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?