Experiment #205

Analysis of hot spots in physical stores

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.

Project Date: January-February 2021

Services: Azure ML, Azure Cognitive Services, Face API, Azure Data Factory, Azure SQL, Power BI

Technology used: Azure ML, Azure Cognitive Services, Face API, Azure Data Factory, Azure SQL, Power BI

Challenge

Exploiting information and analysis for decision making

Idea &
process

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.

Utility

Providing data on customers and their purchases in physical stores

Utility

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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