VRTCLS.AI
Automotive Predictive Intelligence

In-market buyer probability, mapped to model and dealer.

OEMs, dealer groups, and finance arms use predictive intelligence to identify in-market vehicle buyers, model-level intent, and dealer-proximity probability. The result: dramatic improvement in test-drive cost and lead-to-close economics.

Test drive rate
+33%
qualified booked
Lead-to-close lift
+24%
30-day cohort
Model-level intent recall
76%
90d window
Methodology · Signals

What makes Automotive different.

In-market probability

Behavioral signals correlated with active vehicle consideration within a defined purchase window.

Model-level intent

Specificity at the make/model level, not just category-level interest.

Charts · Calibrated

Decay, velocity, and cost — measured.

Per-vertical curves derived from the platform's calibrated model output. Industry averages overlaid for reference.

Lead-quality decay

Hours since first intent signal

Conversion velocity

Days from first contact

CAC reduction · 9-month rollout

Traditional vs. predictive within the vertical

Case Study · Verified

Inside a deployment

Finance+3.1x ROAS

Mid-market lender lifts ROAS 3.1x with behavioral risk + intent overlay

6 months · consumer finance · $1.8M monthly spend

Behavioral risk scoring integrated with intent signals produced cleaner top-of-funnel for a consumer lender. The combined model reduced underwriting waste 38% and lifted return on ad spend 3.1x within two quarters.

FAQ · Schema-marked

Common questions

Can you target by dealer geography?+

Yes. Dealer-proximity probability is a core signal class.

Predictive intelligence · enterprise onboarding

Move from list-buying to probability-buying.

Engage your account team for a calibrated intelligence estimate, methodology walkthrough, and a sandbox environment scored against your own audience.