Checklist for Lead Prioritization: When Scores Are Not Effective

Most lead prioritization models fail to deliver real conversion gains because they rely on thin lead form data and aren’t integrated with loan officer performance.

Here’s a checklist for Lead Prioritization to assess why lead scoring might not be working and how to fix it

Are we relying solely on self-reported lead form data?

  • Are we acknowledging that short-form data lacks depth for meaningful scoring?
  • Are we supplementing lead form inputs with third-party data (real-time email matching, credit soft pulls, property lookups)?
  • Are we running tests to compare conversion rates of short-form vs. long-form leads?

Are we adjusting scoring models based on missing or unreliable data?

  • Are we filtering out leads with incomplete or inconsistent data points?
  • Are we tracking how often missing form fields correlate with lower conversions?
  • Are we weighing certain fields (e.g., credit score, intent signals) more heavily than others?

Are we incorporating behavioral intent signals into scoring?

  • Are we factoring in multi-channel engagement (email opens, website visits, ad clicks)?
  • Are we tracking repeat visits to high-intent pages (e.g., rate calculators, loan program FAQs)?
  • Are we prioritizing leads who interact with multiple touchpoints over one-time form fills?

Are we testing real-time data augmentation?

  • Are we matching email IDs or phone numbers against third-party data sources?
  • Are we incorporating demographic, financial, or behavioral data in real time?
  • Are we analyzing whether augmented data improves conversion accuracy?

Are we tracking conversion rates by lead source and adjusting scores accordingly?

  • Are we scoring direct organic leads the same way as third-party aggregators?
  • Are we analyzing which sources drive the highest quality, not just volume?
  • Are we lowering scores for lead sources with historically poor conversion rates?

Are we incorporating lead scoring into lead allocation?

  • Are we assigning leads based on loan officer strengths instead of treating all LOs equally?
  • Are we tracking which LOs perform better with certain lead types (e.g., FHA, VA, jumbo loans)?
  • Are we dynamically re-assigning leads based on LO performance over time?

Are we continuously refining scoring based on actual closed loans?

  • Are we analyzing why some “low-priority” leads still convert?
  • Are we feeding closed-loan data back into the model to improve future scoring?
  • Are we avoiding static models that don’t adapt to market changes?

Are we considering alternative scoring methodologies?

  • Are we relying solely on statistical modeling, or are we testing rule-based scoring?
  • Are we weighting real-time behavior higher than form inputs?
  • Are we experimenting with AI-based scoring to improve predictive accuracy?

Are we tracking the financial impact of lead prioritization?

  • Are we comparing revenue generated from high vs. low-scored leads?
  • Are we analyzing whether scoring improves Cost Per Funded Loan (CPFL)?
  • Are we tracking wasted sales effort on misclassified leads?

Are we ensuring lead prioritization integrates with the broader sales strategy?

  • Are we aligning lead scoring with marketing and sales KPIs?
  • Are we involving loan officers in scoring discussions to refine criteria?
  • Are we making adjustments based on real-world loan officer feedback?

Final Thought:

Lead scoring without a solid data foundation leads to misplaced priorities, missed opportunities, and inefficiencies. A successful model must go beyond lead form inputs, integrate real-time data augmentation, and align with loan officer performance.

Use this checklist for lead prioritization to audit and maximize your conversion rates.

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