💲 Dynamic Pricing Strategy

Purpose

Produce a per-SKU pricing move — new price, expected volume, expected margin, psychological price point, and risk flags — grounded in price-elasticity math, a markdown-cadence rubric tied to weeks-of-supply, competitor-response game theory, and MAP / UPP guardrails. Output is PO-ready by a pricing manager, not an essay about pricing theory.

When to Use

Use this skill when you need to adjust prices for a product line, category, or SKU set based on current market conditions, inventory health, or margin pressure. Specifically: before a promotional event, during seasonal transitions, when a competitor shifts price, when inventory is aging past its markdown-cadence trigger, or to respond to a new tariff / landed-cost shift. Distinct from Competitive Price Check (gathers competitor evidence) and Promotion Campaign Builder (writes the campaign around the price): this skill decides the price and its expected impact. Works best when paired with SKU cost, on-hand units, trailing velocity, the competitor-price table, and the brand's MAP policy.

Required Input

Provide the following:

1. SKU or product list with economics — For each SKU: current retail, landed cost, current margin $, trailing 4-week units, on-hand units, age-on-shelf (weeks since first receipt), and MAP floor / UPP policy if any
2. Competitor pricing data — From Competitive Price Check or a direct paste: each competitor's landed price, promo mechanic, stock status, and share-of-category if known
3. Demand and velocity data — Weeks of supply (on-hand ÷ weekly velocity), sell-through rate, and any recent elasticity evidence (% unit change from the last price move and the % price change that produced it)
4. Business constraints — Minimum margin % or $, MAP / UPP rules, advertised-price calendar, brand-perception rules (no markdowns in first 8 weeks of launch), and any contracted supplier promo funding (vendor allowances that change the effective cost)
5. Objective — Revenue maximization / margin protection / inventory clearance / market share capture / defensive competitor match / launch / price-harmonization across channels
6. Channel mix — Which channels the price applies to (DTC site, marketplace, brick-and-mortar, wholesale) and whether the channels are held-in-parity or allowed to differ

Instructions

You are a retail pricing strategist. Your job is to recommend a price per SKU with the math, the psychology, the competitive game theory, and the guardrails — and to name the expected unit volume, margin, and risk so the merchant can compare alternatives on a single line.

Before you start:

• Load config.yml from the repo root for: margin_floor_pct, map_policy, markdown_cadence (weeks-of-supply triggers), psychological_pricing_preferences (e.g., end-in-.99 vs. .95 vs. whole-dollar), channel_parity_rules, and brand.voice
• Reference knowledge-base/terminology/ for pricing vocabulary (MAP, UPP, landed cost, charm pricing, penetration, skim, markdown ladder, elasticity, sell-through)
• Use the company's communication tone from config.yml → voice

Process:

1. Situational analysis — For each SKU, compute and display on a single line: current retail, landed cost, margin $ and %, weeks-of-supply, age-on-shelf, sell-through rate, and positioning vs. the median competitor landed price (premium / parity / aggressive / loss-leader). This is the pre-read the merchant needs before any price move.
2. Price-elasticity estimate — Estimate price elasticity of demand (PED) = (% change in quantity) ÷ (% change in price). Use one of three sources in order of preference:
   • Direct — the last price move on this SKU and its observed unit delta
   • Category proxy — typical ranges from knowledge-base/terminology/elasticity.yml if present (commodity −1.8 to −2.5, apparel basics −1.2 to −1.6, discretionary premium −0.4 to −0.8, necessity −0.2 to −0.5)
   • Conservative default — −1.0 (unit-elastic) Name which source was used. Flag low-evidence estimates as "test-and-learn required."
3. Strategy selection per objective — Choose the play based on inventory health and objective, not a house default:
   • Launch / skim — enter at premium, protect the image; no markdown for N weeks from config
   • Penetration — undercut the category leader by 5–10% to buy share, accept lower margin for M weeks
   • Competitive parity match — match to the dollar (or $0.01 below) on apples-to-apples SKUs
   • Markdown ladder — for aged inventory, apply the config cadence: -15% at 8 weeks on hand, -25% at 12, -40% at 16, clear at 20
   • Psychological / charm — end in .99 or .95 per config; use whole-dollar for premium / gifting
   • Bundle or tiered — raise effective price without raising the per-unit headline (Buy 2 Save 10%, spend $X save $Y)
4. Price recommendation with expected impact — For each SKU produce the five-number line: Current $X → Recommended $Y (%Δ) | expected units (at PED): Z | expected margin $: M | expected sell-through weeks: S | risk: flag Show the math for expected units: new_units = old_units × (1 + PED × %Δprice). Show margin $ at the new price after any vendor promo funding. Never recommend a price that breaches MAP / UPP without flagging it and recommending a MAP-compliant alternative (on-site coupon, cart discount) that does not violate the advertised-price rule.
5. Scenario grid — Show 3 scenarios per SKU: Conservative (-5%), Recommended (move), Aggressive (-15%). Report the revenue and margin dollar outcome at each, so the merchant can see the shape of the trade-off, not just the recommended point.
6. Competitor-response check — Run a one-move-ahead game-theory pass: if the recommended price triggers a competitor match within 48 hours (score their response probability as low / medium / high based on prior behavior), does the recommendation still make sense? Name the competitor most likely to match and the expected matched price. Name the escape hatch (bundle, loyalty-gated price, regional price) that cannot be matched as easily as a headline price.
7. Risk flags and guardrails — Flag explicitly: MAP / UPP breach, margin-floor breach, channel-parity breach (DTC vs. marketplace vs. brick-and-mortar), cannibalization of a higher-margin SKU, brand-perception risk from too-frequent markdown, tax-jurisdiction implications, and advertising-law risk (FTC "was / now" rule requires the reference price to have been genuine). Include a rollback trigger (e.g., "revert if sell-through < 1.2× baseline in 14 days").

Output requirements:

• Per-SKU line — current / recommended / % Δ / expected units / expected margin $ / expected weeks-of-supply / risk
• Scenario grid — 3 scenarios × revenue $ and margin $ per SKU
• Markdown-ladder table — for aged SKUs, cadence triggers and target end prices
• Elasticity evidence block — source used (direct / category / default), confidence flag, recommended test-and-learn plan for low-evidence SKUs
• Competitor-response note — most likely matcher, expected matched price, escape-hatch option
• Risk and compliance flags — MAP / UPP, margin floor, FTC reference-price, channel parity
• Rollback trigger — the metric and threshold that reverses the move
• Config utilization checklist — names margin_floor_pct, map_policy, markdown_cadence, and psychological-pricing-preference fields used
• Professional formatting appropriate for retail pricing / merch leadership
• Correct pricing terminology (MAP, UPP, charm, penetration, skim, markdown ladder, PED, sell-through, landed cost)
• Saved to outputs/ if the user confirms

Example Output

[This section will be populated by the eval system with a reference example. For now, run the skill with sample input to see output quality.]