To deliver value through location intelligence, some fundamental processes are critical: Location Discovery, Location Visualisation, Location Analytics, and Location Optimisation. Location intelligence combines location data and business data to get insights to drive business decision- making.
Location Discovery
At this stage, the problem is broken down so that the organisation will be clear about what they want to achieve with location intelligence. Location discovery entails ensuring that any business data with spatial reference is represented on a map to prepare it for further spatial representation and analysis.
At its core, location intelligence combines business and location data; all these components must be identified and mapped. First to be mapped is the location data of various points of interest to the business, including customers, suppliers, and competitors.
This forms the basis of the business data, which is then collected and layered on top of the location data, which includes business metrics of the company such as average spend per customer, the products they buy, what time they purchase them, revenue per each store location, revenue data, logistics data etc.
There is no limit to the number of business data layers that can be added. It is important to note that the data must be as accurate as possible because poor data leads to poor insights or often misleading insights.
Location Visualisation
When the data is gathered, it is then visualised to see if any patterns can be identified before any analytics is done. Some meaningful insights can be identified at this stage. While location visualisation has been put as a second stage of the process, it is essential to know that it applies to all the other stages in the framework, be it location discovery, analytics and optimisation. The data or insights will have to be visualised for all these stages.
Location Analytics
Location analytics comprises two key components, which are analysis and forecasting.
Analysis
This is the stage were some analysis is done depending on the objective. These analyses include trade area analysis, feasibility analysis, site selection analysis, hotspot analysis, network analysis, overlap analysis, and competitor analysis. This list is not exhaustive as they are diverse and are mainly a function of the organisation's objectives. The relationship between the location data and business data is closely analysed, and any correlation is noted. The insights derived from the analysis are then examined so businesses can understand what they mean concerning their operations.
Forecasting
Two major categories of models can also be used to derive more insights from the data: deductive models and predictive models. Through deductive models, a supermarket chain can deduce that its branches within districts with a specific socio-demographic structure have higher figures for unexplained losses.
Another example is a fast food company can deduce that despite almost having a similar trade area size, branch X has more revenue than branch Z because customers in that trade area have more young working couples who prefer takeaways (fast foods) than customers in the trade area for branch Z who are older and have bigger families, hence, they prefer cooking at home.
With predictive models, previous data patterns can be used to predict future outcomes. For instance, a supermarket chain can use demographic data, income data, traffic data and sales data to forecast the potential revenue of a new prospective location. Such analytics shows the power of location data in transforming businesses by helping them make informed decisions.
Location Optimisation
The results from the analytics phase will then inform the formulation of the solution. In cases where a business wants to set up a shop, warehouse or factory, possible sites will be identified and ranked according to their qualities. This is where data informs the decision-making process. These insights will be used to improve the operational efficiency and the customer-facing components of businesses.
Companies can then use tools like geomarketing, where they send promotional messages to prospective clients who will be within a specified distance from their shops. Another example is when delivery companies integrate real-time routing technologies into their systems to shorten delivery time.
Conclusion
Today’s businesses need to be optimised through the use of location intelligence. The benefits are many; improved data quality, enhanced quality of business decisions, operational efficiencies etc. All these benefits put a company on a trajectory of improved profitability.
