List segmentation is key in targeted direct marketing, which is why the AccuList team offers clients help in defining best-performing customer segments via predictive analytics services and data management services. Over the years, we’ve learned that the secret to success is as much a matter of strategic mindset as technical expertise. A recent MarketingProfs article by Mitch Markel, a partner in Benenson Strategy Group, makes that point by identifying some of the common strategic errors that can trip up a segmentation effort.

Obvious Parameters and Old Strategies Dig a Rut

Marketers need to be aware that segmentation models can slip into an ROI rut. Use of obvious profiling parameters and assumptions is one reason. Certainly, demographics (or firmographics), stated needs, and past purchase behavior are essential in grouping for likely response and lifetime value, but people don’t make decisions solely based on these factors. Markel urges research that also looks at fears, values, motivations and other psychographics in order to segment customers or prospects not just as lookalikes but also as “thinkalikes,” which can be especially helpful in crafting personalized content and messaging. Markel cites the examples of car buyers grouped by whether they value safety over performance, and food purchasers sorted for whether they stress healthy lifestyle or convenience. Past success is another reason segmentation can get stuck in a rut. Because segmentation requires an upfront investment, marketers tend to want to stick with proven targeting once the segmentation study is completed. But today’s hyper-personalized, digital environment has accelerated the pace of change in markets, perhaps shifting customer expectations and preferences away from an existing segmentation model. Markel advises an annual “look under the hood” of the segmentation engine to see if segments are still valid or need appending/updating. An annual audit can avoid the expense of a broader overhaul down the road.

Big Data Blindness Ignores Potential Audiences

One outcome of segmentation based on existing customers is blindness to potential audiences. Segmentation research often uses the existing customer base and surveys of people that marketers assume should be targeted. This can create marketing campaigns that miss groups that Markel calls “ghost segments,” people who could be among a brand’s best prospective customers. Markel suggests a periodic look at non-customers for conversion potential as one way to capture these “ghosts.” And, of course, if a new product or service is in the works, research should ask whether it will attract new groups differing from the existing customer profile. Another reason ghost segments are common is that marketers, overwhelmed by the task of sifting “big data,” fall back on whatever data sets are handy. Markel suggests that it would be better to bring in big data at the tail end of segmentation. He advises analysts to start by creating segments using primary research, add existing customer “big data” to target those segments more efficiently, and then plug segments into a data management platform for insights on other products, services, interests, and media that may correlate.

Analytics Miss Without a Companywide Strategy

Finally, Markel stresses that a segmentation study that ends up residing only with a few marketing decision-makers will fail to live up to its ROI potential. Customer and prospect insights have relevance for multiple departments and teams, from sales to customer service to finance. In order to deliver a seamless, personalized customer experience, Markel suggests creating 360-degree customer personas and promoting them throughout the organization. Management can start with workshops to educate employees on the use and importance of those personas both for their departments and the organization, and then can schedule check-ins to show team members the resulting benefits of segmentation and targeting implementation. If segments are made relatable, it will ensure they are used and embraced across the organization.