Data Normalization 101: Matching Disparate SKU Names Across Retailers for a Unified View

The client was a beverage alcohol analytics and advisory firm supporting: Bourbon and tequila brands Regional distributors Multi-state liquor retailers Investment and market research teams Their objective was clear: Build a single, clean dataset that allows apples-to-apples comparison of liquor prices across retailers and regions. However, their internal analysts faced constant friction due to inconsistent product naming, pack-size representation, and attribute formatting.

At first glance, matching “750ml Bourbon” with “Bourbon 0.75L” sounds trivial. In reality, this problem multiplies rapidly when scaled across thousands of products and stores.

Common SKU Variations Observed Across the three retailers, Food Data Scrape identified variations such as: Bourbon 750ml Bourbon – 750 ML Bourbon 0.75L Bourbon 75cl Bourbon Bottle 750ml Bourbon Whiskey 750ML Despite referring to the same physical product, these SKUs were treated as unique entries in raw datasets.

Without normalization, downstream analytics become unreliable.

Business Risks of Unnormalized Data

• Incorrect price comparison
• Duplicate SKUs inflating catalog size
• Misleading regional price gaps
• Faulty promotion and discount analysis
• Broken dashboards and BI reports

For liquor brands and retailers, this can directly impact pricing strategy, distributor negotiations, and revenue forecasting.

Food Data Scrape worked with structured and semi-structured data extracted from: ABC Fine Wine & Spirits (Florida market focus) Spec’s Wine Spirits & Finer Foods (Texas market focus) Top Ten Liquors (Minnesota market focus) Each retailer followed its own internal catalog logic, making direct SKU matching impossible without transformation.

The first step was not normalization. It was data profiling. Food Data Scrape ingested raw product listings and analyzed: Product titles Brand fields Size descriptors Alcohol category tags Unit and pack indicators

Sample Raw Data (Before Normalization)

At this stage, none of the rows were technically identical.

Instead of treating SKU names as strings, Food Data Scrape decomposed each product into structured attributes.

Core Attributes Extracted

• Brand name
• Product line
• Alcohol type
• Volume value
• Volume unit
• Packaging type

Example Decomposition

This step laid the foundation for deterministic matching.

One of the biggest sources of mismatch was volume representation. Food Data Scrape implemented a standard unit policy: All liquid volumes converted to milliliters Canonical size stored alongside original value

Unit Conversion Rules

• 0.75L → 750ml
• 75cl → 750ml
• 1L → 1000ml

Sample After Unit Normalization

This alone eliminated a large percentage of false mismatches.

Retailers often use inconsistent punctuation, casing, or abbreviations. Examples observed: Maker’s Mark vs Makers Mark Don Julio vs DonJulio José Cuervo vs Jose Cuervo Food Data Scrape applied: Controlled brand dictionaries Unicode normalization Stop-word removal Alias mapping tables This ensured brand-level consistency before SKU matching was attempted.

With structured attributes in place, deterministic rules were applied:

Primary Matching Rules

• Same normalized brand
• Same alcohol type
• Same canonical volume
• Same product line keywords

Example Match

This created a single master SKU ID.

Not all products follow clean patterns. Limited editions, packaging variants, and gift packs required fuzzy logic. Food Data Scrape used: Token similarity scoring Weighted keyword matching Confidence thresholds Fuzzy matching was always: Logged Audited Manually reviewable for critical SKUs This hybrid approach balanced accuracy with scalability.

The final output was a retailer-agnostic master SKU catalog.

Sample Unified SKU Table

Each retailer’s product ID was mapped back to this master SKU.

Downstream Price Intelligence Example
Once SKUs were normalized, true price comparison became possible.

Sample Normalized Price View

Without normalization, this table could not exist.

For Analytics Teams

• Clean dashboards with zero duplication
• Accurate regional price gap analysis
• Faster reporting cycles

For Brands

• MSRP compliance monitoring
• Region-wise pricing discipline
• Distributor performance visibility

For Retailers

• Competitive benchmarking
• Promotion effectiveness analysis
• Margin leakage detection

Unlike generic ETL pipelines, Food Data Scrape builds domain-aware normalization logic.

Key differentiators:

• Liquor-specific attribute modeling
• Retailer-aware naming patterns
• Scalable SKU identity framework
• Auditable matching decisions

This ensures the system works not just once, but continuously as catalogs evolve.

The same normalization framework can be extended to: Multi-pack and gift sets Barrel-proof and limited editions Ready-to-drink cocktails International liquor retailers The logic adapts as new retailers and naming conventions are added.

SKU name inconsistency is one of the biggest hidden blockers in liquor price intelligence. Without normalization, data remains fragmented and insights remain unreliable. Through structured attribute extraction, unit standardization, rule-based matching, and controlled fuzzy logic, Food Data Scrape transformed messy, retailer-specific product listings into a unified master catalog. The result was not just cleaner data, but trustworthy intelligence that brands, retailers, and analysts could confidently act on. In liquor analytics, normalization is not a backend task. It is the foundation of every decision that follows.