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Retailers flourish in the long run by developing a loyal base of customers. Nurturing relationships across their customer base to have widespread high customer lifetime values gives retailers the edge they need to stay ahead of competitors. This is the case with grocers like Trader Joes who have customers return weekly and it is also the case with luxury brands like Hermes who have customers return when they want to expand their collection of hand bags. However, retention is difficult, and the estimated annual cost of churn in the United States alone is $1.6 trillion (Accenture). Across retail, businesses have relied on a variety of methods to reactivate customers who churn. The problem lies in the difficulty of identifying customers who will churn ahead of time and reactivating them before it is too late. Businesses that do not overcome these challenges often fail to reactivate customers who churn and overspend on customers who weren’t actually at risk of churn.
AI helps your marketers develop intimacy across your customer base by predicting which customers are likely to churn within a defined period of time. AI will inform your marketers of the top contributing factors of each customer’s likelihood of churn, allowing them to have a thorough understanding of how these relationships can be improved. As a result, your marketers will have clarity on which customers they should reactivate and send personalized incentives to. By having this information ahead of time, customers are more likely to return to your business. This allows you to maximize the productivity and return of your incentives, such as discounts or special perks, by focusing on high risk customers instead of sending them to customers who were going to purchase your product or service regardless.
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Retailers deliver better demand forecasting, marketing efficiency, transparency in the supply chain, and profitability through advanced AI applications and a myriad of AI use cases throughout the value chain: from store-level demand and out-of-stock (OOS) predictions to marketing channel modeling and customer LTV predictions. With the abundance of consumer data, changing consumption patterns, global supply chain shakeups, and increased pressure to drive better forecasting, retail businesses can no longer ignore the potential of AI in their industry.