In the early days of online marketing, there were leads. Plain-old leads.

As a marketer, you could go out and buy lists of leads for your sales team, which meant with enough budget, you could always keep the top of the funnel full.

Eventually, however, sales teams got sick of having to sort through all of those (mostly terrible) and largely unqualified sales leads in order to find the people who were actually serious about buying.

The Birth of MQLs & SQLs

So as marketers, we stepped up our game. We said, “Here’s what we’re going to do:

We’re going to make our leads fill out these lengthy forms, and we’re going monitor all of their actions and behaviors, and if they do these certain things, we’ll give them our Marketing stamp of approval.”

And voilà, the marketing-qualified lead (MQL) was born.

Shortly thereafter, the sales-qualified lead (SQL) was born, which was the result of salespeople looking at MQLs and saying: “Nope, still not good enough.”

In order to achieve SQL status, a lead has to meet additional qualification criteria set by the sales team.

But in both cases, there’s no tangible evidence that those leads are actually ready to buy.

To quote Emmanuelle Skala’s Sales Hacker article:

“The problem is that we follow up with leads whose behavior doesn’t necessarily indicate interest in your product. No wonder conversion rates are so low, and getting worse.”

The Rise (and Fall) of the PQL

Flash forward a few years, and SaaS companies begin to discover a new metric for aligning marketing and sales: the product-qualified lead (PQL).

Unlike MQLs and SQLs, PQLs are people who have signed up for a free or freemium version of a product.

So instead of the qualification criteria being set arbitrarily by marketing and sales teams, with a PQL, a person is considered qualified when they actually engage with the product.

While PQLs were definitely a step in the right direction, at Drift we eventually discovered that PQLs failed to account for everyone who was entering our funnel and converting into customers.

We offer a free version of our product, which generates PQLs, but we also engage with a lot of our web traffic via live chat, which brings in even more leads.

In some cases, a lead can go from having their first chat with us to buying in a matter of hours.

For those chat leads, the question then became: Who gets the credit when they convert into customers?

They weren’t PQLs, since they hadn’t signed up for our product. They weren’t MQLs or SQLs, since they hadn’t filled out a form (we had gotten rid of all our lead forms), and they hadn’t entered any nurturing flows…

Ultimately, we realized that the marketing metric we needed didn’t exist yet.

So we invented it.

Why We Switched to Conversation-Qualified Leads (CQLs)

The lead qualification metrics we had been using were rooted in a world that no longer exists — a world where company priorities and processes are put ahead of customer experience.

Specifically, we found that MQLs, SQLs, and PQLs all suffered from the same two issues:

They slowed down the buying process. Becoming MQLs, SQLs, and PQLs all require that people fill out forms and wait for follow-ups. They ignore the on-demand, real-time buying experiences that people have grown accustomed to these days.

They failed to explain why people were buying. Lots of data points are gathered when generating MQLs, SQLs, and PQLs, but there’s zero qualitative feedback. As a company, we wanted our lead qualification process to include why leads were thinking about buying and how they were planning on using our product.

And that’s how we came up with the conversation-qualified lead or CQL™.

A CQL is someone who has expressed intent to buy during a one-to-one conversation with either A) an employee at your company, or B) an intelligent sales assistant (bot).

At Drift, the overwhelming majority of our CQLs come in via live chat, but any conversation counts.

It’s not about the communication channel, it’s about hearing from someone first-hand that they’re interested in buying and then understanding why they’re interested in buying.

Unlike the other lead types that came before it, a CQL isn’t just a name, an email address, and a list of information you’ve gathered — it’s an archive of all of the conversations you’ve had with that particular lead.

And at Drift, we’ve found those conversations to be treasure troves in terms of understanding what types of leads are likely to become customers, and which features of our product those people find most appealing

By focusing on conversations with CQLs, we’re getting back to basics.

That being said, we’re using modern technology to help make it happen.

How Artificial Intelligence Makes Generating CQLs Easier

In a perfect world, marketing and sales teams would be able to respond to every new lead within five minutes.

As I mentioned in an earlier Sales Hacker post, responding in ten minutes versus five minutes can decrease your odds of qualifying a lead by 400%.

So, how many B2B sales team are actually following this lead qualification best practice?

When we conducted a survey of 433 B2B companies earlier this year, we found that just 7% were able to respond to new sales inquiries within that five-minute window.

I mean, let’s face it:

Being on-call, 24/7, so you can respond to leads and start conversations with them in real-time just isn’t scalable….

Or is it?

Here’s our secret weapon: We use chatbots to ask people the same qualifying questions our (human) sales reps ask. That way, we can keep generating CQLs around the clock, even when our sales reps are asleep, or away on vacation.

And using chat targeting, we can make sure we’re only displaying those bot campaigns to the website visitors who meet our target criteria in the first place.

With CQLs, the point isn’t to forget about your existing qualification rules, it’s to make those rules actionable.

So when someone meets your lead scoring model, you can have a bot proactively trigger a conversation.

Depending on how that conversation goes, the bot can then route that lead directly to a sales rep (if available), or share a sales rep’s calendar and help the lead schedule some time for a demo, or (if the lead isn’t a good fit) the bot can end the conversation and say, “Thanks for stopping by.”

Providing that type of real-time experience just isn’t possible using traditional tools, and that’s why you can’t measure it using traditional metrics.

Final Thought: CQLs = Sales & Marketing Alignment

By switching to the CQL, we’ve put having real-time conversations at the forefront of our sales and marketing strategy.

As a result, we’ve seen our sales cycle shrink from months and weeks to days and hours, and the alignment between our sales and marketing teams has never been stronger.

Remember that screenshot I showed earlier of one of our account executives sharing a deal she’d just closed? What I didn’t show were the responses from our VP of Sales, Armen, and our Director of Marketing, Dave:

Switching to the CQL is how we’re adapting to the real-time, on-demand expectations of today’s buyers, and it’s a metric that our sales and marketing teams are both rallying around.

The way we see it:

If you’re not having conversations with your leads, you’re wasting them.