Marketers often make assumptions and misplace value on MQLs. Though useful, they can get in the way of a more evolved view of what an Opportunity truly looks like.

In some ways, MQLs remind me of the oil and gas industry. Gas powered cars are not going away anytime soon, but the emergence of EVs and more eco-friendly forms of transport are making a dent now and will overtake carbon-based transport in the not too distant future. In the meantime, the two will co-exist as the infrastructure of gas stations, car dealerships, car repair shops, etc. begin to shed their skin and become something else — something new.

So why isn’t the MQL evolving to meet modern expectations? How can we get traditional market and sales organizations to start recognizing the EV equivalents of the MQL on our way to something better?

Modern MQLs

Right now, the MQL is hurting organizations in many ways. It’s amazing how so many B2B marketing and sales organizations rely solely on the MQL as the start of their process. This ‘MQL and nothing more’ status quo is limiting what can be seen as a Total Addressable Market — B2B consumer behavior has changed radically, while the MQL has stayed the same since back in the days of bingo cards (before the Web, people actually filled in cards and sent them via mail back to a vendor) and the dreaded BANT (the self-reported gold standard from days gone by, when prospects checked off whether or not they were worthy and qualified based on whether they were actual buyers or not).

Since the MQL is here to stay for the foreseeable future, it is up to us, the community of strategic thinkers and data scientists to present additional forms of qualification that can form the basis of a richer, deeper pipeline. One could (and should) argue that the MQL is getting in the way of seeing 20 – 30% more qualified behaviors that eclipse those represented by an MQL. Because we now have available a plethora of new and scientifically sound signals based on behaviors that can be combined with the MQL or go beyond it to create a non-MQL way of qualifying accounts. In the next installment of this blog series, we will cover this concept of “beyond the MQL”.

For now, I would like to focus on the biggest assumption made when driving pipeline through a MQL-only approach. That assumption is that the MQL represents the beginning of a marketing/sales process as it relates to pipeline/opportunities. Unfortunately, it doesn’t. The MQL is both the end of the beginning, and the beginning of the end of the process.

The End of the Beginning

We all know that anyone willing to give up their anonymity and actually fill in a form has hit a wall and now must surrender to the process. The goal of the B2B buyer/researcher is to remain anonymous for as long as possible and do as much research as they can outside of a vendor website or even vendor branded content. There are a number of reasons people finally fill in forms on a vendor website, all of them suggest they need help of some kind:

  • They need a quote
  • Need to talk to a subject matter expert to get a deeper understanding of issues like technical integration, the need for scale in infrastructure, and bandwidth
  • Wanting information about an evaluation period or POC
  • On their way to evaluating if you should be in their consideration set for the RFP or project under consideration

The good news is that you have smoked them out into the light of day. The bad news? They are about 50 – 70% into their buyer journey. Herein lies one of the major moments of breakage in the marketing/sales process.

One critical use case of Buyer Intent and/or Behavioral Technographics data is the ability to go back in time and view when an account actually started their research journey.  Depending on your product/solution that could be anywhere from 2 – 18 months or beyond.  We can use the buyer journey by looking at the time and intensity the account has spent researching.

Let’s look at real example:

You can see what each swim lane represents by the label to the right. Starting from the bottom up we have:

  • How many people from the account have come to the site
  • In this case, how many ads were served to this account (this can also represent the number of people coming from any and all campaign activities)
  • The total number of UNIQUE devices involved in the research journey
  • The actual date that this account became an opportunity based on an MBL being submitted
  • The overall INTENT score given to this account over time

On the right-hand side, you will see:

  • How the Opportunity was labeled when it was opened in Salesforce (the info box that is overlaid on top of the swim lane showing the actual OPEN date
  • And on the below right, the various categories that the account was researching

I am now going to ruin the suspense by telling you this client LOST this opportunity.  But I am going to tell you why:

By not using the behavioral signals available, the client could not see all the research that was done leading up to the week of October 28th, the moment of MQL. Because the MQL is being treated as the beginning of the beginning, so to speak, the MQL is assigned a 10% probability in Salesforce. Based on this, two bad things are about to happen (and is the direct reason why they lost this opportunity):

  • Based on their behavior, this account is going to buy from someone in the next 6 – 8 weeks, or at least prepare an RFP
  • Because the MQL was placed into Salesforce as a 10% probable opportunity:
    • None of the context and intelligence from the buyer journey signals makes it way into the opportunity — there is a lack of urgency and a lack of context
    • This opportunity was not assigned, and no further relevant communication took place that you would expect when an opportunity is well down the sales funnel

How many of these misconstrued and misaligned MQL’s do you have in your pipeline? How many times do you get surprised that a 10% probable opportunity closed well before what you perceive as a normal time to sales?

Here is a great opportunity to begin using behavioral intent signals to flesh out, correct and redefine where an account is in its research journey.

There are quite a few other examples of MQL misalignment.  I could go on — suffice it to say — there are so many ways to optimize the traditional marketing/sales funnel by supporting the MQL with modern, scientifically sound data sources.

To learn more, listen to TechConnectr’s ABM Thought Leaders interview with Charlie Tarzian and tuned for the next blog in the Aberdeen Intent Data Series.

Always happy to take questions and thoughts: charlie.tarzian@aberdeen.com