Location Intelligence definition (LI) is about making location data speak business. Precisely, it is the capacity to visually associate regular business data with its location to analytically extract value from the underlying proximity and surrounding environment relations. A mouthful that I will try to clarify by providing its operational requirements and business use-cases.
For profitable businesses purposes, Location Intelligence needs seamless data plumingand reliable content.
Pluming for data access needs to be easily operational and to seamlessly integrate on top of data warehouses (BI), customer relationship management (CRM) systems and most forms of databases and IT systems, cloud or on premise. Poor integration, for example not accounting for user access rights and authorizations, results in security issues that discourage demanding users. Perseverant techies may succeed in building one-off connections and integrations, but these constitute isolated islands of success that do not lead to the adoption nor growth of Location Intelligence.
Content comes from the above data sources and optionally from third parties. Content is made of customer data records, products, sales figures, suppliers, receipts, anything related to the business of the client and being stored according to a data model. But content can also come from outside specialized parties and in most cases this is socio-demographic data freely available or more or less expensive depending on how difficult it is to obtain it. Think of competitor’s prices, where humans or bots may be necessary to constantly update them. Or of the trace of X, Y coordinates left by people moving around with their phone.
The implementation of LI requires geo-maps that display the proximity and surrounding environment relations of the business data being manipulated. Geo-maps have the advantage of being a universal language, everyone knows what North is, and from there locations fall into place. This does not exclude indoor maps from this definition, nor imaginary maps as those used in computer games, because LI works with them too. The important point here is to have a way of:
Location Intelligence users are analysts that rely on visualization and analytical features to examine their data, to make data speak, with the ultimately purpose of generating knowledge that can help the businesses improve performance. Some major capacities the software must have are:
For business users the most common use-case for Location Intelligence is what we call Territorial Performance. It includes one or more of the following decision steps:
1. Definition of geographical territories
2. Estimating territory potential
3. Operational extraction of potential from territory
4. Cross examination of extracted results
To illustrate let us consider the case of a beverage company facing competition. How to best organize sales teams and marketing investments? We know the customer base, from 3rd party providers we know all the potential points of sale (bars, hotels, clubs, etc.). In step 1, we define updated territories by looking at the existing maps, what others have done before. The areas so defined are normally a set of contiguous postal codes as represented by the polygons below.
Figure: Steps 1 and 2
In step 2, we can color code the polygons to represent the sales potential. Potential comes from 3rd parties and/or is calculated with the help of predictive models. In step 3, we assign areas to sales staff in their CRM system and pinpoint prospect and client sites. We provide staff with field help by, for example, updating the calendar and providing real-time best routing as shown below.