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.