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ppa-assortment-diagnosis
GitHub诊断价格-包装组合，识别空白机会与竞争差距。适用于SKU组合分析、竞品地图、渠道/区域覆盖评估及NBA建议。支持多语言触发，结合HTML仪表盘与TomTom地图展示地理洞察。
Trigger Scenarios
分析SKU组合
按价格包装映射竞争格局
寻找产品组合中的空白点
评估各价格层级的覆盖情况
诊断渠道或区域的价格架构缺口
对比自身组合与竞争对手
PPA
价格包装架构
surtido
portafolio de SKUs
espacios en blanco
white space
brechas de precio
análisis de empaque
competitive landscape by pack size
Install
npx skills add NeverSight/learn-skills.dev --skill ppa-assortment-diagnosis -g -y
SKILL.md
Frontmatter
{
"name": "ppa-assortment-diagnosis",
"description": "Diagnoses current price-pack assortment, identifies white spaces and improvement opportunities. Trigger when asked to: analyze SKU portfolio, map competitive landscape by price-pack, find white spaces in assortment, evaluate coverage across price tiers, diagnose PPA gaps by channel or region, or benchmark own portfolio vs competitors. Also trigger for: \"PPA\", \"price pack architecture\", \"surtido\", \"portafolio de SKUs\", \"espacios en blanco\", \"white space\", \"brechas de precio\", \"análisis de empaque\", \"competitive landscape by pack size\". Always renders inline HTML dashboard + TomTom map when geographic\/channel data is present. Includes marketer NBA and benchmarks."
}
ppa-assortment-diagnosis
Diagnoses current price-pack assortment across competitors, channels, regions and pack sizes. Identifies white spaces, over-indexed SKUs, and strategic gaps. Always outputs dashboard first, then NBA + market context.
Plain Language: What This Does
"¿Qué SKUs tenemos vs la competencia?" → Competitive landscape mapping
"¿Dónde no tenemos pack/precio cubierto?" → White space identification
"¿Qué tamaño de pack prefiere el shopper?" → Consumer behavior analysis
"¿Cómo evolucionó el precio por kg/ml?" → Price-pack trend tracking
"¿En qué canal falta qué SKU?" → Channel × pack gap matrix
"¿Dónde geográficamente hay mayor oportunidad?" → TomTom zone mapping
Core insight (EY PPA Framework): The right product at the right price in the right pack for the right channel. A brand may have 20 SKUs and still have 3 critical white spaces that competitors exploit.
Core Equations
Price-per-unit metrics
# Price per volume unit (eq. for cross-pack comparison)
price_per_kg = price_shelf / (weight_grams / 1000)
price_per_ml = price_shelf / (volume_ml / 1000)
price_per_use = price_shelf / servings_per_pack
# Relative price index vs category average
rpi = brand_price_per_unit / category_avg_price_per_unit
# RPI > 1.0 = price premium RPI < 1.0 = price discount
# Pack size trend (eq. from Polestar methodology)
trend = (price_current - price_historical) / price_historical
White space scoring
# White space score for a (pack_size, price_tier) cell
ws_score = (competitor_coverage - own_coverage) * demand_weight
# ws_score > 0.5 = actionable white space
# ws_score > 0.8 = urgent gap
Assortment coverage index
# % of price-pack cells covered by own portfolio
coverage = own_cells_filled / total_competitive_cells
# Benchmark: category leaders typically > 0.65
Workflow
Step 1 — Load & profile data
python scripts/competitive_landscape.py \
--data /mnt/user-data/uploads/sku_data.xlsx \
--market [Mexico/Colombia/Chile/...] \
--category [beverages/snacks/personal_care/...] \
--output results/landscape.json
Step 2 — Track price-pack trends
python scripts/price_pack_trends.py \
--landscape results/landscape.json \
--periods 12 \
--output results/trends.json
Step 3 — Find white spaces
python scripts/white_space_finder.py \
--landscape results/landscape.json \
--own-brand "[Brand Name]" \
--output results/white_spaces.json
Step 4 — Analyze consumer behavior
python scripts/consumer_behavior.py \
--transactions /mnt/user-data/uploads/transactions.csv \
--output results/consumer.json
Step 5 — Geographic zone analysis (when channel/region data present)
→ Use TomTom MCP: tomtom-fuzzy-search + tomtom-area-search
Query POIs by channel type (supermarkets, convenience, traditional trade)
per city/zone to map distribution opportunity vs white space
→ Display results with tomtom-dynamic-map or places-map-display
