5 suggestions for moving beyond MQLs

The marketing qualified lead (MQL) is one of the most widely used metrics in marketing — but it’s increasingly under fire. Executives crave an easy, unequivocal measurement of a customer’s readiness to buy. But this metric is worse than fiction; it’s disinformation. 

Scoring customer interactions to determine engagement fitness is a classic practice. From the 1980’s Miller-Heiman sales methodology to today’s causal AI, companies have monitored signals to learn about customer journey progress. The data supporting MQLs can assist this vital analysis. 

However, many companies demand much more from that data, and these expectations cause problems. 

MQL marks the border between marketing and sales silos

The MQL was born a couple of decades ago when sales management realized customers increasingly use digital media to make buying decisions. Before digital, the sales team had contact with customers earlier in the process than they do today. While scoring methods existed then, sales teams relied heavily on intuition and experience to assess buying probability.

Data produced by digital interactions offered the intriguing possibility of a more scientific, deterministic score. Sales productivity could skyrocket if companies had a solid indicator of customer readiness.

But digital was “owned” by marketing, and marketing and sales worked in two segregated silos. Companies needed a way to hand off prospects from marketing-owned-digital to sales-owned-interpersonal. Consultants invented a linear, staged process that bridged this divide while maintaining traditional organizational power centers. 

MQL became the marker defining the edge where marketing supposedly stopped its work and threw (hopefully) qualified leads over the silo wall. When automation came to marketing, producing even more data, the MQL became hammered into company processes. The funnel process’s staged linearity played well with how software worked and allowed new martech companies to form a profitable niche.

Dig deeper: Why the MQL model is failing B2B marketing and what to use instead

MQLs are poor indicators of customer readiness to buy

Despite an increase in data, MQLs have rarely delivered on their promise. Here’s why.

Real-world customer journeys aren’t linear

Although it is generally true that buyers go through a cognitive process of awareness, interest and consideration before purchasing, real-world journeys look more like a child’s scribble than precise stages. Journeys can zip forward, slide sideways, slip back and stall before inching forward again. This non-linearity means that MQLs are plucked out at an effectively random point and are bound to give false confidence to sales teams. 

‘First digital, then interpersonal’ is a myth

Customers bounce between interaction methods and customers use digital media throughout their journey. Up to 83% of purchase activity consists of independent learning and internal consensus building, per Gartner estimates. By maintaining silo walls between marketing and sales, companies reduce the accuracy of customer readiness analysis and, even worse, inhibit quality customer experience because customers find organizational walls (a.k.a. “not my job”) irrelevant and annoying.

Additional data is needed for predictability 

While thoughtful marketers score MQLs with the best data they have, it’s unlikely they have the data they need. The fact that someone engages with website content does not necessarily signal near-term intent to buy your product. The company may have already chosen a vendor and is simply gathering benchmark information to justify its selection.

Historical data and data from outside marketing systems are needed to identify trends and indicators. For example, one company discovered that prospects who had inquired about customer service practices several months prior were the most interested buyers.

Dig deeper: The problem with B2B marketing: Misaligned measurement is stifling innovation

Moving beyond the MQL

A valid business intent for instituting any marketing metric should be to inform more effective go-to-market (GTM) strategies that result in higher, more reliable revenue streams over time. Improved productivity would also be beneficial.

Achieving this requires giving up the “how” of earlier eras and adapting to evolving customer buying methods. Here are some suggestions for adaptation. 

1. Accept the uncertainty of customer readiness

Your sales team may want only to talk to customers who are at least 80% through their buying process, but you won’t be able to consistently locate these with certainty. Variability is due to the complexity of customer markets and has little to do with the quality of your efforts. The best you can do is measure the probability of a customer’s interest level. A 60% probability means, of course, that it will be incorrect 40% of the time.

2. Avoid surrogation

Expand your intelligence dashboard to include a broader range of metrics. No single metric can be a surrogate for customer readiness or marketing’s value and emphasizing MQLs encourages inappropriate reliance on short-term performance marketing. 

Performance marketing is like coffee. It gives you a temporary boost but does nothing to increase your physical energy. Energy requires generative habits like good nutrition, exercise and adequate sleep. Quality go-to-market must include generative practices such as brand development and customer loyalty, investments that compound like stock marketing investments. 

3. Upskill analytics

More sophisticated analytics methods, such as marketing mix modeling (MMM) or causal AI, can provide clues as to which interaction types most correlated with purchase. Although certainty about customer readiness is impossible, more sophisticated analysis can identify trends and indicators to inform a better probability “score.” 

Dig deeper: Why causal AI is the answer for smarter marketing

4. Set goals for metrics but manage them flexibly

Because customer journeys are volatile and complex, chasing consistent results is futile. Look for long-term progress. For example, every baseball player would love a batting average of over .300, but very few achieve this. Players try to improve their average over time while acknowledging they will perform better in some seasons than others. The same is true for marketing. 

5. Integrate the organization

Companies must increase integration of teams, processes and data to better match today’s customer journeys. Legacy silos exist only because of history and no longer provide the flexibility needed for how customers buy in the messy real world. It’s time to break down the silo walls.

But warning! This doesn’t mean subsuming traditional marketing into traditional sales. That step would be backwards, given that digital means more marketing influence, not less. Instead, rethink the roles of both current teams to create integrated intelligence-augmented processes that reflect how customers really buy — and will improve customer experience, too.

Here’s good news about measuring GTM: In thousands of sandboxes worldwide, leaders are trying new ways to go to market. As with anything new, some won’t work out — but a few will and these will spread. Guaranteed: successful new ways will either abandon the MQL entirely or significantly change its definition or use. 

Be a leader who gains this advantage early.