sales_ops · realestate · workflow
Databricks closes 169% more slipped deals using Clari Revenue Platform
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.
How it works
Common implementation structure
How this type of workflow is generally built, generalized across documented cases — not tied to any one vendor's stack. Click any stage to read what happens there. Specific products that implement these stages appear in “Tools commonly seen” below.
Stage 1 · Quarterly gap identification
The revenue team uses Clari to proactively identify and fill gaps in upcoming quarters.
Tools used
Clari
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.
What failed first
Spreadsheet-based forecasting was error-prone and time-consuming, wasting hours to achieve a level of visibility that still left details hidden.
Results
Time saved50%
Volume169%
Cost replaced13%
Grounding & classification
Source type: vendor customer story
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forecastingpredictive analyticsfailure mode describedhuman review describedmetric backednamed customerproduction runtime claimedsource backedtools describedworkflow describedsoftwarecycle time reductionemployee productivityrevenue increasetime savedvendor customer storyfinance opssales opsai draft human approvaldata sync enrichment