Quotes and orders
Monthly volumes by cost object and order vintage
Case study / B2B services / Revenue forecasting
How a key-account pipeline was converted into a defensible revenue forecast.
For one multinational key account, a vintage model replaces the previous correlation logic. Run-off curves translate orders, call-offs and backlog into a monthly revenue forecast from n+1 to n+6.
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Company profile
B2B-Servicegruppe
01
Pilot within a DACH services group with several thousand employees
02
Multinational key accounts with fragmented cost-object structures across regions and source systems
03
Non-recurring services across quotes, orders, call-offs and delivered output
04
Customer-owned DWH system as the target environment
What the Finance solution needed to achieve.
01
Determine how much of the order pipeline will convert into revenue in the coming months
02
Turn an observed correlation into a predictive model
03
Represent opening backlog explicitly as the missing state variable
04
Track peaks and run-offs over time and include them in the forecast
Data foundation
The data foundation behind the model.
Call-offs
Call-off business as a separate volatile service source
Delivered output
Realised work used to estimate historical run-off curves
Opening backlog
Explicit opening state so output can exceed new orders
Architecture and implementation
From separate data to a production Finance workflow.
Finton combines source data, Finance logic, controls and delivery in one maintainable operating model.
Validation before modelling
The prior analysis is re-derived from raw data and tested for sample size, lags and controls
Vintage run-off curves
Each order month becomes a cohort with conditional conversion from n+1 through n+6
Pooled model
Relevant data points are combined across cost objects and data sources
Plausibility gate
Small vintages, unreconciled backlog and unresolved booking logic produce a question instead of a number
Production in the customer-owned BI & DWH environment
The monthly model runs in the client's existing data platform
Operational output
What Finance receives in ongoing operations.
The solution delivers decision-ready outputs in the client's existing system landscape.
Monthly revenue forecast
Conversion by order vintage from n+1 through n+6
Backtest and model quality
Prior-year comparison as the go or no-go basis
Explicit backlog
Undelivered volume remains an explicit model state
Rollout-ready method
Additional key accounts become a data question rather than a new modelling project
Documented operation
Internal owners can maintain and extend the model
Business impact
Measured impact in the Finance workflow.
Akkurat
The observed correlation was replaced with conditional vintage run-off curves and explicit backlog
n+1 to n+6
Each order vintage gains a monthly conversion horizon
One to many
The method is designed for more key accounts and recurring services
Zero new tools
The solution runs in the client's existing data platform
Your Finance workflow