Wilco's Ad Setup Skill

This skill implements Wilco's analytics-driven PPC strategy from the LegacyX FBA course. It creates a 5-campaign Sponsored Products launch package focused on exact match targeting, strict budget isolation, and data-driven keyword selection. Ready for upload via Amazon Bulksheets 2.0.

Strategy Philosophy

Wilco's method is the industry-standard approach used by large brands. It prioritizes control and data over reach:

• Exact match only — ads show only for the literal keyword, its plurals, capitalizations, and misspellings. No stray search terms.
• 1 ad group per campaign — Amazon cannot distribute budget across ad groups reliably. Isolate everything.
• Max 5 keywords per keyword campaign — Amazon starves bottom keywords even with unlimited budget. Keep it tight.
• Max 10 product targets per campaign — slightly more tolerant than keywords, but still limited.
• Analytics-driven selection — pick keywords based on purchase rate, conversion data, and relevance scores, not intuition.
• Minimal negation needed — exact match prevents wasted spend on irrelevant terms.
• Lower velocity, lower risk — explicitly acknowledged trade-off. This method produces fewer sales initially but wastes less ad spend.

User Inputs Required

Input	Required	Example	Notes
Main keyword	Yes	bamboo cutting board	Primary search term for the niche
Product SKU	Yes	BB-CUT-001	Seller SKU for the product ad
Budget level	No	conservative or aggressive	Defaults to conservative
Product name	No	Bamboo Cutting Board	For campaign naming. Derived from keyword if not given

Workflow

Phase 1: Market Research & Competitor Collection

Run research_products to identify top competitors:

research_products(keyword="<main_keyword>", focus="balanced", product_limit=20)

Collect from results:

1. Top 10-12 ASINs sorted by monthly_revenue descending
2. Market data: avg_price, avg_reviews, search_volume, opportunity_score, market_grade, avg_cpc
3. Note brands, concentration level, and dominant brand

Important for Wilco's method: Focus on ASINs that are well-established (higher reviews, stable/growing sales trends). These are the competitors whose keyword portfolios are proven to convert. Unlike Kunze's gap-hunting approach, Wilco targets proven converting keywords that top sellers already rank for.

Phase 2: Batched Keyword Research (SUB-AGENT DELEGATION)

CRITICAL: Delegate to sub-agents to protect the main context window.

Split competitor ASINs into batches of 3-4 and run keyword research in parallel.

Batching rules:

• Maximum 3-4 ASINs per batch
• Use limit=50 per batch
• Use min_search_volume=300 to filter noise
• Run batches in parallel

Sub-agent prompt template:

You are a keyword research assistant for an analytics-driven PPC strategy. Run keyword research and return conversion-focused results.

Run this tool call:
amazon_keyword_research(asins=["ASIN1", "ASIN2", "ASIN3"], limit=50, min_search_volume=300)

From the results, extract the top 20 keywords sorted by PURCHASE RATE descending (not search volume).

Return results as a STRUCTURED LIST with one keyword per line in this exact format:
KEYWORD | search_volume | purchase_rate | cpc | relevance_score | ASIN1_rank | ASIN2_rank | ASIN3_rank

Example:
bamboo cutting board | 125235 | 5.33 | 1.28 | 892 | 12 | 45 | -
cutting boards for kitchen | 321719 | 3.61 | 2.39 | 756 | 8 | - | 23

Use "-" if an ASIN is not ranking. Use pipe delimiters (|) for easy parsing.

Filter OUT:
- Keywords with search_volume below 300
- Brand names of competitors
- Single generic words (e.g., "bottle", "board", "wood")
- Keywords where fewer than 2 of the input ASINs rank in the top 100

The last filter is key: for Wilco's analytics approach, we want keywords where MULTIPLE competitors already rank — this is data evidence that the keyword converts for this product type.

