Help! Can I use MQLs to build quality pipeline?
Sales: “Your MQLs are 💩! I can’t sell to any of them!”
Sound familiar?
That’s because most B2B marketers struggle at some point with Marketing Qualified Leads (MQLs).
Balancing the qualification criteria so you can get quantity, quality, and flexibility is not easy.
But it’s not impossible either. And in this post, we’re going to show you why.
We’ll cover the main ingredients you need to build quality pipeline with your MQL:
Intro: What is an MQL?
Step 1: Align and define your MQL with business goals
Step 2: Track and measure your MQLs
Step 3: Capture quality leads and build pipeline 🚀
TL;DR
Your MQL qualification criteria needs to be: 1) aligned with Sales and your ICP, 2) based on a strong conversion that signals buying intent.
You need to track and measure your MQL conversion to later pipeline stages. This helps you monitor the quality of your MQLs and the business impact of your efforts.
Understand what marketing motions are capturing your best quality leads with complete go-to-market data.
What is an MQL?
Marketing Qualified Lead (MQL) definition:
A marketing qualified lead is a lead (prospective customer) that the Marketing team has determined (qualified) as having the potential to become a customer, and so enter the Sales pipeline.
This qualification is based on simple criteria, typically conversion events such as a demo request, ebook download, or newsletter sign-up.
The idea is that once the lead performs an action they are ready to be nurtured and sold to by the Sales team.
In this way, MQL is also used by B2B marketers to benchmark business impact, i.e. how well marketing is performing.
But here’s the catch. The weaker the qualification criteria, the easier it is for marketing to capture these Leads, but not necessarily for Sales to close.
For instance, qualifying a lead as MQL following an ebook download might see a ton of leads generated, but not necessarily any who actually want to buy your product. Especially, if performance tracking stops at the MQL.
“In many cases, the value of MQLs have become diluted because you got so focused on just driving that number up, that you forgot to actually look off what did actually become of that number that we drove up.” Steffen Hedebrandt, CMO Dreamdata
It is in this way that MQL has earned its bad rap.
Which brings us to the core question of this post:
Can MQLs be used to build quality pipeline?
The answer is yes, it can… so long as it’s done properly.
Step 1: Define your Marketing Qualified Lead with business goals in mind
“I picture an MQL like walking into a party with a friend. I want to introduce this friend to somebody else. What do I feel about this friend? Is this somebody I want to introduce or not?” Allan Wille, CEO Klipfolio.
The marketer needs to be proud of their MQLs. Is your MQL the friend you’re happy to introduce to your Sales team?
The first step to getting this right is knowing both the type of friends Sales likes to make, and how many friends they need to be introduced to.
Alignment with Sales - MQLs
We get it, Sales complaining about the quality of MQLs might be is annoying.
But, you’ve GOT to hear them out. It’s Sales who’s in the trenches with the leads.
They know who’s good to sell to and who isn’t.
So their complaints are actually an invaluable part of the process. Not to mention that your Return on Ad Spend (ROAS) is wholly dependent on Sales closing deals… so if they’re not picking up your leads, you’ll only have costs! 👀
There are two things you need to align with Sales on to nail your MQL criteria: 1) the ideal customer profile (ICP) and 2) pipeline velocity - how many quality leads they need and how often to meet objectives.
Ideal Customer Profile (ICP)
Sales will know (and have data on) on what type of customer is easiest to sell to quickly. Think the size of company, industry, funding stage, location, etc. (More about ICP here and here).
This info will help refine who it is that you’re after when you’re setting your MQL criteria. i.e. what actions will these leads perform that show buying intent.
Pipeline velocity
On the quantity side of things too, it’s Sales who acts as the barometer. Are there too many or too few (quality) MQLs? How quickly do you need to be generating these MQLs? How do you need to tweak your marketing to meet Sales’ needs?
Read more about Sales and Marketing alignment in this post.
Define your MQL with actions that show buying intent
Once Sales has given their input, you can move to setting the qualification criteria.
That is, the condition a Lead will have to meet to be tagged as a Marketing Qualified Lead.
The main question here is does the action show actual buying intent?
Too often are marketers happy to label an MQL as any lead that’s interacted with your brand in some way.
Ad click, ebook download, newsletter sign up?
Are these Leads really showing they’re interested in your product, now?
Even if some of the Leads coming through these motions are ICP, it’s not guaranteed they’re in the buying space - yet.
Buying Intent
So your MQL qualification criteria needs to be pinned to buying intent. The closer the conversion reflects someone who wants to buy your product the better.
In B2B, Demo requests and Sign ups to Free offerings or Trials are good early proxies for buying intent.
Naturally, the intent level will vary amongst the leads who do convert. But as it’s a metric relatively early in the journey, this margin of error is acceptable.
That’s why there are subsequent conversion stages in the go-to-market funnel (more on this below).
