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/

Evaluating Martech Buzzwords: Hype vs. Results

Marketers are bombarded by advice on how to seize buzzword opportunities such as personalization, "big data," omnichannel, real-time marketing, and, most recently, artificial intelligence (AI). Those struggling to find room in real-world budgets often worry that they're falling behind in an escalating martech arms race! New research by Resulticks—a survey of over 300 marketing pros across industry verticals—offers interesting perspective. For example, "Big Data" was the hot topic at the 2013 DMA Annual Conference, with 50% of marketers enthusiastic about investing. But making practical sense of those data torrents turned out to be more difficult than expected. Resulticks finds that only 16% of today's marketers have fully implemented big data solutions, 20% have given up on the concept, and just 27% rank big data as a top priority now. Part of the problem is overhyped, underperforming martech platforms, per the survey, with 21% of marketers complaining that vendors overpromise and underdeliver. In contrast, personalization has done better in fulfilling expectations, with 60% of today's marketers reporting full or partial implementation. The only fly in the ointment: Tech investments have not always kept pace with enthusiasm, and only 20% rate their software ability to deliver personalization as "excellent." Back in 2014, many retailers were optimistic about an "omnichannel" approach to create a seamless customer experience across all channels. Resulticks finds that only 9% of today's marketers describe their approach as omnichannel, compared with 63% who use less comprehensive multichannel marketing. Only 35% have fully or partially implemented the required software platforms for omnichannel, and, among those who have bet on platforms, 58% rank vendor execution as "poor" to "fair." There's a better report card for the "real-time marketing" that rapidly uses data across channels for more timely, targeted engagement in the customer journey. Resulticks reports that 49% of marketers rate their real-time marketing ability as "good" to "excellent," that half say they have fully or partially implemented real-time marketing solutions, and that 47% say real-time is a priority for their organizations today. Most recently, social media giants have been betting on AI, and many marketers are following their lead. However, Resulticks' survey finds almost half (47%) of the marketers polled already rate AI as overhyped. Here's a big source of that skepticism: 43% of marketers believe martech software vendors overpromise and underdeliver, and 69% rate their vendors' ability to execute AI as "fair" to "poor." For more, see http://www.acculistusa.com/skeptical-of-marketing-tech-buzzwords-youre-not-alone/

Addressing 'Big Data' Issues in Agency Agreements

The era of "big data" has created both greater opportunities and greater complications for marketers in terms of access and use of data. In fact, Advertising Audit and Risk Management (AARM), a North American provider of independent advertising audit and consulting services, recently urged advertisers and marketers to review agency contracts to make sure they address evolving "big data" issues. Data can drive a precisely targeted marketing strategy by leveraging insights from transactional and customer behavioral data--assuming that the advertiser has the right to receive and use that data. Based on their experience, AARM cites at least six key, but often unanswered, data questions that should be covered in contracts: Who owns the data; where the data is stored; how long the data is stored; how secure the data is; whether the data is kept separate from that of other advertisers; and whether the data is being used to aid other advertisers. AARM points out that data ownership is not automatically ceded to an advertiser or marketer despite investment in a media buy generating a data stream. Many within the media chain may try to claim the generated data: Ad agencies, trading desks, publishers, demand-side platforms, and third-party ad servers all may seek unrestricted access, if not ownership, of valuable customer data. That's why marketers and advertisers need to be sure that legal agreements clearly and consistently spell out data ownership rights, privacy considerations, security and access rights for first-, second- and third-party data. For more, see the full post at http://www.acculistusa.com/what-data-questions-should-agency-agreements-address/

How to Use Campaign Data to Budget for Success

Overwhelmed by the pace and volume of "big data," marketing campaign analysis can become a shortsighted focus on lead metrics and quick reaction. We'd like to thank Bonnie Crater, president and CEO of Full Circle CRM, for her emarketingandcommerce.com article reminding marketers that campaign data is also key to long-term ROI--via improved marketing budgets and campaign development. Here are five tips on using campaign data to budget for future success: Start by using prior-year data on lead generation, close rates, average deal dollars, and ROI to provide a roadmap to current-year marketing budget targets. Second, gauge marketing performance and adjust forecasts according to industry benchmarks of realistic lead generation and campaign close rates. Third, in deciding budget allocations, don't measure campaign ROI just by first and last touchpoints; look at the detail of multi-touch, multi-channel, multi-campaign marketing to accurately credit each campaign with revenue generated. Fourth, weight the different elements affecting campaign touches (such as campaign types, user roles, when the touch occurred in the sales cycle) to better allocate spending to maximize campaign ROI. Finally, while digging into campaign data, take note of process problems, such as lead hand-off gaps and sales bottlenecks, and take action to improve efficiency of marketing and sales spending. For detail from the complete article, go to http://www.emarketingandcommerce.com/article/5-tips-using-campaign-data-build-your-next-marketing-budget/1

