GoldenZero SKU Rationalization Advisor
Overview
SKU Rationalization Advisor systematically evaluates every SKU in a category or portfolio against performance, strategic, and operational criteria to recommend keep/delist/consolidate/replace actions. The goal is to reduce complexity costs and free shelf space or warehouse capacity for higher-potential items without sacrificing meaningful consumer choice.
In CPG retail, the long tail is expensive. Industry analysis shows that the bottom 20% of SKUs typically contribute only 1–3% of revenue but consume 15–25% of inventory carrying costs, supply chain complexity, and shelf space. However, naive pruning destroys value—some low-velocity SKUs serve as strategic traffic drivers, assortment signals, or niche segment anchors. This skill applies a multi-criteria framework to distinguish truly unproductive SKUs from strategically necessary ones.
When to Use
- Annual assortment reset or planogram reset cycle
- Warehouse or shelf-space capacity constraints require SKU reduction
- Category has grown to excessive SKU count through years of additions without removals
- Supply chain or procurement team flags complexity costs
- Preparing for private label expansion (need shelf space for new PL items)
- Post-acquisition portfolio integration requiring duplicate elimination
- User provides SKU-level data and asks which items to cut
Required Inputs
| Input | Required | Description |
|---|---|---|
| SKU master list | Yes | Full catalog with attributes: brand, size, flavor, segment, pack, price |
| Sales data (12+ weeks) | Yes | Units, revenue, and ideally margin by SKU; 52 weeks preferred |
| Inventory data | Recommended | Average inventory $, turns, days of supply, stockout rate |
| Margin/cost data | Recommended | Gross margin $, gross margin %, COGS, landed cost |
| Distribution data | Recommended | Store count, %ACV, or warehouse allocation |
| Substitution/switching data | Optional | Which SKUs shoppers switch to when one is unavailable |
| Supplier/vendor data | Optional | Supplier dependencies, exclusive arrangements, MOQ requirements |
| Strategic item flags | Optional | Items with contractual obligations, brand commitments, or strategic role |
Methodology
Step 1: SKU Performance Scoring
Score every SKU on four dimensions (each 0–100):
A. Revenue Contribution Score
Revenue Score = (SKU Revenue / Category Revenue) × 10,000
Normalize to 0–100 scale. Pareto benchmark: top 20% of SKUs should score > 60.
B. Profitability Score
Profit Score = (SKU Gross Margin $ / Top-SKU Gross Margin $) × 100
If margin data unavailable, use ASP relative to category average as proxy.
C. Velocity Score
Velocity Score = (SKU Units per Store per Week / Category Avg Units per Store per Week) × 100
Cap at 100. Measures inventory productivity and consumer demand intensity.
D. Trend Score
Trend Score = 50 + (SKU YoY Growth Rate − Category YoY Growth Rate) × 5
Centered at 50; above 50 = outperforming category trend, below = underperforming. Cap at 0–100.
Composite Score (weighted):
Composite = (Revenue × 0.30) + (Profit × 0.30) + (Velocity × 0.25) + (Trend × 0.15)
Step 2: Strategic Role Assessment
Assign each SKU a strategic role that may override pure performance scores:
| Role | Definition | Protection Level |
|---|---|---|
| Traffic Driver | Generates store/site visits; high search volume, destination item | High — keep even if margin is low |
| Basket Builder | High attach rate; frequently bought with other items | Medium-High |
| Margin Anchor | Above-average margin %; may have low velocity | Medium |
| Assortment Signal | Represents a segment that defines category credibility | Medium — keep at least 1 SKU |
| Niche/Loyalty | Small but fiercely loyal customer base; high repeat rate | Medium — evaluate switching risk |
| Filler/Redundant | No unique role; interchangeable with other SKUs | Low — primary delist candidate |
Step 3: Redundancy & Cannibalization Analysis
Identify clusters of overlapping SKUs:
- Group SKUs by attribute similarity (same brand + same segment + similar price ± 10%)
- Within each cluster, calculate Incremental Contribution: revenue that would be lost if the SKU were removed (net of substitution to remaining SKUs)
- Apply a Substitution Rate assumption: typically 40–65% for CPG (i.e., 40–65% of a delisted SKU's sales transfer to remaining items)
Net Revenue at Risk = SKU Revenue × (1 − Substitution Rate)
Step 4: Action Classification
Based on Composite Score, Strategic Role, and Redundancy:
| Composite Score | Strategic Role | Redundancy | Recommended Action |
|---|---|---|---|
| > 60 | Any | Any | Keep — core performer |
| 40–60 | Traffic/Basket/Signal | Low | Keep — strategically important |
| 40–60 | Filler | High | Consolidate — merge with stronger variant |
| 20–40 | Any non-critical | High | Replace — swap for higher-potential alternative |
| < 20 | Filler/Redundant | Any | Delist — remove from assortment |
| < 20 | Niche/Loyalty | Low | Review — manual decision required |
Step 5: Impact Simulation
Simulate the impact of recommended delistments:
Total Delist Revenue = Σ (Delisted SKU Revenue)
Retained Revenue = Σ (Delisted SKU Revenue × Substitution Rate)
Net Revenue Loss = Total Delist Revenue − Retained Revenue
Complexity Savings = # SKUs Removed × Cost per SKU (inventory carry + handling + space)
Net Impact = Complexity Savings − Net Revenue Loss
Industry benchmark: Cost per SKU ranges from $5,000–$50,000/year depending on category and supply chain complexity.
