Sales ops ·
Revenue team drives forecast speed and pipeline visibility with Clari
The problem
The existing Salesforce setup required extensive configuration to surface real-time, meaningful data, limiting fast and flexible pipeline forecasting.
Workflow diagram · grounded in source
1
Forecast data request
trigger
“With Clari, Sandy was immediately won over by how quickly he could forecast”
2
Real-time data drill-down
output
“breaking down the data by various layers of management, drilling into individual contributor data in real-time, and "changing filters to get exactly what we wanted"”
3
Multi-dimensional pipeline view
output
“the team reviews the pipeline from many dimensions: Individual contributors vs. teams, regions vs. region, marketing vs. SDR vs. Account Executive, and this quarter compared to the previous quarter”
Reported outcome
Sandy was immediately won over by Clari's forecasting speed and filter flexibility; over the following 6-12 months the team built more sophisticated dashboards to review the pipeline across multiple dimensions.
Reported metrics
Forecasting speedhow quickly he could forecast
Dashboard sophisticationmore sophisticated dashboards
Reported stack
ClariSalesforce
Frequently asked questions
What did this team achieve with this AI workflow?
Sandy was immediately won over by Clari's forecasting speed and filter flexibility; over the following 6-12 months the team built more sophisticated dashboards to review the pipeline across multiple dimensions.
What tools did this team use?
Clari, Salesforce.
What results were reported?
Forecasting speed: how quickly he could forecast; Dashboard sophistication: more sophisticated dashboards (source-reported, not independently verified).
How is this sales ops AI workflow structured?
Forecast data request → Real-time data drill-down → Multi-dimensional pipeline view.