Case Study: Data-Driven Pricing

As promised, we’ll be discussing PrettyNerd‘s decision to increase her prices. PrettyNerd has played piano for fourteen years, and has given private lessons for over a year. Each time she’s started teaching a new client, I’ve tried to convince her that she’s undervaluing her service. Putting aside that she makes 5x her teaching wage as a professional pianist (with multiple performances each week), she doesn’t even incorporate mileage reimbursement or commute time into her fees. She’s in the process of signing two new clients, so I decided to just show her the data. I put together a basic spreadsheet showing what she should charge to achieve her desired gross profit for each lesson (namely, $15/hr of instruction) and the difference from the current rate for each client.

This pricing scheme is normalized across clients, including reimbursements for mileage at the federal rate, paid commute time, and clustering to “share” commuting time costs across clients. We applied this calculation uniformly to all her clients. (The clients’ name and neighborhood has been changed to protect client privacy.)

We note a $25 weekly discrepancy, which equates to a “loss” of $1300 annually. Using this data, we worked together to determine a strategy to adjust her rates to “close the gap” between her current and desired profit, while not endangering any client relationships. (We took into account PrettyNerd’s knowledge of what her clients can afford.) While we didn’t “recover” the full amount, once the price changes are effected, she will recover half, a large step toward her desired profit.

Can the mathematical model be improved? Undoubtedly. Will the time spent developing a better approximation pay off? Doubt it.

The lesson is that data should be used to make more informed decisions regardless of the size of the business, from Fortune 500 companies to students earning some money on the side.

Now who wants me to quit mucking around with spreadsheets and help her share her beautiful music online? Any preferences to streaming vs. downloads vs. podcast?