How Modern SaaS Companies Think about MQLs

Among B2B SaaS marketing experts, marketing qualified leads (MQLs) have been a topic of much debate. Some argue that MQLs are losing their relevance, while others believe they are still crucial for success. So, before you question everything you know about lead qualification, wait! Let’s take a step back and examine the truth behind this claim.

Defining MQLs in the Modern SaaS Landscape

Previously, companies defined marketing-qualified leads based on a lead’s direct engagement with a company’s website or content, such as downloading a whitepaper or attending a webinar. However, the definition of an MQL is evolving to incorporate a more comprehensive view of a lead’s interest and readiness to purchase. This evolution brings with it a host of benefits, providing a more accurate picture of a lead’s intent and allowing for more targeted marketing efforts.

For example, a small SaaS company offering a project management tool might define an MQL as someone who has downloaded at least one whitepaper, attended two webinars, and visited at least three testimonial pages. As long as the content is customer-centric, this is still a valid way to gauge overarching interest. It should NOT be considered a warm or hot lead.

Buyers are increasingly researching online before visiting your websites. According to Gartner, marketers lack visibility into 50% of the buying journey, making growth opportunities harder to influence. However, many companies use intent data to expand their MQL definition.

By incorporating intent data, you can identify leads actively researching topics related to their products or services, even if they haven’t directly interacted with the company’s website or content. This powerful tool allows companies to prioritize leads and tailor outreach and nurturing efforts to their interests and needs, even if they only visit the website late in their buying journey. This strategy empowers companies to make more informed decisions and feel confident in their lead qualification process.

How this comes to life

Consider you’re a small SaaS company that offers a project management tool.  You define an MQL as a lead engaged with your content, such as the “Ultimate Guide to Project Management” or “10 Tips for Successful Project Delivery” webinar, and visited your pricing and customer review pages. With intent data, you identify leads who are actively researching topics like “project management software,” “top project management software,” and “project management comparison,” even if they haven’t visited your website or engaged with your content. You can combine this with firmographic data to segment and tailor content by company size, industry, and revenue.

Debunking Common MQL Myths

Before we dive deeper into how to create a winning marketing-qualified leads strategy, let’s take a moment to debunk some common myths surrounding MQLs:

Myth 1: MQLs are a superficial metric

Some people believe MQLs are a vanity metric, representing surface-level interest rather than a genuine intent to buy. However, when properly defined and used with other metrics, MQLs can effectively indicate a lead’s engagement and likelihood to convert.

Myth 2: MQLs are a “one-size-fits-all” solution

Another common misconception is that MQLs are a blanket solution that can be applied similarly to every SaaS business. In reality, the definition of an MQL can vary significantly based on the industry, company size, target audience, product complexity, and sales cycle length.

Myth 3: MQLs are the only metric that matters

While MQLs are a good metric to track, they should be part of a broader experimentation strategy. Tracking metrics such as SQLs, opportunity creation rates, and customer acquisition costs is essential to understanding marketing and sales performance.

Addressing the Product-Led Growth (PLG) Model

Some may argue that marketing-qualified leads are less relevant in a product-led growth (PLG) model. While PLG emphasizes the product’s importance in driving growth, MQLs can still be valuable in this context.

In a PLG model, MQLs can identify high-value accounts requiring more personalized outreach and support. By leveraging MQL criteria incorporating product usage data, such as feature adoption or user engagement, SaaS companies can prioritize accounts that are more likely to benefit from additional support resources and attention. This targeted approach can help drive expansion and retention within existing accounts, complementing the broader PLG strategy.

Evolving Your MQL Strategy

As your SaaS company grows and evolves, so should your approach to MQLs. Continually refine your MQL strategy based on data-driven insights and the best practices to remain effective and aligned with your broader growth goals.

For smaller SaaS companies just getting started with MQLs, focus on establishing a solid foundation:

1. Clearly define your MQL criteria in collaboration with sales, considering your unique business context and target audience.
2. Implement a lead scoring and grading system that assigns points and a grade to leads based on key engagement metrics and persona-based, firmographic, and technographic data that align with your ideal customers.
3. Develop targeted demand generation and lead nurturing campaigns that deliver relevant, valuable content to MQLs based on their interests and stage in the buying journey.

As your company grows and you gain access to more advanced tools and data sources, you can begin to incorporate more sophisticated tactics into your MQL strategy:

1. Leverage customer data with predictive analytics and machine learning to identify patterns and predict future buying behavior.
2. Integrate intent data from third-party providers to identify leads actively researching topics related to your product or service, even if they have not directly engaged with your brand.
3. Implement account-based marketing (ABM) strategies that engage and convert high-value accounts rather than individual leads.

