Win/Loss Analysis

I just [won/lost] a deal. Help me analyze what happened and extract lessons.\n\nDEAL DETAILS:\n- Company: [company name]\n- Deal size: [value]\n- Sales cycle length: [how long from first touch to close/loss]\n- Decision maker(s): [who was involved]\n- Competitor(s): [who we were up against, if known]\n- Our champion: [who was on our side internally]\n\nTIMELINE:\n[Walk through the deal chronologically — key meetings, turning points, objections, what went well, what went poorly]\n\nOUTCOME: [Won/Lost]\nIF LOST: The stated reason was [what they told you]\n\nAnalyze this deal:\n1. TURNING POINTS: What were the 2-3 moments that most influenced the outcome?\n2. WHAT WORKED: What did we do well that we should repeat?\n3. WHAT DIDN'T: What should we have done differently?\n4. REAL REASON: Based on the timeline, is the stated reason for losing (or winning) the actual reason? What was really going on?\n5. SIGNALS WE MISSED: Were there early warning signs (or buying signals) we should have caught sooner?\n6. PLAYBOOK UPDATE: What should we change in our sales process based on this deal?\n7. PATTERN MATCH: Does this remind you of any common deal patterns? (e.g., 'champion left mid-deal', 'bought on emotion then got cold feet', 'committee decision killed momentum')

Here are all the deals we closed (won and lost) this quarter. Analyze them as a group and find patterns.\n\n[Paste deal summaries — for each deal include: company, size, outcome, sales cycle length, competitor, stated reason for win/loss, and 2-3 sentences of context]\n\nI want to know:\n\n1. WIN PATTERNS\n- What do our wins have in common? (industry, deal size, champion profile, sales cycle length)\n- Which reps are winning and what are they doing differently?\n- What's our average winning deal size and cycle length?\n\n2. LOSS PATTERNS\n- What do our losses have in common?\n- Where in the sales cycle are we losing most deals?\n- Are we losing to specific competitors? Which ones and why?\n- What's the most common stated reason for loss? Is it the real reason?\n\n3. COMPETITIVE ANALYSIS\n- How do we perform against each competitor?\n- What positioning works when we win against [competitor]? What fails?\n\n4. PROCESS GAPS\n- At what stage do deals stall most often?\n- Are there qualifying criteria we should add to prevent wasted cycles?\n- What's our conversion rate at each stage?\n\n5. ACTIONABLE RECOMMENDATIONS\n- Top 3 things to change in our sales process next quarter\n- Specific playbook updates for each major competitor\n- Training priorities for the team based on where we're weakest

I've exported our closed deal data from [CRM name]. The file is at [file path].\n\nRun a comprehensive win/loss cohort analysis. Break the data down by:\n\n1. BY TIME PERIOD: Win rate by month/quarter. Are we getting better or worse?\n2. BY DEAL SIZE: Win rate by deal size bracket. Where's our sweet spot?\n3. BY INDUSTRY: Which industries do we win most in?\n4. BY SOURCE: Inbound vs. outbound vs. referral — which source produces the best win rates?\n5. BY REP: Win rate by sales rep (anonymize if you prefer — Rep A, Rep B, etc.)\n6. BY SALES CYCLE LENGTH: What's the optimal cycle length? Do deals that take longer than X days have materially worse outcomes?\n7. BY COMPETITOR: Win rate when competing against each known competitor\n8. BY ENTRY POINT: Does the first product/feature discussed correlate with win rate?\n9. BY STAKEHOLDERS: Do deals with more stakeholders close better or worse? What's the ideal number?\n10. BY DISCOUNT: Do discounted deals close more often? What's the impact on deal size?\n\nFor each cohort, calculate:\n- Win rate\n- Average deal size (won vs. lost)\n- Average sales cycle length\n- Sample size (flag any cohorts with fewer than 10 deals)\n\nGenerate:\n1. A summary report with key findings\n2. A CSV with all the breakdowns\n3. Top 5 most actionable insights with specific recommendations

Here are our lost deals from the last 6 months with the 'loss reason' field from our CRM:\n\n[Paste the data — company, deal size, loss reason, any additional notes]\n\nThe problem: reps select generic loss reasons from a dropdown. 'Budget', 'Timing', 'Went with competitor' don't tell us enough.\n\nFor each lost deal:\n1. Based on the deal notes and context, what's the REAL reason we lost? Recategorize using these more specific categories:\n   - Never had budget (bad qualification)\n   - Had budget but we lost the ROI argument\n   - Champion left or was reassigned\n   - Committee killed it (too many stakeholders)\n   - Competitor won on price\n   - Competitor won on product/features\n   - Competitor won on relationships\n   - Implementation concerns (too complex or risky)\n   - Status quo won (decided to do nothing)\n   - Timing — deal was real but delayed past our tracking window\n   - Bad fit — should never have been in pipeline\n\n2. After recategorizing all deals, show me the real distribution of loss reasons vs. the CRM dropdown distribution\n3. For the top 3 real loss reasons, give me specific recommendations to address each one\n4. Flag deals that were 'bad fit' — these represent wasted pipeline. What qualifying criteria would have filtered them out early?

Here are the won deals from our top 3 reps and our bottom 3 reps over the last 6 months:\n\n[Paste deal data for both groups]\n\nCompare the two groups and find what top performers do differently:\n\n1. DEAL SELECTION: Do top reps pursue different types of deals? (size, industry, stakeholder profile)\n2. SPEED: How quickly do they move from stage to stage vs. bottom performers?\n3. STAKEHOLDER ENGAGEMENT: Do they involve more or different stakeholders?\n4. PRICING: Do they discount more or less? How do they handle pricing conversations?\n5. OBJECTION HANDLING: What objections come up in their deals vs. the bottom group?\n6. SALES CYCLE: Is their cycle faster? If so, where do they save time?\n7. LOSS PATTERNS: When top reps lose, why? Is it different from when bottom reps lose?\n\nThen create:\n1. A 'Top Performer Playbook' — the 5 specific behaviors that differentiate winners\n2. A coaching plan for bottom performers — what to focus on first\n3. Qualifying criteria updates based on the types of deals top performers choose to pursue

Here are this week's closed deals (won and lost):\n\n[Paste this week's deal outcomes]\n\nQuick analysis:\n1. For each deal, one-sentence lesson learned\n2. Any patterns across this week's outcomes?\n3. Anything that should trigger an update to our objection playbook, competitive battlecards, or sales process?\n4. Compare this week to our trailing 4-week average — are we trending up or down on close rate?\n5. One thing to discuss at Monday's sales meeting based on this data

Based on our win/loss analysis, here are the key findings:\n\n[Paste your analysis summary]\n\nNow help me turn these into concrete process changes:\n\n1. For each finding, propose a specific, measurable change to our sales process\n2. Estimate the impact: if we implement this change, what's the likely effect on close rate?\n3. Prioritize: rank changes by (impact x ease of implementation)\n4. Create an implementation plan: who needs to do what, by when?\n5. Define success metrics: how will we know if each change is working?\n6. Draft a 'state of sales' memo I can share with the team that presents the findings, the changes, and the rationale — without finger-pointing at individual reps