The real problem
Plan, actual, and forecast results are more useful when the movement can be separated into understandable business drivers instead of reported as one unexplained variance.
Revenue forecasting & variance
From forecast movement to a clear business story.
I built forecasting and revenue-tracking workflows to make recurring analysis faster, more consistent, and easier to explain. This interactive recreation shows how I compare plan, actuals, and forecast while isolating the drivers behind the variance.
Financial modelingExcelSQLPython
All customers, periods, and financial values in this public demo are fictional.
Recorded workflow
Watch the analysis build.
The recording runs the same fictional scenario shown below and populates each period, KPI, and variance driver as the analysis completes.
Behind the project
Financial analysis translated into an interactive model.
What I built
I built forecasting and variance models that organize financial data, calculate performance changes, and isolate volume, price, mix, and material-cost drivers for decision support.
Why this version is different
The interactive model uses fictional periods, customers, and values. It recreates the analytical approach without publishing employer forecasts, pricing, margins, or operating results.
Technical focus
Forecast structure, variance logic, driver decomposition, scenario analysis, data validation, and decision-ready reporting.
Interactive analysis
FY26 Revenue Forecast
Adjust the commercial assumptions, then run the model.
Period detail
Plan, actual, and forecast
Ready to run
| Status | Period | Plan | Actual / forecast | Variance | GM |
|---|---|---|---|---|---|
| Run the analysis to populate the forecast. | |||||