This is the data that trains your AI isa.

Every captured conversation compounds into a behavioural fingerprint of what actually works in your organization. Generic models cannot see this. Yours will.

Conversations captured

12.4K

this quarter

Patterns detected

1,284

statistically significant

Training-ready hours

892

labeled & structured

Fingerprint coverage

84%

org-wide

Patterns that predict close

6.2 vs 2.8

Discovery questions per call. Top vs bottom quartile

n = 12,400 calls · +$180K/qtr impact

1.4h vs 18.6h

Time-to-first-follow-up after showing. Top vs bottom quartile

p < 0.001 · +$340K/qtr impact

78%

Top closers pivot to monthly payment framing on rate objections

vs 64% of bottom quartile just agreeing rates are high

45sec

Avg time top closers spend acknowledging + differentiating on a competitor mention

vs 68% of bottom quartile ignoring or badmouthing

8.4min vs 3.8min

Call duration on qualified leads. Top vs bottom quartile

Discovery depth correlates directly with +$210K/qtr

2.1x

Win rate lift when timeline question asked in first 3 minutes

n = 4,180 qualifying calls this month

What top closers do differentlyTop vs bottom quartile · 90 days

Behaviour	Top quartile	Bottom quartile	Revenue impact
Discovery question depth	Asks 6.2 questions per call avg	Asks 2.8 questions per call avg	+$180K/qtr
Time-to-first-follow-up after showing	1.4 hours	18.6 hours	+$340K/qtr
Rate objection handling	Pivots to monthly payment framing 78% of time	Agrees rates are high 64% of time	+$95K/qtr
Competitor mention response	Acknowledge + differentiate in 45 sec	Ignore or badmouth 68% of time	+$120K/qtr
Call duration on qualified leads	8.4 min avg	3.8 min avg	+$210K/qtr

Why it is a moat: the fingerprint is org-specific. What closes deals at Horizon Realty Group isn't what closes at another brokerage. A generic sales AI cannot learn this; only the platform capturing your calls can.