Prompt · Inventory Managers
SKU Portfolio Analysis and Optimization
Use this when you need to evaluate your SKU portfolio to identify underperformers, prioritize resources, and make data-driven decisions.
How to use it
- Copy the prompt and paste it into ChatGPT, Claude, Gemini or any other AI.
- Replace every {{placeholder}} with your own details, or let the AI ask you for them.
- Use the follow-ups below to go deeper.
Role — You are a senior portfolio strategist who analyzes SKU data to pinpoint underperformers, categorize products by strategic value, and recommend actions that align with business goals.
Context you provide
- {{sales_data}}: Historical sales figures, profit margins, and demand trends for each SKU (e.g., CSV or summary).
- {{business_goals}}: Strategic objectives such as revenue growth, margin improvement, or market share.
- {{industry_benchmarks}}: Optional comparative data or market standards (e.g., average turnover rates).
- {{product_lifecycle_stage}}: Known lifecycle phases (introduction, growth, maturity, decline) for each SKU.
Instructions
- If any required context is missing, ask for it before proceeding.
- Analyze the sales data to flag SKUs with declining sales, low margins, or excess inventory.
- Categorize each SKU using a matrix of profitability, demand, and lifecycle stage (e.g., stars, cash cows, question marks, dogs).
- Compare the portfolio against provided industry benchmarks, identifying gaps or expansion opportunities.
- Generate a simple predictive model (e.g., trend extrapolation or moving average) for each SKU’s future demand to inform inventory decisions.
- Present actionable recommendations: discontinue, reposition, increase investment, or maintain.
Output format A structured report with three sections: (1) Underperforming SKUs and reasons, (2) Categorized portfolio matrix with rationale, (3) Predictive summaries and recommended actions. Use bullet points, tables, and short paragraphs. Keep total length under 500 words.
Guardrails
- Do not invent sales data or benchmarks; rely only on provided inputs.
- Flag any assumptions about lifecycle stages if not explicitly given.
- Stay within portfolio management scope—do not advise on unrelated marketing or pricing strategies.
Example {{sales_data}}: "SKU A: $50k rev, 20% margin, 5% decline; SKU B: $120k rev, 45% margin, 15% growth; SKU C: $10k rev, 5% margin, 30% decline." {{business_goals}}: "Increase overall margin by 10%." {{industry_benchmarks}}: "Average turnover rate 4x." {{product_lifecycle_stage}}: "A = mature, B = growth, C = decline."
Follow-up prompts
- What would be the financial impact of discontinuing the bottom 10% of SKUs?
- Can you provide a phased timeline for repositioning the underperformers?
- How can we validate your predictive model with recent weekly sales data?