Calculating a Basic Lead Score Using Data

When venturing into the world of inbound marketing, one primary concern is attracting an ample number of leads into the sales funnel. However, as you amass these leads, the challenge then becomes distinguishing between those who are genuinely interested in your product and those who are merely exploring. This is where utilizing data to calculate a basic lead score can make a significant impact.

As you generate a high volume of leads, determining the quality and intent behind each lead becomes critical to focus your efforts effectively. This is where understanding and applying lead scoring can transform your marketing strategy. This piece will guide you through various lead scoring models, key data to assess, and best practices for optimizing your lead scoring efforts.

Table of Contents

Understanding Lead Scoring

Lead scoring involves assigning numerical values to leads based on their likelihood to convert into customers. Factors for scoring include professional information and the extent of their engagement with your online presence. This system enhances the focus of your sales and marketing teams, enabling a more efficient path to customer conversion.

Discover more about lead scoring through this informative video:

Every organization utilizes unique models to assign these values, commonly drawing on past lead data to develop a scoring system. Begin by analyzing attributes of leads who converted to customers against those who did not. This insight helps weight attributes based on their indication of a good fit for your product.

While lead scoring seems straightforward, complexities arise with different business models and leads. To simplify, we’ll explore the basics of creating a lead score, including essential data points and calculation methods.

The Relevance of Lead Scoring

Is lead scoring still effective? Absolutely. The approach has evolved, but its necessity remains for pinpointing potential customers. Modern lead scoring aids in prioritizing and managing leads more efficiently, especially given that 53% of sales professionals in 2023 report increased challenges in selling.

Mark Osborne, B2B sales expert and founder of Modern Revenue Strategies, emphasizes that economic factors make discerning valuable leads more crucial. “Remember the 80/20 rule,” he advises, highlighting that most revenues stem from a small client segment.

Without robust lead scoring, businesses risk thinly spreading resources, losing prime opportunities to competitors who excel in timely engagement.

Exploring Lead Scoring Models

Effective lead scoring models reflect a lead’s true compatibility with your offer. Typically, these models range from 0 to 100 points. Below are six key models based on different types of data:

1. Demographic Details

Demographics play a vital role if you target specific groups like young parents or CIOs. Form questions on landing pages can gauge how well leads fit your target demographic. Consider eliminating outliers by deducting points for non-target categories.

2. Company Facts

For B2B organizations, factors like company size, industry, or type are significant. Tailor form questions to award points for fitting your target business profile and deduct points for non-matching criteria.

Tracking external data such as leadership changes or new investments enhances this model.

3. Behavioral Indicators

Behavioral data offers deep insight into lead intent. Analyzing site engagement, content interaction, and visitor frequency helps determine genuine interest.

Higher scores may be attributed to leads frequently visiting critical pages or consistently engaging with high-value offers.

4. Email Interaction

Email engagement levels reveal a lead’s interest. Open rates and click-through rates provide a clearer picture, enabling focused follow-ups on the most engaged recipients.

5. Social Media Activity

Social engagement also indicates interest, with a strong correlation to high-quality lead generation through referrals. Monitor interactions such as clicks, shares, and comments on social posts.

6. Spam Indicators

Identify and deduct points for spam-like behaviors in form submissions, such as uncapitalized names or excessive keyboard patterns, and monitor suspicious email domains.

Determining Key Data Points

Identifying valuable data points involves collaboration with your sales team, customer feedback, and analytics. These insights will help determine the most influential content for lead conversion.

Sales Team Insights

Your sales team’s direct interaction with leads provides critical insights into effective marketing materials that drive conversion. Utilize their feedback to guide your point distribution.

Customer Feedback

Customer interviews reveal what convinced them to purchase, offering diverse perspectives, especially from varied sales cycle experiences.

Analytical Data

Dive into marketing analytics to identify content that leads to conversions. Use this data to allocate points to impactful offers and emails.

Run contact reports and attribution analysis to pinpoint high-conversion activities for more precise scoring.

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Multiple Lead Scores

If you target various customer segments, multiple lead scoring systems can better serve your needs. Consider these examples:

Fit vs. Interest –

Evaluate leads on both fit (e.g., geographical, industry-specific) and engagement levels to prioritize high-potential contacts.

Different Personas –

Score leads based on distinct personas to align leads with the appropriate sales teams effectively.

New Business vs. Up-sell –

Create separate scores for new prospects and existing customers, recognizing that their signals differ significantly.

Calculating a Basic Lead Score

Here’s a straightforward method for calculating lead scores:

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Manual Lead Scoring Method

1. Calculate Lead-to-Customer Conversion Rate

Determine your lead-to-customer conversion rate by dividing the number of new customers by the number of leads. This rate serves as your baseline.

2. Identify High-Quality Lead Attributes

Choose attributes indicative of high-quality leads, such as industry relevance or specific actions taken (e.g., requesting a demo).

3. Determine Close Rates for Each Attribute

Calculate how often leads with certain attributes convert to customers to establish the relative value of these attributes.

4. Assign Point Values Based on Close Rates

Awards points for attributes outperforming average close rates, ensuring consistency in point value assignment based on relative performance.

Advanced Lead Scoring Techniques

• Logistic Regression: Employs data mining for precise scoring by accounting for interactions between multiple lead attributes.
• Predictive Lead Scoring: Utilizes machine learning for streamlined and continuously optimized lead scoring.

Lead Scoring Best Practices

Implement the following best practices for effective lead scoring:

Leverage AI and Machine Learning

Incorporate AI to enhance personalisation and efficiency in lead scoring, with 66% of professionals finding AI beneficial in understanding customers.

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Lead with Data

Base your lead scoring on solid data to make informed, objective decisions on lead prioritization.

Real-Time Adjustments

Stay agile and adjust your lead scoring models based on current data to remain responsive to market changes.

Enhance Sales with Lead Scoring

By correctly scoring your leads, you can identify those most likely to convert and focus your efforts on them. Feel free to use our lead scoring templates to set up your own scoring system today!

Editor’s Note: This post was originally published in January 2019 and has been updated for comprehensiveness.

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