How Vehicle Service Contract Administrators Use Experience Curves to Stay Ahead of Cancellations If you’re administering Vehicle Service Contracts, cancellations aren’t just noise—they’re a direct hit to profitability. Refund exposure. Margin compression. Loss ratio volatility. Whether contracts are sold direct-to-consumer or through dealerships, cancellations are a real and rising cost. And yet, most administrators aren’t […]
About: Gina Cocking
Recent Posts by Gina Cocking
If you’re selling Vehicle Service Contracts (VSCs) directly to consumers, you’re operating in a high-cancel environment. Many marketers see 40–80% of contracts cancel, and flat cancels—those within 30 days—are especially painful. They erase margin, trigger chargebacks, and undermine performance metrics. But not all cancellations are created equal. Contracts that cancel later are far more profitable. […]
In October 2024, 1.31 million new vehicles were estimated to have been sold across the United States.
The Challenge Our client, an investment bank, often constructs cancellation and claims frequency and severity curves for their clients’ financial products. These curves are critical for predicting future cancellations, claim timing, and severity. However, the process is labor-intensive, requiring significant time and manual effort to build each curve from raw data. Updates are even more […]
Our client, a direct-to-consumer marketer of financial products, faced a hidden challenge that was significantly impacting their marketing efficiency and profitability. By purchasing mailing data from multiple vendors, they were unintentionally remailing the same potential customers—resulting in wasted resources and missed opportunities. With a direct mail database of 18 million records, the inefficiencies were costing […]
Colonnade Advisors, an M&A investment bank, faced a significant data challenge when a client provided customer data in an Excel file and CSV format. The data was disorganized, with customer information spread across multiple rows—sometimes as many as ten per customer. With hundreds of thousands of rows, the fragmented data made it nearly impossible to efficiently manipulate or analyze the dataset.