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318 lines
10 KiB
Markdown
318 lines
10 KiB
Markdown
# Custom Prompt Instructions for Pipeline Management Agent
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This guide explains how to customize the Pipeline Management Agent's behavior through custom prompt instructions in Agentforce Builder.
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## Overview
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Custom prompt instructions let you:
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- Focus the agent on specific business priorities
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- Align suggestions with your sales methodology (MEDDIC, BANT, SPICED)
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- Add company-specific context and terminology
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- Control update aggressiveness and risk tolerance
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## Where to Add Custom Instructions
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**Setup → Einstein Agentforce → Agents → Sales Management Agent → Prompt Templates**
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Or via deployed agent bundle: Edit `sales_management_agent.agent` file
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## Template Structure
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```yaml
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customInstructions:
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- instruction: "[Your custom instruction here]"
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topic: "All Topics" # Or specific topic like "Update Opportunity Fields"
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```
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---
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## Example 1: Methodology Alignment (MEDDIC)
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```text
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When analyzing opportunities, strictly follow the MEDDIC framework:
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Metrics: Always quantify business impact in dollar amounts or percentages
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Economic Buyer: Identify the person with budget authority and highlight their involvement
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Decision Criteria: Extract specific evaluation criteria the customer is using
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Decision Process: Note approval steps, stakeholders, and timeline
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Pain: Focus on business problems, not feature requests
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Champion: Identify internal advocates and note their influence level
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Prioritize suggestions that address gaps in these six areas. If an opportunity lacks Economic Buyer identification or quantified Metrics, flag this as high priority.
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```
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**Impact**: Agent will surface MEDDIC gaps and suggest fields like "Economic Buyer", "Champion", "Pain Statement"
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---
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## Example 2: Risk-Aware Recommendations
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```text
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Be conservative with opportunity stage progression:
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- Only suggest stage advancement if explicit customer commitment exists
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- Require written confirmation (email) for stages beyond Proposal
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- Flag regression risks when activity decreases by 50%+ in 14 days
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- Never suggest "Closed Won" without signed contract or PO evidence
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For high-value opportunities (>$100k), require executive-level engagement evidence before advancing past Qualification.
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```
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**Impact**: Agent becomes more cautious, reducing false positives in pipeline progression
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---
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## Example 3: Industry-Specific Context
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```text
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Healthcare Industry Context:
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Our sales cycle has these unique phases:
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1. Clinical validation with medical staff (usually 60-90 days)
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2. IT security review (30-60 days, often blocking)
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3. Procurement/contracting (45-90 days)
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When analyzing conversations:
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- "IT security review" = likely 60-day delay, suggest stage hold
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- Mention of "clinical trial" or "validation study" = 6-12 month cycle
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- "HIPAA" or "compliance" keywords = route to legal review stage
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- Fiscal year end for hospitals is typically June 30
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Adjust close date estimates based on these industry timelines.
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```
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**Impact**: Agent provides healthcare-realistic timelines instead of generic estimates
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---
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## Example 4: Company Terminology Mapping
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```text
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Translate customer language to our internal fields:
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Customer says → Map to field:
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- "Go-live date" → Implementation_Start_Date__c
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- "Business case" → ROI_Justification__c
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- "Champion" → Internal_Advocate__c
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- "Blocker" or "risk" → Risk_Assessment__c
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- "Next steps" → Next_Step__c (always include dates and owners)
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- "Decision maker" → Economic_Buyer__c
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Ignore product feature discussions unless they relate to decision criteria or blockers.
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```
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**Impact**: Better field mapping for custom objects
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---
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## Example 5: Update Aggressiveness Control
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```text
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Autonomous Update Policy:
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Fields approved for auto-update (no review needed):
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- Next_Step__c (if mentioned explicitly in last 7 days)
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- Last_Customer_Interaction__c (factual date)
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- Competitor_Mentioned__c (list only)
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Fields requiring user review (suggestive mode only):
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- Stage (any change)
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- Close_Date__c (if moved by >30 days)
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- Amount (any change)
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- Decision_Maker__c (name changes)
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Never auto-update:
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- Owner
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- Record Type
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- Custom checkbox fields affecting reporting
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```
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**Impact**: Balances automation with control for sensitive fields
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---
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## Example 6: Data Source Prioritization
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```text
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When conflicts exist across data sources, prioritize:
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1. Email (most reliable, written commitment)
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2. Einstein Conversation Insights (recorded calls)
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3. Manual notes (may be subjective)
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Weighting:
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- An email saying "we'll sign next Tuesday" overrides a call where customer said "maybe next month"
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- For close date: use latest communication's date if more recent than 48 hours
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- For next steps: combine all sources, but flag if sources contradict
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If sources fundamentally contradict (e.g., email says "no" but call says "yes"), flag for user review rather than auto-suggesting.
