In the world of B2B lead gen, the bickering between enterprise B2B marketing and sales organizations rarely ends. Each points the finger at the other’s shortfalls: Sales complains that “these leads are not what I need; I need buyers with intent,” and marketing responds “Why can’t you just do a better job at closing the leads we send you?”
The reality, of courses, is that both are somewhat off-base in their accusations. Despite their complaints about value, sales is actually receiving high quality leads and is doing a good job closing them. The real problem is the volume of qualified leads. Even for best-in-class companies, according to SiriusDecisions’ statistics, only 10% of all inquiries become marketing qualified leads (MQLs) and if yours is an average performing company, a little under four percent of your demand generation activities deliver as promised.
Reputation and inquiry attraction costs money—and lots of it. When you look at the throughput from an inquiry-to-deal perspective, marketing manages a top-of-the-funnel process in which at best, 96% of their inquiries never close and therefore never help the company. For average performing companies, the carnage rate, or inquiries that don’t close, is an astounding 99%, according to SiriusDecisions. All of this misunderstanding, fuels the fire in which B2B sales leaders seek more support for personal interaction type B2B lead gen activities that undermine marketing’s requests for increases in demand generation budgets. After all, why support a process for which most of what is created, ends up in the trash?
In an environment in which most B2B purchasing begins on the web and where data is the foundation for understanding customer life stages and the delivery of relevant interaction, B2B until recently has had some real challenges. Whereas their B2C counterparts were drowning in rich transactional data that drove effective customer development, B2B teams were struggling to make the data they had barely useful. B2B had explicit, but fictitious self-reported BANT (Budget, Authority, Need and Timing) and buyer profile data co-mingling with static bizographics that together provided no reliable prediction for the buyer intent that sales demanded.
Adding to the challenge was the fact that in-vogue marketing automation tools like Marketo relied on rules-based scoring schemes designed by sales and marketing teams to both inform nurture campaign design and then score the prospects that became the leads that marketing handed off to sales. These scoring results were, in reality, an intuitive outcome dressed in a scientific guise that promised a lot more than it delivered.
We are now effectively mining the entire web. With the advent of DMPs, access to massive stores of media data and advances in predictive analytic functionality in tools like SAS, relative newcomers R and Python, as well as embedded functionality in marketing automation tools like Eloqua and Marketo, the prospects for greater expectations with regard to B-to-B customer profiling and scoring is flourishing. What is happening is that big data scraped from the web is being combined with “owned” email interaction and account-based in contact level cross web activity thereby creating a new kind of transactional currency for B2B. Though not quite as powerful as the traditional sales and e-commerce-based transactions used in B2C, the new data stores available to B2B marketers have enabled something pretty close to predictive intent.
