Better leads. Faster conversions. Lower spend.
The promise of Pinpoint Predictive’s insurance marketing analytics – powered by your data.
Overview
Our actuary-built, research-backed models help carriers turn first-party data into a centralized insight engine for lead profitability analytics, lead conversion prediction, and direct mail response rates. With Pinpoint, you can predict profitable leads, filter out high-churn prospects, and optimize marketing channels where they’ll make the biggest impact.
Maximize Lead Quality For Higher Conversion Rates
Discover how to automate better lead quality and higher conversion rates with our smart, compliant, custom models.
Lead Profitability
Mail Response Likelihood
Conversion Likelihood
Predict Loss Potential Before You Quote
Pinpoint’s Lead Profitability model combines advanced lead profitability analytics with a lead quality scoring tool to give carriers clear visibility into profit before quoting. Support your underwriting objectives with smarter lead gen filtering and lead list filtering.
Improve quote efficiency by deprioritizing low-value or high-churn leads
Boost marketing ROI without increasing acquisition costs
Integrates seamlessly with existing quoting or lead management workflows
Focus on Leads Likely to Convert
Pinpoint’s Conversion Likelihood model uses advanced lead conversion prediction and sales conversion analytics to help your teams identify high-value prospects. Built as a binary classifier, it delivers precise prospect conversion scoring so marketing and sales can prioritize outreach on leads most likely to convert from quote to bind.
Improve acquisition rates without increasing marketing spend
Reduce acquisition friction and lower CAC by focusing on high-likelihood leads
Streamline sales and marketing workflows by minimizing redundant tasks
Target the Right People, Cut Campaign Waste
Boost direct mail response rates and trim wasted spend with Pinpoint’s Mail Response Likelihood model. Using historical first-party data, this mail campaign targeting model scores contacts based on their likelihood to engage with physical mailers. The result: smarter targeting, leaner budgets, and higher ROI.
Increase engagement by focusing on individuals most likely to respond
Predict mail response with precision; no broad A/B testing required
Allocate budget more effectively for maximum campaign impact
Key Marketing Benefits
Higher Marketing ROI
Spend less, earn more from every campaign.
Smarter Targeting & Prioritization
Reach the right leads at the right time.
Workflow Efficiency
Save time and reduce acquisition friction.
Case Studies
Predictive Power at Work
Auto Case Study: Loss Cost Model to Optimize Marketing Spend
Pinpoint helped reduce Loss Ratio by 4 points on personal auto Client: National Auto Carrier Line of Business: Auto Insurance Number of Policies: 714,577 Average Premium: $2,495 Use Case Focus: Customer Acquisition Challenges for Auto Insurers: The combined ratio for personal auto has worsened from 92.5% in 2020 (the best result in 25 years) to [...]
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Auto Case Study: Claims Severity Predictions
Pinpoint helped reduce Loss Ratio by 3 points with Claims Severity Predictions Client: National Auto Carrier Line of Business: Auto Insurance Number of Policies: 714,577 Average Premium: $2,495 Challenge for Auto Insurer: A national auto insurance carrier looked to augment their modeling and solve the problem of balancing business against increasing claim payouts, increasing costs [...]
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Auto Case Study: Litigation Risk Scores
Pinpoint Helped Identify an Additional 6% of Claimants Likely to Seek Attorney Representation Client: National Auto Insurance Insurer Line of Business: Auto Insurance Model Trained: 130,000 Claimants Use Case Focus: Claims and Litigation A national insurance carrier partnered with Pinpoint to receive Risk Scores identifying the likelihood of Third-party Claimants to seek Attorney Representation. With [...]
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Home Case Study: Claims Frequency Prediction to Optimize Growth
Pinpoint helped reduce Loss Ratio by 3 points to optimize profitable growth Client: National Home Insurance Carrier Line of Business: Home Insurance Number of Policies: ~300,000 Average Individual Premium: $2,900 A national home insurance carrier partnered with Pinpoint to receive Loss Predictions at the top of the funnel (before the customer journey begins). Their current [...]
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Home Case Study: Loss Cost Prediction to Reduce Non-Weather Water Losses
Pinpoint helped reduce Loss Ratio by 4 points during pre-application Client: National Home Insurance Carrier Line of Business: Home Insurance Number of Policies: ~150,000 Average Individual Premium: $1,291 A national home insurance carrier partnered with Pinpoint to receive Loss Cost Predictions at the top of the funnel (before the customer journey begins). Their current models [...]
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Auto Case Study: Loss Cost Prediction to Optimize Growth
Pinpoint helped reduce Loss Ratio by 4.6 points by focusing on Individual Drivers Client: National Auto Carrier Line of Business: Auto Insurance Number of Policies: 80,000 Average Premium: $1,500 Challenges for Auto Insurer: A national auto insurer partnered with Pinpoint on generating Loss Cost predictions to better understand the profitability on individual drivers and identify [...]
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Home Case Study: Claims Frequency Predictions for Book Acquisition
Pinpoint helped reduce Loss Ratio by 7 points by focusing on Book Acquisition Strategy Client: National Home Insurance Carrier Line of Business: Home Insurance Number of Policies: 200,000 Average Premium: $2,813 Use Case Focus: Book Roll A national home insurance carrier used Pinpoint's platform to generate Loss Predictions. Their existing models were only available after the [...]
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Frequently Asked Questions
What types of problems does Pinpoint help insurance carriers solve?
Pinpoint empowers carriers to make smarter, faster, and more accurate decisions across marketing, underwriting, ratemaking, claims, and more. Our predictive models plug into existing workflows (API or batch) to prioritize high-value acquisition, sharpen pricing and filed rating, improve bind quality, and triage claims early so costs don’t compound, customized to each carrier’s specific experience and outcomes.
How quickly can Pinpoint’s solutions be deployed?
Most solutions deploy in under a week. They integrate seamlessly with any core system or backend process. With a few clicks, our automated platform deploys new models via configurable APIs, turning hypotheses into real-world results with minimal IT disruption.
Do I need large amounts of historical data to get started?
No. Pinpoint’s solutions don’t always require historical outcomes or conversion data. Pinpoint’s lead profitability model is available for scoring, without training on any of your prior loss experience, shortening the window and minimizing the amount of work it takes to realize value in production. Models like conversion likelihood and mail response likelihood however, are custom-models, tuned to your existing outcomes for maximum precision, or mail response likelihood, leveraging historical first-party data.
How does Pinpoint improve marketing ROI?
By prioritizing leads based on profitability, conversion potential, and mail response likelihood, Pinpoint’s marketing analytics solutions allow carriers to focus only on high-value leads. This eliminates wasted spend and sunken costs on low-yield campaigns and ensures marketing budgets are allocated where they’ll have the biggest impact and likelihood to have positive ROI.
What types of marketing analytics models are available in the Pinpoint product suite?
Pinpoint offers three core models for insurance marketing analytics: lead profitability, which identifies and prioritizes profitable leads before quoting. mail response likelihood, which targets individuals most likely to respond to direct mail. conversion likelihood, which predicts which leads are most likely to convert from quote to bind.
Fast-Track Profitable Growth
Data Science That Delivers Business Impact
Pinpoint is an AI company purpose-built for insurers. We combine deep actuarial expertise with modern data science to solve real-world problems across underwriting, marketing, and distribution. Our mission is simple: make insurance more precise and more profitable—one model at a time.