Geospatial analytics techniques will give you the information you need to make tough decisions around consolidating and optimizing physical business locations.
COVID-19 has impacted business everywhere – especially ones that require customers to visit a physical location. Customers are more selective about where they visit in person and have expectations about their experience once they do visit. This has caused businesses to scramble to reassess their real estate investments while continuing to meet customer needs.
In some cases, simply enhancing or retooling a location is sufficient. This is the case with many grocery stores who now offer curbside pickup and no-contact payment options. In other situations, businesses are finding that it’s more cost effective to close some locations and redirect resources and customers to a different location. However, knowing what locations to consolidate is not always straightforward. The good news is: the answer is in your data—you just need the right data strategy and tools to extract, combine, and analyze your data and glean relevant insights.
Note: this demo uses a data set for banks, but this solution can be built and applied to any company that has physical locations.
The first visual shows four data points:
Identify your locations, clients, and competitors within a territory
But at this zoom level, it’s difficult to see any specifics. Since there is a concentration of customers in the central region (lots of blue), that is a good place to drill down to begin the optimization analysis.
Too often, data sources are ignored because customers aren’t aware of them or don’t know how best to integrate them.
In this zoomed-in view, census block polygons (in purple) were added. This free dataset was integrated to show the concentration of homes that are occupied in each bank territory. The darker the purple, the more homes are occupied.
Drill down of customer concentration, branch locations, and census data
This sets us up nicely to view optimization opportunities at existing branch locations.
We can see that a heavy concentration of customers (in blue) centered around the two branch locations (in orange), showing that these branches service a relatively large number of customers.
Two branch locations are serving a large number of their customers
As we move to the right of the map, we see a few branches that are in close proximity to one another servicing a much smaller number of customers than the previous image.
Other locations in close proximity to each other are serving much fewer customers
In more profitable times these branches served as an opportunity to expand into areas where many homes were on the market. But due to a changing market, each location might not be necessary to meet customer demand. More investigation is needed to learn more about each branch.
By zooming into three branches that are in question, we can utilize some additional geospatial analytics techniques to make more informed decisions.
The pop ups can provide a lot of information for each branch with a single click. For this demo, we’re going to look at behavioral and utility costs for each branch. The following images tell us that the Richland County and Assembly Street branches have a significantly higher payroll and utilities expenses than the Columbia Main branch.
Analyze branch location total costs
Though all three branches served a purpose during profitable times, branches with high maintenance costs should be assessed further, especially during lean times.
Google Street View
By double-clicking a branch point, Google Street View provides a “boots on the ground” perspective and allows us to view amenities and the physical space around each branch.
Analyze street view of each branch
Takeaways from this street view analysis:
So far, it looks like we should reconsider the usefulness of the Assembly and Richland County branches. Both branches cost significantly more to run and offer seemingly less in value than the Columbia Main branch.
But before we jump to conclusions, we must consider the ramifications to our customer base if we were close one branch and redistribute the resources.
By adding competitor points back to the map (green) we see that some are located near our branches.
Location of competition in proximity to branch locations
If we were to close one of our branches, it is possible that we would lose the customers to competitor branches. However, given the low density of customers around these locations (blue), it’s highly unlikely we’ll lose a significant number of customers.
Now that we’ve spent time investigating our current expenditures and risks associated with each location, it will be simpler to decide how to redistribute resources. For example:
By augmenting internal data about each branch with free data accessible through Google and The Census, we were able to make confident decisions about branch closures and optimizations.
If you need help analyzing or augmenting your data to make similar decisions, schedule a free, 30-minute data strategy session with our analytics experts.
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