lease spaces, but for health reasons became remote-only. The risk associated with a vacant commercial property is significantly higher than an occupied one (for reasons of vandalism, crime, or unmonitored hazards). Using foot traffic data, it is possible to get a general sense of visitors to commercial spaces, so that insurers can flag which properties or clusters of properties are likely vacant and thus at higher risk.
    • Business Open Hours: Similarly to business vacancy, as the economy recovers many businesses continue to have limited open hours. This information is vitally important for insurers (and potentially for lenders as well) as they consider which businesses and industries are recovering faster than others.

Combining Geolocation Data with Other Data Types

The typical geolocation solution also merges data from other sources, such as business credit bureaus, business intent data, and business transactions data. The goal is to create a 360 degree picture of a business for insurers and lenders. As a result, Demyst has not only partnered with geolocation players such as MobileWalla, Unacast, and Safegraph, but we’ve also connected point-of-interest data in these data sets with data from Experian, Equifax, and 180byTwo (intent data), among others.

The Demyst team is actively guiding our customers through the data discovery, evaluation, and productionalization of geolocation data into use-cases in these verticals, while also providing feedback to upstream vendors to assist them in maturing their product offerings for the banking and insurance verticals.

Upcoming Webinar

For additional information, join us on Wednesday May 12, 2021 at 1pm EST for a live webinar between Demyst and Unacast. We'll be reviewing the above information, as well as some recent changes in privacy guidelines impacting geolocation providers.

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