PREDICTIVE ANALYTICS IN RETAIL CASE #202: IMPROVE YOUR PERSONALIZATION
Predictive analytics brings science to retail marketing. It enables targeted marketing campaigns for increased revenue lift and there are many customer retention use cases, but personalized marketing is one of the most popular uses of predictive analytics. Personalization increases conversion rate by presenting relevant content to each customer.
Retailers often promote products that they want customers to buy, but many customers are more interested in something else. If the job of marketing is to grab attention and get a response, showcasing the products to each customer that they themselves are interested in significantly increases your chances. Customers who engage on your website might infer their product interest, but how do you personalize the product experience of customers who have not engaged recently? New approaches to predictive modeling make it easy to know every customer’s likely interest in every product during a specific future timeframe, so you can personalize dynamic emails or postcards, target the most likely buyers for your digital ad promotion, arrange products on your website, cross-sell at your service desk, or make recommendations on any channel to increase conversion rate.
Our client, a national apparel retailer, used predictive analytics to populate dynamic email templates with personal product recommendations for every email recipient. The results were compared to static promotional emails they used to broadcast to all customers.
The result? Emails with personalized product content consistently outperformed the static promotions in terms of revenue lift per customer. See chart below which includes the number of marketing emails the retailer sent and the respective marginal lift during the test campaign.
Peter Moloney is CEO of Loyalty Builders, a cloud-based predictive analytics service enabling marketers to get revenue lift from more relevant communications to their customers.