Personalization Driven by Bad Data Ends Up Crashing

Personalization is a fundamental necessity for direct marketing response today. So why do personalization efforts still flop? It's usually a data problem. A recent post by Spider Graham for bizjournals.com highlighted five common personalization mistakes and illustrates our point. First, pretending to know a recipient when the marketer clearly has nothing more than a name is a response killer. True personalization is about crafting message, offer and value proposition based on understanding the target and his or her needs--which requires recent, multifaceted, quality data. On the other hand, getting the name right is still the first, most basic requirement. Using the wrong name or a misspelled name is a data sin, but even using the wrong version of a name can backfire, especially with opt-in e-mail or telemarketing, Graham points out. Spam suspicions rise when someone who always subscribes as Bob is addressed as Robert. That's why it's important to consider list data sourcing when personalizing, especially in terms of hygiene and rented versus house quality. Third, Graham reminds that marketers are chasing moving targets; many attributes, from address to relationship with a brand, change over time. Good personalization relies on up-to-date data about multiple demographic factors and purchase patterns. A promo for stuffed toys to a household where all kids are grown and gone stirs up nostalgia not dollars. Fourth, don't lie and imply a relationship that doesn't exist based on a one-time visit or query, warns Graham, or "come back" and "we miss you" will not only alienate but waste an opportunity to start a new relationship. Finally, check your basic data quality! Dedupe, normalize, update and watch for missing data fields as well as faulty fill logic so you don't end up with "Dear FirstName" or "We miss you, N/A." The good news is that the worst personalization errors can be avoided by committing to smart data hygiene, logic and processing. See the complete article: http://www.bizjournals.com/bizjournals/how-to/marketing/2016/06/5-personalization-mistakes-marketers-make.html

Survey Finds Big, Dirty Challenge in 2016: Customer Data

Direct marketers know that quality data is at the heart of their success. Yet three-quarters of customer service, data, marketing, sales and tech professionals told the latest Experian Data Quality survey that they will be struggling with inaccurate data in 2016--undermining efficiency, customer satisfaction and profits. From a recent Direct Marketing News magazine report of Experian findings, respondents cited their main data quality problems as incomplete/missing data (60%), outdated info (54%), duplicate data (51%), inconsistent data (37%), and typos (30%)--and more than half attributed that bad data to human error. Indeed, when it comes to the biggest obstacles to improving data quality, respondents cite the two top challenges as lack of internal knowledge/skills and lack of internal human resources. As a data services provider, we're happy to see that underperforming data quality vendors is at the very bottom of the list of impediments to better data (cited by just 7%). So what kind of projects can we expect from clients this year if they join the push to tackle data issues? Those surveyed said they'd be working on data cleansing (37%), data integration (37%), data migration (31%), and data enrichment (31%). If you are still wondering if data quality is worth the investment, consider the top five reasons given for improving data quality: increasing efficiency (56%), enhancing customer satisfaction (41%), enabling more informed decisions (39%), saving on costs (39%), and protecting brand and reputation (34%)--all goals with a positive impact on the long-term bottom line. For more report details, read the DM News story at http://www.dmnews.com/dataanalytics/managing-customer-data-in-2016/article/469162/

Don't Let Data Glitches Stymie B2B Lead Efforts

Business-to-business marketers dedicate chunks of budget and time to gathering qualified leads. Unfortunately, we've seen basic data problems undermine the effectiveness of hard-won B2B prospect and customer databases. So we'd like to pass along a recent MarketingProfs article alerting marketers to six of the most common data pitfalls. No. 1 on the list posted by Rob Manser, acting director of marketing at contact validation firm Service Objects, is relying on a single contact method in lead data. Focusing solely on e-mail outreach, for example, increases failure from address errors or poor channel response. By gathering or appending multichannel contact options--phone, e-mail and mailing address--the chances of connection climb. As Manser points out: "An e-mail or a phone call might never be returned, but a clever direct mail piece may catch a prospect's eye." Problem No. 2 arises from incorrect data gathering--incomplete, typo-riddled, misformatted or just plain bogus contact information. It doesn't mean all bad-data contacts must be tossed; many can be cost-effectively salvaged today via data verification, validation and appending software. Pitfall No. 3 is out-of-date information. Valuable contacts change companies, move to other locations in the same company, change titles and departments, etc. Frequent and thorough contact-data updating is required. That said, even when info is technically correct, Pitfall No. 4 occurs because data is not contact-specific enough; using a headquarters phone and address instead of the contact's division location will miss response in a geo-targeted campaign, for example. No. 5 on Manser's list of prospecting mistakes: Lead data that doesn't include a company's key targeting criteria--such as title or company size--which creates costly sales and marketing misfires. The final error compounds all others: allowing a contact database to become a pool of wasted opportunities by failing to fix data problems. Manser argues that there is no excuse now that marketers can turn to database services for quick, automated data-appending, data-verification and data-validation programs for clean-up--and we agree!  For more: http://www.marketingprofs.com/opinions/2015/28978/six-huge-lead-generation-pitfalls-that-are-hurting-your-business 