Also report on separate lines:
TOTAL_FOUND: [number]
PASSED_FILTERS: [number]
TOP_PURCHASE_RATE: [keyword1], [keyword2], [keyword3], [keyword4], [keyword5]

Launch as parallel sub-agents:

Agent(subagent_type="general-purpose", name="kw-batch-1", prompt="<template with ASINs 1-4>")
Agent(subagent_type="general-purpose", name="kw-batch-2", prompt="<template with ASINs 5-8>")
Agent(subagent_type="general-purpose", name="kw-batch-3", prompt="<template with ASINs 9-12>")

Phase 3: Build Analytics-Driven Keyword List

After all sub-agent batches return, merge and process:

3a. Merge & Deduplicate

• Combine all keyword arrays from all batches
• Deduplicate by keyword text (case-insensitive)
• For duplicates: merge competitor rank data, keep highest purchase_rate value

3b. Competitor Coverage Scoring

• For each keyword, count how many of the 10-12 competitor ASINs rank for it
• Coverage score = number of competitors ranking / total competitors analyzed
• Keywords with coverage >= 50% are "proven converters" — strong evidence these keywords drive sales in this niche

3c. Wilco Score (Analytics-Driven Ranking) Calculate a composite score for each keyword using min-max normalization (scale each metric to 0-1 within the keyword set):

Normalization: normalized_value = (value - min) / (max - min)  [0 if max == min]

wilco_score = (purchase_rate_normalized * 30) + (coverage_score * 25) + (search_volume_normalized * 20) + (relevance_score_normalized * 25)

Where:

• purchase_rate_normalized * 30 — highest weight on conversion potential (Wilco's primary metric). Normalize the raw purchase_rate values across all keywords using min-max.
• coverage_score * 25 — proven converter signal. Already 0-1 scale (competitors ranking / total competitors).
• search_volume_normalized * 20 — volume matters but is secondary. Normalize raw search_volume using min-max.
• relevance_score_normalized * 25 — relevance prevents wasted spend. Normalize raw relevance_score using min-max.

Maximum possible score: 100 (all metrics at their max within the set).

3d. Categorize & Select

Sort all keywords by wilco_score descending and assign to 3 keyword campaigns:

Campaign A — Top Converters (5 keywords):

• Take the 5 highest wilco_score keywords
• These have the best combination of purchase rate, competitor coverage, and relevance
• These are your bread-and-butter exact match targets

Campaign B — Volume Converters (5 keywords):

• From remaining keywords, take the 5 with highest search_volume that also have purchase_rate >= 3%
• These provide reach while maintaining conversion quality

Campaign C — Niche Converters (5 keywords):

• From remaining keywords, take 5 with highest purchase_rate regardless of volume
• These are low-competition, high-conversion gems
• If search_volume < 1000, can group up to 10 keywords per Wilco's rule for low-volume terms

Product Targeting — Top 10 ASINs:

• Take the top 10 competitor ASINs by revenue from Phase 1
• Max 10 per Wilco's rule (not 15-35 like Kunze)
• Bid at average CPC

Phase 4: Generate Amazon Bulksheets 2.0 CSV

STRONGLY RECOMMENDED: Use Python csv.writer to generate the CSV reliably.

Create the output directory first: mkdir -p ./campaigns

Generate a single CSV with all 5 campaigns using the 29-column Amazon Bulksheets 2.0 format:

Product,Entity,Operation,Campaign Id,Ad Group Id,Portfolio Id,Ad Id,Keyword Id,Product Targeting Id,Campaign Name,Ad Group Name,Start Date,End Date,Targeting Type,State,Daily Budget,sku,asin,Ad Group Default Bid,Bid,Keyword Text,Match Type,Bidding Strategy,Placement,Percentage,Product Targeting Expression,Audience ID,Shopper Cohort Percentage,Shopper Cohort Type

Campaign naming convention: [Product Name] - [Type]

• Example: Bamboo Cutting Board - Auto, Bamboo Cutting Board - Exact A, etc.

