As the digital age continues to unfold, retailers are increasingly implementing machine learning technologies to gain a much-needed competitive edge. From demand forecasting andto dynamic pricing, customer behavior analysis, fraud detection, and customer lifetime value estimation, ML algorithms are used for various tasks in retail companies.
Crashing Under $50,000? Bitcoin Is Suddenly Braced For Another ‘Crucial’ $9 Billion Earthquake After $2 Trillion Ethereum, XRP, Solana And Crypto Price Wipe Outbrings us some curious examples of ML models producing unexpected results, such as a $14,000 closet on Wayfair or Uber prices rising by 400% during periods of collective unrest. It's unlikely that companies anticipated these outcomes when they implemented machine learning systems.
One example involves inventory management. Suppose a store has a product in stock, but it isn't selling. Algorithms might deem this item unsaleable and recommend excluding it from the assortment. However, what if the product is just out-of-shelf? Most machine learning algorithms cannot account for such details. It took us a few years to train our models to catch such anomalies and take them into account.
Weather conditions, for instance, also impact the demand for specific products. Similarly, major events like soccer matches can trigger sales spikes in the host cities and nationwide through online streaming.
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