Applications of Machine Learning in Retail
The capability of machines to learn without being explicitly programmed, known as machine learning (ML), has traversed technology beyond predictive analytics and algorithms. ML applications are now growing exponentially parallel to the current developments of new and existing companies. Although ML promises great advancements technologically, it is still easily accessible and pocket-friendly to startups and SMBs (small and midsize business). Although the use cases and applications of ML create a lot of confusion about what the term means, there are three ways that retailers of any size can utilize it to drive more value from the data than they already have at their disposal.
Normally, it would be very difficult and inefficient for retailers to try and guess the customer’s wish to stop using the business’ service. Machine learning, however, can leverage a number of data points to identify the risk of losing a particular customer and present new opportunities to re-engage through special offers and personalized communication.
Boost Average Order Size
ML is now being heavily utilized to spot important connections between two or more products which were previously impossible. Such insights can help businesses to take appropriate actions to improve their overall sales.
Reducing Waste in Marketing Collateral
ML also allows businesses to analyze live sales data and determine the products with best consumer response. This is also helping marketers to adapt faster to changing tactics.
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