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The Scoring Pipeline

Score Every Lead. Win Every Bid.

9 scoring models across Medicare, P&C, auto, solar, and final expense. A sequential pipeline evaluates every lead in real-time, transforming lead buying from flat-rate guessing into precision-priced acquisition. Medicare models validated across the AEP 2026 season.

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The Sequential Pipeline

Every model in the pipeline, from pre-bid scoring to post-acquisition identity resolution to retention intelligence.

ZipLync:MA
Real-time ping-stage evaluation in under 3ms. Determines whether a lead is worth acquiring before you bid. Analyzes phone-geographic concordance across 29 features to predict Medicare conversion propensity. 88x lift between best and worst leads.
ProLync:MA
Medicare Advantage conversion scoring. 23 features, 750x lift between best and worst deciles. The flagship model — filters non-converters with 97-98% NPV accuracy. 15-80ms latency.
ProLync:DSNP
Dual Special Needs Plan scoring for dual-eligible leads (Medicare + Medicaid). 32 features trained on dual-eligible enrollment outcomes. 99.8x lift. Identifies leads most likely to enroll in D-SNP products.
ProLync:CSNP
Chronic Special Needs Plan scoring for leads with chronic health conditions. 35 features engineered for chronic-condition enrollment prediction. 48.6x lift.
ProLync:SNP
Combined Special Needs Plan scoring (DSNP + CSNP). 45 features, highest AUC of all models (0.776). Top decile captures ~50% of all SNP conversions. 91x lift.
ZipLync:Homeowner
Pre-bid geographic risk intelligence for P&C insurance leads. Scores 37,000+ US ZIPs across 89 features from 7 government data sources — FEMA hazards, home values, flood claims, crime, and more. Adaptive scoring with phone concordance and IP geolocation. Sub-1ms.
ZipLync:Auto
Pre-bid scoring for auto insurance leads. Combines street-level crash risk, vehicle density, and geographic patterns to identify high-intent auto shoppers before you bid.
ZipLync:Solar
Lead scoring for residential solar. Combines property characteristics, solar irradiance data, utility rates, and demographic propensity signals across 59 features to predict conversion likelihood.
ZipLync:FE
Pre-bid scoring for final expense life insurance leads. Two-factor expected-value model combining conversion probability and persistency to identify leads worth acquiring.
BidLync Auction Engine
Every bid is mathematically indexed to the lead's predicted score, your CPA target, and real-time budget pacing. Price-per-Probability bidding replaces flat-rate guessing.
ShieldLync Book Protection
Retention scoring for Medicare agencies. Identifies at-risk members in your book of business before they disenroll, so you can intervene during AEP and OEP with targeted retention campaigns.
Closed-Loop Learning
The platform ingests final sales dispositions from your CRM to continuously improve scoring accuracy. Models get sharper with every sales cycle.
MULTI-MODEL INTELLIGENCE

Nine Models. Sub-200ms.

ZipLync evaluates at the ping in 1-3ms across five verticals — MA, Homeowner, Auto, Solar, and FE. Four ProLync models — MA, DSNP, CSNP, SNP — score post-acquisition against 256M consumer records in 60ms. The full pipeline runs under 200ms.

1-3ms

ZipLync

60ms

ProLync (4 models)

<1ms

ZipLync:Homeowner

PIPELINE STABLE

Live Scoring Output

ZipLync:MATIER 7 | 0.517
ProLync:MADECILE 9 | 0.728
ProLync:DSNPDECILE 7 | 0.541
ProLync:CSNPDECILE 5 | 0.389
ProLync:SNPDECILE 8 | 0.612
ZipLync:HomeownerTIER 6 | 9.672
Match QualityHIGH
Integrations

Works with your existing stack

27 live integrations across call tracking, lead distribution, contact centers, and CRMs. Add BidLync as a target in your platform in under 5 minutes.

Contact Centers & Dialers
ConvosoFive9NICE CXoneGenesys
CRM & Lead Management
SalesforceHubSpotVelocify

How the platform works

Specifics on latency, model structure, and how scoring fits an existing stack.

Why does pre-bid lead scoring have to run in milliseconds?

A real-time lead auction resolves in a few hundred milliseconds end to end, so any scoring meant to inform the bid has to fit inside that window. RiskLync's pre-bid model returns a score in 1–3ms from phone number and ZIP code, and the full ping-to-bid pipeline runs under 200ms. A model that takes a second to answer cannot participate in the decision it exists to inform, however accurate it is. This is the constraint that separates pre-bid scoring from post-purchase lead analysis, quality dashboards, and fraud filtering, which all run after the money is already committed.

What is pre-bid lead scoring?

Pre-bid lead scoring evaluates a lead before you pay for it, during the ping stage of a real-time auction. The scoring model sees only the data available at ping time — typically phone number and ZIP code — and returns a conversion propensity score fast enough to inform the bid. RiskLync's ZipLync returns a pre-bid score in 1–3ms.

What data do you need to score an insurance lead?

Pre-bid scoring needs only a phone number and ZIP code, because that is all a publisher sends at ping time. Post-acquisition scoring adds name, age, and gender to perform identity resolution against a consumer reference set, which produces a substantially more accurate score once the lead is already purchased.

What do AUC and lift actually mean for a lead scoring model?

AUC measures how well a model separates converters from non-converters across all thresholds, where 0.5 is random and 1.0 is perfect. Lift measures the ratio between the best and worst scored groups — an 88x lift means leads in the top band convert 88 times more often than those in the bottom band. Lift is the number that maps directly to bidding decisions; AUC describes overall model quality.

Why use separate models for MA, DSNP, CSNP, and SNP?

The populations behave differently and the signals that predict conversion are not the same. Dual-eligible and chronic-condition special needs plans have different eligibility constraints, different enrollment timing, and different demographic concentrations than general Medicare Advantage. A single blended model averages these differences away, so RiskLync runs a dedicated model per product.

Does lead scoring fit into an existing ping-post stack?

Yes. Scoring is delivered as an HTTP API call that returns a score and reason code, so it sits inside an existing ping-post flow rather than replacing it. Latency is the binding constraint, which is why the pre-bid model is built to answer in single-digit milliseconds.

    Platform — Predictive Lead Scoring for Insurance | RiskLync