7 Data-Driven Reasons Why B2B Demand Gen Fails 2. THE DATA ABOUT THESE ENORMOUS NUMBERS IS FLAWED To deal with big numbers like the above, you need Big Data. Big Data helps you paint a mathematical picture of what your B2B environment looks like. Unfortunately, most of the widely available B2B data elements are deeply flawed. The three most prevalent data elements used to build B2B lead lists are industry codes, company headcount, and top line revenue. These three elements are flawed in not only the way they are designed and collected but also in the way that they are used. The top 3 US-based industry coding schemes are SIC codes, NAICS codes, and “home-grown” taxonomies, such as the one used by Linkedin. Each of these has serious design limitations. First and worst, the logic used to assign codes lacks rigor and consistency. Second, these coding schemes lack a reliable way to measure the relative similarity or difference between industry codes. Third, there are no incentives for companies to verify the accuracy of their own data and no central system within which to operate. Therefore, industry codes are frequently vague or simply incorrect. Headcount and revenue data suffer similar woes. First, category bins are created with arbitrary ranges that limit precision. Second, as we saw with industry codes, there is no system to verify data although the government does provide some degree of pressure to induce accuracy. Therefore, headcount and revenue outside of publicly traded companies are generally inaccurate or outdated. Despite these limitations, B2B companies still rely heavily, if not exclusively, on these three data elements to extract data for the top of their sales funnel. The three most prevalent data elements used to build B2B lead lists are: ● industry codes ● company headcount ● top line revenue These three elements are flawed in not only the way they are designed and collected but also in how they are used. www.leadcrunch.ai 4 www.leadcrunch.com