When it comes to lead generation and new customer opportunity qualification, many businesses have traditionally relied on the waterfall approach which leverages the concept of a Marketing Qualified Lead (MQL). However, the emergence of buyer intent (BI) data has introduced a new analytical method that focuses on understanding the intent and behavior of potential customers.

These two concepts came into play in a recent conversation with a CGO of a B2B Identity and Access Management company who asked:

“What are the pros and cons of using the waterfall versus the intent approach for developing a new opportunity scoring model and which approach would I recommend.”

I wrote this post because I’ve been asked this question by other companies seeking to optimize pipeline management. In this article, we will cover the top and bottom-line benefits of better-qualified opportunities, explore the pros and cons of using the MQLs versus Buyer Intent for scoring, highlight the differences, discuss how to create models for each, and answer the question posed by the CGO regarding which approach is best for B2B companies.

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Before recommending an approach, let’s explore the pros and cons of each individual model.

Pros & Cons of the MQLs for Qualifying New Customer Opportunities

MQLs refer to leads that are qualified opportunities, based on predefined criteria and indicators. Marketing, business development, and other teams dedicated to lead generation typically use various tactics such as gated content, webinars, and forms to capture leads and evaluate their fit with the company’s target audience.

These are the top 3 pros and cons of the MQL approach:

PROs:

1. Standardized criteria: MQLs provide a standardized and measurable way to assess opportunity quality, ensuring that all opportunities are evaluated based on the same criteria.
2. Scalability: MQL models can handle large volumes of leads, allowing Marketing teams to efficiently, and quickly prioritize and distribute them to the Sales team.
3. Alignment with Marketing efforts: MQLs provide insights into the effectiveness of marketing campaigns and provide insights into which strategies and channels generate the most qualified leads.

CONs:

1. Lack of context: MQLs often lack sufficient context about the prospective customer’s intent, making it challenging to understand their specific needs and level of interest.
2. Limited opportunity insights: MQLs typically provide basic information, such as contact details and demographic data, but typically lack behavioral insights that can offer a deeper understanding of the prospective customer’s interests and preferences.
3. Potential misalignment with Sales: MQLs may not always accurately reflect the readiness of an opportunity to engage with the Sales team, leading to inefficiencies and lost opportunities.

Pros & Cons of the Buyer Intent for Qualifying New Customer Opportunities

Buyer Intent accounts for fit and focuses on analyzing and interpreting behavioral data to identify potential customers who are actively showing interest in a product or service. By tracking online activities, such as website visits, content consumption, and search behavior, businesses can gain valuable insights into the intent of a prospective customer.

These are the most common pros and cons of the buyer intent approach:

PROs:

1. Enhanced personalization: BI data enables businesses to personalize their marketing and sales efforts based on the specific needs and interests of the potential customer.
2. Timely engagement: By identifying prospective customers who are actively researching or considering a purchase, businesses can engage with them at the right moment, increasing the chances of conversion.
3. Better opportunity quality: BI data provides a more nuanced understanding of the intent of a prospective customer, allowing for more accurate opportunity qualification and prioritization.

CONs:

1. Data complexity: Analyzing and interpreting buyer intent data can be complex, requiring the use of advanced analytics and data processing techniques.
2. Privacy concerns: Gathering and utilizing behavioral data may raise privacy concerns among prospects, necessitating a transparent approach and adherence to data protection regulations.
3. Incomplete picture: Most Marketing organizations only capture BI data from online activities, which typically does not fully represent a potential customer’s overall buying journey or intentions. Investing in second-party and third-hand data can help fill this gap.

4 Important Differences Between the MQL and BI Approaches

While there are pros and cons for each, both the MQL and BI approaches aim to qualify opportunities, ultimately leading to a higher customer acquisition rate. From our perspective, here are how the approaches differ in their focus and methodology:

How To Create Effective MQL and BI Models: 3 Steps Each

Both models score opportunities. Creating models for both the MQL and buyer intent approaches involves a combination of technology, data analysis, and domain expertise. Your organization’s capabilities may impact which approach you take.

Here’s a brief overview of the process for each:

3 Essential Steps to Build an MQL Model:

1. Identify the criteria: This step entails defining the specific criteria that indicate an opportunities’ readiness to engage with the Sales team. Demographic data such as job title, company size, and industry are often used and we refer to these as fit criteria.
2. Establish how you will score the opportunity: Assign scores to opportunities based on their fit with the criteria and their level of engagement with marketing activities.
3. Set thresholds: Determine the minimum opportunity score required to qualify the opportunity to be ready to be passed to the Sales team.

3 Essential Steps to Build a Buyer Intent Model:

While creating a BI Model is a bit more complex, with today’s analytical tools it is within reach of most organizations.  Here’s what the steps entail.

1. Gather data: The BI approach takes behavioral data to create. You will need to be able to collect and aggregate behavioral data from various sources, including website analytics, content consumption, and search data.
2. Define intent signals: This step requires you to identify key indicators of buyer intent, such as specific content consumption patterns, frequency of website visits, or specific search queries.
3. Analyze and interpret data: You will need to utilize advanced analytics techniques, such as machine learning algorithms, to analyze the data and identify patterns and signals of intent.

