True confessions of a CMO, part 2: I cheat

In my last article, I shared my true confession — I’ve never really cared about MQLs. I explained some of the issues an overreliance on MQLs can cause, and why I believe there’s a better way to measure not only the health of your pipeline but also the effectiveness of your marketing and sales teams.

OK, so about my cheating…I wasn’t always a cheater. Like many of you, I once tried to untangle the marketing metrics hairball by scoring and analyzing everything from buyer intent, to lead and account strength, to attribution, to MQL-to-SQL conversion rates (Read more...) I was TOFO, MOFU and BOFU’d to death!

The truth is, I don’t want to deal with a data lake, or pay for dozens of tools and data sources, or stitch together twenty-five point solutions, or hire a team of data scientists so I can impress everyone with my cascading waterfall of conversion graphs.

Even if we do these things, sales still ends up giving the stank eye since they want more relevant and meaningful account insights.

What are we missing between marketing and sales?

We partnered with Heinz Marketing to find out what’s holding back companies as they engage customers and predictably grow revenue.

One finding, while not incredibly surprising, stood out: competing metrics still cause misalignment between sales and marketing. Marketers lean on MQLs, while sales turn toward more qualitative measures like account and persona engagement, deal velocity and accounts in-market.

Which of these metrics do you currently track and report on?

A lot of this split is historical. Marketing has owned the top of the funnel — awareness, brand positioning and lead gen. Sales had education, nurturing and closing deals.

But these lines are now blurred. Marketing is in charge of WAY more than before, especially around the buyer experience. Marketing and sales need to be aligned on a set of common measures if they want to predictably grow revenue.

So, I cheat. Instead of using arbitrary and subjective scoring against an ever-moving target (my assessment of MQLs), I use AI to decide what constitutes a good account or lead for sales to “work.”

At 6sense, we drink our own champagne: we use the same AI-based predictive analytics in our platform as we offer our customers.

As a result, we don’t have to guess at what makes an account worth going after. Nor do we have to get into any subjective wrangling with the sales team about the “best” accounts.

We call it the 6QA (6sense Qualified Account), and when an account “6QAs,” sales is all over it. The 6QA brings process and science to lead and account scoring, which to date has been largely a rules-based exercise based on subjective human judgement.

We merge historical opportunity data with these scoring criteria:

• Account Profile Fit Score – A measure of how similar a company is to your ideal customer profile (ICP), using firmographic and technographic factors.
• Contact Profile Fit Score – A measure of how similar a person is to your typical buyer or buying committee members using demographic factors.
• Contact Engagement Score – A measure of how engaged a person is with your first party sales and marketing tactics compared to previous buyers using engagement activities.
• Accounts In-Market – The Holy Grail: a measure of the commercial opportunity available to your company right now based on accounts matching your ICP and currently in-market for your products or solutions.

There’s no arbitrary point system — the scores are driven by Big Data and AI predictions. Subjectivity is out of the equation (no more, “I think an eBook download is worth 4 points, and sales thinks it’s worth 2 points”), and the scores are “back tested” to prove the algorithms work. Again, no debating — it’s just math.

What about measuring the top of the funnel?

To understand what’s happening “pre-pipeline,” I set my sights on getting accounts through the prospect journey and to 6QA — or, more simply put, move accounts “in-market” for our solution so sales can more effectively engage.

Again, this process requires a deep understanding of where the prospect is in the buying journey — which 77% of buyers feel is a complex process — and exactly what’s needed to move them toward opening an opportunity.

You need to account for every intent signal of the entire buying team (anonymous and known, across first and third party interactions); which personas matter and are engaged or need to be; and the ability to predict not only where each is in their own unique buyer journey but also the next best action to take to move the account.

Organizing our funnel using the traditional “demand gen waterfall” (target demand, to MQL, to SQL) may help marketers sleep at night. But honestly, the waterfall really isn’t much more than a security blanket in today’s B2B buying environment.

I can hear you asking, “How do we ensure leads or accounts in-market get turned into pipeline and ultimately ‘worked’ if there isn’t an MQL to SQL handoff?”

Beyond the 6QA, predictive analytics related to buying stage offer full funnel insights to inform and coordinate my marketing and sales activities. We also track activity in dynamic segments, where accounts automatically progress and regress through the buying journey based on behavioral intent.

Together, this lets us trigger the right campaigns and activities so our BDRs, sales, and customer support teams can engage accounts at the exact right time using the right channel and the right message. All the while, marketing can provide appropriate aircover, content and tactics.

Some tactics we orchestrate with sales to engage accounts through their buying journey include:

• Awareness Stage: Accounts are beginning to show signs they realize they have a problem
  • Tactic: Trigger display ads to ensure the prospect connects our brand to a keyword or topic they care about.
• Consideration Stage: Accounts want to learn about potential solutions to an identified problem
  • Tactic: Personalized content hub based on keywords the account cares about and trigger automatic account enrichment to build out the buying team.
• Decision Stage: Accounts are still researching and learning but open to engaging — this opens the window of opportunity to engage
  • Tactic: Trigger BDR outbound cadences: a 21-part series of personalized, relevant and high-value communications based on the prospect’s role, need and areas of interest.
• Purchase Stage: Accounts are narrowing the field and looking to make a decision
  • Tactic: BDR secures a meeting using cadences, but if not, trigger direct mail.

Of course, there are an endless variety of triggers and tactics you can deploy based on the campaign, budget and experience you want to deliver.

The point is, the buyer’s behavior informs the AI to orchestrate the tactics — based on buying stage — that will drive engagement across sales and marketing.

So, I confess: I let AI make decisions on what accounts are worth pursuing, ready to engage, likely to buy from us, and then the best actions to take.

However, I have one more confession THAT MAY SHOCK YOU! OK, maybe a little click-baity, but in my next article, I’ll confess my true feelings about marketing sourced pipeline and revenue targets.