As Frederick the Great (King of Prussia, 1712-1786) once observed, “If you are not advancing, you are retreating.” His wisdom is nowhere more apparent than in the world of customer data. There is no steady state where the data is “clean” and you can move on to other issues. If you are not constantly improving the timeliness, accuracy, completeness and consistency of your data, you will fall further and further behind.

 

This column focuses on why you must have people, processes and technology to continuously advance on the data quality front. And, even with the best processes, scorecards and incentive plans in place, you will never achieve 100 percent data quality. Information about complexity and rate of change will, however, help you to set achievable customer data quality objectives and create a business case for a program of ongoing quality enhancements.

 

According to statistics maintained by D&B, the average small business (fewer than 100 employees) in the U.S. has 1.6 names and addresses. Larger companies in the U.S. average four names and addresses.1 The variety of names and addresses comes about because companies have legal names that are different from their doing-business-as names (e.g., Smith Enterprises and Bob’s Quick Tax Preparation) and because the company itself or someone within your own organization uses a short name (e.g., D&B and Dun & Bradstreet). The frequency of multiple names and addresses varies a little from country to country, but the D&B statistics are generally representative of the size of the multiplicity problem. The result of multiple names and addresses will be unrecognized instances of the same entity, often called file fragmentation.

 

Companies are also complex because many are embedded within larger corporate trees. D&B estimates that between 10 to 12 percent of entities within any country are parts of a corporate structure.2 Linked entities are often parts of very large organizations, but small business entrepreneurs often create multiple business entities to limit liability. If you correctly identify these linkages, you can create real cross-sell and up-sell opportunities.

 

In general, people have fewer instances of multiple names than companies. But, some names are commonly abbreviated (William, Wm., Bill and Will), women in many countries change their names when they marry and people do legally change their names. The complexity of name and identifying information is then made worse by the fact that customers are constantly changing.

 

Established companies move, change their phone numbers and even change their names. According to data collected by D&B about companies in the U.S., 21 percent of all companies will move in a year, 18 percent will change their phone number and 12 percent will change their name.3 The rate of change is dramatically higher in emerging markets for at least two reasons: rapid growth often forces across-the-board identity changes, such as revised postal and telephone area codes; and business practices may be more tolerant of rapid transitions.

 

In any given year, companies will start up and some will fail. According to statistics from the U.S. Small Business Administration, about 670,000 new businesses were created in 2005 (the last year with data available), about 545,000 businesses closed and about 40,000 business filed for bankruptcy. And, mergers and acquisitions change the shape of corporate family trees. According to a recent article in the Financial Times, there were more than 33,000 mergers in 2006 and about 24,000 through the first three quarters of 2007.4 Individuals and households change, too. People marry, divorce, have children, etc. One concrete example of the rate of change for consumers is the U.S. Census Bureau estimate that nearly 14 percent of the U.S. population moved during 2005.5

 

The very nature of customer data complexity and volatility means that you will never achieve perfect data - you must constantly be attacking quality issues because your customer data is deteriorating as you read this column. You can, however, set realistic data quality expectations, budget for ongoing maintenance and broadly share your quality metrics. Your data quality goals should drive improvements in customer data that will ensure your company’s profitable revenue growth within risk and regulatory boundaries.

 

Although you will never completely eliminate all file fragmentation, identifying and eliminating as many multiple instances as possible can help you target your sales campaigns and can reduce the costs of marketing campaigns. You will never have a perfect view of all linked relationships because they are in constant flux, but driving up the quality of your corporate linkage and consumer household information will reveal cross-sell and up-sell opportunities; will convince the customer that you do actually know who they are, which will drive up loyalty on their part; and will permit your company to better manage its risk exposures. With realistic goals, victory is possible.

 

References:

 

  1. D&B internal research, shared with customers in sales and marketing presentations.
  2. D&B.
  3. D&B.
  4. Financial Times, September 28, 2007, Page B1.
  5. U.S. Census Bureau. Geographical Mobility: 2005 to 2006 Detailed Tables, Table 1.

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