You Can’t Afford to Guess Most direct-to-consumer marketers in the vehicle service contract (VSC) and home warranty space know their topline numbers. Sales volume. Cancel rate. Cost per lead. But those numbers don’t tell you what’s really happening. They don’t show you which reps are killing margin, which campaigns are quietly failing, or where your […]
Home sales in May 2025 reflected a market at a crossroads—nudging upward in some areas while falling back in others. On the surface, total U.S. home sales climbed to 446,000 units, up from 414,000 in April. That 7.7% month-over-month bump sounds like momentum. But look closer, and the picture’s more complex. Behind the headline numbers […]
Home sales across the U.S. jumped in March 2025, signaling a typical spring bounce in buyer activity—but that momentum was layered atop a still-fragile foundation. Compared to February, total home sales rose by more than 20%, with gains across both new and existing home categories. However, viewed in the broader context of persistent affordability issues, […]
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.