Sales teams live in Salesforce, but sales processes rarely live in one place.

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A “simple” deal can start as a lead in Sales Cloud, move into CPQ for quoting, hit approvals in another workflow tool, trigger e-sign in a separate system, and then land in billing and finance. The result is that you get dashboards for each stage, but not a true view of how the end-to-end process actually runs.

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That’s where process mining fits in: it reconstructs the real paths your deals take, using system event data, so you can spot where revenue gets stuck and why.

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‍Why Salesforce data alone doesn’t show the full story

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Salesforce is great at tracking objects and stages. But many of the delays that slow revenue happen between systems and teams:

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• a quote created in CPQ, then waiting for pricing approval
  
  
• a contract sitting in legal review with no visibility
  
  
• handoffs between Sales Ops, Finance, and delivery that aren’t measured consistently
  
  

Process mining stitches those steps together into a single “digital twin” of lead-to-cash, so you can see variants, rework, waiting time, and exceptions across the full journey. Salesforce

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The processes worth mining in Salesforce Sales Cloud

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If you want “Google juice,” you’ll want clear, specific use cases that match how people search. These are the big ones.

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1) Lead-to-Cash (end-to-end revenue flow)

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Lead-to-cash covers the full cycle from initial interest to payment, often including quote-to-cash and order-to-cash activities. Salesforce Ben +1

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Salesforce also documents lead-to-cash architectures where CPQ and Billing work together across the cycle. Salesforce

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What process mining can reveal:

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• Leads that “age out” before first contact
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• Stages that are skipped (or looped) before close
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• Deals that bounce back to earlier stages due to missing info
  

• Handoffs that create long idle time (Sales → Sales Ops → Finance)

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KPIs to track:

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• lead response time
  
  
• time-in-stage by segment and owner
  
  
• rework rate (stage regressions, quote revisions, reopened opportunities)
  
  
• approval cycle time and exception rate

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2) Quote-to-Cash (pricing, CPQ, contracting, billing)

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Quote-to-cash includes configuring a quote, producing a proposal, contracting, order processing, invoicing and payment. Salesforce +1

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What process mining can reveal:

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• discount approvals that cause repeated “quote edits”
  
  
• contracts that stall due to missing product or legal fields
  
  
• orders that fail downstream due to mismatched data between Sales Cloud and Billing

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3) Opportunity management (where revenue slips quietly)

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Most teams think they have an “activity” problem. Often, they have a process problem:

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• inconsistent next-step definitions
  
  
• duplicated opportunity creation
  
  
• “late-stage” deals that never truly progress
  
  

Process mining shows the actual opportunity paths that correlate with wins vs. losses, and which variants consistently produce clean closes.

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Typical Salesforce + sales process problems (and what mining shows)

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These come up constantly across sales orgs:

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• Approval bottlenecks: too many approvals, unclear thresholds, or approvals triggered late
  
  
• Handoff delays: Sales Ops queues, finance checks, contract reviews
  
  
• Data quality failures: missing fields that force rework in CPQ/Billing
  
  
• Variant chaos: every rep sells differently, so forecasting and enablement suffer
  
  

Process mining quantifies these issues, so you can stop arguing about opinions and start prioritizing fixes by business impact.

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What data you need (practical, not theoretical)

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You don’t need a perfect data lake to start. A strong first pass usually includes:

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• Salesforce events (lead created, first activity, stage changes, close date changes, opportunity created/updated)
  
  
• CPQ quote events (quote created, revisions, approval events)
  
  
• Contracting / e-sign milestones (sent, viewed, signed)
  
  
• Billing and order milestones (order created, invoice issued, payment received)
  
  

The goal is a consistent case ID (Opportunity ID, Quote ID, or an agreed mapping) plus timestamps for key steps.

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Quick wins you can implement after insights

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A good Salesforce process mining project doesn’t end with “interesting dashboards.” It ends with fixes like:

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• tightening stage entry/exit criteria (stop fake progress)
  
  
• simplifying discount approval rules based on real risk
  
  
• standardizing “happy path” variants for top segments
  
  
• building alerts when deals enter known “stall patterns”
  
  
• improving required field logic based on what actually breaks downstream

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Bottom line: process mining turns your platform data into a clear view of bottlenecks, rework, and process variants so you can improve speed, compliance, and adoption with confidence. Reach out for further insights and tips, and we’ll share practical next steps based on what we typically see in companies using this platform.

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