Qualifying leads isn’t about ticking boxes anymore. With the concept of SQLs gaining nuance, it’s time for a more sophisticated framework.

Your modern tech stacks collate data from multiple sources, which marketing uses to qualify leads to the next funnel stage as Sales Qualified Leads or SQLs. This qualification process is based on aggregated behavioral signals from email opens, subscriptions, responses, and chats.

But recently, sales have noticed a serious disjuncture.

Often, when SDRs approach the leads handed down by marketing, they hit a wall. These so-called SQLs don’t actually have buying intent or interest in progressing the conversation.

They were all false positives.

Imagine a lead downloads a whitepaper, opens three different emails, kicks off marketing’s score threshold, and is labeled as an SQL. When sales initiate contact, they realize the lead was merely executing competitor research or browsing casually.

This handoff was a dead end and a waste of time, eroding sales’ trust in marketing scoring models.

In the long run, this has caused observable friction between teams, inducing cross-departmental misalignment that results in low-quality leads that don’t convert.

This begs the question:

The truth is that marketing’s approach is not entirely incorrect.

They are inaccurately analyzing the intent signals. It’s not about finding out that leads are engaging but about why they are.

This is known as intent context.

Most scoring models are straightforward. These traditional methods attribute specific scores to engagement signals. If whitepaper downloads are 10 points, subscribing to the newsletter is 20.

This might be a crucial step in the lead qualification process, but without context, these signals can be easily misread.

For example, an account might download your whitepaper or eBook, and signals are usually assigned a high score in traditional systems, but it is actually a competitor or researcher. There’s no buying intent.

Your SDRs end up wasting time and resources on leads that look great in the CRM but aren’t sales-ready.

A high score doesn’t equate to high buying intent, especially without context. And then comes intent, followed by sales-readiness. In simpler terms, MQLs’ qualification into SQLs requires precision.

But why?

Because sales-qualified leads aren’t just here for research or casual strolling.

SQLs are leads who are past the stage of primary awareness or casual interest and illustrate a strong intent to purchase. They are sales-ready accounts that are ready to negotiate and talk numbers with your sales team.

At this stage, SQLs consume content that’ll help distinguish you as a potential vendor for their business challenges. So, they generally interact with case studies and pricing models to set your solutions apart from competing options.

An SQL generally demonstrates high-purchasing intent.

Sometimes, teams botch this up. Most teams use MQLs and SQLs interchangeably.

But they aren’t the same and shouldn’t be.

The clear demarcation is the intent to buy.

MQLs are leads who are actively aware of your brand and have shown interest in knowing more. They are trying to research and gain knowledge about their own pain points while exploring other options in the market.

Their buying intent is almost null.

Meanwhile, SQLs fall on the other side. They demonstrate high buying intent. And at this stage, they are evaluating their options.

The content these two engage with is distinct. But how do you map your strategies in the first place without a complete grasp of the subject matter?

It all just leads to over-scoring and low-quality MQLs who never convert.

If the qualification process is where your leads take the blow, how will they ever progress further?

Structure a framework to accurately identify SQLs: what makes a lead sales-ready?

Start by asking the right questions and listening.

BANT is one of the most effective and powerful qualification frameworks out there. Developed by IBM in the 1950s, it has proved to be a treasure trove for modern marketing.

And every other framework, from MEDICC to NOTE, is a reiteration of BANT. But BANT has become obsolete in the modern landscape.

For example, take NOTE.

It poses a crucial advantage in today’s customer-first marketing landscape compared to the older models.

• N – Need: Understand the problem prospects face, i.e., what’s not working?
• O – Opportunity: What’s the upside if the problem is addressed and tackled?
• T – Team: Who’s pulling the strings for the final purchase?
• E – Effect: What would the short-term and long-term effects of integrating this solution look like?

NOTE underscores the long-term customer experience, while BANT has helped businesses efficiently close deals. It’s not enough to make a purchase; you need to assess whether the client is fit to adopt and integrate the solution into their systems.

This makes NOTE imperative for the modern buying landscape. It focuses not on the business’s need to sell but on the customer’s need for the solution.

Older qualification systems quickly push the sales process.

But NOTE acknowledges the organization’s need and timeline to ensure that SDRs gauge where the leads are in their buying journey. And whether this solution will set them up for long-term success.

If you think about it, the need for an appropriate budget is a single facet of qualification.

The newer model focuses on the nuances of decision-making, addressing its nonlinearity in B2B.

It isn’t about the obvious anymore. But truly listening to what leads are here for.

Nuanced qualification models help SDRs and marketing recognize the client’s growth potential.

And listen to understand what this purchase means to the leads.

By determining this, sales and marketing can move a lead toward being sales-ready.

Establish persona filters to segment relevant leads.

Most often, ill-fitting leads are still handed off from marketing to sales because they engaged with your brand.

This is where the disconnect begins.

Just because an account downloads the whitepaper doesn’t mean they are relevant. They might even hold purchasing intent, but are they in the market for your solutions?

This is where persona filters swoop in.

These filters, such as industry, pain points, and behavior, among others, help segment out the relevant accounts that fit your buyer persona or ICP. For example, the lack of budget may categorize an account as an MQL because they can’t afford to progress into a sales conversion.

Meanwhile, the wrong industry would establish that your solution wouldn’t help solve their pain points. This account is irrelevant to what you have to offer.

Persona filters help spotlight this demarcation.

And if the account continues to engage, they are likely doing so only for the information. They cannot move into being an SQL.