Using a Scientific Approach to Direct Marketing

Thanks to evolving technology, today's direct marketing can rely more on scientific discipline than risky, intuitive market reads. A recent article in Direct Marketing News highlighted a five-step scientific approach that you can apply to almost any marketing channel -- social media, e-mail, mobile, display ads and, of course, direct mail -- courtesy of Matt Dopkiss, CEO and founder of Dynamit, a data, design, and technology company. Here's Matt's "big bang theory meets big data." First, identify the hypothesis, or marketing question, that a campaign is seeking to answer (say, which offer gets to highest response). Make sure that the test hypothesis is specific, measurable and actionable. Second, use a control model for comparison of results. Compare the response of subscribers receiving a new e-mail version with a set of subscribers not receiving the e-mail, for example. Third, use statistics to verify your theory, collecting enough data to draw valid conclusions -- but not such a huge data set that noise overwhelms actionable results. Fourth, repeat tests to make sure results can be reproduced and are not due to timing or extraneous factors (such as weather, news events, etc.). Finally, be certain you are applying the right technical tools and methods (and trained staff) to extract actionable data. For more detail, go to http://www.dmnews.com/the-big-bang-theory-meets-big-data/article/369346/

How to Feed 'Good' Data to Your Marketing Database

A challenge in the Big Data era of myriad potential data feeds is to select inputs that are usable, useful and cost-effective for marketing ROI. Not all data is created equal! To that end, we'd like to pass along a recent MarketingProfs six-point questionnaire to help you score data candidates for integration into your central customer and prospect database. Half of the questions deal with the crucial need for a unique identifier on imported data (such as an e-mail address or phone number) so that new data can be matched to the existing marketing database. If the identifier doesn't exist, can it be created (appended, for example)? Next, consider the overlap between a data feed and the customers and prospects already in the database: If the goal is inexpensive integration of targetable info (behavioral, purchase and demographic attributes, or additional contact points, for example), high overlap is good. (For database growth, not so good, we would add.) Finally -- and this is the key issue to our mind -- you need to determine the potential financial benefit from new data in terms of increased revenue. That, of course, assumes a basic understanding of customers from existing data. For the full MarketingProfs data-scoring article, see http://www.marketingprofs.com/opinions/2014/25212/is-your-data-any-good-six-questions-to-help-score-your-data-resources

Survey Finds Marketers Bullish on Big Data

Marketers are pretty united in their positive attitude about the opportunities of "Big Data." According to a 2013 survey of marketers from the Direct Marketing Association (DMA), data-driven marketers are generating increasingly profitable returns and are bullish on marketing programs based on Big Data. The DMA's “Quarterly Business Review” for the third quarter of last year, developed in partnership with Winterberry Group, surveyed 220 marketers and found that 75.4% expressed confidence about data-driven marketing, with 49.5% “strongly agreeing” that marketing informed by data is positioned to expand in the months ahead. See the report summary at http://adage.com/article/btob/marketers-report-strong-data-oriented-results/290866/?btob=1

Celebrity Endorsements Don't Win Fans for Brands

Think a famous name touting your product or service will impress customers? Think again. Brand endorsements by celebrities and athletes are trusted less than all other methods of brand promotion, including digital ads, traditional ads and company-sponsored social media, according to a new global study by The Boston Consulting Group. Consumers put the most store in what family and friends say offline about a brand, followed by product reviews, expert opinions, and information from Google and other search engines. One reason trust matters: Brands that show their customers that they can be trusted are able to gather at least five to 10 times more information about customers to fill the Big Data marketing mill. Still, marketers must tread carefully because consumers are choosy about what info they entrust to companies, and financial and health data definitely rank higher on the privacy scale than brand preferences, age and gender. Millennials, despite a social sharing addiction, are about as concerned with data privacy as their elders. As a result, the data practices of financial institutions, social media, search-engine companies, and government arms garner more than twice the consumer concern than do branded manufacturers, airlines, hotels, cable providers or retailers offering loyalty cards. For more, see MediaPost's Marketing Daily story at http://www.mediapost.com/publications/article/212917/endorsements-dont-earn-trust-for-marketers.html

Retailers Can Use 'Big Data' for Bigger Holiday Sales

OK, retailers, if you've been gathering reams of online and offline data about your prospects and customers, it's time to use it to wrap up a holiday sales gift to yourselves. How? A recent MarketingProfs article suggests three tips for using "big data" to deliver a merrier holiday season to e-commerce and brick-and-mortar merchants. No. 1: Combine customer profiles and buying/shopping histories with real-time situational data (location, time/seasonality, limited-time offers, remaining inventory, etc.) and apply that data across devices and channels for more targeted, personalized and relevant promotions in real time (in-store mobile ads, for example). No. 2: Retarget site abandoners with video ads, which drive more engagement than display ads and e-mails--and use data to make sure videos are relevant and timed to the customers' buying cycle. No. 3: Don't stop with customer acquisition. Deliver multi-channel welcoming experiences to new customers for retention, cross-sell and upsell to keep sales flowing into the New Year. For specific implementation ideas, see the article at http://www.marketingprofs.com/articles/2013/11924/how-to-harness-big-data-for-better-holiday-shopping-experiences