13-15 JUNE 2023

Nowadays, many buzz words such as Big Data, Business Analytics or Machine Learning, are spread pretending to help companies in their daily tasks and issues. But how those solutions add real value to retail companies in their decision process?

Being data wealthy

For sure, having a large database is a first good and powerful tool for companies in order to understand their customers, competitors, environment in general… But how? And is the Big Data really a game changer?
The idea is to gather millions of data about a specific area, from many different sources (internal or external) from companies. Those data can be sociodemographic, socioeconomic, banking one’s, commercial figures, about the competition, the pedestrian traffic, etc. that, in the end, will help retailers understanding issues they have mostly with their consumers or competitors.  As a consequence, companies are now running to gather those data: internal ones as company KPI (sales revenue, product mixt etc.), or external data from third party sources. The goal would be then to know how to manage all those data …

The Location Intelligence, a key element for decision making

At this precise moment, Location Intelligence turns to be really useful and powerful. Indeed, once the data is gathered thanks to the Big Data, how to treat and understand those millions of figures?
Then the idea is simple: there are many different types of Location Intelligence tools but most of them help retailers visualizing their data on a map. Thus, it really helps to be clearer on how some competitors’ point of sales are displayed on a particular area, what is the commercial dynamic of a neighbourhood or how is the pedestrian traffic on that precise street retailers are interested in.  Thanks to this, retailers can take decisions backed on objective data and can understand what is happening on their store and why.
Indeed, it gives them the possibility to understand what are the external factors that are affecting their stores and thus to understand the performances of each store regarding its environment. For example, if thanks to their Location Intelligence tool they can understand the pedestrian traffic, an income average and being close to a transport station, this will affect positively their sales. Obviously, a store having those positive external factors can’t be compared to one with a low pedestrian traffic, with super high income on a residential area.  But what next?

Going a step further

And what if the tool can give already made analysis? Indeed, some advanced Location Intelligence tool use machine learning science in order to create mathematical models to anticipate results in real life. These tool is on the top of what can be done to extract the most from initial diverse sources and disordered data. Linked with geo related data, it can help understanding an environnement and its dynamics. Backed with millions of data, it  even give  the possibility to retailers to use this solution to extract insight from their store portfolio.
Indeed, where Location Intelligence basic functionalities can read actual and factual data about what is going on in an area: population censused, disposable income distribution, commercial point of sales etc., more advanced one, can go even further anticipating trends of peculiar figures. Then this kind of tool turns out to be even more powerful allowing company to cross their internal data (store/product performances, product mixt, customer information …) with external one and thus to go deeper into the analysis. Retailers will then be able to analyse their store performances and to adapt it to the area they are in, clustering some stores that have the same characteristics or making sale forecast about a new opening.
Finally, Location Intelligence  tool are impressive in their results but can’t work without being backed by Big Data. Thus, having both wrapped into a same solution just like in Geoblink app for example, is really a game changer for retailers in their daily life, allowing them to understand and analyse blurred figure and issues and so to take the best decisions for their brand.
Marie Chaigneau