Sales & Revenue Forecasting

Know what next quarter looks like before it starts

Forecast revenue, pipeline, and bookings with the accuracy of a data science team — without one.

Outcome
85%

average forecast accuracy across pilot customers

The problem

What's broken today

1

Quarterly forecasts swing wildly because reps anchor on last quarter, not signal.

2

Finance and sales argue about the number instead of acting on it.

3

Pipeline coverage looks healthy until two weeks before close.

How Predict Labs solves it

Built for the way operators actually work

Top-down + bottom-up reconciliation

Predict Labs forecasts at the deal, rep, segment, and region level — and reconciles them automatically.

Pipeline scoring you can defend

Every deal gets a probability and a key-driver explanation rooted in your historical close behavior.

Continuous re-forecasting

Models retrain as deals advance. The number you see at 9am is the number that reflects yesterday's CRM activity.

Sample questions

Ask Predict Labs anything

What will Q2 revenue look like by region?
Predict Labs
Building chart and analysis…
Which deals in our pipeline are most likely to close?
Predict Labs
Building chart and analysis…
Are we on track to hit annual plan?
Predict Labs
Building chart and analysis…
How it works

Three steps to your first prediction

STEP 1

Connect your CRM and finance data

Salesforce, HubSpot, NetSuite, or a CSV — we accept what you have.

STEP 2

Ask for the forecast you need

"Show me committed and best-case revenue for Q2 by region."

STEP 3

Share, alert, embed

Pin to a dashboard, send weekly to leadership, or alert on variance.

Getting started

What you'll need

These are the typical data sources for sales & revenue forecasting. You don't need every source on day one — start with what you have.

Typical data sources
  • Salesforce or HubSpot
  • Closed-won + closed-lost history
  • Quota + plan data
  • Optional: marketing attribution

Try Sales & Revenue Forecasting on your own data

Free for 14 days. No credit card. No data scientist required.