Results
More than 20 years of results, found in the numbers.
Victor Long has built forecasting models, receivables systems, and sales tools inside operating businesses — as a call center manager, director, general manager, and COO. He's run these operations himself — the results below came from doing the work.
What does a decade of statewide accountability look like?
For ten years, Victor led IGT's end-to-end technology contract with the Wisconsin Lottery, building data-driven decision-making into the foundation of the operation. He designed a customized customer scorecard tracking KPIs across every critical area of the business, then partnered with a cross-functional team to roll out a similar version to all lottery customers nationwide. The scorecard became a direct channel for feedback from the Wisconsin Lottery, driving continuous improvements to operations — reflected in consistently strong customer satisfaction ratings, with most areas scoring 4 or 5 out of 5.
Case Study · Receivables
How do you cut past-due receivables in half?
New Horizons Computer Learning Centers · Director of Customer Service, Enterprise Accounts
By tracking every invoice, every collection call, and every dollar still recoverable — in one system.
As Director of Customer Service for Enterprise Accounts, Victor tackled a costly problem: too many invoices more than 120 days past due. Rather than guessing at causes, he built an Access database consolidating every invoice and information from every collection call, then analyzed the data to isolate the real drivers of late payment — chiefly invoice errors and gaps in following customers' accounts payable procedures. Armed with that root-cause data, Victor and his team targeted the fixes that actually moved the needle, cutting 120+ day aging in half.
Reduction in receivables aged over 120 days.
New Horizons Computer Learning Centers — Enterprise Accounts, Corporate Office.
Case Study · Forecasting
How accurate can a forecast get?
Progressive Insurance · Call Center and Underwriting Operations
Accurate enough that a single variable — policies in force — predicted call volume with an R² of .95.
As a call center manager at Progressive, serving Texas, Louisiana, Arkansas, and Oklahoma, Victor built a linear regression model forecasting call volume from policies in force. The relationship was unusually strong — not a typical result — and the center used it to plan staffing, improving efficiency and service levels.
Two years later, he built the staffing model for underwriting operations across the same four states: robust enough to guide hiring and cross-training decisions, and to hold up under the corporate controller's scrutiny.
The .95 R² is the exception, not the expectation — most forecasting relationships are weaker and messier than this one. Not to mention, you must meet a number of assumptions before you can use a regression equation for forecasting. What carries over to any business, regardless of size, is the discipline behind it: test whether a relationship in the data is real and strong enough before you build hiring, staffing, or budget decisions on top of it.
R² of the call-volume forecast.
The Method
Why do these projects work?
Because the analysis starts with the operation, not the data. Victor learns how a business actually runs — who does what, where the money moves, what breaks — before he opens a spreadsheet. The numbers only give answers when you know the right questions.
The next result could be yours.
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