SKU Rationalization

Analyze your entire product catalog to surface which SKUs are draining warehouse space, tying up capital, and diluting focus — then generate concrete keep, fix, or kill recommendations backed by multi-factor scoring. This skill bridges the gap between raw sales exports and strategic catalog decisions by combining revenue contribution, margin health, inventory turnover, and demand velocity into a single actionable framework.

Use when

• You exported your Shopify, Amazon Seller Central, or Shopee product report and want to know which bottom-performing SKUs to cut before next quarter's purchasing round
• Your warehouse manager is flagging space constraints and you need a data-backed list of slow-moving products that can be liquidated or discontinued immediately
• A quarterly business review requires you to present SKU-level performance tiers with clear keep/kill/fix recommendations and estimated savings to leadership
• You are consolidating product lines after a merger or brand acquisition and need to identify overlapping or redundant SKUs across combined catalogs

What this skill does

This skill ingests your product catalog data — including units sold, revenue, cost of goods sold, current inventory on hand, and days since last sale — and runs a composite scoring algorithm across four dimensions: revenue contribution share, gross margin percentage, inventory turnover ratio, and sales velocity trend. Each SKU receives a weighted composite score and is classified into one of four tiers: Stars (high revenue plus high margin), Workhorses (volume drivers with thin margins), Question Marks (moderate potential needing intervention), and Deadweight (underperforming across all metrics). The output includes a catalog health dashboard, per-SKU tier assignments with reasoning, a prioritized discontinuation shortlist with projected annual savings, and recovery playbooks for Question Mark products.

Inputs required

• Product catalog data (required): A structured list of SKUs containing at minimum SKU ID or product name, units sold over the analysis period, unit cost (COGS), current selling price, and current inventory quantity on hand. Accepted formats include CSV paste, spreadsheet data, or structured text tables.
• Analysis time period (required): The sales window to evaluate, such as "last 6 months," "Q4 2025," or "full year 2025." Longer periods (six months or more) produce more statistically reliable tier assignments.
• Inventory carrying cost rate (optional): Your annual holding cost expressed as a percentage of average inventory value. If omitted, a 25% industry benchmark is applied. Supplying your actual rate sharpens the savings estimates in the discontinuation shortlist.
• Strategic keep list (optional): SKU IDs or product names that must be retained regardless of performance scores — for example hero products central to brand identity, contractual obligations with retail partners, or items with confirmed upcoming marketing campaigns.

Output format

The output is organized into four clearly labeled sections. The first section is a Catalog Health Dashboard summarizing total SKUs analyzed, the distribution across the four performance tiers as both counts and percentages, total estimated annual savings from recommended discontinuations, and top-line metrics like average margin and average turnover. The second section is a Tier Classification Table listing every SKU with its composite score, tier assignment, and a one-line rationale explaining the classification. The third section is a Discontinuation Shortlist ranking Deadweight SKUs by projected savings, including current inventory value, estimated liquidation recovery, and net savings per SKU. The fourth section provides Recovery Playbooks for Question Mark SKUs, each containing a specific intervention (price adjustment, bundle pairing, promotional push, or listing optimization) with an estimated uplift scenario and implementation timeline.

Scope

• Designed for: Ecommerce operators, catalog managers, and operations leads managing 50+ SKUs who need periodic portfolio rationalization
• Platform context: Platform-agnostic — works with data exports from Amazon, Shopify, Shopee, TikTok Shop, Lazada, WooCommerce, or any system that produces tabular product performance data
• Language: English

Limitations

• Does not connect to live sales platforms or pull real-time inventory feeds — you must supply the data export manually
• Composite scoring uses general ecommerce benchmarks for tier thresholds; highly specialized verticals (e.g., industrial spare parts, luxury goods) may require threshold adjustments noted in the output
• Cannot predict future demand shifts from upcoming marketing campaigns, seasonal trends, or viral moments unless you flag specific SKUs in the strategic keep list