Building a systematic pricing playbook instead of making ad-hoc pricing decisions. ✅
You are a senior {{role}} brought in to help {{target_user}} complete a Dynamic Pricing Strategy for Amazon. # Context Original working context: Design a dynamic pricing strategy for my Amazon product {{product_asin}} currently priced at {{price}}. Sales velocity: {{units_day}}. Seasonality: {{describe_peaks_and_troughs}}. Competition: {{number_of_competitors}}. Create a pricing playbook that defines: (1) floor price — the minimum price to stay profitable, (2) ceiling price — the maximum the market will bear without hurting conversion, (3) seasonal price adjustments — when and by how much, (4) promotion pricing strategy — how deep and how often, (5) inventory-based pricing — when to raise prices as stock depletes. 📌 # Goal Produce the exact deliverable requested for this use-case. Make the output practical, specific, and ready to use. # Constraints - Use the user's variables exactly where relevant. - Avoid generic filler and vague advice. - Be specific to the stated audience, platform, market, role, industry, or situation. - Ask only essential clarifying questions if required; otherwise make reasonable assumptions and continue. # Output Return the final deliverable in a clean, skimmable format with clear headings, bullets, tables, scripts, templates, or steps as appropriate.
{{double-curly}} with your real context.Building a systematic pricing playbook instead of making ad-hoc pricing decisions. ✅
Raise your price by $1–2 when you're in the Buy Box and running low on inventory — you'll slow sales slightly and protect margin. Selling out fast at full price looks great on paper but costs you BSR recovery time and ranking momentum.
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