For larger, more mature SaaS companies with extensive resources and advanced marketing tech stacks, the possibilities for MQL optimization are nearly endless:

1. Use AI-powered chatbots and conversational marketing platforms to engage with MQLs in real time, answering their questions and guiding them through the buying journey.
2. Leverage data visualization and advanced analytics to optimize every stage of the funnel.
3. Implement comprehensive lead lifecycle management processes that ensure seamless handoffs between marketing, sales, and customer success teams, driving greater alignment and revenue growth.

Leveraging B2B Technology for Enhanced Marketing Qualified Leads Strategies

In the last five years, B2B SaaS companies have gained access to many powerful tools and platforms that can revolutionize your marketing strategy and sales engagement. By leveraging these technologies, you can gain deeper insights into customer behavior, optimize marketing and sales processes, and create more personalized, compelling experiences for your target accounts. With that comes the possibility of gaining significant insights into your buyers, the buying team, and more sophisticated go-to-market strategies. For example: 

Conversation Intelligence

Platforms like Gong or Chorus by ZoomInfo enable sales teams to analyze customer interactions, providing valuable insights into buyer sentiment, objections, and preferences. By integrating conversation intelligence data into your MQL strategy, you can refine your lead scoring and grading models, tailor your messaging, and equip your sales team with the insights they need to have more effective conversations with qualified leads.

Experimentation and Optimization

Tools like Optimizely and AB Tasty allow marketers to test and optimize their content, messaging, and user experience across various touchpoints. By continuously experimenting and refining your marketing assets based on data-driven insights, you can improve the quality and relevance of your MQLs, increasing the likelihood of conversion.

Account-Based Marketing (ABM) Platforms

Solutions like 6Sense and Demandbase empower companies to identify and target high-value accounts with more precision. By leveraging intent data, firmographic information, and predictive analytics, these platforms help you focus on the accounts most likely to convert, ensuring that your sales team engages with the right leads at the right time.

Sales Engagement Platforms

Tools like Salesloft and Outreach streamline and automate many sales outreach and follow-up tasks. By integrating these platforms with your MQL process, sales can engage with qualified leads in a timely, personalized manner while also gathering valuable data on the effectiveness of different outreach strategies.

The Role of Predictive Analytics and AI in Lead Scoring and Grading

As your SaaS company grows, predictive analytics and artificial intelligence (AI) become increasingly crucial in optimizing lead scoring, grading, prioritization, and nurturing. By leveraging machine learning algorithms, you can analyze vast amounts of data to identify patterns and predict which leads are most likely to convert, ultimately enabling you to forecast revenue more accurately.

Let’s consider an example of how a SaaS company can evolve its lead scoring and grading approach as it grows:

Early Stage: Basic Lead Scoring and Grading

When your SaaS company is in its early stages, you may start with a basic lead scoring and grading system using a marketing automation tool like Pardot. This system assigns points to leads based on their website engagement and grades them based on demographic and firmographic criteria, such as job title and company size.

For instance, you might assign 10 points to a lead who visits your pricing page, 5 points for watching 75% of a webinar, and 5 points for reading a blog post. Leads who accumulate certain points within a specific timeframe (e.g., 100 points in 14 days) are considered MQLs and passed on to the sales team for nurturing.

You can use a sales enablement platform like Salesloft to create a structured sales nurturing process at this stage. This process could include a series of personalized emails from sales representatives, leveraging the platform’s ability to track engagement and provide insights for further personalization. If appropriate, the platform can facilitate phone calls, LinkedIn messages, and emails to create a multi-channel nurturing approach.

Growth Stage: Incorporating Predictive Analytics

As your company grows and accesses more advanced tools and data sources, you can incorporate predictive analytics and machine learning into your lead scoring and grading process. These techniques allow you to analyze historical customer data to identify patterns and predict future conversion likelihood.

For example, consider you’re a mid-sized SaaS company selling a lead routing and scheduling platform. By analyzing past customer behavior, you discover that leads who fit specific demographic and firmographic criteria, complete an online course, and watch at least three customer success story videos are three times more likely to convert than those who only download a whitepaper and visit the pricing page. Based on this insight, you can adjust your lead scoring model to assign higher points to these high-value behaviors and prioritize these leads for sales follow-up.

Mature Stage: Leveraging Intent Data and AI for Predictive Revenue

Lead scoring and grading can become even more precise and effective for larger, more established SaaS companies with access to intent data and advanced AI capabilities. By analyzing leads’ search history, content consumption, and engagement with competitor websites, you can identify leads actively researching and considering products or services similar to yours.

Consider a scenario where you’re a large SaaS company selling a customer support platform. By leveraging intent data and AI, you discover that a lead who has visited your competitor’s website multiple times and searched for “customer support software” is showing strong buying signals. Your AI-powered lead scoring system assigns a high lead score to this individual and prioritizes them for personalized outreach and nurturing.

