Ppa value perception

ppa-value-perception

Measures consumer value perception, models decision hierarchies, estimates WTP via logit and conjoint, and detects perception failures. Always outputs dashboard first.

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Plain Language: What This Does

"¿Cuánto pagaría el consumidor por este pack?"  → WTP estimation (logit/conjoint)
"¿El pack 1L es percibido como mejor valor que 500ml?"  → Perception failure check
"¿Qué atributo importa más: precio, marca o tamaño?"  → Decision hierarchy + value drivers
"¿Cómo comunicar mejor el valor de este SKU?"  → Value communication A/B test
"¿El segmento premium paga más por conveniencia?"  → WTP by segment × occasion

Core insight (EY PPA Framework p.3): A larger pack that costs MORE per kg/ml than the smaller pack creates a negative brand impression — the shopper feels cheated. This is a perception failure that reduces brand equity even if the absolute price is rational.

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Core Equations

WTP from logit model (choice-based conjoint)

# Logit utility model:
U(alternative) = β_price × price + β_size × size + β_brand × brand + ε

# WTP for attribute X (eq. from Lancaster-McFadden):
WTP_X = -β_X / β_price

# Where β_price < 0 (price always reduces utility)
# β_X > 0 → consumer pays MORE for attribute X
# β_X < 0 → consumer pays LESS (negative preference)

# Example: β_brand_premium = 2.3, β_price = -0.8
# WTP_brand_premium = -2.3 / -0.8 = $2.88 above reference

Perception failure detector

# Price-per-unit consistency check across pack sizes:
price_per_unit_i = price_shelf_i / volume_i   # for each SKU i

# Violation flag: larger pack should be CHEAPER per unit
for each (small_pack, large_pack) in same_brand_same_tier:
    if price_per_unit(large_pack) > price_per_unit(small_pack):
        flag = "PERCEPTION_FAILURE"
        penalty_score = (ppu_large - ppu_small) / ppu_small  # % premium paradox

# Benchmark: >5% paradox triggers brand equity risk (EY PPA p.3)

Value Score index

# Composite perceived value:
value_score = (quality_perception × w_quality +
               price_fairness    × w_price +
               pack_convenience  × w_convenience +
               brand_trust       × w_brand) / Σw

# Weights from conjoint/MaxDiff by segment
# Benchmark: value_score > 0.65 = strong value proposition

MaxDiff scaling (Sawtooth methodology)

# Best-Worst scaling for attribute importance:
importance_score_i = exp(utility_i) / Σ_j exp(utility_j)

# Output: ranked importance 0–100 for each attribute

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Workflow

Step 1 — Load SKU + price data

# Always run data-intake-normalizer first
python scripts/perception_failure_detector.py \
    --skus /mnt/user-data/uploads/sku_data.xlsx \
    --brand "BrandName" \
    --output results/perception_failures.json

Step 2 — Model decision hierarchy

python scripts/decision_hierarchy.py \
    --survey /mnt/user-data/uploads/conjoint_survey.csv \
    --method logit \
    --attributes "price,size,brand,format,flavor" \
    --output results/decision_hierarchy.json

Step 3 — Estimate WTP

python scripts/wtp_estimate.py \
    --hierarchy results/decision_hierarchy.json \
    --segments "value_seeker,mainstream,premium" \
    --output results/wtp.json

Step 4 — Identify value drivers

python scripts/value_drivers.py \
    --transactions /mnt/user-data/uploads/transactions.csv \
    --attributes "price,size,brand,channel,promo" \
    --output results/value_drivers.json

Step 5 — Test value communication (optional)

python scripts/value_communication_tester.py \
    --messages /mnt/user-data/uploads/messages.csv \
    --segment "mainstream" \
    --output results/communication_test.json

Step 6 — Dashboard

python scripts/value_perception_report.py \
    --perception results/perception_failures.json \
    --wtp results/wtp.json \
    --drivers results/value_drivers.json \
    --output dashboard_data.json

Output sequence:

1. [bash_tool] perception_failure_detector.py → immediate flags
2. [bash_tool] wtp_estimate.py + decision_hierarchy.py
3. [web_search] WTP benchmarks [category] [market] [year] +
                "price perception consumer behavior [category] LATAM"
4. [show_widget] Dashboard: perception failure alerts + WTP curves +
                 decision hierarchy + value driver chart
5. [text] NBA + market context (brand, category, SKU, segment)
6. [text] Caveats: stated vs revealed preference, survey bias

