First used in the late 1800’s, Isochrone Maps, which depict the transport times in every direction from a distinct point, have long been an important component in the business analyst’s toolbox. But with modern data streams and Location Intelligence, a deeper understanding of customer travel and activity can be had using catchment areas derived from mobility data.
With wide-ranging applications in geomarketing, real estate investment, retail strategy and much more, isochrones have been employed for years to create customer catchments.
Seeing all points-of-interest within a 15-minute walking-distance, isochrone catchment can help real estate investors to better determine the kinds of residents or commercial entities they should target for leasing. Using a 15+ minute drive-time isochrone catchment can help a local retailer pick targets for out-of-home advertising and make other geomarketing decisions.
Isochrones are an extremely useful tool for getting a sense of a location’s relationship with its surroundings. And while using isochrones in the above fashion is sure to make for more informed decision-making, they do not tell empirical truths about consumer behavior in any way.
Advertising targets living within an 15-minute drive-time isochrone catchment area may be more likely to visit a retail location. And while that reasoning is sound, without evidence, that assumption can never be more than a hypothesis on its own.
Luckily, the advent of modern, always-on data streams has equipped us with the empirical evidence we need to create customer catchments that are no longer hypothetical, but true snapshots of human behavior.
Using a modern data stream like mobile phone or foot traffic data can create a more specific view of customer catchment for a target location. By digging into and cleaning the data (in the case of our below example, they used Unacast foot traffic data) from these streams and matching pings, our data scientists are able to model trips that result in traffic around our location and map the origins and destinations of those journeys.
Once cleaned and aggregated for counts, these origin-destination pairs reflect an empirical view of catchment that more accurately reflects where customers are coming from and travelling to while being in the vicinity of our location.
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As a bit of a sanity check, we can certainly see similarities when overlaying our new mobility catchment based on OD pairs with the previous isochrones.
When we look at our walking isochrones overlaid with our aggregated mobility catchment data, the results are much the same. Aggregated data reflects that counts are high within the 15 minute walking distance of our store location, so making marketing or investment decisions around demographics and points of interest within that catchment is a fair bet.
Things get far more interesting when we look at our new mobility catchment areas in relation to our drive time isochrones.
In some ways, our drive time isochrone does a fair job of reflecting our catchment. The spike in the isochrone northwest of our anchor store is due to shorter drive time for customers travelling along the Hollywood Freeway. The edge of our 15 minute drive time isochrone in the Hollywood Hills and Downtown LA does match up with some of the destinations of devices that passed our store.
That said, that 15 minute drive-time isochrone does not include some of the key areas where our foot traffic is originating from. Specifically West Hollywood into Beverly Hills to the West, and Glendale to the Northeast show particularly high trip origin counts in our mobility catchment.
If we were using only Isochrones to make our out-of-home geomarketing decisions or basing investment decisions off of demographics and firmographics within the 15-minute isochrone catchment, we would not be making those decisions based on where all of our likely customers are actually coming from.
Similarly, if we were using one of our longer drive-time isochrones, we may decide to invest advertising dollars into neighborhoods like Burbank and Mount Washington that, though they fall within our 30-minute drive time isochrone, do not seem to be the origin or destination for much of our foot traffic.
Human mobility data, derived from mobile devices is a powerful tool and the catchments that can be created by cleaning and aggregating this data can offer retailers and investors a clearer picture of who their target customers are. That said, there are some particular challenges when working with this data to solve business problems.
First off human mobility data can be “noisy” and can require significant cleaning before it is actionable. Once aggregated, the data isn’t point specific, and is instead represented as a region where a mobile device will have passed through. Additionally, this data can be demographically biased depending on your data provider. (It may be useful to check your origin data against home and census data to make sure that you are working with data that is a representative sample of the surrounding population.
Instead of thinking that it’s time to throw away our isochrones and fully commit to building out mobility catchment areas based on GPS data, recognize that this is another useful technique to add to the Location Intelligence toolbox and modernize business decision making. Having a wide range of tools and techniques, with their various benefits and challenges, can allow businesses to stay agile and make sure they are always using the right tool for every Location Intelligence job.
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