Delectable AI: 4D Product Intelligence

Four Dimensions of Intelligence.
One Enriched Product.

Raw transaction data tells you what was purchased. Delectable's 4D enrichment tells you why it was purchased, who it was purchased for, and what should come next.

150+

Enrichment Dimensions per SKU

8

Food Science Databases

70K+

Enriched Product Profiles

2.3

Personas per Loyalty Card

Beyond Transactions: Multi-Dimensional Product Understanding

Each dimension adds context that transforms raw data into actionable intelligence.

Dimension 1: Food Intelligence

What the product IS at the molecular level

● Macro/micro nutritional decomposition (calories, macros, vitamins, minerals)
● Allergen classification (8 major allergens + 120 trace allergens)
● Dietary compatibility scoring (18 diets: keto, paleo, FODMAP, whole30, Mediterranean...)
● Ingredient quality grading (processing level, additive count, clean label score)
● Proprietary health score (composite of 12 nutritional factors)
● Freshness classification (fresh / frozen / shelf-stable / prepared)

8 food databases integrated

Dimension 2: User Dimensions

WHO the product is for and WHY

● Persona affinity scoring (per household persona, 0-1 scale)
● Life-stage relevance mapping (baby, toddler, teen, adult, senior)
● Dietary restriction compatibility matrix per persona
● Price sensitivity scoring per persona segment
● Brand loyalty propensity (switching likelihood 0-1)
● Recipe-to-product linking (which recipes feature this product)

Loyalty + behavioral data

Dimension 3: Behavioral Dimensions

HOW the product fits into shopping behavior

● Purchase frequency modeling (weekly/biweekly/monthly/seasonal)
● Basket co-occurrence patterns (what pairs with what)
● Session context signals (time of day, device type, browse path)
● Replenishment cycle prediction (days until next purchase)
● Cross-category affinity mapping
● Substitution graph (what swaps for what, directional)

Transaction + session data

Dimension 4: Market & External Signals

WHEN and WHERE the product is relevant

● Seasonal demand curves and holiday patterns
● Local inventory and availability signals
● Promotional sensitivity scoring (deal-elastic vs. brand-loyal)
● Competitive price positioning vs. category
● Trending dietary patterns (social media, health trends)
● Recipe popularity signals from viral content

Market + external feeds

How Nielsen Data Flows Through Delectable Enrichment

Every Nielsen data asset becomes more valuable when enriched with Delectable's 4D intelligence.

Nielsen Data Assets

Syndicated POS Data

Category performance, brand share, pricing trends, volume tracking across retailers

Homescan Panel Data

250K+ households, basket-level purchase data, cross-retailer shopping behavior

Product Master Data

UPC hierarchies, category taxonomy, brand ownership, product attributes

NIQ Activate Segments

Demographic and behavioral audience segments for targeting and activation

4D Enrichment Pipeline

Product Enrichment

Raw UPC → 150+ dimension enrichment via 8 food databases + ML classification. Nutritional decomposition, allergen mapping, dietary scoring, and health grading applied to every SKU.

Persona Engine

Panel household → K-Means clustering → 2.3 distinct personas with individual preference vectors. De-averages household data into actionable individual profiles.

Behavioral Modeling

Transaction history → 27+ ML models → purchase propensity, basket affinity, replenishment prediction. Temporal patterns and substitution graphs extracted.

Knowledge Graph

Products, personas, recipes, dietary needs connected in a queryable multi-hop graph. Enables reasoning across dimensions that flat tables cannot support.

Value Creation by Stakeholder

For Nielsen

De-averaged panel data, persona-level brand affinity, enriched product taxonomy. Transform panel households into individual-level intelligence that commands premium pricing.

For Retailers

Personalized recommendations, agentic shopping experiences, optimized assortment. Drive higher basket sizes and loyalty through intelligent product discovery.

For CPG Brands

Household-level intelligence, persona targeting for retail media, closed-loop attribution. Know exactly which personas buy your product and why they switch.

For Consumers

Relevant recommendations, dietary-safe suggestions, recipe-driven discovery. An intelligent shopping experience that understands individual needs.

Before & After: Raw Data vs. Enriched Intelligence

See the transformation from flat transaction records to multi-dimensional product profiles.

