Ever since technology took over the shopping experience, it has also taken over the way retailers draw conclusions and predictions about their services. Data-driven insights are not a new phenomenon, but with Artificial Intelligence, retailers can increase their accuracy and hence their operational efficiency. In an industry that operates on low margins, efficiency is imperative and Machine Learning can assist businesses to make better decisions and development plans.
NATURAL LANGUAGE PROCESSING
Product Trends in Social Media
With the advent of Social Media, economic demand for products has turned into a dynamic landscape. Traditional methods of product demand forecasting and trend analysis become unable to cope with the nature of social media content. Retail businesses engage AI to identify popular trends, using Named Entity Recognition to identify them, and promote similar products in their stores in order to stay ahead of the curve.
Shelf Management in Retail
Every year, retailers lose billions in sales due to products being out of stock and improper shelving practices. Ensuring a good retail experience is crucial to winning customer loyalty and increasing customer value. AI and ML can be leveraged to automate the tedious manual gap check process by identifying all availability issues on shelves. Visual scanners aid in recognizing product locations, products that are out of stock and those that are past their best by date.
Inventory Shrinkage Management
When retail inventory disappears without being paid or accounted for, it is called ‘Shrinkage’. This isn't necessarily caused by theft, but also by accidentally bypassing the register or self-checkout scanners. Companies are embracing AI to tackle this problem using visual scanners and cameras to monitor the checkout area. Cues like hand movements under the barcode scanner and number of items in the trolley at self-checkout are visual aids in this process.
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