Location data use cases: Location data can be used to Inform investment decisions, detect changes, and measure effects.
We work with a wide range of unique clients and partners on a range of location data use cases. Yet, no matter the vertical, there are three overarching location data use cases. Each use case covers a fair bit of ground in terms of variants and application. I use the sticky note diagram below as a visual reference.
#1) Inform a (Investment) decision
The most common location data use case is to inform a decision; very often an investment decision. The bigger the investment to be made, the more information people want to look at.
There are two variations on this location data use case:
Identify areas of interest
In these instances, the location data use case is to apply location data to geo-target the marketing campaign, or use mobility data from different venues to assess potential store locations. Either way, the intent is to inform an investment decision before it is made.
Compare sets of areas
For example, a municipality is going to build a new bus line, or community clinic, and needs to evaluate different areas for their suitability. In another scenario, a commercial real estate investor wishes to evaluate different multifamily residential development opportunities. The municipality may pay much attention to foot traffic patterns around points of interest, whereas the CRE may dig-in around migration patterns and emerging areas indicators.
In both location data uses cases the ultimate driver is using location data to make a better-informed investment decision.
#2) Measure the effect of a change
Once you’ve made a decision, you want to monitor things so you can see how your decision is working out, which gets us to the #2 location data use case -- Measuring the effect of a change.
The bigger the change, the more important and complex it can be to measure. Typically, people are looking for location data to help them measure change in one of two ways:
Compare points in time
For example, a new e-bike vendor, or Uber launches in Small Town A. How does that change mobility patterns?
If the same thing happens in Small Town B what will that look like by comparison?
As a CRE investor, what can location data tell me about foot traffic in the area around the properties I own, and what can that tell me about what rent growth, or rent default, may look like down the road?
In each case, the intent is to use location data to help measure the effect of change over given points in time.
Identify collateral effects
For example, municipalities are expected to be good at measuring the effects of change. Is that new clinic serving its intended purpose, or would a new downtown arena disrupt traffic on key commuting routes?
The intent of these questions is to measure change not just in an immediate area or timeframe, but with reference to the surrounding area and over an extended period of time. So, identifying collateral effects is really the art of measuring the ripples of that investment.
#3) Early detection of change
It seems odd, but the wish to detect change often comes after the need to measure it. Perhaps that’s because detection is a much more predictive and prescriptive location data use case compared to measurement, which is simply explaining what has already happened.
In any event, the sooner you know about change, the sooner you can manage downside risk or improve upside opportunity. There are two main variants on the early detection of change use case for location data:
Detect deviations in areas of interest
For example, a real estate developer wants to find areas with changing population demographics indicating the need for a high-end residential development. By studying location along with census data and observing migration patterns, it is possible to measure how a given area’s population and income base are growing or contracting.
A growing population combined with an increase in average income, as we have seen in many areas of Florida, is a strong indication of investment opportunity.
Detect changes in behavior of population segments
Many urban centers are now younger than they were pre-COVID. Some neighborhoods are getting wealthier, others are ghost towns. The behavior of those who live in these different areas is changing, too.
Everything from the places we go, to the people we encounter, to the brands we buy from is influenced by our mobility. All of which can be better informed by location data.
Do you have another location data use case we’re not thinking of? email@example.com