The situation
A senior loan officer in Denver, refi-focused, with an 812-contact past-client database accumulated over six years of W-2 origination at a previous lender. The database had real refi opportunity — but the LO had no system for surfacing it. Quarterly newsletter touches weren’t doing it.
What got shipped
The snapshot’s rate-drop alert workflow became the centerpiece. The LO imported the past-client database (original loan amount, original rate, original close date, current cell, current email), and within 24 hours the daily benchmark watch was active.
Additional pieces shipped:
- The refinance calculator embedded on a dedicated refi landing page.
- An “annual equity update” email to past clients, fired quarterly, with cash-out scenarios for borrowers showing material equity growth.
- The pre-qualification automation tuned for refi (faster docs, smaller intake — most refi borrowers already have an LOS file from origination).
Illustrative outcomes
Over the first 60 days:
- 94 rate-drop alerts fired across the database (roughly 12% of the contact base).
- 31 borrowers replied or clicked through to the refi calculator.
- 11 refis entered the pipeline.
- Estimated commission pipeline added: ~$112k (assumes average loan size ~$340k, ~1% commission split across LO + brokerage).
What worked
The rate-drop workflow’s specific message language seems to matter. The default message references the borrower’s actual current rate, projected monthly savings, and break-even — all numbers specific to their loan. Borrowers reply because the math is theirs, not a generic blast.
The equity-update email did less well in the short term. Equity-cash-out conversations move slower than rate-and-term refi — the email seeded conversations that may close in Q3 or Q4 rather than within the 60-day measurement window.
What we’d do differently
We’d add the AI receptionist earlier. Initially the LO chose to handle replies personally (which was fine — they’re a senior LO with time), but as alert volume grew, the AI could have handled the first-touch qualification and saved the LO time for the highest-intent conversations.
Caveat
This is an illustrative scenario. Actual refi pipeline depends on market rate environment, past-client data quality, and dozens of variables. A rising rate environment produces a different outcome than a falling rate environment — most originators see refi pipeline cluster in periods when benchmark rates have dropped at least 75-100 basis points below the average rate in their database.
“I knew my past-client database had refi money in it. I didn't have a system to surface it. The snapshot's rate-drop workflow surfaces it daily — I just walk into the calls.”