Example 1: Product Profile

Raw Nielsen Product Data

UPC:         0041130286
Description: CHOBANI GREEK YOG VAN
Category:    YOGURT
Subcategory: GREEK YOGURT
Size:        5.3 OZ
Price:       $1.79
Units Sold:  2,847 (4 weeks)
Share:       4.2% of category
        

Delectable-Enriched Profile

UPC:              0041130286
Name:             Chobani Greek Yogurt, Vanilla
Category:         YOGURT > GREEK > SINGLE-SERVE

// Food Intelligence
nutrition:        {protein: 12g, sugar: 15g, fat: 0g, calcium: 15% DV}
allergens:        ["milk"]
dietary_scores:   {keto: 0.3, low_sugar: 0.4, high_protein: 0.92, mediterranean: 0.7}
health_score:     7.2 / 10
processing_level: "minimally processed"

// User Dimensions
persona_affinity: {athlete: 0.87, health_mom: 0.72, child: 0.41, senior: 0.55}
life_stage:       ["adult", "teen", "senior"]
recipe_links:     ["smoothie-bowl", "overnight-oats", "protein-parfait"]

// Behavioral Dimensions
replenish_cycle:  8.3 days
basket_companions: ["granola", "berries", "honey", "bananas"]
substitutions:    ["Fage 0%", "Siggi's Vanilla", "Store Brand Greek"]
purchase_pattern: "weekly staple"

// Market Signals
promo_sensitivity: 0.34 (low - brand loyal)
seasonal_curve:   "stable year-round, +12% Jan (resolutions)"
trend_score:      0.68 (high-protein trending)
        

Example 2: Household Intelligence

Raw Nielsen Panel Record

Panelist ID:     HH-28471
HH Size:         4
Income:          $75K-$100K
Total Trips:     142 / year
Avg Basket:      $87
Top Categories:  Produce, Meat, Baby, Dairy
Channel Split:   78% in-store, 22% online
        

Delectable De-Averaged Household

Loyalty Card:    HH-28471
Personas:        3 detected (K-Means, silhouette: 0.74)

PERSONA A: "Health-Conscious Mom" (45% of trips)
  dietary:       organic=0.82, low_sodium=0.78, whole_foods=0.85
  focus:         produce, dairy alternatives, clean snacks
  sensitivity:   price=medium, quality=high
  brands:        Organic Valley, Annie's, Earthbound Farm
  replenish:     weekly (Sat morning pattern)

PERSONA B: "Athlete Dad" (32% of trips)
  dietary:       high_protein=0.92, performance=0.78
  focus:         meat, sports nutrition, bulk grains
  sensitivity:   price=low, convenience=high
  brands:        Perdue, Quest, Muscle Milk
  replenish:     biweekly (bulk buy pattern)

PERSONA C: "Parent - Baby" (18% of trips)
  dietary:       allergen_sensitive=0.95, organic_baby=0.88
  focus:         baby food, formula, diapers
  sensitivity:   price=high (comparison shops), safety=critical
  brands:        Earth's Best, Gerber Organic, Pampers
  replenish:     weekly (needs-based)

CROSS-PERSONA:
  shared_items:  milk, bread, eggs, bananas (all 3)
  conflict_items: nut products (Persona C allergen risk)
  allergen_guard: tree_nuts BLOCKED household-wide
        

The Data Flywheel: Each Side Makes the Other More Valuable

A compounding data advantage that deepens with every transaction.

Nielsen Data

More representative sampling calibrates Delectable's behavioral models

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Delectable Enrichment

Richer product + persona dimensions enhance Nielsen's panel value

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Consumer Experience

Personalized interactions generate new behavioral signals

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Measurement

Attribution data proves ROI, attracting more CPG investment

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Year 1: Foundation

Enrichment of 70K SKUs across top grocery categories. De-averaging of 250K+ panel households. Initial behavioral model training on Nielsen-calibrated data.

Year 2: Scale

Cross-retailer behavioral models trained on Nielsen-calibrated data. 3x enrichment depth (450K+ SKUs). CPG brand intelligence products launched.

Year 3: Dominance

Industry-standard consumer intelligence layer. Defensible data moat across 90+ countries. Attribution-as-a-service for retail media.

The Moat Deepens With Every Transaction

Unlike static datasets, Delectable's enrichment improves continuously. Every shopper interaction trains the models. Every Nielsen data refresh recalibrates the intelligence. Competitors would need to start from zero.

Four Dimensions of Intelligence.One Enriched Product.