Avoid These Segmentation Errors for Max List ROI

List segmentation is key in targeted direct marketing, and 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, identifies some of the common strategic errors. First, 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." 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. One outcome of segmentation based on existing customers or surveys of people marketers assume are the right targets is blindness to potential audiences that Markel calls "ghost segments." 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 segments more efficiently, and then plug segments into a data management platform for insights on other products, services, interests, and media that may correlate. Finally, Markel stresses that a segmentation study will fail to live up to its ROI potential unless it informs the whole organization. Customer and prospect insights have relevance for multiple departments and teams, from sales to customer service to finance. Markel suggests creating 360-degree customer personas and promoting them throughout the organization via workshops and periodic team updates on results. For more, see https://www.acculist.com/avoid-segmentation-missteps-to-boost-list-roi/

Many Publishers Not Fully Mining Audience Data

Business periodical marketers come to AccuList USA for help with audience building via multi-channel campaigns, but as data experts, we'd like to remind them that their audience data offers other revenue streams worth mining. Most publishers know that targeted audience data is key to competing for ad dollars; for improved subscriber response via personalization; and for better targeted content marketing. A recent Adweek article by Jason Downie suggests several other ways to monetize audience data. Downie urges publishers to build “off-the-shelf” audience segments that can be sold directly to advertisers, for example. Consider how a seminar promoter could use a business magazine's data if the publication built an audience of people interested specifically in his topics or proven seminar buyers; the advertiser would be able to enjoy the benefits of tapping not just a business-engaged audience but a strategically targeted set of potential buyers more likely to convert. Audience segments can also offer insights that can be further monetized. For example, analytics could show that seminar attendees are four times more likely to share content online. That makes them online influencers, and since influencers are extremely valuable, the publisher can demand a higher CPM. Additionally, an audience segment can open the door to new advertisers and marketers, including non-endemic spending. A business publisher's analytics may show a subscriber segment visits golf sites as well as the magazine site, for example. The publisher can now woo clients looking to target "golfers." Another way publishers can take advantage of data is in the RFP process, according to the Adweek article, noting that the average publisher spends up to 1,600 hours per month, or 18% of revenue, responding to advertiser RFPs. Publishers can develop a customized response to an advertiser RFP, starting with first-party data to build out the RFP-requested audience and then enriching that database with third-party data appending. Digital campaigns can expand targeting by adding lookalikes. Author Downie advises running a portion of an ad campaign without audience or contextual targeting to identify additional audiences, interests, actions and behaviors of those who respond well to the campaign but were not included in the initial targeting. Still another option for publishers with high-quality audience data is to sell it as "second-party data," either directly to another company through a second-party data exchange or through a programmatic data exchange. And, of course, subscriber lists can be monetized as "third-party data," earning regular rental revenue on the open market and via data brokers. For more detail, see http://www.acculistusa.com/many-business-publications-fail-to-fully-mine-audience-data/