What Does the Subprime Mortgage Crisis Have to Do With B2B Marketing?

The current real estate boom is off the charts, with home prices ballooning 17% year over year, inventory at record lows, and stories of mind-boggling bidding wars making news in markets across the country.

Maybe that’s what got me thinking about the last housing boom — and the catastrophic crash that followed. As I was recalling how that house of cards came tumbling down, I couldn’t help but connect the dots between that fiasco and the current state of B2B marketing.

That crash — which we all know now as the subprime mortgage crisis — was preceded by an about-face in how borrowers qualified for loans. Before the early 2000s, getting a mortgage required reliable income, a hefty downpayment, and a respectable credit score that gave banks reasonable confidence they’d be repaid.

But that all changed when banks started lending to less-qualified borrowers without requiring any trustworthy proof of ability to pay. Around three-quarters of these mortgages were adjustable rate — meaning that even if the borrowers could afford the initial payments, they were much more likely to default when the interest rates ballooned.

The subprime mortgage crisis was, of course, a wake-up call for everyone involved. But from my vantage point, it’s a cautionary tale for all of us — including those of us in B2B marketing. Here’s why.

Our Own House of Cards

While there’s plenty of blame to go around with the mortgage crisis, the crux of the problem was the use of faulty data to make big bets. At every level, the individuals and institutions involved in creating the crisis accepted bogus information and ran with it — paystubs written on the backs of napkins, workarounds for cruddy credit scores, shoddy bond ratings from credit agencies with too much skin in the game. They allowed this useless data to inform decisions that would change the world.

And right now, in B2B marketing, we’re making the exact same mistake. Just like how the big banks made big bets on so-called “qualified borrowers” who were anything but, marketing and sales teams put all their money on “marketing qualified leads (MQLs)” that, if we’re being honest, aren’t necessarily qualified. And just like in the subprime mortgage scenario, there are stakeholders at every point in the process who should be raising red flags and asking, “Wait, is this legit?”

The answer, from my vantage point is no, not anymore. It’s understandable that we got to this point: We’ve been trying to meet ambitious goals with limited information. But given the exponential leaps we’ve seen in data and technology in the past several years, it’s time to rethink the MQL and what place, if any, it has in measuring marketing’s success, forecasting revenue, and making business decisions.

The fact is that the entire MQL process is subjective, open to manipulation, and, most importantly, not based on how modern buyers buy. To understand where we’re going wrong, I want to shine a light on how the system for designating MQLs works.

While the process varies from organization to organization, designating MQLs generally involves a lead-scoring system designed by the marketing department, sometimes in coordination with sales. The system takes into account an individual’s known activity — in other words, the times they’ve raised their hands to tell us they’re interested in what we have to offer. If someone downloads an ebook, they’ll be assigned X points. Shared their email address at a trade show? That’s worth Y points. Opened an email? Here’s a point for that.

When an individual has accrued enough points, we dub them “qualified” and send them over to sales, who then pour their resources into trying to get them to buy.

But the problem is modern B2B buyers don’t make purchasing decisions the way they used to (more on that in the next section). And technology has advanced so much in the past few years that we now have access to insights we never could have imagined when we began putting our faith in MQLs.

So while the MQL system might have once been the best we could do, that’s not the case today. What we all want — optimized sales and marketing efforts that weed out junk and surface the best accounts that are ready to buy — is possible, but not with the system we’ve all become accustomed to.

We Need Data-Informed Selling & Marketing Instead

It’s little wonder that, using the MQL system, we keep seeing our predictions fail, sales and marketing are at each other’s throats, and our big bets don’t pay off as often as they should.

It’s time for the MQL house of cards to tumble. We have the opportunity to bring it down, and to replace it with a strong, sturdy structure that will deliver predictable, reliable revenue. Here’s how we need to shift our thinking if we want to build a marketing approach built on real data, instead of flimsy guesswork.

Understand how modern buyers buy, and what it means for B2B go-to-market teams

We know three important things about today’s B2B buyers that make MQLs useless: they’re anonymous, they’re fragmented, and they’re resistant.

Modern buyers prefer to remain anonymous.

If we’re only attributing value to activities where people raise their hands and make themselves known, we’re oblivious to loads of potential customers who are actually in-market to buy. Modern buyers prefer to remain anonymous until very late in the buying journey, meaning they do most of their research before they ever make themselves known to a seller. In fact, some research points to the fact that B2B buyers get through 70 percent of their buying process without ever speaking to a sales person. That means that a lot of our opportunity to engage with buyers passes before we even realize they’re potential customers for us.