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```
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**Impact**: Reduces suggestion conflicts from multiple data sources
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---
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## Example 7: Opportunity Size Thresholds
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```text
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Opportunity Value Tiers:
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Strategic (>$500k):
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- Require executive engagement evidence
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- Flag if no activity in 7 days
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- Suggest bi-weekly check-ins minimum
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- Never auto-update stage without explicit approval
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Enterprise ($100k-$500k):
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- Weekly activity expected
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- Require decision process documentation
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- Auto-update low-risk fields only
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Standard (<$100k):
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- Full autonomous update approved
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- Focus on velocity and efficiency
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- Suggest stage progression more aggressively if activity supports it
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```
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**Impact**: Differentiated agent behavior based on deal size
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---
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## Best Practices
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### ✅ DO
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- **Be specific**: "Extract budget approval date" not "Look for budget info"
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- **Use examples**: Show the agent what good looks like
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- **Set boundaries**: Explicitly state what NOT to do
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- **Align to methodology**: Reference your sales framework (MEDDIC, BANT, etc.)
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- **Test incrementally**: Add one instruction, test, refine, add next
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### ❌ DON'T
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- **Be vague**: "Improve opportunity quality" (how?)
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- **Conflict with SF metadata**: Don't override field validation rules
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- **Assume data exists**: "Always fill Economic Buyer" (what if unknown?)
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- **Over-constrain**: Too many rules make agent overly cautious
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- **Ignore testing**: Prompts that sound good may behave unexpectedly
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---
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## Testing Your Instructions
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### Manual Test (Agentforce Builder)
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1. **Setup → Einstein Agentforce → Agents → Sales Management Agent**
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2. Click **Test** button
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3. Enter test conversation:
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```text
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Customer: "We'll need board approval, but our CFO Sarah Johnson is supportive. We're targeting a January 15 go-live."
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```
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4. Review agent output for your custom instruction impact
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### Production Test
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1. Add a note to a real opportunity with test instructions keywords
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2. Wait for scheduled flow run (or trigger manually)
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3. Check **Opportunity History** for agent-generated suggestions
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4. Verify suggestions align with your custom instructions
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### A/B Test Different Instructions
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1. Clone agent, add different instructions to each
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2. Assign different users to each agent via permission sets
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3. Compare suggestion quality after 2 weeks
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4. Adopt winning instruction set
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---
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## Common Issues & Solutions
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### Issue: Agent ignores custom instructions
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**Solution**:
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- Check instruction is under correct topic
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- Verify agent is activated
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- Ensure instruction doesn't conflict with base system prompt
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- Use imperative language ("Do X") not suggestive ("You might want to...")
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### Issue: Suggestions become too cautious
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**Solution**: Add explicit "approved scenarios" list where agent should be aggressive
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### Issue: Agent hallucinates data
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**Solution**: Add constraint: "Only use explicitly stated information. If data is unclear, flag for user review rather than guessing."
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### Issue: Wrong fields get updated
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**Solution**: List exact field API names in instructions with update permissions
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---
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## Advanced: Methodology-Specific Templates
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### MEDDIC Sales
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```text
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Every opportunity must have MEDDIC qualification data:
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Required fields to populate:
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- Metrics: Business_Impact__c (quantified ROI)
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- Economic Buyer: Decision_Maker__c (name + title)
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- Decision Criteria: Evaluation_Criteria__c (list)
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- Decision Process: Approval_Steps__c (timeline with stakeholders)
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- Pain: Pain_Statement__c (business problem)
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- Champion: Internal_Champion__c (name + influence level)
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Suggest stage progression only when all 6 elements have data. Flag incomplete MEDDIC as "Risk: Qualification incomplete" in Next_Step__c.
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```
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### BANT Sales
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```text
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Focus on BANT qualification:
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Budget: Extract dollar amounts and approval status → Budget_Status__c
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Authority: Identify decision maker title and approval rights → Decision_Authority__c
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Need: Summarize business problem and urgency → Business_Need__c
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Timeline: Extract purchase timeline and fiscal constraints → Target_Close_Date__c
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Disqualify opportunities early if any BANT element is clearly absent or negative.
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```
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### SPICED Sales
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```text
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Use SPICED for complex enterprise sales:
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Situation: Current state and context → Customer_Situation__c
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Pain: Business problem and impact → Pain_Points__c
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Impact: Cost of inaction (quantified) → Cost_Of_Inaction__c
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Critical Event: Forcing function or deadline → Critical_Event__c
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Decision: Process and stakeholders → Decision_Process__c
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Focus on Impact quantification - every opportunity needs a dollar value for cost of inaction.
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```
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---
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## Integration with Stage Descriptions
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Your custom instructions should align with stage descriptions (see `assets/stage-descriptions/`).
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Example alignment:
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- **Stage**: Prospecting
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- **Stage Description**: "Initial outreach and lead qualification. MEDDIC Focus: Begin gathering Metrics..."
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- **Custom Instruction**: "In Prospecting stage, prioritize Metrics and Pain discovery..."
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This creates consistency between what stages mean and what the agent looks for.
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---
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## Further Reading
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- [Field Completion Prompt Templates](../references/field-completion-prompt-template.md) - For custom field prompts
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- [Stage Descriptions](../assets/stage-descriptions/) - MEDDIC, BANT, SPICED stage definitions
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- [Salesforce Agentforce Documentation](https://help.salesforce.com/s/articleView?id=sf.agentforce.htm)
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