In the quest for significant competitive advantage, business transformation and unbeatable customer experiences, many organisations are turning to spatial technology. In this blog, we outline what spatial is, how it’s being used and what options are available in-market now.
What is spatial
Spatial technology is used everywhere from business intelligence and enterprise reporting, to trend analysis and retail outlet planning. But what is it?
In a nutshell, spatial technology is a framework that allows businesses to manage, visualise and analyse data combined from multiple sources including; geospatial (location), demographic, traffic, time, weather, store locations and more.
This then provides unique insights, reveals hidden relationships, patterns, and trends that drive stronger, more actionable decisions. Spatial technology is used by the world’s best businesses to gain highly competitive, actionable insight simply by layering many types of data.
How spatial technology is used
The retail industry has used spatial technology for years. Just a few examples include; analysing the demographics of customers per store (allowing them to adjust stocked product lines to maximise profits), to map how stores are performing against each other and competitors (to make decisions on store revenue targets, staffing, OPEX and more), to analyse weather data per region (allowing them to adjust when to hold the end of season sales to maximise revenue) and even help decide whether to open, close or move stores for better profitability.
Consider this scenario. Company A has only one competitor in Taranaki but they’ve heard that an Australian competitor is about to enter the market. Company A’s management team need to see how this will impact their revenue per store and therefore what to do as a pre-emptive strategy.
Using spatial technology, they map their store locations against the existing and new competitors as well as drive times, traffic data and customer loyalty per store data.
The first map (below) shows company A’s stores (five orange circle symbols) and their competitors (eight green circle symbols). The relative size of each store is indicated by the relative size of the circles. The grey bands around each store show the 2km driving radius. Company A’s team can see a little bit of overlap between two of the stores (making them benefitting sites) but otherwise, outlets are quite well separated.
Now let’s look at what the competitive landscape looks like. The second map (below) indicates the same 2km competitor driving radius in orange. Instantly, company A’s management team can see there is a lot of competitive overlap. So much so that the four largest stores are saturated with competition. Company A’s team can also see that the competition is gaining a lot of customers from outside their stores’ driving radius.
The management team decide to layer on more data to see if they can defend against the new competitor with customer loyalty or pure logistics. They also want to see if the stores are in the right place to maximise the area’s performance, not just individual stores.
See below, they combine map 1 (colour coded distances to drive to the closest store) and map 2 (store loyalty) resulting in map 3. This results in the opposite of the loyalty map (map 2), showing customers who prefer not to shop locally, and usually do their shopping on an outlet further away from their local store is. Usually, the reasons for this are due to traffic, and sometimes because the local shop does not provide something specific that the customer is after.
The interesting zones are the area left of New Plymouth (purple circles), which seem to indicate customers go to Inglewood instead, probably because of traffic. The blue dots even further left all probably go to New Plymouth or Inglewood rather than Opunake, also probably due to traffic.
So, when taking all these analytics into account, are these outlets in the optimised location in the first place? That is what map 4 shows the management team:
- Inglewood and Stratford are pretty much spot on, but New Plymouth and Hawera should move a bit (if possible) to address those traffic issues.
- The one that is farthest off is Opunake – moving it North some 15 km will resolve the need for a new site.
Optimising the site locations would resolve all issues found from the analysis and boost revenue for all stores while possibly killing off some of the competition.
With this insight in hand, the management team request their analysts to prepare some customer segmentation and additional trends, then enable dashboards for them to start monitoring how well the stores are doing (and whether recommended changes are making an impact). Example of a dashboard from Esri Insights is below.
A couple more examples include:
The Australian Cancer Atlas – a non-specialist analytics tool that can be used to view all types of cancers against demographics like age, gender, ethnicity etc. in geographical areas to see whether location affects the numbers of specific cancer types.
The university campus security light map – this map layers together the light cast by the campus emergency blue boxes along with the map of the campus, enabling the security team to position additional blue lights in the campus for optimal safety.
Spatial technology available now
There are a few simple spatial technology applications available right now. A popular free one is Story Maps, an infographic-style tool that allows users to tell a story on a spatial data set. But for serious business leaders, this won’t do.
Here at Zag, our preferred technology of choice is actually a combination of SAP HANA and Esri. Full disclosure here that we also sell and support these solutions. We prefer SAP HANA not just because it is a natively spatial database but also because it is super-fast. As SAP explains, “SAP HANA is an in-memory, column-oriented, relational database management system.” In reality, what this means is that it is really flexible because not only can it perform advanced analytics but it can also extract and transform data which allows for the deepest insight by better modelling the intrinsically messy real world.
For technically–minded experts, I explain SAP HANA’s flexibility in a lot more detail in my full blog Harnessing the Power of Spatial.
While Esri is simply the most powerful mapping and spatial data analytics technology currently available.
So, there you have it. Spatial technology is a powerful tool that enables businesses to become highly competitive through unique insights, hidden relationships, patterns, and trends that drive stronger, more actionable decisions by integrating many types of data.