Keep MQL criteria early in the journey
It’s important to bear in mind that the conversion event cannot come too late in the buying journey either.
The MQL needs to remain early in the journey so that you can adjust tactics if demand, or the quality of the demand, suddenly drops.
Step 2: Tracking and measuring your MQLs
Constant communication between Sales and Marketing about Leads, while immensely useful, is impractical at scale.
Not to mention that anecdotal evidence often lacks precision and accuracy.
That’s why tracking impact on the next stage(s) of the pipeline is fundamental to building quality pipeline with your MQLs.
Dreamdata’s Audience Hub
Dreamdata Audience Hub offers the easiest way to create the most relevant audiences from your go-to-market data anywhere. Which can then be synced directly to LinkedIn and Google Ads at the click of a button.
MQL conversion rate/ ratio
The main metric is MQL conversion rates to different stages of the pipeline. Here we’re looking at two stages:
Sales Accepted Lead (SAL)
Sales Qualified Lead (SQL)
MQL to SAL conversion
A Sales Accepted Lead is the first stage following the MQL that verifies whether the MQL is in fact ICP.
The SAL essentially means that a Lead has been approved by the sales team as having the potential to sell to. After becoming a SAL, the sales process begins.
In this way, the SAL helps us measure:
How many of marketing’s Leads are of opportunity quality - and thus the effectiveness of marketing in generating pipeline.
Here you want your conversion rate to be as high as possible. As you want to be pulling in as many ICP Leads as possible, and not wasting time on any undesirables.
Of course, this will never be 100% as non-ICPs will not self-qualify and click through - especially for Free offerings.And oppositely, how many SALs Sales might be able to close.
This helps further connect and distribute the funnels between teams and identify where there are shortcomings. And holds both teams accountable to each other!
MQL to SQL conversion
The next conversion to monitor is the MQL to SQL - or alternatively, SAL to SQL - rate.
The SQL stage focuses on the likelihood of Sales closing a deal
This conversion rate is important in identifying whether or not your ICP criteria and MQL conversion event are accurate. I.e. if there are tons of SALs (ICP MQLs) but none (or only very few) are converting, your ICP definition is out of sync.
According to Klipfolio, a good conversion rate here is approx. 15%.
This pipeline stage also helps Sales prioritise Leads to ensure that maximum effort is spent on those leads that are most likely to become customers within the sales SQL-to-Won timeline.
You can read more about how to set up a B2B go-to-market funnel that better aligns Sales and Marketing here →
MQL Time to value
You also need to keep an eye on your Time to Value at each stage. That is, how long it’s taking your MQL to convert to subsequent stages.
This will not only inform you about how long your buyer journeys are, but in doing so can help you predict revenue and plan what actions you need to hit your business goals.
Using MQLs as a source of predictable revenue
When you know how many MQLs Sales needs, how many MQLs convert, and how long each stage takes, you can reverse engineer the process and predict revenue and pipleine based on different inputs.
For instance, say MQL to SQL takes you three months on average. And you know 50% of MQLs become SAL, and 20% go through to the sales pipeline, then you can predict how many SQLs you need and when.
You can find a downloadable calculator and learn more about how to use pipeline data to predict revenue in this post →
Step 3: Start capturing more MQLs
While tracking conversion rates between stages gives a very good indication that efforts are working, it says little about exactly what activities are capturing those MQLs.
Was it your LinkedIn ad campaign? Your Blog? Webinar? G2 reviews? 🤷
Which brings us to the final piece of the puzzle: performance.
That is, doing more of what works so that you can capture more of the best quality MQLs, as often as possible.
To be able to get down to this level, you need to measure the cause and effect of each and every one of your activities.
And for this you need data. Lots of data.
Tracking go-to-market data from first-touch to closed won
To test which of your efforts far up the funnel are influencing accounts to convert to MQL - and beyond - you need to be tracking as many of your activities as possible.
For the B2B marketer facing long and complex customer journeys, this means three things:
tracking user behaviour on your site
connecting all the tools on your go-to-market tech stack
having the data models in place to transform this data into actionable insights
In practice there are two ways to put this together: building the setup in-house or buying a solution like Dreamdata off-the-shelf.
You can find out more about what it takes to put this together in this build vs. buy article.
Scaling your activities to generate quality MQLs
Once you’ve connected all the dots in the customer journey, you can accurately track and measure the success of all your activities.
That is, by connecting all your tracked go-to-market data to pipeline and revenue, you can accurately identify which of your activities are most valuable.
What Google ad campaigns are generating the most revenue?
What content is impacting your funnel and when?
Are your LinkedIn ads actually producing SQLs?
Example:
Check out how Dreamdata CMO, Steffen Hedebrant uses Dreamdata to see the value of his Google Ads.
You can see more ways to use Dreamdata for B2B go-to-market analytics here.