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Market Context (always include)

• Market: country + city/zone
• Category + sub-category
• Brand + competitive set
• Segment: value seeker / mainstream / premium / super-premium
• Occasion: on-the-go / household / HoReCa / gifting
• Channel: MT / TT / convenience / e-comm
• Data source: conjoint survey / panel / transaction data

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Dashboard Panels

1. KPI bar — perception failures count, avg WTP gap vs current price, decision hierarchy top attribute, value score index, % SKUs with price paradox
2. Perception failure table — SKU pairs where large pack > small pack per unit, with paradox % and brand equity risk rating
3. WTP curves — price vs probability-of-choice by segment (S-curves)
4. Decision hierarchy — horizontal bar chart of attribute importance (MaxDiff scores)
5. Value drivers — feature importance from logit/RF model
6. Value score map — brand × tier × channel heat map of perceived value

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Marketer Insights Layer (MANDATORY)

Search before benchmarking

web_search: "willingness to pay [category] [market] consumer research [year]"
web_search: "price perception pack size paradox [category] CPG [year]"
web_search: "conjoint analysis WTP FMCG LATAM benchmark [year]"

Translate metrics to business language

Technical	Business meaning
WTP = $24.50	"Segment will pay up to $24.50 before switching — current price $21 = $3.50 headroom"
β_brand = 2.3	"Brand preference is worth $2.88 in additional WTP vs generic"
paradox_pct = 12%	"Pack 1L costs 12% MORE per ml than 500ml — shopper perceives brand as unfair"
importance(price) = 45%	"Price is the #1 decision driver — brand only contributes 20% to choice"
value_score = 0.42	"Below benchmark (0.65) — price, quality and convenience messages misaligned"

NBA — Next Best Actions

Always produce 5-6 specific actions:

• Fix perception failures first: "SKU [X] 1L costs $[A]/ml vs 500ml at $[B]/ml — reduce 1L shelf price by [C]% or increase 500ml price to restore economic logic"
• Capture WTP headroom: "Segment [Y] has $[Z] WTP above current price — test +[X]% price increase in [channel] with no volume risk"
• Hierarchy insight: "Price is attribute #1 for [segment] (45% importance) — value messages must lead with economy, not quality or brand"
• Reframe value communication: "For premium segment, communication should emphasize [top driver] not [current message] — drivers show [X]% more impact on choice"
• Segment-specific pricing: "Value seekers WTP = $[A], premium = $[B] — [C]% gap justifies a dedicated entry SKU below $[D]"
• Channel differentiation: "WTP in convenience is $[A] vs supermarket $[B] — price the same SKU $[C] higher in convenience without switching risk"

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Key Caveats

• Stated vs revealed preference: Conjoint/survey WTP overstates actual behavior by 20–40% on average — apply deflation factor before pricing decisions
• Hypothetical bias: Survey respondents overstate premium brand preference; cross-validate with actual transaction data (value_drivers.py)
• Segment stability: WTP shifts with inflation — retune model every 6 months in high-inflation markets (MX, ARG, COL)
• Perception failure ≠ always wrong: Some brands intentionally price large packs at premium per-unit (luxury positioning) — flag but confirm strategic intent first
• Survey design matters: Choice-based conjoint requires ≥200 respondents per segment; MaxDiff requires ≥150

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Integration with OS

Skill	Handoff
data-intake-normalizer	Always first — validate SKU/price/survey data
ppa-assortment-diagnosis	Diagnosis finds white spaces → perception tells what value to offer there
ppa-portfolio-optimizer	WTP by segment feeds optimal price-pack mix
price-demand-optimization	WTP = upper bound for p_opt estimation
customer-segmentation-clustering	Segments feed WTP estimation per persona
trade-promotion-roi	WTP gap = headroom for promo depth without brand damage

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References

• references/wtp_methodology.md — Logit WTP derivation + conjoint design guide
• references/perception_failure_cases.md — EY case studies + paradox benchmarks
• references/value_driver_benchmarks.md — Attribute importance by category (LATAM)