How is lead scoring adjusted over time based on results?

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Adjusting lead scoring over time based on results is a critical part of maintaining an effective and efficient sales and marketing funnel. A lead scoring model is not a "set it and forget it" tool; customer behaviors, market conditions, product offerings, and business goals all evolve, and so too must your lead scoring.

This iterative refinement process relies heavily on data analysis and cross-functional collaboration between sales and marketing. Here's how it's typically done:

1. Establish Baselines and Initial Model:
Define Initial Criteria: Start by collaborating with sales to identify attributes (demographics, firmographics, job title, industry) and behaviors (website visits, content downloads, email opens, webinar buy telemarketing data attendance, demo requests) that historically indicate a good fit and strong intent.
Assign Initial Scores: Assign points (positive and negative) to these criteria based on initial hypotheses and industry best practices.
Set Thresholds: Define the score at which a lead becomes an MQL (Marketing Qualified Lead) and/or SQL (Sales Qualified Lead).
Implement: Put the initial lead scoring model into action within your CRM and marketing automation platforms.
2. Monitor Performance Metrics:
Once the model is active, continuously monitor key performance indicators (KPIs) related to lead progression and sales outcomes. This data is the foundation for adjustment.

MQL to SAL Conversion Rate: How many of the leads marketing considers "qualified" are actually accepted by sales?
SAL to SQL Conversion Rate: How many accepted leads does sales successfully qualify further?
SQL to Opportunity Rate: How many sales-qualified leads turn into active opportunities?
Opportunity to Win Rate: What percentage of opportunities convert into closed deals?
Sales Cycle Length: How long does it take for leads with different scores to close?
Average Deal Size: Do higher-scoring leads result in larger deals?
Lead Source Performance: Are leads from certain sources consistently scoring higher and converting better?
Lost Lead Analysis: For leads that were scored highly but didn't convert, what were the reasons for loss?
3. Gather Feedback from Sales:
Data tells you what is happening, but sales feedback tells you why (or at least provides crucial context).

Regular Meetings: Schedule recurring (e.g., monthly or quarterly) meetings between sales and marketing to discuss lead quality.
Qualitative Insights: Sales reps are on the front lines. They can provide invaluable anecdotal evidence:
"Leads from X campaign are scoring high, but they are consistently asking for features we don't offer."
"The leads with Y job title are very engaged, but they never have the budget."
"When a lead mentions Z competitor, they always churn out of the pipeline, regardless of their score."
CRM Feedback Mechanisms: Implement fields in the CRM where sales reps can provide feedback on lead quality (e.g., "poor quality," "not ready," "good fit but wrong timing") or specific reasons for lead disqualification.