It’s not because they lack the budget, urgency, or authority, but because their wants don’t align with the industry you cater to. It serves as a qualification and an elimination process.

By focusing on leads that fit your ICP and buyer persona, SDRs expend their resources on high-intent leads with promising conversion potential.

Gauge buying intent from behavioral and third-party data.

Tech innovations have established a new channel for marketers to revamp their efforts.

It’s not about services that are marketable to a diverse section of the population. Each effort has become more niche, and the comms strategies are more targeted – the customer is at the center.

B2B buying was never linear, and it has become even more complex.

So, savvy marketers have invested heavily in leveraging behavioral data to understand their audience base, whether by studying the sequence of specific actions or even analyzing third-party data.

There would be two different scenarios here:

1. A prospect is researching queries related to your services on third-party sites – this is third-party intent data. These touchpoints are outside your jurisdiction, but they still illustrate that the prospect is in-market.
2. Another prospect from the same company is interacting with service pages across your website. And then, they proceed to download eBooks and respond to emails. These behavior sequences are direct proofs of engagement.

Both these data types help marketers pinpoint very different behaviors and also gauge the purchase intent of a lead. They facilitate marketers to move beyond guesswork and underscore buyer behavior with precision.

And when you combine third-party data with these behavioral sequences, specific patterns emerge.

The overlap is where SQLs emerge from.

Instead of relying on either internal or external signals, why not merge both for an accurate analysis?

Alone, third-party data often lacks context, and behavioral data doesn’t always signal sales-readiness. But combined, the data spotlights the relevance and timeline.

With this, SDRs get a clearer picture of what to say, to whom, and when.

CRM enrichment for confident sales qualification.

Traditional CRM systems entail basic information, such as industry, name, email, and company. By itself, this data cannot help you gauge whether a lead is sales-ready.

It requires contextual data that offers you better insight into who a lead is and what they are really in the market for. It could also be that they know the problem and are actively researching it, but aren’t confident where to look for solutions just yet.

This is where CRM enrichment can help your brand become the lead’s saving grace.

It helps transform raw lead data into actionable data by layering context to each data point. Each lead account is paired with contextual information that can’t be gauged from basic form filling or whitepaper downloads.

This additional data could actively change how you prioritize and qualify your leads.

Think:

A prospect enters your CRM through gated content. On the surface, you only know their name, email, and company. But what if CRM enrichment follows this up with – the lead is the IT director of a mid-market cybersecurity company? And your solution can perfectly integrate with tools in their existing tech stacks.

The company has growth potential and is actively looking to invest in new infrastructure.

So, they might not just be consuming your content, but could still be prospective buyers.

Moreover, CRM enrichment also works alternatively. In isolation, some data may flag an account’s engagement, but paired with context, it might not illustrate buying intent.

On the other hand, another account may be actively researching solutions in your market, but their interaction with your brand is quite limited. You can leverage this data from enrichment providers to help marketing reach out to them and nurture them forward.

CRM enrichment doesn’t add more data to an already exhausted database. It adds better data for sales to target the right leads at the right time, refining the sales qualification process.

Assess the lead source to gauge lead quality early on.

Not all leads are created equal, and neither do they come from the same source. When a lead enters your sales funnel, the channel they entered through gives the slightest clue about what’s their intention.

A lead arriving from a search for “best CRM systems” might hold far more intent than a like on a social media post. Both are at distinct stages of the buyer journey. While one is aware of the problem and is actively evaluating solutions, the other is casually browsing.

When these activities are tracked across past behavior, marketers come to see a pattern.

Leads from inbound marketing channels, such as content marketing and SEO, have higher conversion rates. They might move through the sales cycle faster or even have high customer lifetime value.

These patterns offer marketing and sales with predictive insight, improving the SQL identification process.

When paired with CRM enrichment, the collated data showcases that the leads from particular sources and channels have a higher possibility of fitting the SQL criteria. This helps marketing and sales develop a comprehensive understanding of what ‘qualified’ or ‘sales-ready’ actually means.

Overall, lead sources might not directly help with qualification. But it adds intent context behind the data available in your CRM. It enhances your qualification model and helps:

1. Prioritize high-quality leads early on.
2. Facilitate better segmentation.
3. Tailor nurturing for leads not yet ready.

Underscoring lead score gives you a head start on leads’ sales potential and helps your SDRs invest time in accounts with higher conversion possibilities.

The bottom line: The more information your teams have on a lead, the easier it becomes to qualify them as sales-qualified leads.

It’s not about closing deals per se. But elevating efficiency in a way that works best for your business.

And neither is it about segmenting SQLs and MQLs.

It’s about gaining a more strategic mindset that streamlines your efforts with the desired outcomes.

With the advent of rapid digital transformation, the traditional definition of a sales-qualified lead has collapsed.

Today, there are more channels and touchpoints. And the concept has become more nuanced. So, the qualifying framework isn’t a lens to perceive leads as mere data points but to spotlight the nuances of the buyer journey.

A lead may be considered an SQL in the traditional sense, but it still requires passing through multiple criteria and nurturing stages before interacting with sales.

The focus isn’t just on demographic or firmographic data, though their significance remains. Behavior-based qualification has gained momentum, leading to more personalized experiences.

The buyer has come to occupy the central position in marketing and sales.

And owing to the subtle shift from forceful sales tactics to relational and consulting approaches, SQLs are no longer perceived as numbers.

They are now at a vital juncture in the customer-relationship-building process, alongside establishing marketing-sales alignment.

Propelling a much-needed shift in sales’ role: closing business deals to educating and consulting.