Moreover, you can create a predictive revenue model by integrating your predictive lead scoring system with your CRM and sales data. This model considers lead score, past conversion rates, average deal size, and sales cycle length to forecast revenue accurately. For instance, your model might predict that leads with a score above 150 have a 40% chance of converting within the next 30 days, with an average deal size of $10,000. By aggregating these predictions across your pipeline, you can create a more reliable revenue forecast and make data-driven decisions about resource allocation and growth strategies.

As your SaaS company evolves, so should your approach to lead scoring, grading, and revenue forecasting. By continuously refining your models based on new data and insights, you can stay ahead of the curve and make informed decisions that drive predictable, sustainable growth.

Balancing Automation and Human Interaction

According to McKinsey, almost 80% of B2B decision-makers prefer remote human interactions or digital self-service. While technology and automation play crucial roles in modern lead qualification, balancing automation and human interaction is essential.  

Relying solely on marketing automation can lead to a lack of personalization and authentic engagement, which can hinder the effectiveness of your MQL strategy.

To achieve this balance, consider the following:

1. Use technology to enhance, not replace, human engagement: Leverage insights from conversation intelligence, experimentation, and ABM platforms to inform and guide personalized outreach and follow-up by sales and marketing teams.
2. Invest in training and coaching. Provide your teams with the skills and knowledge needed for effective, empathetic conversations with leads, tailoring their approach to prospect’s unique needs and challenges.
3. Emphasize the importance of authenticity: Encourage your teams to prioritize genuine human connections with leads while leveraging technology to scale and optimize their efforts.

While automation can help you identify and prioritize high-potential MQLs, a personalized touch builds trust, fosters relationships, and drives conversions.

Aligning Marketing and Sales

The most effective marketing-qualified leads strategies require alignment between marketing and sales, regardless of company size or resources. 

According to the Salesforce State of Marketing report, 31% of B2B marketers find it challenging to share a unified view of customer data across business units. Meanwhile, 62% of business buyers feel they’re communicating with separate departments rather than one company. This, in turn, impacts your brand and customer experience.

Regular communication and collaboration between marketing and sales teams can ensure that both teams work towards the same goals and have a shared understanding of what constitutes an MQL.

As companies grow, they can implement more formal processes and tools to facilitate alignment, such as shared dashboards, regular pipeline reviews, and service-level agreements (SLAs) that outline each team’s expectations and responsibilities.

For larger SaaS companies with access to intent data and ABM platforms, alignment can be further enhanced by providing marketing and sales teams visibility into target accounts’ research and engagement history. This allows both teams to prioritize accounts based on their level of interest and readiness to buy and to coordinate their outreach and engagement efforts for maximum impact.

Overcoming Alignment Challenges

Aligning marketing and sales teams on defining and refining your marketing-qualified leads strategy is essential. Common alignment challenges include:

• Differing priorities and goals between marketing and sales
• Lack of communication and collaboration
• Inconsistent definitions of MQLs and other key metrics
• Resistance to change and adoption of new processes

To overcome these challenges, consider the following strategies:

1. Secure executive buy-in: Ensure senior marketing and sales leadership is aligned on the importance of MQLs and committed to supporting the necessary changes in process and structure.
2. Establish shared goals and metrics: Define common objectives and KPIs both teams can rally around, such as revenue targets, conversion rates, and customer acquisition costs.
3. Foster regular communication: Implement regular meetings, such as weekly pipeline reviews or monthly strategy sessions, to ensure ongoing alignment and collaboration between marketing and sales.
4. Training and enablement: Invest in training and resources to help both teams understand the marketing-qualified leads process, criteria, and best practices for engagement and follow-up.
5. Celebrate successes together: Recognize and reward the achievements of both marketing and sales teams in generating and converting high-quality MQLs, reinforcing the importance of collaboration and alignment.

Metrics to Track Throughout the MQL Journey

Marketers are tracking more metrics than ever, with 72% of high-performing marketers analyzing marketing performance in real time.

As you develop and refine your marketing-qualified leads strategy, track critical metrics that can help you gauge your effectiveness and areas for improvement. Here are some metrics to consider:

MQL Volume

This metric tracks the total number of MQLs generated monthly. As you refine your lead scoring and nurturing processes, you should aim to see a steady increase in MQL volume over time. This metric alone is not a sufficient indicator of growth.

MQL-to-SQL Conversion Rate

This metric measures the percentage of MQLs your sales team accepts and converts into Sales-Qualified Leads (SQLs). A high MQL-to-SQL conversion rate indicates that your lead scoring and nurturing processes effectively identify and prepare leads for sales follow-up.

SQL-to-Opportunity Conversion Rate

This metric tracks the percentage of Sales-Qualified Leads (SQLs) converted into sales opportunities. By monitoring this metric over time, you can gain valuable insights into the quality of leads passing from marketing to sales. This can help you identify areas for improvement in your sales process and optimize your lead generation efforts.

