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

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