Finding a location for a coffee shop that has good footfall － consisting of people in a rush, with money to spare －is an obvious example of how to use location analytics. Yet it’s an approach far beyond physical stores.

While retail locations are perhaps the most cited examples, location analytics isn’t inherently about shop placement or even B2C. It’s simply the art and science of visualising one or more data sources in a geographic form －usually a map －to help you better interpret and use the data.

This may involve finding the perfect real-estate for something other than a store: for instance, location analytics can pinpoint the sweet spot to build a factory that perfectly blends the right rent, business rates, supply of local labour and supply links.

Sometimes it isn’t about buildings or facilities at all. It could be localised marketing, such as crunching the demographics from census data to find the best site for localised customer target campaigns (for example, bus shelter posters). The same data could help make direct marketing, such as door-to-door leafleting, more effective.

Another use is in logistics; transportation company Con-way Freight used location analytics to highlight inefficiencies in its routes by cross-referencing traffic jams, customer locations and facility opening hours.

Location analytics can even be about what you do, rather than where you do it. If data on local schools, nightclubs and health clubs is combined with population demographics, local retailers might identify the mix of clothing lines that they will want to stock.

Whatever your business set-up, some key elements guarantee best use of location analytics. Firstly, you need to identify relevant factors and where the appropriate location data sets might be obtained. Don’t be afraid to be imaginative: data can come from places as diverse as public statistics to your own consumer data and transaction record history. Don’t forget online data. One survey found 96% of visits to a website over 30 days created location data in site logs.

Secondly, you need to ensure your location analytics software can cope with different data sources and combine them in a useful manner. Ideally you’d use a package that makes it easy to add or remove sources, change the emphasis you place on each, tighten or loosen filters, and immediately see the effects without lengthy reprocessing.

Finally, remember to think laterally about how you use location analytics. You want to use your data to get specific answers, but ideally you should also be open to the analysis unexpectedly divulging conclusions and questions you hadn’t even thought to ask.