Engagement overview
Client | National accounting & advisory firm |
|---|---|
Solution | Revenue Forecast |
Timeline | 5 weeks |
Users | Finance and business unit leaders |
Use Case | Revenue forecasting & board reporting |
Key outcomes
Forecast within 10% of plan compared with roughly 20% variance in prior forecasts.
Recurring reporting automated, with the most frequent pipeline and board reporting automated and the ability to query data in plain language.
Pipeline visibility by cost center and partner for the first time, with data unified across multiple systems.
Bottom-up forecasting by business unit replaced top-down estimates that leaders couldn't act on.
Why disconnected data undermines forecasting
The firm runs a partner-led model across multiple business units and cost centers. New business pipeline lived in several systems, and the records did not line up. Opportunities were missing fields, partner and cost center assignments were inconsistent, and the same engagement could appear differently depending on where you looked.
Without a single reliable view, the finance team built the forecast top down. Leadership would set a number and work backward, and the result routinely missed by around 20%. That gap made it hard to commit to hiring, capacity, and investment decisions with confidence.
Board reporting carried its own cost. Each cycle, the team pulled data by hand to answer the same recurring questions: where the pipeline stood, which units were on track, and how bookings would convert to revenue. Questions that came up mid-meeting often waited until the next cycle for an answer.
Building a better forecast
Chassi connected and reconciled the firm's data, built a weighted forecast, and automated reporting in five weeks.
Consolidated and cleaned the pipeline data
Chassi connected the firm's source systems and reconciled new business pipeline into one model, broken down by cost center, partner, and business unit. Along the way, the team resolved duplicate and conflicting records and filled in or flagged missing information so the numbers could be trusted.
Built a weighted pipeline forecast
With the data structured, Chassi applied machine learning to historical conversion patterns to forecast bookings. The result is a weighted pipeline that estimates what will close and when, and carries that through to how bookings will convert into revenue.
Automated reporting and enabled natural-language queries
Chassi automated the majority of the firm's recurring pipeline and board reporting. Leaders can now ask questions of the data in plain language and get answers to the specific requests that come from the board, without waiting on a manual pull.
Turning better forecasts into better decisions
Beyond accuracy, the bigger change is what leaders can do with the forecast. Built from the pipeline up, each business unit leader can see what drives their number and make decisions at the unit level, which wasn't possible before. The plan is now something the team can act on, rather than a target to explain after the fact.
Before | After | |
|---|---|---|
Forecast accuracy | Typically off by about 20% | Within 10% of plan |
Forecast method | Top down | Bottom-up, by business unit |
Pipeline view | Fragmented across systems | One reconciled view by cost center and partner |
Board reporting | Manual pulls each cycle | Majority automated, with natural-language questions |
Beyond the numbers
For a partner-led firm, revenue depends on hundreds of individual relationships and engagements. A forecast built from that pipeline, rather than imposed on it, gives finance and the board a number they can plan around, and gives each unit leader a clear view of where to focus.
“We finally have a forecast we can trust. Instead of spending time reconciling numbers across systems, our teams can focus on what's driving revenue and make decisions with confidence.”
— CFO
National accounting and advisory firm
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