Output Specification
1. SKU Action Summary
| SKU | Brand | Segment | Revenue | Composite Score | Strategic Role | Action | Net Revenue Risk | Substitute SKU |
|---|---|---|---|---|---|---|---|---|
| — | — | — | — | — | — | Keep/Delist/Consolidate/Replace | — | — |
2. Portfolio Impact Dashboard
- Total SKUs: current → proposed
- Revenue at risk (gross) and retained (net of substitution)
- Complexity cost savings
- Net margin improvement
- Shelf space / warehouse slots freed
3. Delist Candidate Detail Cards
For each delisted SKU:
- Performance scores breakdown
- Top 3 substitute SKUs and substitution rationale
- Customer impact assessment (loyalty segment affected)
- Supplier impact (any volume commitments or deductions at risk)
- Recommended exit timeline
4. Consolidation Recommendations
Clusters of SKUs to merge, with proposed surviving SKU and rationale.
Analysis Framework
Key Metrics
- SKU Productivity: Revenue per SKU (benchmark: top quartile > 2× category average)
- Pareto Ratio: % of revenue from top 20% of SKUs (benchmark: 75–85%)
- Long-Tail Burden: % of SKUs contributing < 0.1% of category revenue
- Inventory Turns by SKU: Turns < 4×/year in center store signals overstock or low demand
- Incremental Contribution: Revenue uniquely attributable to the SKU (not transferable)
- Complexity Cost per SKU: Fully loaded cost including procurement, warehousing, handling, shelf management
- Customer Reach: % of total buyers who purchased the SKU in the last 52 weeks
Decision Thresholds
- Delist threshold: Bottom 15–25% of SKUs by composite score, unless strategically protected
- Minimum viable assortment: At least 1 SKU per decision-tree node that has demonstrated demand
- Maximum delist batch: Recommend no more than 15% of SKU count in a single reset to manage disruption
- Substitution rate validation: If observed switching data is available, use it; otherwise apply category-level default (50%)
Examples
Input: "We have 1,200 SKUs in our Home Cleaning category. Management wants to reduce to 900 SKUs while maintaining at least 95% of current revenue. Here's our 52-week sales, margin, and inventory data."
Output:
- Scoring: 1,200 SKUs scored; 187 score below 20 (delist zone), 124 score 20–40 (replace zone), remaining 889 score > 40
- Redundancy: 93 SKU clusters identified with high overlap; 156 SKUs are redundant within clusters
- Recommendations: Delist 168 SKUs, consolidate 84 into 42, replace 48 → net reduction of 258 SKUs (to 942)
- Impact: Gross revenue at risk $4.2M (3.8%); estimated retained after substitution $2.9M; net loss $1.3M (1.2%). Complexity savings: $2.1M/year. Net benefit: +$0.8M/year.
- Top delist clusters: 14 niche air freshener variants (0.3% of revenue, 1.2% of SKUs), 22 legacy packaging sizes in dish soap, 11 private label duplicates with identical formulations
Guidelines
- Never recommend delisting the only SKU serving a valid consumer need state — that creates an assortment gap
- Apply a "buyer count" safety check: if a SKU has > 2% of category buyers, flag for manual review even if scores are low
- Consider seasonal SKUs separately — annualized metrics understate their in-season importance
- Respect contractual and promotional commitments; flag timing constraints for delistments
- Communicate delist recommendations with sensitivity: provide data-backed rationale, not just scores
- Stagger delistments over 2–3 reset windows rather than a single mass cut
- Monitor post-delist performance for 8–12 weeks; have contingency to relist if substitution doesn't materialize
- Always pair rationalization with gap-fill recommendations (use Assortment Gap Analysis skill) — rationalize and renovate simultaneously
Validation Checklist
- Every SKU has been scored on all four dimensions
- Strategic roles are assigned and protection levels applied
- Redundancy analysis uses attribute-based clustering, not just performance data
- Substitution rates are documented and defensible
- Net revenue impact is calculated (not just gross delist revenue)
- Complexity cost savings are estimated
- No single-representative segments are left empty after delistments
- Seasonal SKUs are evaluated on in-season performance
- Supplier and contractual constraints are flagged
- Delist count stays within the recommended maximum per reset cycle