How Payflow cut forecast deviation in half

Payflow's revenue team had the same problem most growing companies have: forecasts that drifted by more than 20% in either direction every quarter. Reps under-called confident deals and over-called shaky ones. The data the forecast ran on was three days stale by the time the meeting started.

Three months after rolling out Piper AI, Ramon Coll, VP of Sales, reports forecast deviation down by 50%. We asked him what changed.

What changed in pipeline review

The pipeline review used to be a verification meeting. Half the time was reps confirming or correcting fields the system had wrong. Now reps come in with the fields already current, and the meeting is about decision-making instead of data entry.

The biggest single shift was on stakeholder mentions. Before, deals would carry a single contact for weeks, even when transcripts showed three or four people involved. Piper AI surfaces the stakeholder graph as it forms, and the team can spot single-threaded deals before they stall.

What Ramon told us

'The information I have in the forecast is much more accurate. Thanks to Piper AI, our forecast deviation has been reduced by 50%.' He added that the team got time back, but the time isn't the headline. The trust in the data is.

What we'd watch next

We're tracking two things at Payflow over the next quarter: whether the deviation reduction holds across a different forecasting environment, and whether the time saved gets reinvested in more outbound or just absorbed. The honest answer at this point is: both.