SALES INFRASTRUCTURE & OPERATIONS
Forecasting when the data lies.
Your CRM is dirty, your reps are optimistic, and your board wants accuracy by Friday. The forecasting problem is almost never the model.... it's the inputs.
📄 GUIDE
TARGET AUDIENCE
LEADERSHIP
PUBLISHED
JUL 2026
BY
CHRISTINA MERRILL
LAST UPDATE
JUL 2026
IN THIS GUIDE
01
Why forecasts are wrong (and it's not the model)
02
How to identify problems with input
03
How to forecast from three angles instead of one
04
Rep-by-Rep calibration
05
How to defend a hard number to your stakeholders
SALES INFRASTRUCTURE & OPERATIONS
Forecasting when the data lies.
Your CRM is dirty, your reps are optimistic, and your board wants accuracy by Friday. The forecasting problem is almost never the model.... it's the inputs.
Forecasting when the data lies.
Your CRM is dirty, your reps are optimistic, and your board wants accuracy by Friday. The forecasting problem is almost never the model.... it's the inputs.
KEY INSIGHT
A clean forecast on dirty data is more dangerous than no forecast at all. Confidence in a wrong number gets people fired.
Forecasting when the data lies.
Your CRM is dirty, your reps are optimistic, and your board wants accuracy by Friday. The forecasting problem is almost never the model.... it's the inputs.
01
Why forecasts are wrong (and it's not the model)
02
How to identify problems with input
03
How to forecast from three angles instead of one
04
Rep-by-Rep calibration
FIELD INSIGHT
A clean forecast on dirty data is more dangerous than no forecast at all. Confidence in a wrong number gets people fired.