Opportunity-to-Customer Conversion Rate

Measures the percentage of sales opportunities resulting in closed-won deals to assess marketing and sales effectiveness and identify areas for optimization. Measures the percentage of sales opportunities resulting in closed-won deals. By tracking this metric, you can determine your overall marketing and sales effectiveness and identify areas for optimization.

Customer Acquisition Cost (CAC)

This metric calculates the total cost of acquiring a new customer, including all marketing and sales expenses. By tracking CAC over time and comparing it to your customers’ lifetime value, you can ensure that your MQL strategy generates a positive return on investment. While a good CAC may vary based on industry, a CAC payoff of less than 12 and no more than 24 months is usually ideal.

Alternatives to MQLs

While marketing-qualified leads remain widely used, there is a growing trend towards alternative approaches that better reflect the complexity of modern B2B buying processes. One such approach focuses on “Qualified Accounts” or “Engaged Accounts” that consider the collective engagement of multiple stakeholders within a target account rather than individual lead activities.

For example, a SaaS company targeting enterprise clients may define a Qualified Account as one where multiple decision-makers have engaged with the company’s resources page, attended events, or requested demos. 

Most B2B and B2B2C marketers (89%) use account-based marketing platforms to coordinate targeted campaigns with sales and service teams. These platforms help to streamline the marketing process and ensure better collaboration between different teams.

An account-based approach recognizes that B2B purchasing decisions often involve multiple stakeholders and that the collective engagement of the account is a more robust indicator of intent than individual lead activities. A more advanced approach considers the overall go-to-market strategy related to each buying team member’s needs. 

Suppose a company sells revenue intelligence software. In that case, it must consider the buyer’s journey through various departments, including marketing, product, sales, revenue operations, legal, tech, and finance. It must also consider the broader buying team’s perspectives and requirements based on their specific job responsibilities or the “job to be done.” This is where the MQL process merges with the broader marketing and sales funnel needs. 

Setting Up Your MQL Strategy

When setting up your marketing-qualified leads strategy, you must tailor your approach to your company’s unique needs, resources, and expertise. While implementing an advanced MQL strategy may seem daunting, especially for smaller SaaS companies, you can start somewhere and gradually build upon your lead qualification efforts as you grow and scale.

To get started, focus on the following key considerations:

Define Your MQL Criteria

• Collaborate with sales and marketing to establish a shared definition of MQLs that aligns with your business goals and target audience.
• Tailor your criteria to your specific product, sales cycle, and customer journey. Incorporate lead scoring, grading, and intent data to prioritize leads based on engagement and likelihood of purchase.
• Establish a straightforward handoff process between marketing and sales, with agreed-upon SLAs and regular communication.

Implement Lead Nurturing

• Develop targeted nurturing campaigns that keep MQLs engaged throughout the funnel, considering the psychological and emotional factors influencing decision-making.
• Use a mix of automated and personalized touchpoints to build relationships and trust with leads, tailoring your messaging and content accordingly.

Leverage B2B Technology

• Implement conversation intelligence platforms (e.g., Gong, Outreach) to analyze customer interactions and inform lead scoring, grading, and messaging.
• Use experimentation and optimization tools (e.g., Optimizely, AB Tasty) to refine your marketing assets and continuously improve MQL quality.
• Leverage account-based marketing platforms (e.g., 6Sense, Terminus, Demandbase) to identify and target high-value accounts with more precision and accuracy.
• Integrate sales engagement platforms (e.g., Salesloft, Outreach) to streamline and personalize outreach to MQLs.

Adapt to Changing Landscapes

• Regularly review and adjust your MQL criteria, considering shifts in customer behavior, market conditions, and business priorities.
• Collaborate closely with sales and customer success teams to understand your target accounts’ evolving needs and challenges.
• Focus on delivering clear, compelling value propositions that address your ideal customers’ specific pain points and priorities.

As your company grows and your resources expand, you can incorporate more advanced tactics and technologies, such as predictive analytics, account-based marketing, and sales engagement platforms. Remember, the key is ongoing refinement and optimization of your broader demand generation strategy based on data, feedback, and changing market conditions.

So, there you have it! The MQL is not dead but instead evolving to meet the needs of a modern go-to-market strategy. You can effectively identify, nurture, and convert high-quality leads into loyal customers by adapting your marketing-qualified leads approach to fit your evolving business needs and leveraging the latest tools and technologies.

Remember, the key to success with MQLs is continually testing, refining, and optimizing your approach based on data-driven insights. 

You can create a demand generation engine that drives sustainable business growth by tracking KPI metrics throughout the MQL journey and staying attuned to your target audience’s evolving needs and preferences.

So, don’t be afraid to experiment, iterate, and have fun with your marketing strategy. With the right approach and creativity, you can master lead qualification and demand generation.