With another IRCE in the books, we’ve taken some time to reflect on our experience this year. During the show we met a large variety of retailers, but we noticed that a lot of them had something in common. B2B retailers were heavily represented among this year’s IRCE attendees.
Despite this similarity, we’re very aware that no two retailers are exactly the same. Because of this, personalized marketing campaigns are important for retailers in the B2B space. More specifically, data-driven emails can lead to increased customer engagement and retention.
To take this further, retailers who leverage predictive data in their email marketing can achieve a higher level of personalization and relevancy with these messages.
Predictive analytics, a broad term describing a variety of statistical techniques to predict future events, is one of the most efficient and effective ways to mine marketing data intelligently. Having access to predictive analytics creates a strategic opportunity for B2B marketers to reach customers by zeroing in on predicted behavior and triggering email campaigns based on these actions.
We offer multiple predictive marketing solutions for B2B retailers. Specifically, customer retention, product replenishment, and cart recovery campaigns that incorporate both dynamic product recommendations and predictive reorder dates, ensure that relevant and personalized messaging is sent to customers at exactly the right time.
In this blog, we’ll focus on another predictive marketing campaign that’s been especially successful for our clients, the Win-Back series.
Keeping B2B customers engaged at all times can be a difficult task. Win-back messages serve as the reminder that some customers need to return to your brand to make another purchase.
USCutter has two different versions of their win-back series. One for 1-2x purchasers and another for more engaged customers who have made at least three orders.
The 3+ purchaser series is sent based on the individual buyer’s predicted order date. Normally, win-back campaigns are sent based on static dates such as 60, 90, and 120 days since the last purchase. However, with data science these campaigns can be triggered on metrics like predicted order date, which is a projected next purchase date tailored to a specific user’s order history.
To learn about other top campaigns for B2B retailers, download our new B2B industry case study.