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

Study Shows Bad Data Undermining Marketers

If you worry that customer data problems are hurting your marketing results, join the crowd. Poor data quality continues to drag down ROI for the majority of U.S. businesses. In fact, the average U.S. organization believes that one-third of its data is inaccurate, and a whopping 91% of companies believe revenue is being negatively affected by inaccurate data in terms of wasted resources, lost productivity, or wasted marketing and communications spending, according to the Experian Data Quality study released this year. One of the most common culprits cited was poor address data quality. The study definitely reflects the kind of data quality challenges that direct mailers (and their list brokers, printers and agencies) bring to DBM Designs' database marketing services. What are the tasks that usually need to be tackled? Data processing prior to mailing provides address format standardization for proper mail sorting, postal discounts, avoidance of invalid addresses, and removal of wasteful duplicate records from all sources. And with 17% of Americans moving each year, a mailing list check against the National Change of Address (NCOA) database is essential to avoid costly wrong addresses and duplicates. Thorough data processing also helps to detect other data problems, such as missing, incomplete or miscoded customer information, and to create a single-customer view across channels for better segmentation, targeting by channel, and response tracking. For more on the results of the Experian Data Quality study, go to http://www.experian.com/blogs/news/2015/01/29/data-quality-research-study/