Case Study: New Auto Analytics Help Attract Future Loyal Customers

July 9, 2025 / 2 min read

This is the third post in a series sharing insights from our white paper, Auto Policy History Analytics: The Future of Risk Segmentation. Amid record-setting shopping behavior with few signs of slowing, the white paper features analysis and innovations to unlock predictive policy insights at Rate Call 1 to drive profitable, sustainable growth.

With Verisk’s Coverage Verifier Analytic Objects (CVAO), dozens of actionable analytic objects enable use cases that can enhance quoting-engine customer experience and profitability.

Confidently predict future outcomes at Rate Call 1
Harness next-generation policy insights to help inform critical upfront decisions
Spot and attract future loyal customers at the point of quote

Innovative auto policy history analytics act as unique lenses and yield deeper, more meaningful insights that can be harnessed in multiple ways.

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CVAO quickly delivers diverse insights, providing insurance-ready analytics that seamlessly integrate with minimal demand for IT resources.

Here’s how insurers can act immediately on this newly available information:

Risk management

Determine appropriate bind channel, diversify the application process to verify questionable eligibility data, and fast-track qualified leads.

New business eligibility

Capture facts related to eligibility criteria such as length of prior continuous insurance, prior limit history, and number of address changes.

Pricing and tiering

Leverage CV data and analytics to help power tiering, surcharging, and discounting decisions while aligning the rate with the risk.

Payments and lifetime value

Negate nonpayment and reinstatement cycles with appropriate down payments and facilitate profitable growth via better retention and lifetime value analysis.

Case study: Micro-segmenting rates to attract future loyal customers¹

Harness the power of CVAO to help solve intricate industry challenges. Policy history analytic objects are building blocks for solving nuanced problems. Precisely defined objects from targeted insight domains can be combined to help discern risk, develop rating factors, and tailor workflows with adjustable lookback periods.

Here’s the value this expanded solution set offers.

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Amid record shopping and carrier-switching, are you doing enough to capture sustainable, profitable new business? Explore how tomorrow’s loyal customers are already showing they’re ready to be won, if you know where to look.

Read the previous series post: “Analytic Objects Are the Next Evolution of Auto Risk Segmentation“

Take a deeper dive into auto policy analytic strategies when you read our white paper.

Download the white paper

Verisk, Auto Policy History Analytics: The Future of Risk Segmentation, March 2025, < https://www.verisk.com/resources/campaigns/auto-policy-history-analytics-the-future-of-risk-segmentation/>, accessed on June 27, 2025, Case study is an Illustrative example.
Ibid. Verisk study combining CVAO data with ISO Statistical Plan loss cost data, 2025. Limited to policy history data and statistical plan data available for research. Results may vary based on full production data and the risk profile of individual insurers’ books of business, as well as the sophistication of their current rating plan. Actual data points not shown due to the proprietary nature of the data. (Analysis based on 5-year lookback period.)
Ibid.(Analysis based on most recent policy term.)

Case Study: New Auto Analytics Help Attract Future Loyal Customers

Case study: Micro-segmenting rates to attract future loyal customers1

Case study: Micro-segmenting rates to attract future loyal customers¹