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.
Carnage rates stifle demand generation investment
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.
Big Data, the New Analytic Currency
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.
Mirroring the direct marketing adage regarding campaign success and the relative value of targeting, offer, and creative, leveraged data has become the new king for marketing in the digital age. When combined with emerging technologies for people-based customer engagement, data and engagement management are leading a revolution in segmentation and journey planning. Intuitive rules are being replaced with scientific methodologies and machine learning, which, along the way, will also empower an efficient, data-driven account-based marketing process.
We believed we had moved beyond what Forrester had termed the “hype cycle” for predictive analytics to a place where accurate prediction of prospect intent and life stage would improve interaction engagement and lead quality. Analytic teams had developed a marketing ecosystem that connected self-reported facts about individuals and the company they worked for with digital campaign interaction, web social factors and financial reporting behavior. This evolution had enhanced marketing automation, smart search and display, and web personalization outcomes thereby enabling marketing to provide sales with a volume and quality of leads that they would relish… or at least that was the plan.
Problems in Paradise
After all, what could be known, was now known … and mined. Marketing was evolving to a more scientific approach that layered explicit and implicit data plus target interaction to create strong surrogates for buyer intent. However, the promise of a new world order has been dashed; look-alikes apparently do not reveal true intent. What marketing saw as hope, sales still perceived as hype. In fact, a 2016 survey from eMarketer indicated that just 28 percent of sales leaders were satisfied with marketing’s scored leads, while 35 percent were dissatisfied and another 37% did not even know or care to know if scored leads performed any better than what they were getting before.
“Now” … A Tipping Point
Despite the previous failings of analytic breakthroughs to placate sales with the leads that they want, it looks like we have hit a tipping point. Digging deeper into bigger data, new insights are moving us beyond fit to the best customer look-alikes that are in the market now; and in doing so they are delivering reliable versus predictive buyer intent.
What has changed? Advanced predictive analytic schemes are leveraging observations from a company’s own site and engagement interactions with individual and account-based behavior across the web, revealing what individuals are searching for and what competitive site interactions they are having now. The outcomes of contemporary predictive analytics today can tell us who is searching on which terms for products that you sell, which of your customers are visiting competitor pages for the categories of products and services you sell to them today and what the pace of buyer interaction is. The analytic enterprise and the agency support they rely on as well as niche players like 6Sense, Lattice Engines and Mintigo are proving every day that they can accurately predict buyer life stage and future revenue.
Why You Should Care
In an environment where most buyers do not raise their hands and engage with sales folks until short lists are about to be approved, knowing about now changes everything in so many ways. Consider these scenarios-
- Customer Acquisition- Aggregated competitive interaction can tell you that a prospect company you had scored in the middle of the funnel is now in the later stages of the decisioning process. Therefore, sales can and should more readily engage.
- Nurture- The incremental data overlays provided by companies in this space can be used to make your marketing automation messaging more relevant thereby driving greater engagement.
- Scoring- Knowing that interaction has increased or decreased can change the scores that trigger the hand-off from marketing to sales which should improve both marketing qualified leads and sales acceptance rates.
- Opportunity Prioritization- The opportunity that was placed on the back burner of an overwrought sales person can move to the front burner based on behavior observed now.
- Cross and Upsell- The great dilemma- how soon to reengage with a recent, new customer. How about when you notice that they are looking at your competitors’ sites for the same solutions you have, but have not pitched yet.
- Retention- Predictive analytics can alert you when your best customers are searching for and shopping the competition for solutions you have provided to them.
It is not the ingredients versus the chef that make a great meal, it is the ingredients and the chef that make a great meal. The data are there in abundance, the tools are available to mine it, and the analytic capability is there to understand and leverage it. The answer is big data and predicative analytics combined. Making that a great meal requires the real-time execution of now.