Why the Lead Scoring Model Is Structured This Way

Overview

The B2C Prospect Lead Scoring model is designed around three core principles:

1. Intent

2. Qualification

3. Recency

These principles ensure the model identifies prospects who are most likely to apply for membership while preventing older or low-quality activity from artificially inflating scores.

Intent Signals Receive the Most Weight

The model assigns the highest scores to actions that historically indicate strong buying intent.

Examples include:

• Form submissions

• Meeting bookings

• Sales email replies

• Phone conversations

These signals demonstrate that a prospect is moving beyond passive research and is actively considering membership.

Sales Interactions Are Strong Indicators of Readiness

Direct engagement with sales representatives typically occurs later in the decision process.

Examples include:

• Calls with representatives

• Booked meetings

• Direct replies to sales emails

Because these signals indicate a high level of interest, they contribute significantly to engagement scoring.

Fit and Engagement Are Evaluated Separately

A prospect may appear highly engaged but still not represent an ideal fit.

Separating fit from engagement prevents situations where:

• Highly engaged but unqualified prospects dominate the score rankings

• Sales teams spend time on contacts unlikely to convert

Evaluating both dimensions ensures the model prioritizes qualified prospects who are actively interested.

Data Completeness Improves Lead Quality

Contacts who provide additional information are easier for sales teams to contact and qualify.

Examples include:

• Email address

• Phone number

• Location information

• SMS opt-in permissions

More complete records generally correlate with higher conversion rates.

Lead Source Influences Expected Conversion

Some acquisition channels historically generate more qualified prospects than others.

Sources with stronger conversion performance are given additional fit weighting.

Examples include:

• Organic search

• Direct traffic

• Paid search

• Referral traffic

Negative Signals Reduce Priority

The model subtracts points when signals indicate a prospect is unlikely to convert.

Examples include:

• Opt-outs

• Already being a member

• Explicit lack of interest

• Disqualification by sales

These signals help prevent unnecessary outreach and ensure sales time is focused on viable opportunities.

Score Decay Maintains Relevance

Engagement scores gradually decrease over time to reflect current interest rather than historical activity.

Without decay, contacts who interacted months earlier could remain artificially high-scoring despite losing interest.

Decay ensures the score represents recent buying behavior.

Summary

The scoring model is designed to prioritize contacts who are:

• A strong prospect fit

• Actively engaging with CHM

• Currently showing intent

This approach helps align marketing activity with sales outreach and improves overall lead prioritization.