“So what *exactly* goes into defining the MQL?” our new SDR asked.
And extremely concerned.
By the tone of his voice, I could tell there was more to the story.
I explained that our MQLs were based on the usual - FIT + INTENT - but we’re using a few tools to enrich the user data and proactively find the technographic fit, the demographic fit, role, and then grade and score accordingly.
INTENT matters a ton, and they have to be pretty engaged in our blog, website, and content to meet the score requirements.
“Wow. That’s way better than what we did at
company name omitted,” he said.
“MQLs were a completely different story at
company name omitted. We had a mass email database - like 60,000 people - and the data was only half correct.
“There were emails like
email@example.com and we used to just guess who it was. Marketing would email blast to certain segments - mostly based on industry and location, but if the recipients opened the emails or clicked on just one thing, they MQL’d. Then it would turn over to Sales as a round-robin.”
“Everyone hated those MQLs. The conversion rate is like 4% into a demo. Sales close rate is like 28%. You do the math.”
I hate to admit it, but this is not the first time I’ve heard Sales complain about poor quality MQLs.
The marketing qualified lead was created to signify both the handoff of a marketing-generated lead to sales while also highlighting the diamonds in the rough.
If someone had the right FIT and showed some amount of INTENT, then they would “MQL”, pass on to Sales, and from there, either the prospect was ready to talk to someone, or they’d get nurtured until they were.
It sounds incredibly simple - I promise you it’s not. But if marketing does its job really well, it can be crystal clear and make Sales insanely happy.
Oz Content, a content marketing platform, was able to increase their marketing-sourced qualified leads by 20x - that’s 2000% - and 4x their total volume of their MQLs using these exact methods.
This post covers exactly how to do that - make MQLs that makes Sales insanely happy. But the answer might surprise you.
I’m not going to propose you simply send more emails, do more drip campaigns, and up your retargeting spend.
I’m going to suggest you dig deeper - and that starts with Sales, the data, and the very definition of marketing qualified lead in the first place.
Review the Sales Team’s Exact Prospecting and Sales Process
You’ll read a lot out about “marketing and sales alignment”, and I want to take a quick second to demystify it.
If someone hasn’t spelled it out directly, alignment means having an actual conversation with - surprise - the Sales team.
And no - it doesn’t have to feel like some painful, teeth-pulling process. Approach the conversation with an open mind and a willingness to improve. I promise it’ll go great.
Here’s some questions to ask your top closers (account execs, VPs, etc.) and spears (outbound prospectors, SDRs, inbound sales leads, etc.):
For the Spears:
- When you receive a lead or find a company to prospect, who do you typically look for? And why?
- What other information do you typically look for to determine if they’re a good fit? Location, size of the company?
- What tools are you using to qualify? Clearbit? Datanyze? What information are you getting from these tools to help you decide?
- How are you disqualifying leads?
For the Closers:
- Who signs the deal / contract when opportunities close?
- What do amazing opportunities look like? What about them makes them “easy” or “seamless” to close?
- What do terrible opportunities look like? What are the red flags that pop-up during the sales cycle?
The feedback and insights you’re going to get are just one piece of the puzzle to make Sales love your MQLs.
You’re essentially opening up the door to a real feedback loop - which is an amazing start. Next, you’ll need to take the information you just heard and translate it into something your MQL process can actively adjust to.
If you’re like us - a B2B SaaS company - there’s a huge chance that a specific set of demographics, technographics, and behavior are incredibly important to you.
But you need that information translated into whatever is helping you determine MQL in the first place - which is probably your marketing automation system, or some custom process in CRM.
Enriching Your CRM or Marketing Automation Platform
Data enrichment often goes underrated.
Something as simple as getting data in the right place actually has a massive butterfly effect - starting with marketing and trickling down to sales, customer success, and then into product, and even operations & development.
Enriching your CRM and marketing automation, however, is exactly what’s going to improve the overall quality of your definition of MQL, and therefore the quality of the MQL.
That’s why there’s tons of data enrichment options out there - so marketing and sales can find and identify the quality leads faster with data from other tools.
Whatever you’re using, you need that data inside the platform performing the MQLs decisions.
Even still, it begs the question: why are so many MQL processes out there so bad?
There’s several potential answers, but the three that stick out the most are: 1. Marketing doesn’t understand who its buyers are (FIT) 2. Marketing doesn’t understand the buyer journey (INTENT) 3. The process itself is broken or missing information
But even inside #1 - FIT, if Marketing understands what data is required to establish FIT, there’s still the possibility that the data can’t get into the platform that determines MQL in the first place.
That’s why #3 is so important. There’s a huge chance the process is broken to begin with.
This leaves Marketing in a precarious position: either figure out how to get the data in the right place, or suffer from a less accurate, and therefore less quality definition.
That’s why choosing tools with the ability to receive the data you need is so important.
You could literally be locked out of creating a better definition MQL strictly because you can’t get the right data.
Luckily, there’s a way we can fix this, too.
Attributes and Events
When defining the MQL, many marketing teams use “attributes” or fields in their marketing automation tool or CRM (or both). Attributes are typically used to determine FIT.
There’s also the opportunity to use “events” or activity. Activity is typically used to determine INTENT. Events can include like
viewed page or
clicked demo button or
started live chat.
Viewed Pricing Page is probably one of the most common events marketers use (next to maybe
Submitted Demo Request) to define the MQL.
But there are more to events than meets the eye. They’re part of the MQL formula (FIT + INTENT) and if chosen wisely, indicate INTENT.
Traditionally, the events that marketing automation or CRM platforms can acknowledge, record, and score are limited to the platform itself.
For example, Pardot can’t acknowledge and record a prospect’s chat conversation from Intercom because it doesn’t natively integrate with Intercom.
So how do we get around platform limitations like that?
You must transform that event data into a field in the platform you need the information in. And it’s not an uncommon practice.
But for more complex transformations and 100% data reliability, you can bring in more powerful integration solutions.
Redefining the Marketing Qualified Lead
The combination of qualitative data from your sales team, quantitative information about closed:won deals, and your own understanding of your buyer’s journey will open the door to a better defined marketing qualified lead.
Using the right combination of attributes and events will shape your MQL into something that Sales genuinely looks forward to reaching out to.
And then, of course, there’s the results.
For Lengow, an e-commerce solution for retailers and brands, Sales fights over their MQLs. That’s how good they are.
Oz Content has seen results as much as 4x their MQLs and moving from 20% to almost 100% marketing-sourced qualified leads - moving sales back to closing deals, not sourcing them.
I say all of this to say that when an MQL is well-defined, it brings results along with it.
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Atlanta's SaaSiest demand gen manager