B-to-B Prospecting Data Just Keeps Getting Better

The most reliable and scalable approach to finding new B-to-B customers is outbound communications, whether by mail, phone or email, to potential prospects, using rented or purchased lists. B-to-B marketers typically select targets from prospecting lists based on such traditional variables as industry, company size and job role, or title. But new research indicates that B-to-B prospecting data is much more detailed these days, and includes a plethora of variables to choose from

The most reliable and scalable approach to finding new B-to-B customers is outbound communications, whether by mail, phone or email, to potential prospects, using rented or purchased lists. B-to-B marketers typically select targets from prospecting lists based on such traditional variables as industry, company size, and job role or title. But new research (opens as a pdf) indicates that B-to-B prospecting data is much more detailed these days, and includes a plethora of variables to choose from—for refining your targeting, or for building predictive models—to pick your targets even more effectively.

My colleague Bernice Grossman and I recently conducted a new study (opens as a pdf) indicating that B-to-B marketers now have the opportunity to target prospects more efficiently than ever before. In fact, you might say that business marketers now have access to prospecting data as rich and varied as that available in consumer markets.

To get an understanding of the depth of data available to B-to-B marketers for prospecting, we invited a set of reputable vendors to open their vaults and share details about the nature and quantity of the fields they offer. Seven vendors participated, giving us a nice range of data sources, including both compiled lists and response lists.

We provided each vendor with a set of 30 variables that B-to-B marketers often use, including not only company size and industry, but also elements like the year the company was established, fiscal year end, Fortune Magazine ranking, SOHO (small office/home office) business indicator, growing/shrinking indicator, and other useful variables that can give marketers insight into the relative likelihood of a prospect’s conversion to a customer. We learned that some vendors provide all these data elements on most of the accounts on their files, while others offer only a few.

We also asked the participating vendors to tell us what other fields they make available, and this is where things got interesting. In response to our request for sample records on five well-known firms, the reported results included as many as 100 lines per firm. Furthermore, two of the vendors, Harte-Hanks and HG Data, supply details about installed technology, and their fields thus run into the thousands. The quantity was so vast that we published it in a supplementary spreadsheet, so that our research report itself would be kept to a readable size.

Some of the more intriguing fields now available to marketers include:

  • Spending levels on legal services, insurance, advertising, accounting services, utilities and office equipment (Infogroup)
  • Self-identifying keywords used on the company website (ALC)
  • Technology usage “intensity” score, by product (HG Data)
  • Out-of-business indicator, plus credit rating and parent/subsidiary linkages (Salesforce.com)
  • Company SWOT analysis (OneSource)
  • Whether the company conducts e-commerce (ALC)
  • List of company competitors (OneSource)
  • Biographies of company contacts (OneSource)
  • Employees who travel internationally (Harte-Hanks)
  • Employees who use mobile technology (Harte-Hanks)
  • Links to LinkedIn profiles of company managers (Stirista)
  • Executive race, religion, country of origin and second language (Stirista)

Imagine what marketers could do with a treasure trove of data elements like these to help identify high-potential prospects.

Matter of fact, we asked the vendors to tell us the fields that their clients find most valuable for predictive purposes. Several fresh and interesting ideas surfaced:

  • A venture capital trigger, from OneSource, indicating that a firm has received fresh funding and thus has budget to spend.
  • Tech purchase likelihood scores from Harte-Hanks, built from internal models and appended to enhance the profile of each account.
  • A “prospectability” score custom-modeled by OneSource to match target accounts with specific sales efforts.
  • PRISM-like business clusters offered by Salesforce.com (appended from D&B), which provide a simple profile for gaining customer insights and finding look-alikes.
  • “Call status code,” Infogroup’s assessment of the authenticity of the company record, based on Infogroup’s ongoing phone-based data verification program.

We conclude from this study that B-to-B prospecting data is richer and more varied than most marketers would have thought. We recommend that marketers test several vendors, to see which best suit their needs, and conduct a comparative test before you buy.

Readers who would like to see our past studies on the quality and quantity of prospecting data available in business markets can access them here. Bernice and I are always open to ideas for future studies. We welcome your feedback and suggestions.

A version of this article appeared in Biznology, the digital marketing blog.

Author: Ruth P. Stevens

Ruth P. Stevens consults on customer acquisition and retention, and teaches marketing at companies and business schools around the world. She is past chair of the DMA Business-to-Business Council, and past president of the Direct Marketing Club of New York. Ruth was named one of the 100 Most Influential People in Business Marketing by Crain's BtoB magazine, and one of 20 Women to Watch by the Sales Lead Management Association. She is the author of Maximizing Lead Generation: The Complete Guide for B2B Marketers, and Trade Show and Event Marketing. Ruth serves as a director of Edmund Optics, Inc. She has held senior marketing positions at Time Warner, Ziff-Davis, and IBM and holds an MBA from Columbia University.

Ruth is a guest blogger at Biznology, the digital marketing blog. Email Ruth at ruth@ruthstevens.com, follow her on Twitter at @RuthPStevens, or visit her website, www.ruthstevens.com.

3 thoughts on “B-to-B Prospecting Data Just Keeps Getting Better”

  1. I’ve been in this industry for more years than I care to mention, so I remember when name and address was about all you could get when renting a business or consumer mailing list. The amount of information available today is truely astounding.

    However, while the "plethora of variables" to choose from is great, it doesn’t help when the data is dated and obsolete.

    Recently, I had a client use a B-to-B mail file (from a well known and reputable compiler who I won’t mention) for a mailing to small businesses followed up by telemarketing. The telemarketers reported back that the information provided was poor at best. From wrong telephone numbers, to a contact person was no longer there, to closed businesses and so on.

    While the information for public companies is often accurate, private (small) business data is a real problem. My compiler told me there are something like 40 million businesses in the US, and that their call center can only contact around half of them every year.

    Additionally, there are a lot of businesses that can’t be reached for verification, and many who refuse to give out or confirm information. Yet, the list compilers keep renting the old data.

    All of these selection options are great, but only if they are accurate.

    By the way Ruth, love the title for your posting ("Ruthless B-to-B Marketing")!

  2. Perhaps the biggest influence on B2B prospecting has been the use of modeling with different types of models and database participation. Whether its participation in a database like Meritbase or contributing your data to a business coop from Abacus or I-Behavior, b2b mailers have the ability to not only use traditional b2b firmographic data but combine that with actual dm purchase behavior. And the power of these databases include various buyer reactivation and cross sell modeling opportunities.

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