Sales ops · Production

Databricks closes 169% more slipped deals using Clari Revenue Platform

The problem

Databricks' go-to-market teams relied on error-prone, time-consuming spreadsheet forecasting that wasted hours and still left pipeline details hidden, making the approach unscalable as pipeline velocity and headcount grew rapidly.

First attempt

Spreadsheet-based forecasting was error-prone and time-consuming, wasting hours to achieve a level of visibility that still left details hidden.

Workflow diagram · grounded in source
1
Quarterly gap identification
Trigger
The revenue team uses Clari to proactively identify and fill gaps in upcoming quarters.
▾ source quote
“The Databricks revenue team uses Clari to proactively identify and fill gaps in upcoming quarters”
2
AI forecast and CRM scoring
Ai action
Clari generates its own forecast call alongside an AI-powered CRM score for each deal.
▾ source quote
“compare each team's call to Clari's call, the forecast deal stage and AI-powered CRM score”
3
Leaders review and override
Human review
Revenue leaders star commit opportunities, explore slipping deals, and override rep calls to produce the most accurate forecast.
▾ source quote
“They can star opportunities that make up the commit number, explore slipping deals, and override a rep's call in order to get the most accurate forecast possible”
4
Pipeline gap actioned
Output
If leaders notice a pipeline gap for upcoming quarters, they can activate pipeline generation programs well in advance.
▾ source quote
“If they notice a pipeline gap for the next two quarters, leaders can activate pipeline generation programs well in advance to close that gap”
5
Rep coaching from deal insights
Feedback loop
Managers use instant, accurate deal insights from Clari to coach reps on aspects of each deal that will help them close more business.
▾ source quote
“Managers have instant, accurate deal insights that they can use to coach reps on the aspects of each deal that will help them close more business and hit their quota”
Reported outcome

Databricks achieved a 169% improvement in win rate on slipped deals (aligning to 13% in won revenue), a 19% decrease in slipped deal rate, and 50% time savings on forecasting, freeing reps to focus on higher value activities.

Reported metrics
Win rate improvement on slipped deals169%
Won revenue from rescued deals13%
Slipped deal rate19%
Forecasting time savings50%
Reported stack
Clari
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Source
https://www.clari.com/resources/customer-stories/databricks/
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Frequently asked questions

What did this team achieve with this AI workflow?

Databricks achieved a 169% improvement in win rate on slipped deals (aligning to 13% in won revenue), a 19% decrease in slipped deal rate, and 50% time savings on forecasting, freeing reps to focus on higher value act…

What tools did this team use?

Clari.

What results were reported?

Win rate improvement on slipped deals: 169%; Won revenue from rescued deals: 13%; Slipped deal rate: 19%; Forecasting time savings: 50% (source-reported, not independently verified).

What failed first in this deployment?

Spreadsheet-based forecasting was error-prone and time-consuming, wasting hours to achieve a level of visibility that still left details hidden.

How is this sales ops AI workflow structured?

Quarterly gap identification → AI forecast and CRM scoring → Leaders review and override → Pipeline gap actioned → Rep coaching from deal insights.

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