How to Use First-Party Data to Drive More Efficient Media Spend During the Holiday Season (and Beyond)

With the reported trillion-dollar holiday season rapidly approaching, retailers should smartly take heed of the benefits that customer insights derived from first-party data have to offer.

The holiday season is the highest pressure time of the year for retailers. Holiday customers account for up to one quarter of retailers’ annual revenues, and customer acquisition rates in November and December are 29 percent and 59 percent higher than average, respectively.

And while the rise of e-commerce has redirected the flow of retailers’ various revenue streams, it hasn’t lessened the importance of the holiday season. Quite the opposite, in fact: U.S. retailers’ holiday revenues grew from $556 billion to $589 billion from 2016 to 2017 alone, and eMarketer predicts an additional 3.8 percent increase this holiday season — driven largely by a 15.3 percent spike in online sales.

To maximize their slice of this expanding holiday pie, retailers must cement competitive advantages wherever possible. Consumers are willing to spend big during the holiday period, but figuring out how to nudge them toward spending their hard-won cash on your products is an enduring quandary for retailers. With everyone scrambling to elevate their brand presence, prices in the media marketplace skyrocket during the holidays, propelling metrics like cost-per-impression through the roof.

As such, the deepest competitive advantages are driven not by ubiquity, but by efficiency — spending smarter, not spending more. By pivoting from expensive (and usually ineffective) anonymous cookie targeting to targeting based on customer insights derived from first-party data, retailers have the opportunity to not only emerge victorious from the holiday season, but to extend their winning streak well into the new year.

A Revolution in Smart Customer Targeting

Until recently, retail marketers had little choice but to maintain a two-tiered, strictly siloed targeting system. Customers’ personal information was gathered through a retailer’s point-of-sale system or customer loyalty program and centralized in its customer relationship management (CRM) software, and could then be used to craft marketing communications tailored to specific customer profiles. This enabled retailers to deliver email and direct mail messaging that was carefully calibrated to a customer’s shopping frequency, discount sensitivity, channel preference, and so forth.

Unfortunately, such personalization was nearly impossible on digital channels like search, display, and video. Other than retargeting individual customers who visited their website, retailers had no way to reliably deliver specific ads to specific customers. Instead, they purchased audiences from third-party media vendors that could only be identified by broad demographic characteristics. A retailer could buy ad impressions served to, for instance, 18- to 29-year-old women living within a 50-mile radius of New York City, but it couldn’t target a subset of customers within that audience that it knew comprised its highest value shoppers.

Uniting the personalization potential of CRM targeting with the reach of digital targeting is a surefire way for retailers to improve the ROI of their media spend — and thanks to LiveRamp and Custora’s partnership, doing so is not only possible, but fairly straightforward. Retailers can now move beyond targeting anonymous cookie pools and speak directly to the customers they choose. This precision is necessary for retail success in general, but even more so during the holiday season.

Real holiday success involves acquiring (and retaining) customers who are not only going to make purchases this holiday season, but will return for purchases in January, February, and beyond.

Make a List, Check It Twice

In addition to facilitating the mechanics of precision targeting, first-party datasets that merge CRM data and digital channel data — email engagement data, site browsing data, web analytics data, etc. — can help retailers determine who they should target in the first place.

Lookalike prospecting is as old as retail itself, but customer insights derived from first-party data have the potential to make the process much more efficient. Building lookalike audiences around customers with the highest predicted customer lifetime value (CLV) has been the default of effective prospecting for decades, but its prevalence means that retailers must push beyond this status quo in order to gain a competitive advantage.

Rising above the crowd requires targeting consumers who have the potential to become high-CLV customers down the line, not just customers who have already demonstrated their value — and brand loyalty — through thousands of dollars of purchases spread across many months or years.

By leveraging predictive models powered by cutting-edge machine learning algorithms, a retailer can gain a great deal of insight into customers who have had very minimal engagement with the brand. These models assess the specific product a minimally engaged customer purchased, the acquisition channel that led to the purchase, and any available demographic information as a means of predicting the customer’s potential trajectories with the brand.

The customer might be the archetypal one-time, gift-buying holiday shopper, meaning the retailer would be wasting its resources by targeting them in the future. Alternatively, the customer’s profile might be a close match with the early behaviors of high CLV customers, meaning the retailer should do its utmost to cultivate an ongoing relationship with the customer.

Avoiding the Holiday Hangover

Retailers that supplement their high-CLV prospecting with this bigger picture strategic prospecting are best positioned for long-term success. Consider, for example, that two retailers are competing for the same aging customer base.

One pours all of its marketing resources into targeting the highest CLV customers in that audience through channels like television, radio, and out-of-home. The other dedicates half of its resources to targeting high-CLV customers and the other half to developing relationships with early-stage customers whose profiles resemble those of the current high-value targets through channels like email, display, and social.

Needless to say, the second retailer’s investment in developing new, younger customers will pay huge dividends in the long run, enabling it to leapfrog the first retailer once and for all.

A similar scenario plays out during the fall months, ultimately determining the winners and losers of the holiday season. An astute holiday strategy takes the entire year into account, not just the period between Black Friday and Christmas Eve. January can be a brutal month for retailers, and surviving the “holiday hangover” takes abundant forethought. Meeting holiday goals is important, but doing so by acquiring exclusively one-and-done customers is a recipe for disaster.

Real holiday success involves acquiring (and retaining) customers who are not only going to make purchases this holiday season, but will return for purchases in January, February, and beyond. Doing so requires strategic lookalike prospecting and, subsequently, precise customer targeting — a holiday one-two punch as potent as eggnog and mulled wine.

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