Leveraging intensity maps in your network planning.
As a retailer, you’re probably used to using surveys as a means to understanding customer behavior. Traditionally, once you get the data, you might start working in excel or some other statistical software, cranking out brilliant correlations or percentage checks.
There are plenty of available tools which can help you connect spatial and consumer profile data to better plan and manage services, but before you start applying survey results to business strategy, creating intensity maps could help you gain a better understanding of consumer behavior.
What is an intensity map?
An intensity map can show regional variations of consumer infrastructure density within a city and offer a bird’s eye view of locations where consumers exist within a city. See some examples of intensity maps being used by Kiwi.
What is the benefit of using an intensity map?
Intensity maps allow large quantities of individual point data to be summed up and displayed in an easy to use manner. They indicate important variations in infrastructure on a large scale, so areas of interest and trade zones can quickly be identified. Watch a video about trade zones here.
How can I create my own intensity map?
Once you collect your customers’ locations you can use mapping software to create location maps first. There are lots of mapping software available these days to choose from, see our article on the 5 Mapping Tools that Can Make Your Job Easier for some ideas on what’s out there and could possibly work for you. Once you have your locations, intensity maps of specific stores can be mapped out next. For intensity mapping methods, SharePoint has some instructions that can be used as a starting point for those who understand a bit about GIS.
How can intensity maps help me with store optimization?
Based on the distribution of stores on your intensity map, you can begin to see a much clearer image of where most of your customers are coming from. Basic information, such as gender, occupation, preference, shopping behavior, visit frequency and others all represent data that can be used to do the following: