Analyzes opportunity data and activity notes to generate competitive intelligence including win/loss rates by competitor, patterns, and rep-ready battlecard talking points. Use when user asks "how are we doing against [competitor]", "competitive analysis", "competitor win rate", "battlecard for [competitor]", "competitive landscape", "why are we losing to [competitor]", or "competitor intelligence".
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Analyzes opportunity data and activity notes to generate competitive intelligence including win/loss rates by competitor, patterns, and rep-ready battlecard talking points. Use when user asks "how are we doing against [competitor]", "competitive analysis", "competitor win rate", "battlecard for [competitor]", "competitive landscape", "why are we losing to [competitor]", or "competitor intelligence".
Competitive insights are often locked in deal notes, lost opportunity records, and anecdotal rep knowledge. This skill mines Dataverse to surface structured competitive intelligence: which competitors appear most frequently, where deals are being lost to them, what deal patterns correlate with wins vs losses, and what talking points reps should use. This is the Dataverse-internal equivalent of competitive research โ drawing from closed deal history rather than external sources.
Instructions
Step 1: Identify Competitor or Analysis Scope
Accept input from the user:
Specific competitor name (to analyze one rival)
All competitors (for a full competitive landscape view)
Time range (default: last 12 months)
Segment filter (by owner, territory, deal size, or industry)
Calculate date range for analysis: createdon >= '[start_date]'
Step 2: Identify Lost Deals with Competitor Information
Important: The opportunity entity does not have a direct competitorid field โ the opportunity-to-competitor relationship is many-to-many (). When an opportunity is closed, Dynamics 365 creates an activity record which have a direct lookup. Use for structured competitor data on closed deals.
opportunitycompetitors_association
opportunityclose
does
competitorid
opportunityclose
Query closed-lost opportunityclose records:
SELECT oc.opportunityid, oc.competitorid, oc.description, oc.createdon,
oc.actualrevenue
FROM opportunityclose oc
WHERE oc.statecode = 1
AND oc.createdon >= '[start_date]'
ORDER BY oc.createdon DESC
Then join to the opportunity table by opportunityid to get deal details:
SELECT opportunityid, name, estimatedvalue, actualclosedate, salesstage,
description, ownerid, customerid, closeprobability
FROM opportunity
WHERE statecode = 2
AND actualclosedate >= '[start_date]'
ORDER BY actualclosedate DESC
Note: If opportunityclose is not populated (competitor not selected at close), fall back to text pattern matching in opportunity description fields as described in Step 3.
Fetch competitor names if competitor table is used:
Step 3: Mine Opportunity Descriptions for Competitor Mentions
For opportunities without structured competitor fields, search text fields for competitor names:
SELECT opportunityid, name, estimatedvalue, statecode, actualclosedate,
salesstage, description, currentsituation, customerneed, customerpainpoints
FROM opportunity
WHERE statecode IN (1, 2)
AND actualclosedate >= '[start_date]'
Search description, currentsituation, and customerpainpoints for known competitor keywords or names provided by the user. Categorize each mention as:
Win with competitor present (statecode = 1)
Loss to competitor (statecode = 2)
Step 4: Pull Competitor Annotations from Activities
Search activity notes for competitor mentions:
SELECT annotationid, notetext, subject, objectid, createdon
FROM annotation
WHERE createdon >= '[start_date]'
Search notetext for competitor name patterns. Extract:
Context of mention (evaluation, objection, price comparison)
Outcome (linked opportunity won or lost)
Frequency of mention
Also search phonecall and appointment descriptions:
SELECT activityid, subject, description, regardingobjectid, statecode
FROM phonecall
WHERE createdon >= '[start_date]'
Step 5: Calculate Win/Loss Rates by Competitor
From Step 2, you now have two lists of opportunityids: [lost_opportunityids] (from opportunityclose where competitorid matches and deal was lost) and [won_opportunityids] (from a separate opportunityclose query for won deals). Run an additional query to capture won opportunityclose records for this competitor:
SELECT oc.opportunityid, oc.createdon, oc.actualrevenue
FROM opportunityclose oc
WHERE oc.competitorid = '[competitorid]'
AND oc.createdon >= '[start_date]'
Cross-reference these opportunityids against opportunity.statecode to split into won vs lost buckets. Then aggregate:
Won opportunities where competitor was present:
SELECT COUNT(opportunityid) as won_count, SUM(estimatedvalue) as won_value
FROM opportunity
WHERE statecode = 1
AND actualclosedate >= '[start_date]'
Filter results programmatically to only those opportunityids found in the won opportunityclose results above.
Lost opportunities to competitor:
SELECT COUNT(opportunityid) as lost_count, SUM(estimatedvalue) as lost_value
FROM opportunity
WHERE statecode = 2
AND actualclosedate >= '[start_date]'
Filter results programmatically to only those opportunityids in [lost_opportunityids] from Step 2.
Average deal size (wins) vs average deal size (losses)
Average sales cycle (wins) vs average sales cycle (losses)
Step 6: Identify Deal Patterns
For won deals with competitor present vs lost deals to competitor, compare:
Deal size distribution:
SELECT COUNT(opportunityid) as count, SUM(estimatedvalue) as total_value
FROM opportunity
WHERE statecode = 1
AND estimatedvalue < 25000
Filter results programmatically to only those opportunityids in [won_opportunityids]. Repeat for medium ($25K-$100K) and enterprise (>$100K) segments; run equivalent queries for statecode = 2 filtered to [lost_opportunityids].
Industry distribution:
SELECT account.industrycode, COUNT(opportunity.opportunityid) as count
FROM opportunity
JOIN account ON opportunity.customerid = account.accountid
WHERE opportunity.statecode = 2
AND opportunity.actualclosedate >= '[start_date]'
GROUP BY account.industrycode
Filter results programmatically to only those opportunityids in [lost_opportunityids] from Step 2.
Stage at loss:
SELECT salesstage, COUNT(opportunityid) as lost_count, SUM(estimatedvalue) as lost_value
FROM opportunity
WHERE statecode = 2
AND actualclosedate >= '[start_date]'
GROUP BY salesstage
Filter results programmatically to only those opportunityids in [lost_opportunityids] from Step 2.
Step 7: Identify Common Objections and Pain Points
From text mining in Step 4, categorize recurring themes from notes and activity descriptions when competitor is mentioned:
Price objections: Look for "cheaper", "lower cost", "price", "pricing"
Feature gaps: Look for "missing", "doesn't support", "can't do", "limitation"
Existing relationship: Look for "incumbent", "already using", "switching cost", "prefer"
Brand recognition: Look for "heard of", "well-known", "trusted", "market leader"
Implementation concerns: Look for "complex", "timeline", "integration", "support"
Count frequency of each theme across deal notes.
Step 8: Extract Win Patterns
For won deals where this competitor was present, identify common factors:
Qualification strength in wins:
SELECT budgetstatus, decisionmaker, need, purchasetimeframe, purchaseprocess,
COUNT(opportunityid) as count
FROM opportunity
WHERE statecode = 1
AND actualclosedate >= '[start_date]'
GROUP BY budgetstatus, decisionmaker, need, purchasetimeframe, purchaseprocess
Filter results programmatically to only those opportunityids in [won_opportunityids] from Step 5.
Note which BANT patterns appear most often in wins โ these inform the competitive playbook.
Activity volume in wins vs losses:
Count total activities per opportunity for wins and losses separately using activitypointer to identify engagement differences.
Step 9: Generate Competitive Intelligence Report
COMPETITIVE INTELLIGENCE REPORT
Competitor: [Competitor Name]
Period: [Start Date] โ [End Date]
Generated: [Today's Date]
โโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโ
EXECUTIVE SUMMARY
โโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโ
Deals Analyzed: [n won] wins / [n lost] losses
Win Rate (competitive): [n]%
Revenue Won (competitive): $[value]
Revenue Lost to Competitor: $[value]
COMPETITIVE PERFORMANCE BY DEAL SIZE
โโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโ
Small (<$25K): Win rate [n]% | [n] won, [n] lost
Medium ($25K-$100K): Win rate [n]% | [n] won, [n] lost
Enterprise (>$100K): Win rate [n]% | [n] won, [n] lost
WHERE WE LOSE
โโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโ
Most losses occur at: [Stage] stage ([n]% of losses)
Top industries lost: [Industry 1] ([n] deals), [Industry 2] ([n] deals)
Average deal size lost: $[value] vs average deal size won: $[value]
COMMON OBJECTIONS (from deal notes)
โโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโ
1. Price/Cost concerns โ mentioned in [n]% of lost deals
2. [Feature gap] โ mentioned in [n]% of lost deals
3. [Incumbent relationship] โ mentioned in [n]% of lost deals
WIN PATTERNS (what works in competitive deals)
โโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโ
โ Economic buyer engaged: [n]% of wins vs [n]% of losses
โ Budget confirmed before Propose: [n]% of wins vs [n]% of losses
โ Higher activity volume: [n] avg activities (wins) vs [n] (losses)
โ Shorter sales cycle: [n] avg days (wins) vs [n] avg days (losses)
BATTLECARD: [COMPETITOR NAME]
โโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโ
Their strengths (from deal notes):
โข [Strength extracted from notes]
โข [Strength extracted from notes]
Common objections and responses:
โข "They're cheaper" โ [Response based on win patterns]
โข "[Feature gap]" โ [Response/workaround]
โข "We already know them" โ [Response focused on differentiation]
Where we win:
โข [Scenario 1 โ e.g., enterprise deals with complex integration needs]
โข [Scenario 2 โ e.g., accounts with active support issues with competitor]
โข [Scenario 3 โ e.g., deals where economic buyer is engaged early]
Where to be careful:
โข [Risk area 1 โ e.g., SMB price-sensitive deals]
โข [Risk area 2 โ e.g., industries where competitor has strong presence]
RECOMMENDED ACTIONS
โโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโ
1. [Specific action based on loss patterns]
2. [Coaching recommendation for reps]
3. [Process change to improve win rate]
โโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโ
Output Format
Deliver a two-part output:
Intelligence report โ quantitative win/loss analysis with patterns
Battlecard โ rep-ready talking points, objection responses, and when-to-use guidance
Example Interaction
User Input:
"Show me competitive intelligence on Acme Corp for the last 6 months."