Date format: YYYYMMDD (use today's date)

Budget and bid settings:

Setting	Conservative	Aggressive
Daily Budget per campaign	10	20
Keyword bids	At suggested CPC	10% above suggested CPC
Auto campaign default bid	0.50	0.75
Product targeting bid	At avg CPC	At avg CPC

Use Python csv.writer for reliable column counts:

import csv
from datetime import date

HEADER = ['Product','Entity','Operation','Campaign Id','Ad Group Id','Portfolio Id','Ad Id','Keyword Id','Product Targeting Id','Campaign Name','Ad Group Name','Start Date','End Date','Targeting Type','State','Daily Budget','sku','asin','Ad Group Default Bid','Bid','Keyword Text','Match Type','Bidding Strategy','Placement','Percentage','Product Targeting Expression','Audience ID','Shopper Cohort Percentage','Shopper Cohort Type']
COLS = len(HEADER)  # 29
today = date.today().strftime('%Y%m%d')

def make_row(**kwargs):
    row = [''] * COLS
    field_map = {
        'product': 0, 'entity': 1, 'operation': 2, 'campaign_id': 3,
        'ad_group_id': 4, 'portfolio_id': 5, 'ad_id': 6, 'keyword_id': 7,
        'pt_id': 8, 'campaign_name': 9, 'ad_group_name': 10, 'start_date': 11,
        'end_date': 12, 'targeting_type': 13, 'state': 14, 'daily_budget': 15,
        'sku': 16, 'asin': 17, 'ag_default_bid': 18, 'bid': 19,
        'keyword_text': 20, 'match_type': 21, 'bidding_strategy': 22,
        'placement': 23, 'percentage': 24, 'pt_expression': 25,
        'audience_id': 26, 'shopper_pct': 27, 'shopper_type': 28
    }
    for k, v in kwargs.items():
        if k in field_map:
            row[field_map[k]] = str(v)
    return row

rows = []

# Example: Campaign row
rows.append(make_row(
    product='Sponsored Products', entity='Campaign', operation='Create',
    campaign_id=name, campaign_name=name, start_date=today,
    targeting_type='Auto', state='Enabled', daily_budget=budget,
    bidding_strategy='Dynamic bids - down only'
))

with open(output_path, 'w', newline='') as f:
    writer = csv.writer(f)
    writer.writerow(HEADER)
    writer.writerows(rows)

Campaign 1: Auto Campaign

Structure: Campaign + Ad Group + Product Ad + 4 Auto Targeting rows

The auto campaign uses Amazon's automatic targeting. Always include the 4 auto targeting expression rows with individual bids so you can control and optimize each targeting type independently.

• Targeting Type: Auto
• 1 ad group, 1 product ad, 4 product targeting rows
• Default bid: $0.50 (conservative) or $0.75 (aggressive)
• Dynamic bids - down only
• Auto targeting expressions (each gets its own Product Targeting row):
  • close-match — keywords closely related to your product
  • loose-match — keywords loosely related to your product
  • substitutes — shoppers viewing similar products
  • complements — shoppers viewing complementary products

Campaign 2: Product Targeting (Max 10 ASINs)

Structure: Campaign + Ad Group + Product Ad + up to 10 Product Targeting rows

• Targeting Type: Manual
• Max 10 competitor ASINs (Wilco's hard limit)
• Product Targeting Expression: asin="BXXXXXXXXXX" per ASIN
• Bid at average CPC

CSV escaping: The asin="BXXXXXXXXXX" value contains double quotes. Use csv.writer which handles escaping automatically, or manually escape: "asin=""BXXXXXXXXXX"""

Campaign 3: Manual Exact — Top Converters (Campaign A)

Structure: Campaign + Ad Group + Product Ad + 5 Keyword rows (all Exact match)

• Targeting Type: Manual
• 5 keywords with highest wilco_score
• Match Type: Exact (always)
• Individual bids per keyword at their CPC from research

Campaign 4: Manual Exact — Volume Converters (Campaign B)

Same structure as Campaign A, with 5 different keywords selected for volume + conversion.

