Data intelligence in the optimization of projects at the Point of Sale

[muskanislam25]

The search for information at the point of sale is especially relevant because a decisive battle is being fought in a relatively concentrated space for market access and the achievement of objectives. For this reason, companies that provide services in this area use technologies that allow us to capture relevant information in this regard.



In order to talk about data intelligence in the optimization of point-of-sale projects , we need to answer questions about what happens on the shelves: how much space do I occupy there, at what prices, what offers does the competition have, are there stock-outs or are they expected, what sales volume have I had in this period and my category, if the agreements reached with certain retailers have been implemented , have I conquered new spaces in the center; and many other questions, to understand our performance.



Likewise, this performance is the result of sweden whatsapp lead the team 's work , which must receive clear information about who they are speaking to/should speak to at a point of sale , what characteristics they have, what objectives should be set at that point or type of points depending on the action, any agreements at the chain level that may exist and the commercial strategy.



Another particularly relevant aspect is team management , to understand who should be working at the moment and where, in order to be able to respond in the most effective way possible to unavailability of personnel on the road for very different reasons. Likewise, these sales teams need information on their performance evaluations, support to optimize their work routes, and the ability to manage information quickly and easily. In fact, this is one of the areas where technology is driving the fastest progress (route optimization, Image Recognition, information on POIs, etc.).



This information , much of which is collected by the sales project 's commercial teams , can be complemented by other sources of information (chains, consumer panels, people traffic, geoinformation, etc.) so that we can build models with greater value contribution when distributing and managing the project 's resources in the most optimal way.