Skill Output:
COMPETITIVE INTELLIGENCE REPORT
Competitor: Acme Corp | Period: Sep 2025 โ Mar 2026
โโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโ
EXECUTIVE SUMMARY
Win Rate vs Acme Corp: 38% (vs 61% overall win rate)
Revenue Won: $420,000 | Revenue Lost to Acme: $680,000
COMPETITIVE PERFORMANCE BY DEAL SIZE
Small (<$25K): 55% win rate โ strongest segment
Medium ($25K-$100K): 32% win rate โ significant gap
Enterprise (>$100K): 18% win rate โ critical weakness
WHERE WE LOSE
Most losses at: Propose stage (67% of losses)
Top industries: Financial Services (5 deals), Healthcare (3 deals)
COMMON OBJECTIONS
1. Price โ mentioned in 71% of lost deals
2. Existing integration with their platform โ 43% of lost deals
3. Acme's brand recognition in FSI โ 29% of lost deals
WIN PATTERNS
โ Economic buyer engaged early: 78% of wins vs 22% of losses
โ POC completed before proposal: 64% of wins vs 11% of losses
BATTLECARD: ACME CORP
"They're cheaper" โ "Our TCO over 3 years is [X]% lower due to..."
"We use their platform" โ "Our API integrates in <2 hours โ here's a reference customer..."
Dataverse Tables Used
Table
Purpose
opportunity
Win/loss records and deal details
opportunityclose
Structured competitor data on closed deals (has direct competitorid lookup)
competitor
Structured competitor profiles
account
Industry and firmographic context
activitypointer
Activity volume comparison
annotation
Mining notes for competitive mentions
phonecall
Call descriptions for competitor mentions
Key Fields Reference
opportunity:
statecode (STATE) - Open(0), Won(1), Lost(2)
description (MULTILINE TEXT) - General opportunity notes
currentsituation (MULTILINE TEXT) - Customer's current environment
actualclosedate (DATE) - When deal was closed
salesstage (CHOICE) - Stage at close
Note: opportunity has no direct competitorid field โ the relationship is many-to-many via opportunitycompetitors_association. Use opportunityclose.competitorid for closed deal competitor data.
opportunityclose:
opportunityid (LOOKUP) - Links to the closed opportunity
competitorid (LOOKUP) - Competitor selected at close (direct lookup, use this for competitive analysis)
description (MULTILINE TEXT) - Close reason / notes entered at time of close
actualrevenue (MONEY) - Final deal value at close
statecode (STATE) - Activity completion state
createdon (DATETIME) - When the close activity was created
competitor:
name (NVARCHAR) - Competitor name
strengths (MULTILINE TEXT) - Known strengths
weaknesses (MULTILINE TEXT) - Known weaknesses
overview (MULTILINE TEXT) - General description
opportunitiescomments (MULTILINE TEXT) - Where we win
threatscomments (MULTILINE TEXT) - Where we lose
Configurable Parameters
Analysis period (default: last 12 months)
Minimum deal count threshold for statistical confidence (default: 5 deals)
Competitor name keywords for text mining (configurable list)
Deal size segmentation thresholds
Examples
Example 1: Specific Competitor Analysis
User says: "How are we doing against ACME Corp?"
Actions:
Search opportunityclose records for competitorid matching ACME
Query won and lost deals with ACME as competitor
Calculate win/loss rates and deal value analysis
Mine activity notes for competitive mentions
Generate battlecard talking points
Result:
COMPETITIVE ANALYSIS: ACME CORP (Last 12 Months)
HEAD-TO-HEAD RECORD:
Wins: 8 deals ($420K) | Losses: 12 deals ($680K)
Win Rate: 40% (vs 55% overall win rate)
WHERE WE LOSE:
- Price objection in 67% of losses
- Mid-market segment: 25% win rate
WHERE WE WIN:
- Enterprise deals: 62% win rate
- When technical eval involved: 71% win rate
BATTLECARD:
"They're cheaper" โ "Our TCO over 3 years is 23% lower..."
"We use their platform" โ "Our API integrates in <2 hours..."
Example 2: Full Competitive Landscape
User says: "Show me the competitive landscape for Q4"
Actions:
Query all opportunityclose records with competitor data for Q4
Group by competitor name
Calculate win rates and deal values per competitor
