Oct 20

Marketers: Want to Grow Like the Companies That Actually Exit? 7 Profit Secrets from an Investment Banker Who’s Sold Them

Selling more contracts isn’t the goal. Making more money is. Too many marketers chase top-line volume and ignore the hidden costs eating their margin—cancellations, poor targeting, slow mail, and comp plans that reward the wrong behavior. If you want exponential growth that actually sticks to the bottom line, start here. 1. Study Your Cancellation Curve […]

Sep 16

Warranty Administrators: A cautionary tale—and a fix you can implement today

When it’s time to sell your company, everything gets scrutinized. Especially the numbers. For F&I administrators, few metrics carry more weight with buyers than the loss ratio—and yet that number is only as accurate as the earnings curve behind it. If your curve is wrong, your loss ratios are wrong. And if your loss ratios […]

Jul 18

How the Wrong Earnings Curve Almost Cost an F&I Administrator Millions When Selling the Company

When preparing a company for sale, financial optics matter. For F&I administrators, few metrics carry more weight with buyers than loss ratios—and those ratios are only as accurate as the earnings curve behind them. F&I earnings curve optimization must happen before a company goes up for sale. Here’s a real-world example of how defaulting to […]

Dec 5

Case Study: Streamlining Cancellation and Claim Frequency and Severity Curve Construction

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 […]

Jun 13

Case Study: Streamlining Customer Data for Enhanced Analysis

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.