Common Design and Data Missteps Can Sink Mail ROI

Even if a direct mailer gets everything right when it comes to list selection, offer and copy, certain common design and data mistakes can boost postal costs, reduce deliverability and cut ROI. Our thanks to Printing Industries of America for a handy summary of avoidable mailing mistakes. The PIA list includes design and mail preparation faux pas, such as creating a flat mailer that could have mailed as a letter for reduced postage; creating an unusually shaped mail piece without calculating the higher postage ramifications; failing to check weight and final thickness and so incurring higher postage; and failing to meet USPS requirements for the address block, either as printed on the outer envelope or viewed via a window envelope. Another group of common errors involves data processing. Mailing list data problems that PIA notes--and that DBM Designs regularly addresses with CASS-certified software, NCOA database matching and merge-purge--include lack of standard USPS abbreviations, punctuation (except the hyphen in ZIP+4), no secondary addresses, no pre- or post-street directionals (N, S, etc.), duplicate names and addresses, and failure to meet USPS Move Update requirements. However, by the time a mail campaign goes to press, it's too late for most direct mailers to cost-effectively avoid mail missteps, even if working with the minority of printers offer mailing services.  Based on DBM Designs' successful direct-mail partnerships, early application of postal and data processing disciplines is the best cure for these common direct mail bugs. See details of all top-10 mailing mistakes: http://www.printing.org/page/9380

With Bad Data Costing Average Direct Mailers So Much, Going Beyond the Average Solutions Pays Off

The average U.S. company wastes $180,000 a year on direct mail that does not reach intended recipients because of inaccurate data, per a handy Lemonly.com report on the impact of bad data. That's not so surprising when a recent Experian survey shows marketers rate a third of their databases as inaccurate, particularly when it comes to addressing. The annual cost of wasted mail is also no surprise if you consider the cost per thousand (CPM) by mail type in the Direct Marketing Association's 2015 "Response Rate Report" survey: CPMs range from about $585/M for postcard and letter packages to $1,000/M for oversized envelopes and $1,200 for dimensional mail. A company mailing out oversized pieces to 100,000 a month with 15% bad addresses/undeliverables will end up with over $180,000 in yearly waste in no time! The biggest source of undeliverable mail (76%) per the U.S. Postal Service is consumer and business change of address from the 17% of Americans who move/change addresses each year. Too many mailers assume that when they qualify for First Class and Standard mail commercial pricing by matching against the Postal Services' National Change of Address (NCOA) database, they've cleaned up the problem. Unfortunately, the NCOA database itself has issues because of lags in recording, look-back time limits (18 months and 48 months), and, most important, the up to 40% of consumer and business movers who do not report their change of address. So in addition to NCOA, mailers should match their data against the several additional Proprietary Change of Address (PCOA) databases that draw on private sources, such as magazine subscribers and financial information, to catch the big chunk of address changes and "dead/undeliverable" addresses that fall through the cracks. The savings is easy to calculate: Using several private data sources to find undeliverable and move data not recorded in the standard NCOA database costs between 15 cents and 25 cents per corrected record. These records will otherwise waste anywhere from 60 cents to $1.20 each in direct mail costs. There's also the lost response from prospects/customers who will not receive the intended mail piece, or any other offers. Even at 25 cents per fix, the savings is huge with mail rates as high as they are. Plus, the improved address correction allows for more effective merge-purge deduping and more savings. For a broader calculation of bad data's business impact, see the Lemonly infographic: http://lemonly.com/work/the-cost-of-bad-data/