Are Duplicate Fields Hurting Your Database Marketing?

A basic requirement of cost-effective direct marketing is elimination of duplicates, meaning multiple database records for the same person or company account. But marketers today need to watch out for another, trickier duplication challenge: duplicate data fields. Expanding use of multi-channel, multi-sourced data fuels the problem. The same prospect or customer record is often enriched by data from different online and offline sources--data appending services, lead-gen services, list rentals, predictive and lead scoring services, e-mail validation services, call center entries, online ad and social platforms, events, and so on. The marketer may intend to validate, update and unify this data, but efforts are delayed or incomplete for whatever reason. Then the marketer gets ready to launch a campaign and discovers many contact records have two job titles or four industry codes or three e-mail addresses. If the data entries are not clearly dated and sourced, the marketing team has no clue which data are the most up-to-date, accurate and appropriate for targeted promotion! We were pleased to see a recent MarketingProfs article by Ed King, CEO of the data automation firm Openprise, offer cogent advice on avoiding this costly problem. Obviously, marketers should first strive to unify field content promptly while the data is fresh. If data unification must be delayed, new data should be labeled by its source and age for use in future data consolidation decisions. Whether field data unification is immediate or delayed, the marketing team needs to agree on a data-unification logic. King advises that this logic should be based on at least three key factors: source authority (giving priority to trusted data sources); source focus (preferring sources more aligned/specialized for the marketer's industry/target); and age of data (for example, in B2B, more recent contact name or company size is likely to be more accurate). Consistency and scalability are the goals; ad hoc, manual record decisions are not only less efficient but less likely to yield optimal overall results. While unifying data in fields, the database process should also normalize data so formatting and coding are consistent. Plus, a smart database effort can remap field content for better targeting. King provides the example of consolidating 2,000 industry codes into 10 custom definitions that better fit market targets. For the whole article, see http://www.marketingprofs.com/articles/2016/30069/your-duplicate-data-problem-has-an-evil-twin-that-is-much-worse-duplicate-fields?adref=nlt060816

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/

Survey Cites Obstacles to Data-Driven Marketing Success

Despite technology advances and the bounty of "big data," data quality remains the biggest challenge for data-driven direct marketing. Consider MarketingProfs' report on a new Ascend2 global survey of marketing, sales and business professionals. In terms of the most important goals of data-driven marketing strategy, personalizing the customer experience led the field (60%), followed by measuring data-driven ROI (51%) and targeting individual market segments (50%). Then when it came to the biggest obstacles to success, companies put front-end data issues ahead of back-end analytics by a wide margin. Improving data quality was ranked as the No. 1 challenge to data-driven success (59%), followed by integrating data across platforms (51%), raising the level of data analytical skills (38%) and measuring data-driven marketing ROI (37%). In fact, only 16% of respondents rated the quality of their marketing data as "very good," compared with the 27% who rated data quality as "somewhat poor" to "very poor." The majority (57%) called their data "somewhat good," which is just not good enough for the most cost-effective marketing results! For more from the survey, read http://www.marketingprofs.com/charts/2015/28904/data-driven-marketing-top-objectives-and-challenges