Three B-to-I Data Pitfalls to Address
April 8, 2009 By Hallie Mummert, Editor-in-chief, Target Marketing
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When working with B-to-B databases for campaigns targeted to institutions—such as schools, hospitals and churches—marketers must be prepared to fix some gaps, tidy up some inaccuracies and work with their database marketing services firms to properly match records, notes the white paper, How Data Quality Issues Affect B-to-B Marketers and Modelers. Written by John F. Hood, president of MCH Inc., a compiler of business-to-institution databases and lists, the white paper details three key data challenges and solutions:
1. Firmographics collected on B-to-B firms often aren't accurate value measures for institutions. For example, Hood points out, number of employees is a critical piece of sales information for most B-to-B marketing needs. But employee counts for churches typically don't reflect the high percentages of volunteer workers, which can distort your perception of the size of the organization. Supplement this data, where possible, with specialty database information.
2. SIC Codes also aren't a perfect fit with institutions. When the Standard Industrial Classification system was developed in the 1930s, according to Hood, it was oriented more to B-to-B firms than institutions. As such, he explains, just 5 percent of defined SIC codes exist for institutions, while these organizations represent about 33 percent of the economy. If data categorized according to the North American Industrial Classification system is available, it's preferable to files segmented by SIC code. NAIC is not fool-proof, so be prepared to do some analysis to further enhance your files.
3. Inter-related institutions can have similar names as well as share physical and/or mailing addresses, making it tough to match records for modeling. Leverage your sales force or other internal knowledge source to determine which data attributes might help distinguish institutions that appear similar. You also can minimize your risk by prioritizing the institution types by your "company's sales strategies or product profile," Hood writes. "Let's say you know that both churches and childcare centers are on your customer file. You also know that you spend much more on marketing and sales efforts on the churches and that childcare [center] customers are more or less an afterthought. Your vendor can use that knowledge to prioritize the matching so that churches are matched before childcare [centers]. There may still be errors, but the errors will be skewed towards your priorities and therefore will be less damaging."
To learn more about institutional data, visit MCH Inc.'s resource center on its Web site.
1. Firmographics collected on B-to-B firms often aren't accurate value measures for institutions. For example, Hood points out, number of employees is a critical piece of sales information for most B-to-B marketing needs. But employee counts for churches typically don't reflect the high percentages of volunteer workers, which can distort your perception of the size of the organization. Supplement this data, where possible, with specialty database information.
2. SIC Codes also aren't a perfect fit with institutions. When the Standard Industrial Classification system was developed in the 1930s, according to Hood, it was oriented more to B-to-B firms than institutions. As such, he explains, just 5 percent of defined SIC codes exist for institutions, while these organizations represent about 33 percent of the economy. If data categorized according to the North American Industrial Classification system is available, it's preferable to files segmented by SIC code. NAIC is not fool-proof, so be prepared to do some analysis to further enhance your files.
3. Inter-related institutions can have similar names as well as share physical and/or mailing addresses, making it tough to match records for modeling. Leverage your sales force or other internal knowledge source to determine which data attributes might help distinguish institutions that appear similar. You also can minimize your risk by prioritizing the institution types by your "company's sales strategies or product profile," Hood writes. "Let's say you know that both churches and childcare centers are on your customer file. You also know that you spend much more on marketing and sales efforts on the churches and that childcare [center] customers are more or less an afterthought. Your vendor can use that knowledge to prioritize the matching so that churches are matched before childcare [centers]. There may still be errors, but the errors will be skewed towards your priorities and therefore will be less damaging."
To learn more about institutional data, visit MCH Inc.'s resource center on its Web site.
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