A critical challenge for your B2B firm is differentiating promising leads from a vast pool of prospects. Studies reveal that, on average, only 27% of leads sent to sales are qualified, resulting in wasted resources and effort.
At Revnew, we faced a similar hurdle. Without an established method to assess lead quality, we risked investing time in prospects that might not convert. Statistics show that around 79% of marketing leads fail to convert due to inadequate lead nurturing.
We recognized this gap and understood the urgency of implementing a robust B2B lead scoring system. Why? Companies utilizing lead scoring witness a 77% increase in lead generation ROI.
Therefore, we prioritized establishing a systematic, data-driven approach to prioritize leads. Our goal was to enhance efficiency and empower the team to focus on leads with higher conversion potential.
Drawing from our experience, let’s explore four effective lead scoring best practices backed by statistical evidence. Additionally, we’ll shed light on the crucial role of an efficient lead-scoring model in B2B sales.
But as usual, let’s cover the basics first!
Marketing teams usually use a B2B lead scoring system to evaluate the potential value of a lead for sales opportunities that ultimately can convert into a sales appointment. It’s an integral part of sales success and can be predetermined by implicit, explicit, and negative criteria.
Lead scoring for B2B companies involves assigning scores that signify the following:
In essence, you must incorporate lead scoring into your strategy if your goal is to construct a lead funnel that:
No. Lead scoring differs from deal scoring. Sales reps use lead qualification frameworks like BANT, MEDDIC, and CHAMP to qualify high-scoring sales opportunities passed by the marketing team. It enables them to score the deals they create for more strategic closing.
What Are the Core Data Source Types in B2B Lead Scoring?
The three key types of data sources in lead scoring data include:
| Implicit Data (Based on Behaviors and Interests) | Explicit Data (Measurable and Specific Criteria) | Negative Data (Indicating a Lead May Not Be a Good Fit/ Is Less Likely to Convert) |
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Social Media Engagement
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Technographic Data
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Industry or Geography
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Purchase Intent
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Firmographic Data
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Email Engagement
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Email Engagement
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Demographic Data
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Data Quality:
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Online Behavior
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Intent Data
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Others:
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Understanding the distinction between explicit, implicit, and negative data sources is fundamental to building an effective lead scoring model. Let’s see how.
Now, let’s check out four tried and proven tactics to create an impactful lead scoring framework that helps in lead classification to drive your business toward increased conversion rates and revenue.
Before prioritizing and pinpointing leads, you must delineate the attributes of your intended customers. Building an ICP is one of your pivotal phases in lead assessment.
Explore the following queries as you shape your ICP:
Once you comprehensively understand your ICP, you can target companies that align precisely with the explicit criteria. This method ensures you're not aimlessly pursuing leads but actively engaging and organizing qualified prospects.
Utilizing historical data is essential in the lead scoring process. Why? It enables you to identify patterns and trends essential for predicting future outcomes.
Plus, the analysis of historical data offers valuable insights into the attributes and behaviors of previously converted leads.
To fully grasp the journey of successful conversions, enriching your data with insights derived from past data is imperative.
As such, you can refine your lead-scoring strategies and enhance predictive capabilities for future outcomes.
Setting lead-scoring parameters involves systematically determining the factors influencing your lead generation process. Here's a step-by-step guide to establishing these criteria:
Once you've established your B2B lead scoring criteria, the subsequent phase involves their prioritization. You can follow a tiered categorization based on their relative importance.
Consider structuring the scoring system as follows:
Suppose a lead engages in specific actions, amassing a combined score of 9, positioning them within the "essential criteria" category. This score signifies a notable qualification, prompting the need for the following actions:
Let’s now check out an illustration of a lead scoring model that combines various criteria, their respective scoring scales, and associated actions or queries.
| Criteria | Scoring Scale | Queries/Actions | Directed to Sales Team |
| Company Size | 1 to 5 | Evaluate based on company size or revenue | Yes |
| Industry | 1 to 3 | Determine the lead’s industry or sector | Yes |
| Website Engagement | 1 to 4 | Analyze website interactions and engagement | Yes |
| Email Interaction | 1 to 3 | Track engagement with email campaigns | Yes |
| Job Title / Position | 1 to 5 | Assess the lead’s job title or position within the company | Yes |
| Purchase Intent | 1 to 4 | Analyze intent data such as pricing page visits | Yes |
Explanation:
Typically, criteria with higher scores (like Company Size, Website Engagement, Email Interaction, Job Title/Position, and Purchase Intent) are routed to the sales team for focused engagement and follow-up.
Lead scoring software plays a crucial role in evaluating and prioritizing leads, aiding your sales and marketing teams in focusing their efforts on the most promising opportunities. Here are five leading solutions in the industry.
So, here it is. We've explored the fundamentals of lead scoring and transforming your potential prospects into revenue-generating sales-ready leads. Following a straightforward four-step process, you can establish and refine your lead scoring system as you collect more lead data.
We’re an industry-dominated B2B lead generation agency helping businesses like yours for more than a decade. If you seek deeper insights or wish to explore additional strategies, feel free to reach out for further assistance and guidance on enhancing your profit-driving potential.