Campaign 5: Manual Exact — Niche Converters (Campaign C)

Same structure as Campaign A, with 5 keywords selected for highest purchase rate. Can stretch to 10 keywords if all are low-volume (<1,000 search volume) per Wilco's rule.

CSV Generation Rules

1. Save as UTF-8 CSV: ./campaigns/[keyword]-wilco-campaigns.csv
2. All 5 campaigns in a single file
3. No dollar signs — numeric values only for budget and bid
4. Campaign ID = Campaign Name for new campaigns
5. Ad Group ID = Ad Group Name
6. Match Type is ALWAYS Exact for keyword campaigns — this is the defining rule of Wilco's method
7. Max 5 keywords per keyword campaign (can be 10 for Campaign C if all low-volume)
8. Max 10 product targets in the ASIN campaign

CSV Validation (REQUIRED)

After generating the CSV, always verify column counts before presenting to the user:

awk -F',' '{print NF}' ./campaigns/[keyword]-wilco-campaigns.csv | sort | uniq -c

Expected output: All rows should show 29. If any row has a different count, fix it before proceeding.

Also verify:

• Total row count matches expected: header + 7 (Auto) + 13 (ASIN) + 8×3 (Exact A/B/C) = 45 rows typical
• All keyword rows have Match Type = Exact
• No empty Campaign Name or Ad Group Name fields

Phase 5: HTML Summary Artifact

Present a comprehensive HTML summary to the user. Structure:

<html>
<head>
<style>
  body { font-family: -apple-system, BlinkMacSystemFont, 'Segoe UI', sans-serif; max-width: 900px; margin: 0 auto; padding: 20px; background: #f8f9fa; }
  .header { background: linear-gradient(135deg, #1a237e 0%, #283593 100%); color: white; padding: 24px; border-radius: 12px; margin-bottom: 24px; }
  .header h1 { margin: 0 0 8px 0; font-size: 24px; }
  .header .subtitle { opacity: 0.85; font-size: 14px; }
  .header .method-badge { display: inline-block; background: rgba(255,255,255,0.2); padding: 4px 12px; border-radius: 16px; font-size: 12px; margin-top: 8px; }
  .card { background: white; border-radius: 10px; padding: 20px; margin-bottom: 16px; box-shadow: 0 1px 3px rgba(0,0,0,0.08); }
  .card h2 { margin-top: 0; font-size: 18px; color: #1a237e; border-bottom: 2px solid #3f51b5; padding-bottom: 8px; }
  .scorecard { display: grid; grid-template-columns: repeat(auto-fit, minmax(140px, 1fr)); gap: 12px; }
  .metric { background: #f0f2f5; padding: 14px; border-radius: 8px; text-align: center; }
  .metric .value { font-size: 22px; font-weight: 700; color: #1a237e; }
  .metric .label { font-size: 11px; color: #666; margin-top: 4px; }
  table { width: 100%; border-collapse: collapse; font-size: 13px; }
  th { background: #1a237e; color: white; padding: 10px 12px; text-align: left; }
  td { padding: 8px 12px; border-bottom: 1px solid #e8e8e8; }
  tr:hover td { background: #e8eaf6; }
  .badge { display: inline-block; padding: 2px 8px; border-radius: 12px; font-size: 11px; font-weight: 600; }
  .badge-a { background: #e8f5e9; color: #2e7d32; }
  .badge-b { background: #e3f2fd; color: #1565c0; }
  .badge-c { background: #fff3e0; color: #e65100; }
  .badge-proven { background: #f3e5f5; color: #7b1fa2; }
  .campaign-row { display: flex; justify-content: space-between; align-items: center; padding: 12px 0; border-bottom: 1px solid #eee; }
  .campaign-name { font-weight: 600; }
  .campaign-details { font-size: 13px; color: #666; }
  .budget-summary { background: #e8eaf6; border-left: 4px solid #3f51b5; padding: 16px; border-radius: 0 8px 8px 0; margin-top: 16px; }
  .warning { background: #fff3e0; border-left: 4px solid #ff9800; padding: 12px 16px; border-radius: 0 8px 8px 0; font-size: 13px; margin-top: 12px; }
  .file-link { background: #e8f5e9; padding: 12px 16px; border-radius: 8px; font-family: monospace; font-size: 13px; }
</style>
</head>
<body>
  <div class="header">
    <h1>Wilco's Ad Setup: [Product Name]</h1>
    <div class="subtitle">PPC Launch Package &mdash; [Date] &mdash; Keyword: "[main_keyword]"</div>
    <div class="method-badge">Analytics-Driven | Exact Match | Max Control</div>
  </div>

