Machine learning is a powerful technology that is driving the next generation of consumer-facing businesses. Machine learning powers every mapping app, every photo filter, and it even forecasts the weather. In retail, machine learning can be used to recommend products to customers at the right times (e.g., emailing them an offer for their favorite brand on their birthday), make pricing predictions (e.g., what price will maximize the number of items sold), and increase basket size by understanding shopping patterns (e.g., how quickly shoppers abandon their carts).

Benefits of machine learning services in retail industry:

1) PREDICTIVE ANALYTICS

As mentioned before, machine learning has massive potential across many different industries because it empowers products with consummate skills like voice transcription, face recognition, and language translation.

One major way machine learning is implemented in retail is predictive analytics. Predictive analytics allows retailers to determine the best product for a customer at any given time while considering their context (e.g., where they are, what they’ve purchased before, when they usually shop). This technology can be used both on the customer-facing side of things (recommending products based on previous purchases) and the supply-side (predicting how much of each product should be stocked).

2) PRICE OPTIMIZATION

Price optimization allows retailers to dynamically adjust pricing throughout the day by predicting how customers will react. In this case, computer models consider factors such as seasonal trends and inventory levels and assess each customer’s price sensitivity. For years, this technology has been used by brick-and-mortar stores, but machine learning can be even more precise to maximize revenue and minimize inventory costs.

3) SHOPPING TRACKER

One way machine learning is being implemented into shopping carts is through the use of shopping trackers. These tools can monitor behavior and identify data patterns to provide shoppers with personalized offers and recommendations based on their previous purchases.

4) PRODUCT RECOMMENDATIONS

Another way machine learning is being implemented into retail is through product recommendations. For example, recommender systems extract information from customer purchase history and item metadata to recommend similar products to a given customer by using collaborative filtering, demographic targeting, and other techniques.

5) STOP LOSS ANALYTICS

Stop-loss analytics is when machine learning is used to automatically detect when the inventory in a store needs to be re-stocked based on historical sales data. Stop-loss analytics aims to minimize waste by making sure that products do not go out of stock.

6) CUSTOMER EXPERIENCE MANAGEMENT

Customer experience management refers to the use of machine learning to track customer behavior, determine areas for improvement, and provide personalized support. For example, machine learning can measure the level of engagement with marketing campaigns or identify shopping patterns to provide an optimal shopping experience. A machine learning application in telecom is mostly used for CRM.

7) PRODUCT RECOMMENDATIONS

One of the most common (and impactful) machine learning applications is to recommend products to shoppers. Recommender systems extract information from customer purchase history and item metadata to suggest similar products to a given customer by using collaborative filtering, demographic targeting, and other techniques.

These are just a few of the many ways machine learning is being implemented in retail. The possibilities for this technology are endless, but it will be exciting to see how machine learning continues to evolve in the future.

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