StratAgree™ – Forecasting
Plan with hyper accuracy, with a better revenue and production forecast
Applying much of the data we gather in Flow of Funds and Profitability, we are able to produce highly accurate forecasts. Tighter forecasts can help you determine when and where to best use marketing and product resources.
Revenue Forecasting Process
We begin by performing a line-by-line review of the general ledger and all journals feeding it. The journals are mapped back to the core processing systems where we identify every transaction type that impacts fee income. A model is built where we account for all transactions. We cross check accuracy by multiplying volume by unit price to calculate expected revenue in order to work our way back to the GL totals.
Through mapping, we identify all primary drivers of revenue. Then, using historical transaction data, we develop seasonal models of transaction volume and work out any dependencies. Seasonality is particularly important for certain fee drivers whose behavior fluctuates greatly depending on day-of-week, and month-of-year. Accordingly, factors for day-ality, and month-ality are components of our forecast model.
With solid data in hand, we create a “walk-forward” process that looks at the primary revenue line items and transaction drivers. The walk-forward starts with the previous month’s actuals and makes rate and volume adjustments based on the seasonal projections of driver activity over the forecast period. This forms the basis for forecasting the coming months.
The walk-forward is checked for accuracy then placed into production.
Unit Production Forecasting
We also have developed expertise forecasting unit production. Our omni-channel unit forecast process (covering branch, online, call center and bank-at-work channels) is hyper-accurate and will help you better manage marketing and sales.
Our models address the most common deficiencies of bank projection processes and produce highly-accurate forecasts with clear accountabilities, solid integrity of both the data and model, and enable a smooth, repeatable process for monthly and yearly forecasting. Forecast error is typically below 1%.