Ppa assortment diagnosis

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.

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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.

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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

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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

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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

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Dashboard panels

1. KPI bar — own SKUs, competitor SKUs, coverage index, top white space score, price tier gaps, RPI vs category
2. Price-Pack Matrix — rows=pack sizes, cols=price tiers, cells=brand coverage (color = own / competitor / white space / both)
3. White Space heatmap — score by (pack_size × price_tier × channel)
4. Price trend chart — price/kg or price/ml evolution by brand over time
5. Consumer behavior panel — avg purchase size, price paid, frequency by segment
6. TomTom geo map — channel POIs per zone, colored by white space opportunity
7. NBA panel — 5-6 specific actions

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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"

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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

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References

• references/polestar_ppa_methodology.md — Price-pack architecture framework
• references/ey_ppa_framework.md — EY commercial excellence model
• references/price_tier_benchmarks.md — Category price tier definitions by market