Establish intent scoring: Assign scores to prospective opportunities based on their demonstrated intent signals, allowing for prioritization and personalized engagement.

Regardless of which approach you implement, it is important to note that both approaches require ongoing refinement and optimization based on feedback, performance analysis, and market changes. Continuous monitoring and adaptation are essential for maintaining the effectiveness of either lead qualification model.

How Do Buyer Intent Signals Lead to Better Conversion Rates?

With the pros and cons of each model, and between models, in hand, we can use the Six C’s (contact, connect, conversation, consideration, consumption, and community) to illustrate the differences with a brief example.  Let’s consider a software-as-a-service (SaaS) company that offers project management solutions to businesses.

If a company employs the MQL approach, they will establish predefined criteria such as target industry, company size, and job title to qualify opportunities, such as John Smith, Marketing Director at XYZ Corp, a mid-sized technology company.

Here’s what happens next:

John Smith visits the company’s website and downloads a whitepaper on project management best practices. He also subscribes to the company’s newsletter and attends a webinar on improving team collaboration. Based on the predefined MQL criteria, John meets the requirements as he belongs to the target industry and holds a relevant job title. Even though John is at the contact and connection stage, the Marketing team assigns a high lead score to John based on the content he consumed and passes him on to the sales team for further engagement.

Now, let’s explore the same SaaS company using the buyer intent approach to qualify opportunities based on behavioral data and intent signals.

Sarah Johnson, Operations Manager at ABC Manufacturing, regularly visits the company’s website, specifically spending a significant amount of time on pages related to project planning and resource allocation. She has also engaged with the company’s blog, reading articles on effective project management strategies. Furthermore, she has conducted multiple searches for project management software comparisons and pricing information. She too has downloaded the white paper, attended a webinar, and subscribed to the newsletter.

Analyzing these intent signals, the company’s Marketing team scores Sarah as a high-intent prospect because her behavior suggests she is actively researching and evaluating project management solutions. The Marketing team tailors their marketing efforts to provide personalized content and resources, such as case studies and product demos, to address her specific needs and pain points, which she downloads. Sarah is more than a contact and connection; she has moved to the conversation stage in her journey. She is now ready to be introduced to a member of the Sales team. Better-qualified leads improve conversion rates.

Answered: The Best Choice is Buyer Intent. It Improves Conversion

Answered: Within the B2B sector, choosing the most effective approach for opportunity qualification is crucial for driving sales and maximizing revenue. Both the MQL and BI approaches have their merits, but we believe certain factors make one approach more suitable for B2B.

In the B2B landscape, where the sales cycle tends to be longer and involves multiple stakeholders, the BI approach often proves to be the best choice. Why because it is proven to increase conversion rates.

Gartner research shows that the average conversion rate at the top of the marketing funnel is 6%, but organizations using intent data are two times more likely to have a 10% conversion rate.  A higher success rate at the top of the funnel means compounding returns through later stages.

Buyer intent data improves the conversion rate by enabling:

1. Enhanced Understanding of Decision-Making Processes: B2B purchases typically involve a complex decision-making process, often requiring buy-in from various individuals in various functions within the organization. The BI approach, which focuses on analyzing behavioral data, provides valuable insights into the intent and interests of different stakeholders. This enables businesses to tailor their future marketing and sales strategies and optimize existing activities to address the specific needs and pain points of each stakeholder involved, ultimately improving the chances and speed of conversion.
2. Account-Based Marketing (ABM) Alignment: Account-based marketing has gained significant popularity in the B2B space, as it allows businesses to focus their efforts on high-value target accounts. The BI approach complements ABM strategies, as it enables companies to identify accounts that are actively researching and engaging with their content. By leveraging buyer intent data, B2B companies can prioritize their resources and efforts on the accounts that demonstrate the highest intent, increasing the effectiveness of their marketing and sales activities.
3. Long-Term Relationship Building: B2B relationships often extend beyond a single transaction and rely on long-term partnerships. The BI approach, with its emphasis on understanding prospect behavior and intent, allows businesses to nurture leads over time and build stronger relationships. By consistently engaging with potential customers based on their demonstrated interests, B2B companies can establish credibility, trust, and thought leadership, positioning themselves as valuable partners throughout the buyer’s journey.

It is important to note that while the BI approach may be more advantageous for B2B companies, it does not discount the value of the MQL approach entirely. Some B2B organizations may still find value in using MQLs to establish initial contact and gather basic lead information. However, the BI approach provides the necessary depth and context required for successful B2B opportunity qualification and conversion.

choice between the MQL and BI approach depends on the specific needs and goals of the business. Companies should carefully evaluate their target audience, sales cycle, and marketing strategies to determine which approach aligns best with their objectives and resources. Both approaches have their advantages and limitations in qualifying new customer opportunities. The MQL approach offers standardized criteria and scalability but lacks context and behavioral insights. The BI approach enables personalized engagement and better lead quality but requires sophisticated data analysis and raises privacy concerns.

Consider integrating these approaches to effectively identify and engage with potential customers who are most likely to convert into valuable customers.

Want to explore how to optimize your opportunity qualification process and model? Take advantage of our Ask Laura Advisory Services.