B2B CMOs and CIOs: It’s time to rethink data management
Effective data management is critical to B2B digital marketing success. Columnist Sonjoy Ganguly explains how implementing a rich, B2B-focused data structure and collaborating with the right strategic partners can make all the difference.
In digital media and advertising, data management platforms (DMPs) have become an essential tool that “ingests, classifies, sorts and houses information, and then delivers it in a way that’s useful for marketers, publishers and other businesses.”
But for B2B marketers, these tools have a major flaw.
As the name implies, B2B marketers sell into organizations (e.g., General Electric, Boeing, Nestle), not individual consumer demos (e.g., millennial male in New York DMA, $100K+ income, in market for SUVs). Traditional advertising technologies and data management practices, to date, have predominantly focused on managing individuals rather than companies.
That is core to the issue: How do you market to the right companies if existing systems and practices are predicated upon targeting individual consumers? B2B marketers don’t benefit from solutions that allow them to target “soccer moms,” “tech-savvy millennials” or other consumer-based lookalike audiences. They need a much more precise and relevant targeting scheme.
For example, the CMO at an enterprise software company with long sales cycles (nine to 18 months) needs to simultaneously monitor, analyze and surface insights on cross-departmental stakeholders across levels of seniority and diversity, all within discrete organizations. At large corporations, this can include the Financial Analysts, Director of Finance, CFO, VP Engineering, CTO, Architects, Engineering Directors, Procurement Managers and dozens of other business stakeholders, from the first introductory call to a signed contract and through the process of onboarding, as well as utilization.
With B2B marketing and advertising spend reaching $161 billion — and digital accounting for over half — B2B CMOs and CIOs need to dramatically change their data management approach to avoid drowning in irrelevant data and legacy systems that were designed for an entirely different practice. This is only possible by reorganizing their data infrastructure around B2B’s unique go-to-market needs and working with partners that support these new standards.
First things first: Extend company attributes
Basic audience targeting is too broad and is just not an effective mechanism for B2B marketers. B2C marketers now have a seemingly endless pool of data to define target audiences across devices, from historical purchases and behavioral data to location-based visitor arrivals. But ask any B2B marketer, and their targeting parameters look awfully similar to ones found in playbooks from several years ago: industry, company size (employees), revenue (if public) and office headquarters’ locations.
B2B CMOs need to significantly expand the company attributes they track — ones that provide signals that are uniquely relevant to the products and services they sell — and allow sales and marketing teams to communicate more tailored messages by account. These can range anywhere from, “What other software is installed?” and “Which types of solutions are they researching?” to “How much do they currently spend on R&D?”
Once the extended attributes are strategically identified, CMOs must persistently incorporate these new signals throughout their internal systems to keep alignment across teams. These new signals require organizational commitment to adopt new procedures that utilize and act on the signals to improve their marketing effectiveness.
Build out a rich company-user ontology
Once more granular and vivid company-based information becomes ingrained throughout the marketing and sales organizations, it behooves CMOs and their CIOs to further build out a rich ontology. With the exponential growth of data (which also includes more user-generated content than ever before), creating taxonomies to classify and categorize the data is absolutely critical to making these massive unstructured data assets actionable at scale. But the sheer volume and the denormalized nature of this data make it very complex to classify it correctly.