AI assistant for analysis
Why is Q4 above run rate. Which deals have slipped twice. What the number is without the three that always push. Covirage answers from pipeline history, with the deals behind every figure.
Every forecast has a story. Covirage finds the deals that make the story true or false, and shows them before someone in finance does.
Run rate to forecast, decomposed into new, expansion, slipped and pushed.
Deals that have moved twice or more, with the pattern across reps.
"What is the forecast without the West's pushed deals?" Answered, with the list.
Three steps, in this order.
The CRM export with stage history, not just the snapshot. Two years back if you have it.
Weekly forecast review, monthly close, quarterly board.
Before the call, during the call, and when the CFO asks after.
Short answers. The Help centre has the long ones.
No. It analyses yours. The bridge and the slip history are computed from the pipeline history you already have.
Two quarters gives a bridge. Two years gives slip patterns by rep.
Salesforce and HubSpot, including stage history.
Written for this job: the measures, the data you already hold, and the arithmetic.
How a sales leader measures forecast accuracy per rep from the weekly forecast snapshots and the closed results: error, bias and the split between them, why a rep who is always 20 percent high is more useful than one who is randomly 10 percent out, the per-rep adjustment it produces, and the identity that ties the adjusted forecast to the raw one.
16 Sept 20263 min readThe four common ways to forecast a quarter, rep roll-up, stage-weighted pipeline, run rate and historical conversion, what each needs, what each is good and bad at, a worked quarter where they give four different numbers, and why the right output is not one of them but the bridge between them, with the deals that explain the gaps.
16 Sept 20263 min readA reading guide for a forecast bridge from last period's number to this one: the check that the lines sum before anything is read, the order to read them in, closed, slipped, lost, new, resized, the deal list behind each line as the thing to open, the line with no deals behind it as the one to distrust, the repeat slippers across bridges, and the two lines that set next period's calibration.
16 Sept 20263 min readA reading guide for a pipeline table by rep and stage: why face value is the column to distrust, why weighted by historical conversion is the one to believe, what the slip count column does to both, how to read stage distribution as a shape rather than a total, the needed multiple against coverage, and the two rows to open, the rep whose weighted coverage is lowest and the rep whose face-to-weighted ratio is highest.
16 Sept 20262 min readThe complete pipeline coverage calculation on ten open deals small enough to check by hand: the deals by stage and value, the team's historical conversion by stage at this point in the quarter, the face value, the weighted value, the slip counts and the slip adjustment, the remaining target, coverage three ways, the needed multiple, and the deal list that explains the gap, so a reader can reproduce every figure and then run it on their own snapshot.
16 Sept 20263 min readFive checks on the open pipeline that take a minute from a weekly export and remove most of the argument from the forecast call: close dates in the past, deals without a next step or activity in a stated window, deals in the same stage past the team's own median stage duration, values unchanged since creation on late-stage deals, and duplicate opportunities on one account. Each with the count, the value affected and the rep.
16 Sept 20263 min read