We call this the Dark Funnel™ — the place where buyers are engaging in all the activities that will eventually influence whether and what to buy (and from whom). And all this happens without our knowledge, meaning we’re powerless to influence it if we’re waiting for people to fill out a form or sign up for a newsletter. The fact is there’s gold in the Dark Funnel™, but the MQL model has no way of pointing us toward it. So we’re missing out on great accounts to sell to, all while waiting for “leads” to make themselves known to us. When we have the technology to uncover intent signals in the Dark Funnel™ — not just on our own websites, but also on third-party websites — we’re no longer beholden to the MQL.

Another problem with the MQL system is that all bets are placed on individuals. But we know that in B2B, purchases are made by buying teams of 10 or more people. MQLs keep us operating under the outdated premise that we can be successful by engaging with just one individual, in what’s called a single-threaded deal. But in order to be successful, marketers and sellers need to be multi-threading their deals, meaning they need to act on insights about the entire buying team. Likewise, marketing should be trying to engage as many members of a buying committee as possible. And that takes account-based insights that aren’t possible with the MQL model.

Modern buyers are resistant.

The third important quality of modern buyers is that they’re resistant to sales and marketing efforts, making it harder than ever to get them to engage. When I started in marketing, a robust campaign had eight to 10 touchpoints. Today you need multiples of that to get buyers to take notice. In fact, Salesloft found that multiple touchpoints in a single day results in 2.9x more prospect engagement. So that handoff between sales and marketing, where we say, “Here’s a bunch of qualified leads. Good luck!” won’t work anymore. Modern B2B buying teams need to be proactively engaged throughout the buying process and beyond — so marketing needs to keep up the work, even after determining that an account is showing signals of readiness to buy.

Putting these insights into action

The shift in buying is not just now occurring. It has already happened. We in B2B sales and marketing have a choice to make: We can either watch our numbers take a nosedive with a system that’s already been failing us, or we can raise the red flag and commit to building a new foundation. This new foundation is not lead-based. It starts with high standards for data and technology so we can start unifying sales and marketing around meaningful, results-focused insights.

With a unified approach to data and technology, we take data out of silos and make it possible for everyone on the revenue team to align around the same story — the same account insights, the same view of account engagement, and a common understanding of marketing and sales efforts. Instead of having everyone view the customer (and potential customer) experience from their own little keyhole of data, the whole team can coalesce around a more surgical approach.

This is, in large part, possible because of advances in artificial intelligence (AI). Unlike the current system of scoring leads, AI is not arbitrary or points-based. It’s simple math, without hunches and preconceived notions overlaid onto it. AI is always on, and it gets smarter over time. And with it, sales and marketing teams can get clean, unadulterated insights that actually make a difference in revenue — things like ideal customer profiles, buying team persona models, behavior and keyword insights, and optimum timing scenarios.

All of these insights have a direct effect on marketing and sales activities. And in this new, unified approach, all the execution channels we use to engage potential buyers (think email, ads, direct mail, chat on website, sales outreach) can all be seamlessly, consistently orchestrated as well. But none of this is possible until we let go of the MQL model.

Just like those loans made to unqualified borrowers were the catalyst for failures across the entire mortgage industry, junk metrics like MQLs are the genesis of unpredictable pipeline, poorly performing sales teams, and misaligned expectations. Whether you’re a sales or marketing leader, a CEO, an investor, or just someone who’s interested in business success, it’s time to say goodbye to junk metrics like MQLs.

Instead, place your bets on a data-driven model that sets your entire revenue team up for success — starting with accounts with a higher propensity to buy, finding champions on the buying team, and honing in on an account’s activity. It’s with those insights, not MQLs, that you’ll be able to engage accounts with the right message and tactic at the right time, instead of throwing money and effort at leads who were never going to buy in the first place.

It’ll be a shift, for sure, but here’s the good news: You’re not only going to have more reliable information to work from, you’re also going to start thinking about your end goals in a new way. Instead of obsessing over useless leads, you’ll start to instead focus on outcomes that matter — like predictable month-over-month revenue, better average sales prices, and decreased cycle times.

Those are the things that will have the biggest multiplier effect on your revenue, and with the right foundation, they’re all within reach.