  <!-- Market Scorecard -->
  <div class="card">
    <h2>Market Scorecard</h2>
    <div class="scorecard">
      <div class="metric"><div class="value">[search_volume]</div><div class="label">Search Volume</div></div>
      <div class="metric"><div class="value">$[avg_price]</div><div class="label">Avg Price</div></div>
      <div class="metric"><div class="value">[avg_reviews]</div><div class="label">Avg Reviews</div></div>
      <div class="metric"><div class="value">[opportunity_score]/100</div><div class="label">Opportunity</div></div>
      <div class="metric"><div class="value">[market_grade]</div><div class="label">Market Grade</div></div>
      <div class="metric"><div class="value">$[avg_cpc]</div><div class="label">Avg CPC</div></div>
    </div>
  </div>

  <!-- Keyword List with Analytics Scores -->
  <div class="card">
    <h2>Keyword Selection (Analytics-Ranked)</h2>
    <table>
      <tr><th>Keyword</th><th>Volume</th><th>Purchase %</th><th>CPC</th><th>Coverage</th><th>Wilco Score</th><th>Campaign</th></tr>
      <!-- Top 15 keywords sorted by wilco_score -->
      <tr>
        <td>[keyword]</td><td>[vol]</td><td>[purch%]</td><td>$[cpc]</td>
        <td>[X/Y competitors]</td><td>[score]</td>
        <td><span class="badge badge-[a/b/c]">[A/B/C]</span></td>
      </tr>
    </table>
  </div>

  <!-- Campaign Preview -->
  <div class="card">
    <h2>Campaign Preview (5 Campaigns)</h2>
    <!-- List each campaign with details -->
  </div>

  <!-- Risk/Velocity Warning -->
  <div class="card">
    <h2>Strategy Notes</h2>
    <div class="warning">
      <strong>Expected behavior with Wilco's method:</strong> Sales velocity will be lower than broad/phrase methods during the first 2-4 weeks. This is normal. The trade-off is lower wasted ad spend, no need for negative keyword management, and precise budget control. Monitor conversion rates and CTR per keyword — these are your primary optimization levers.
    </div>
  </div>

  <!-- Budget + Files -->
  <div class="card">
    <h2>Budget Estimate</h2>
    <div class="budget-summary">...</div>
  </div>

  <div class="card">
    <h2>Generated Files</h2>
    <div class="file-link">campaigns/[keyword]-wilco-campaigns.csv</div>
  </div>

  <!-- Analytics Checklist -->
  <div class="card">
    <h2>Analytics Review Checklist (Post-Launch)</h2>
    <ol style="font-size:13px;color:#444;">
      <li><strong>Day 7:</strong> Check CTR per keyword. Pause any keyword with CTR below 0.15% after 1,000+ impressions.</li>
      <li><strong>Day 14:</strong> Check conversion rate per keyword. Keywords converting above average get bid increases; below average get bid decreases.</li>
      <li><strong>Day 21:</strong> Pull search term report from Auto campaign. Extract high-performing search terms and add them as new exact match keywords in Campaigns A/B/C.</li>
      <li><strong>Day 30:</strong> Full ACOS/ROAS review. Pause keywords with ACOS > 2x your target. Increase budget on campaigns with ACOS below target.</li>
      <li><strong>Ongoing:</strong> Use search term mining from Auto to feed new exact keywords into manual campaigns. This is the analytics flywheel of Wilco's method.</li>
    </ol>
  </div>
</body>
</html>

