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Friday, May 15, 2020

Machine Learning in Retail

 Not sure how complete it is, especially with the current state of the business.   More what than effetively how. I would do at least some embedded risk analysis here.   And better modeling of retail key process.  Still some good thoughts.   And embedded examples

Complete guide to machine learning and deep learning in retail  By Adrien Book  in ThePourquoiPas

Stores are changing. We see it happening before our eyes, even if we don’t always realize it. Little by little, they are becoming just one extra step in an increasingly complex customer journey. Thanks to digitalisation and retail automation, the store is no longer an end in itself, but a mean of serving the needs of the brand at large. The quality of the experience, a feeling of belonging and recognition, the comfort of the purchase… all these parameters now matter as much as sales per square meter, and must therefore submit themselves to the optimizations prescribed by Data Science and its “intelligent algorithms” (aka artificial Intelligence in the form of machine learning and deep learning).

The use of artificial intelligence is, above all, a competitive necessity. Indeed, e-commerce players did not wait on anyone: note, for example, the adaptation of online search results to the end customer, or the recommendations made based on a digital profile. These two aspects are impossible for brick and mortar (for now). However, physical commerce has its own strengths. Olfactory, visual, auditory, etc. data can be used to give the consumer a feeling of having experienced something unique and made specifically for them. In addition to customer relationship improvements, artificial intelligence also makes it possible to seek the resolution of problems that have long represented a burden for retailers : better inventory management, optimization of store space, optimization of employee time...

We present below a complete look at deep learning/ machine learning use cases implemented to create the store of the future, supported by real-life examples.  ... " 

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