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Harnessing the Holidays and Beyond with Predictive Modeling

The hype of the post-holiday season can turn frigid pretty quickly if retailers don’t get it right – and that includes delivering more relevant marketing campaigns, messaging, and offers to customers at the right time and in the right place.

Predictive modeling is the way to drive engagement, anticipate customers’ needs, retain customers, and keep them shopping long after the tinsel has been stored. In today’s retail landscape, anticipating a customer’s every need is critical – and it’s especially crucial in the current competitive landscape.

According to Alliance Data’s “Now, New, Next” trends report, to deliver deeply personalized messaging, offers, and experiences, brands are enhancing their data-collection methods and innovating how they leverage analytics and insights. Through predictive modeling, they can accelerate their ability to anticipate behaviors and needs.

On a customer scale, predictive modeling has been around for decades and started to reach maturity as brands moved from general mass marketing to creating databases to capture important customer information. This led to RFM segmentation – looking at how Recently a customer had shopped, how Frequently they tend to buy, and how Much they purchase. While RFM was a generally good predictor of future engagement, it focused on past behavior and ignored nuances around a customer’s shopping cycle and her preferences related to merchandise style, cut, color, and price. 

Enter predictive modeling, which involves using advanced statistical techniques to take the foundation of RFM data and combine it with customers’ demographic and psychographic attributes. This allows brands to unlock a richer view of behavior, what is driving it, and how to use that information to enhance the customer’s journey. 

Your Marketing is Only as Good as Your Data

Winning the hearts (and wallet share) of customers begins with collecting good data and combining it with the voice of the customer and predictive analytics to understand where and when their customers need them most. The answer lies at the intersection of what customers are saying and doing. Advanced computing allows retailers to leverage insights faster than ever and understand purchase patterns and behaviors that will drive everything from marketing and brand messages to rewards and the in-store experience.

In addition to enhancing the customer experience, using quality data to guide marketing efforts allows brands to prioritize how they connect with customers and when. For example, predictive modeling can help determine which potential customers will wait until the last minute to make their big gift purchases, and in the meantime, retailers can focus efforts on procrastination-adverse shoppers.

A Model Approach to Customer Engagement

The predictive modeling journey begins with comprehensive segmentation to understand customers, their value, and their affinity for shopping categories and specific types of merchandise. Many brands even create personas around key customer groups.

From there, retailers need to understand what each customer’s shopping cycle looks like. For example, some customer groups only engage during select events, be it Black Friday or during post-holiday sales. A lot depends on what that cycle is and building your marketing plans around it.

Once brands knows who their customers are and how they like to buy, they can focus on building merchandise affinity, channel migration, and retention models, which are designed to help deepen connections with customers by making them aware of what other merchandise they may like, helping them to engage with the brand through all relevant channels and ensuring that brands are talking to their customers at the right times to keep them engaged.

Discovery and Engagement Drive Long-Term Loyalty

As retailers perfect these methods, they can then build out models to identify other consumers not currently shopping with them and engage them as well. These models help brands compete in a more targeted way to find new customers that resemble some of their existing best customers today.

Predictive analytics can help maximize productivity and allow retailers to more effectively reach their target market, and retailers that use predictive modeling can gain an edge over the competition and gain new customers for life. Especially now, as many retailers received the “gift” of new customers during the strong holiday season!

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