A consultancy made the pipeline review boring (in a good way).

The firm is a management consultancy with six practices, from operations to digital transformation, serving mid-market and enterprise clients across North America. Its revenue depends on a pipeline of large, long-cycle engagements, and the board judges leadership on one thing above all: the accuracy of the quarterly bookings forecast.

Each practice kept its own forecast spreadsheet on top of Salesforce exports. Stages meant different things in different practices, close dates were rarely updated, and 22% of open opportunities had not been touched in 60 days. The weekly pipeline review took three hours, most of it spent reconciling practice spreadsheets with the CRM rather than discussing deals.

The result was a forecast that over-promised consistently. Over the previous four quarters, accuracy averaged 74%, and two misses had forced hiring freezes that, in hindsight, were not necessary.

The firm connected Salesforce, its BigQuery warehouse (which holds utilization and staffing data), QuickBooks and the practice target sheets. Revenue operations then defined, in the Kimo semantic layer, what each stage means, which fields are required to enter it and how weighted pipeline is computed. Every practice now reads the same measures: pipeline, commit, best case, weighted forecast and forecast accuracy.

A hygiene model scores each opportunity on staleness, missing next steps and close-date slippage, and pushes a short list to each partner every Monday morning. Staffing data from BigQuery is joined to the pipeline, so the forecast also shows whether the firm has the people to deliver what it is about to sell.

The weekly review runs from one Kimo dashboard. Partners use Ask Kimo to drill in (“Show commit deals in Operations that slipped more than once”) and every answer arrives with the query and definitions behind it, which ended most debates before they started.