Traditionally, retailers use a normal way to segment consumers based on recency, frequency of purchase and monetary value of purchases (RFM score). Retailers judge a customer based on these factors and market their products. These traditional methods often lead to biased decisions on consumer purchase as the retailer continues to promote the similar products which the consumer bought recently. This strategy would be highly ineffective given this consumer’s typical purchasing habits.
To eliminate traditional and cumbersome manual approaches to retail operations, actionable data is provided through powerful machine learning models with real time insights to improve decision-making and gain competitive advantage. It’s important for retailers to have a dynamic view of the customer to more accurately predict the types of promotions and marketing efforts to which a consumer will respond.
Machine learning platforms have the capability to gain a deeper understanding of a customer over time. These platforms can analyze customer data in conjunction with recent market trends to better predict purchasing behavior, optimize pricing, prevent customer churn, forecast demand, etc. Eventually machine learning’s automation makes it the most effective way to offer personalized promotions.
The automated machine learning algorithms learn the entire purchase history and recognize the customer purchase behavior. To motivate the consumer to make more purchases, the machine learning platform then recommend the retailer to promote lower end, inexpensive products and offer unique promotions through customer’s preferred channel to push their higher price range.
Machine learning is the best way for retailers to predict customer buying habits and adapt marketing strategies. Consumers have high expectations for personalization and will remain loyal to the retailers that truly understand their needs.