Step 6 — Generate report + dashboard
python scripts/assortment_gap_report.py \
--landscape results/landscape.json \
--white-spaces results/white_spaces.json \
--consumer results/consumer.json \
--output dashboard_data.json
Output sequence:
1. [bash_tool] Run all scripts
2. [web_search] Category benchmarks: avg SKUs per brand, price tier distribution,
pack size trends for [market] [category] [year]
3. [TomTom MCP] If geographic data → map channel POIs per zone
4. [show_widget] HTML dashboard: landscape matrix + white space heatmap +
trend chart + geo map
5. [text] NBA + market context (region, city, category, SKU level)
6. [text] Caveats: data recency, panel vs POS source differences
Market Context (always include in output)
Every output must reference:
- Market: country + city/region
- Category: macro-category + sub-category
- Channel: MT (Modern Trade) / TT (Traditional Trade) / E-comm / Club
- Time period: data vintage + trend window
- Competitive set: defined brands in scope
- Price tiers: defined entry / mainstream / premium / super-premium breakpoints
Dashboard panels
- KPI bar — own SKUs, competitor SKUs, coverage index, top white space score, price tier gaps, RPI vs category
- Price-Pack Matrix — rows=pack sizes, cols=price tiers, cells=brand coverage (color = own / competitor / white space / both)
- White Space heatmap — score by (pack_size × price_tier × channel)
- Price trend chart — price/kg or price/ml evolution by brand over time
- Consumer behavior panel — avg purchase size, price paid, frequency by segment
- TomTom geo map — channel POIs per zone, colored by white space opportunity
- NBA panel — 5-6 specific actions
Marketer Insights Layer (MANDATORY)
Web search before benchmarking
web_search: "price pack architecture [category] [market] trends [year]"
web_search: "average SKUs per brand [category] [country] Nielsen [year]"
web_search: "pack size trends [category] LATAM [year]"
Translate metrics to business language
| Technical | Business meaning |
|---|---|
| ws_score > 0.8 | "Competitor owns this space — we have zero presence" |
| coverage < 0.50 | "We cover less than half the competitive price-pack landscape" |
| RPI > 1.2 | "We price 20%+ above category avg — only justified if brand equity supports it" |
| trend > 0.15 | "Prices in this pack size rose 15% — inflation passing or premiumization signal" |
| price_per_kg gap | "Our 500g is 30% more expensive per kg than our 1kg — shopper notices" |
NBA — Next Best Actions
Always produce 5-6 specific actions adapted to market/category context:
- White space priority: "Launch [pack_size]g at [price_tier] for [channel] — ws_score=[X], [N] competitors present, zero own coverage"
- Over-indexed SKU: "SKU [X] cannibalizes [Y] with 85% shopper overlap — rationalize or reposition"
- Price corridor gap: "No SKU between $[A] and $[B] — shopper trading up from entry tier has no step"
- Channel-specific gap: "Traditional trade has no [small_pack] below $[X] — competitors dominate impulse occasion"
- Geographic priority: "Zone [X] has [N] supermarkets with no [category] coverage above [price_tier] — distribution win opportunity"
- Pack size trend: "[Size] pack growing [X]% YoY — validate if portfolio has winning offer in this format"
Integration with OS
| Skill | Handoff direction |
|---|---|
data-intake-normalizer |
Always first — validate SKU/price/channel data |
rgm-analyzer |
PPA diagnosis feeds RGM revenue waterfall |
ppa-portfolio-optimizer |
Diagnosis → Optimizer builds the solution |
price-demand-optimization |
White space price points → demand curve validation |
market-basket-analysis |
Cross-category white spaces → basket affinity |
category-space-planner |
Pack gaps → shelf space reallocation |
trade-promotion-roi |
New SKU launch → promo plan for distribution gain |
References
references/polestar_ppa_methodology.md— Price-pack architecture frameworkreferences/ey_ppa_framework.md— EY commercial excellence modelreferences/price_tier_benchmarks.md— Category price tier definitions by market
Version History
- e0220ca Current 2026-07-05 23:37
