Analysis of hot spots in physical stores with Machine Learning, Cognitive Services and Data Analysis

Experiment #205





There are countless processes in a physical store that produce data that can be used and exploited to generate or increase sales processes. 


In addition to measuring the time spent in the store and register whether the visitor ends up buying something or not, the purpose of experiment #205 is to use facial recognition services to locate hot spots in the store, sentiment analysis to analyze the level of satisfaction of the client, and cognitive services to analyze bags or objects from other brands.

With all this information it’s possible to calculate a predictive stock to automatically supply the store.


The idea arises from the need and the lack of use of the information that can be exploited in a physical store. Nowadays data-driven decision making is crucial for the proper development of a business and thanks to AI this reality can be possible.



First, we will use the store’s cameras to analyze the images. Then, we will develop a software that collects all the information and sends it to the cloud to be analyzed in the Cognitive Services. Once in the cloud, all the necessary analysis, the entire ETL process and a further integration with the corporate DataWarehouse will be carried out and utilized through PowerBI.



Find correlations between the average time spent in a store and the probability that a purchase happens, detect heat sources and patterns for possible rearrangements of the layout, predict stocks, analyse customer sentiment to identify possible dissatisfaction and detect brands that customers consume for segmentation.

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