|Lessons In Loyalty|
My wife does a lot of direct response shopping -- she bought a wrought iron floor lamp from Morocco and a wall clock from France for the kitchen -- and as a result, she receives a ton of catalogs and other promotional mail. Sometimes she even gets two copies of the same catalog, one addressed to her name when she was previously married and one addressed to her current name. Even though we've been married for several years, these catalog companies are obviously using databases that are neither clean nor current.
Clean and current data are but two aspects of overall data quality. According to the Navesink Consulting Group, a New Jersey-based data quality consulting group, data is of high quality "if it is fit for intended uses in operations, decision-making and planning."
That means the
- is free of defects: accessible, accurate, timely, complete, consistent with other sources, etc.
- Possesses desired features: relevant, comprehensive, proper level of detail, easy to read and interpret, etc.
Using these criteria, no customer database is error-free, and the high level of erroneous data has become a costly problem for suppliers and an annoyance for customers. In a recent study, close to 80% of consumers polled said they dislike receiving duplicate pieces of mail for a single promotion. However, names can also be misspelled or mail pieces can be sent to former residents or to the wrong address. High quality data and the sophisticated statistical techniques for analyzing this information are absolutely essential for successful customer programs and processes. All information holds potential importance in customer relationships -- historical purchase data, essential demographics, and lifestyle characteristics -- so suppliers are becoming increasingly concerned.
Not surprisingly, the range of estimated errors in most databases is quite broad. I've seen percentage figures from the low single digits to 40% and higher. Errors result from keying mistakes, misheard names and addresses, or duplicate data entry. Other problems arise from poor list merge-purge programs, errors when data streams are integrated, infrequent cleaning and so on.
Costs associated with poor data quality go well beyond the obvious waste from mailing duplicate merchandise catalogues. They include dealing with customer complaints caused by data errors, and the staff costs involved in checking databases, finding missing data, and fixing incorrect data. Order entry staff, for example, may spend up to 25% of their time performing these tasks. In a company with 20 people on order entry staff, that would equal 5 effort-years annually. Further, if the loaded cost for each of these staff members is $50,000 per year, the annual cost associated with data errors -- in just this one area -- would be $250,000!
Lutheran Brotherhood, a member-owned fraternal benefits organization (mutual funds and annuities, insurance, estate planning, college and retirement programs, etc.) has more than 2 million member names and addresses spread across several product and service line databases. In addition, they have 6 million names in their prospect database. They often ran into customer service problems, one of which centered around delivery of their bi-monthly magazine. If a household had multiple members, and they requested multiple copies, there were questions about why they were getting only one copy. Also, like my wife with merchandise catalogues, the same member might get multiple copies of the magazine even if they should have received only one.
The organization was principally using front-line staff to rectify member data quality problems, but this was both expensive and inefficient. Lutheran Brotherhood established a process to centralize all member information into one database (customer data integration), so that employees would have a complete view of their members. This was a proactive and positive first step, but it didn't eliminate the bigger problem of duplicate names and addresses. Staff members were still responsible for the searching and matching process, functions that were costly, time-consuming and bad for morale.
Lutheran Brotherhood solved this issue by using advanced linking software, in this case provided by Innovative Systems, Inc., to de-duplicate member and prospect names from the database. Their entire file of 8 million names can now be cleaned for duplicates in four hours; and, as JoAnne Gibbs, business analyst for Lutheran Brotherhood, concluded: "Our use of the product saved us from duplicating a total of 482,000 customers and prospects." Much of the problem surrounding magazine delivery evaporated as a result.
Data quality upgrade efforts such as these are usually transparent to customers, and they should be. The real rewards of higher quality data, however, are improvements in areas of customer relationships such as service and market research, and greater efficiency in marketing and other customer-related processes, making CRM activities more productive and consistent.
If only my wife were a member of the Lutheran Brotherhood.
Michael Lowenstein is managing Director of Customer Retention Associates, a customer and staff loyalty program development, research and consulting firm located in Collingswood, N.J. He has three decades of experience in customer and staff loyalty research and has written several books, including Customer Retention: Keeping Your Best Customers, The Customer Loyalty Pyramid and Customer Win-back: How To Recapture Lost Customers - And Keep Them Loyal.
This was first published in March 2002