Competitive Battlecard Assembly

This drill builds structured, data-backed competitive battlecards for each named competitor that appears in your sales pipeline. Unlike static documents, these battlecards are living CRM records that update automatically as new deal data, win/loss outcomes, and competitive intelligence accumulate.

Input

• Attio CRM with deal records that have competitor_mentioned attributes
• At least 3 completed deals where a specific competitor was mentioned
• Clay account for competitor company enrichment
• Anthropic API key for synthesis

Steps

1. Initialize the Competitors object in Attio

If not already created (check first via Attio MCP), create a custom object called "Competitors" with these attributes:

| Attribute | Type | Purpose | |-----------|------|---------| | Name | Text | Competitor company name | | Website | URL | Competitor's primary website | | Mention Count | Number | Total times mentioned across all deals | | Win Rate Against | Number (%) | Deals won when this competitor was in the deal | | Loss Rate Against | Number (%) | Deals lost to this competitor | | Avg Deal Size (Competitive) | Currency | Average deal size in competitive deals with them | | Their Strengths | Text (multi-line) | Synthesized from buyer quotes | | Their Weaknesses | Text (multi-line) | Synthesized from buyer quotes | | Our Differentiators | Text (multi-line) | Where we win most often | | Common Objections | Text (multi-line) | Recurring objections in competitive deals | | Winning Frameworks | Text (multi-line) | Response frameworks with highest win rate against them | | Trap Questions | Text (multi-line) | Questions that highlight our strengths vs their gaps | | Pricing Intel | Text (multi-line) | Known pricing from prospects and public sources | | Recent Changes | Text (multi-line) | Product/pricing changes detected by monitoring | | Last Updated | Date | When this record was last refreshed | | Battlecard Version | Number | Incremented on each refresh |

2. Aggregate deal data per competitor

Query Attio for all deals where competitor_mentioned matches the target competitor. For each deal, extract:

• Deal outcome (won/lost/open)
• Objection data (from call-transcript-objection-extraction results stored in notes)
• Pain data (from discovery notes)
• Decision criteria mentioned
• Win/loss reasons (if closed)
• Buyer quotes about the competitor

Use Claude API to synthesize across all deals:

{
  "prompt": "You are analyzing {n} sales deals where {competitor_name} was mentioned as a competitor. Synthesize the following data into a competitive battlecard.\n\nDeal data:\n{deals_json}\n\nFor each section, use ONLY information from the deal data. Do not invent strengths or weaknesses. Quote buyers directly where possible.\n\nReturn JSON:\n{\n  \"their_strengths\": [\"Strength 1 with supporting buyer quote\", ...],\n  \"their_weaknesses\": [\"Weakness 1 with supporting buyer quote\", ...],\n  \"our_differentiators\": [\"Differentiator 1 — why it matters to buyers\", ...],\n  \"common_objections\": [{\"objection\": \"...\", \"frequency\": n, \"best_response\": \"...\"}],\n  \"winning_frameworks\": [{\"framework\": \"...\", \"win_rate\": 0.xx, \"sample_size\": n}],\n  \"trap_questions\": [\"Question that surfaces their weakness without naming them\", ...],\n  \"pricing_intel\": {\"known_range\": \"...\", \"packaging\": \"...\", \"discount_patterns\": \"...\"},\n  \"deal_patterns\": {\"we_win_when\": \"...\", \"we_lose_when\": \"...\", \"key_decision_criteria\": [\"...\"]}\n}"
}

3. Enrich with public competitive intelligence

Run Clay enrichment on the competitor:

• Company search via clay-company-search for firmographics, headcount, funding, tech stack
• Claygent scrape of their pricing page, features page, and recent blog posts via clay-claygent
• G2/Capterra ratings (if available via Clay enrichment)

Append public intel to the battlecard.

4. Store the battlecard in Attio

Update the Competitor record with all synthesized data. Create a structured note with the full battlecard:

## {Competitor Name} Battlecard v{version}
**Updated:** {date} | **Deals analyzed:** {n} | **Win rate against:** {win_rate}%

### When We Win
{synthesized from deals where we beat them}

### When We Lose
{synthesized from deals where they beat us}

### Their Strengths (from buyer quotes)
{bulleted list with actual buyer quotes}

### Their Weaknesses (from buyer quotes)
{bulleted list with actual buyer quotes}

### Top Objections & Responses
| Objection | Frequency | Best Response Framework | Win Rate |
|-----------|-----------|------------------------|----------|

### Trap Questions
{questions that surface their gaps without naming them}

### Pricing Intelligence
{known pricing, packaging, discount patterns}

### Recent Product/Market Changes
{from competitor-changelog-monitoring if available}

5. Set up automated refresh

Create an n8n workflow triggered weekly (Monday 7 AM):

1. Query Attio for new deals involving each tracked competitor in the past 7 days
2. If new data exists, re-run the synthesis (Step 2) with all historical + new data
3. Update the Competitor record and increment Battlecard Version
4. If win rate against any competitor drops below 40%, send Slack alert
5. Fire PostHog event: battlecard_refreshed with competitor name, deal count, win rate

Output

• Structured Competitor records in Attio with data-backed battlecards
• Weekly automated refresh incorporating new deal data
• Win rate tracking per competitor over time
• Alerts when competitive position degrades

Triggers

• Initial build: Run manually for each competitor with 3+ deal mentions
• Refresh: Weekly cron via n8n, or triggered when a new deal closes involving a tracked competitor
• Alert: Immediate Slack notification if win rate drops below threshold