Sub-Agent Delegation Summary

Phase	Delegate?	Why	Sub-agent Type
Phase 1: Market Research	No	Small result, needed for orchestration	Main agent
Phase 2: Keyword Research	YES	Large results, 3-4 parallel batches	general-purpose per batch
Phase 3: Keyword Processing	Optional	Only if context is getting large	general-purpose
Phase 4: CSV Generation	Optional	Only if context is getting large	general-purpose
Phase 5: HTML Summary	No	Must be presented in main chat	Main agent

Bulksheet Format Quick Reference

29 Column Headers (in order)

Product | Entity | Operation | Campaign Id | Ad Group Id | Portfolio Id | Ad Id | Keyword Id | Product Targeting Id | Campaign Name | Ad Group Name | Start Date | End Date | Targeting Type | State | Daily Budget | sku | asin | Ad Group Default Bid | Bid | Keyword Text | Match Type | Bidding Strategy | Placement | Percentage | Product Targeting Expression | Audience ID | Shopper Cohort Percentage | Shopper Cohort Type

Entity Types & Required Fields

Entity	Required Fields (besides Product, Entity, Operation)
Campaign	Campaign Id, Campaign Name, Start Date, Targeting Type, State, Daily Budget, Bidding Strategy
Ad group	Campaign Id, Ad Group Id, Ad Group Name, State, Ad Group Default Bid
Product ad	Campaign Id, Ad Group Id, State, sku (sellers) or asin (vendors)
Keyword	Campaign Id, Ad Group Id, State, Bid, Keyword Text, Match Type
Product targeting	Campaign Id, Ad Group Id, State, Bid, Product Targeting Expression

Key Values for Wilco's Method

Field	Value	Notes
Product	Sponsored Products	Always
Operation	Create	For new campaigns
Targeting Type	Auto or Manual	Auto for campaign 1 only
State	Enabled	Always for new campaigns
Match Type	Exact	ALWAYS Exact for Wilco. Never Broad or Phrase.
Bidding Strategy	Dynamic bids - down only	Conservative default
Product Targeting (ASIN)	asin="BXXXXXXXXXX"	Max 10 per campaign
Date format	YYYYMMDD	Today's date

Wilco vs Kunze Quick Comparison (for user context)

Aspect	Wilco	Kunze
Match type	Exact only	Broad + Phrase
Keywords per campaign	Max 5 (strict)	Max 5
ASINs per campaign	Max 10	15-35
Keyword campaigns	3 (all exact)	2 (1 broad + 1 phrase)
Keyword selection	Analytics-driven (purchase rate, coverage)	Gap-hunting (low competition)
Negation needed	Minimal	Regular
Sales velocity	Lower	Higher
Risk	Lower	Higher
Analytics investment	High	Moderate

Tool Reference

Tool	When to Use
research_products(keyword, focus="balanced", product_limit=20)	Phase 1: Market scan and competitor ASIN collection
amazon_keyword_research(asins=[...], limit=50, min_search_volume=300)	Phase 2: Keyword research (run in sub-agents, 3-4 ASINs per batch)
Agent(subagent_type="general-purpose")	Phase 2+: Delegate keyword research batches and heavy processing

Error Handling

• Keyword research returns too many results: Reduce limit to 30 or increase min_search_volume to 500
• Fewer than 15 quality keywords found: Reduce Campaign C to 3-4 keywords, or merge B and C into one campaign with up to 10 keywords
• No competitor ASINs found: Ask user for specific competitor ASINs
• Sub-agent fails: Retry once, skip and note in summary if second attempt fails
• All keywords have purchase_rate < 1%: The niche may have low commercial intent. Flag this in the summary and suggest the user verify demand