dynamic-pricing-ecommerce

# Dynamic Pricing for Ecommerce

Turn seller-approved economics and trusted signals into a bounded repricing system with explicit rules, approvals, monitoring, and a kill switch.

## Installation

```bash
npx skills add nexscope-ai/eCommerce-Skills --skill dynamic-pricing-ecommerce -g
```

## Capabilities

- Define SKU eligibility for automatic, approval-required, or manual repricing.
- Calculate contribution-safe floors and commercially justified ceilings.
- Select demand, inventory, competitor, season, and promotion signals without treating noisy observations as facts.
- Create deterministic rule matrices with bounded price steps, cooldowns, and conflict precedence.
- Simulate normal, downside, promotion-stack, stockout, and price-war scenarios.
- Design approval, audit-log, rollback, anomaly-breaker, and emergency-stop controls.
- Produce a staged platform implementation and measurement plan without enabling live changes.

## Usage Examples

```text
Design safe Amazon repricing rules for these 200 SKUs without starting a price war.
```

```text
Create an inventory-aware dynamic pricing plan for my Shopify store.
```

```text
Which products can be auto-repriced, and which should always require approval?
```

```text
Audit these existing repricing rules for margin, promotion, and rollback risks.
```

## Inputs and Collection

Use seller-supplied and inspected evidence first. Collect:

- SKU, variant, channel, marketplace, currency, tax treatment, fulfillment method, and lifecycle stage;
- current price, realized selling price, list or compare-at price, coupons, promotions, bundles, and discount-combination rules;
- COGS, inbound freight, duties, packaging, fulfillment, payment, referral, affiliate, ad, return, and other variable costs;
- target contribution dollars or margin, approved floor, approved ceiling, and brand or MAP constraints;
- inventory on hand, inbound stock, sell-through, age, weeks of cover, replenishment lead time, and stockout risk;
- timestamped traffic, orders, units, realized price, conversion where available, cancellations, and returns;
- comparable competitor offers with variant, pack size, seller, fulfillment, availability, delivered price, source, and capture time;
- current repricing tool, platform capabilities, rule cadence, account permissions, approvers, and business objective.

If required economics or authorization details are missing, ask one consolidated follow-up. If they remain unavailable, design a provisional system but mark affected floors, rules, and automation decisions as blocked.

## Workflow

### 1. Establish the Evidence Boundary

List the exports, pages, cost sheets, platform settings, and seller facts actually inspected. Label each material input:

- **Confirmed:** supported by inspected evidence.
- **Assumption:** an explicit scenario placeholder, not an observed fact.
- **Unknown:** missing information that blocks reliable automation.

Do not invent demand, competitor history, costs, fees, elasticity, conversion, or platform capability. A visible competitor price is a point-in-time observation, not a durable market signal.

### 2. Calculate Economic Guardrails

Use realized seller-funded economics:

```text
Net Revenue = Selling Price - Seller-Funded Discounts - Refund Allowance
Contribution $ = Net Revenue - COGS - Variable Selling Costs
Contribution % = Contribution $ / Net Revenue
```

When percentage fees apply to selling price:

```text
Price Floor = (Unit Cost + Fixed Variable Costs + Target Contribution $) / (1 - Variable Fee Rate)
```

Model base, high-return, high-ad-cost, promotion-stack, and fee-change cases. Keep a contractual or legal minimum separate from the calculated economic floor. Define a ceiling from value, reference-price, policy, and customer-trust constraints; do not create artificial scarcity or an inflated reference price.

### 3. Classify SKU Automation Eligibility

Assign each SKU to one control tier:

| Tier | Appropriate when | Required control |
|---|---|---|
| Auto-eligible | reliable economics, stable identifier, trusted signals, reversible changes | bounded rules, logs, alerts, kill switch |
| Approval-required | launch, high margin risk, large price step, strategic product, sparse data | human review before publish |
| Manual-only | missing costs, MAP/legal ambiguity, bundles, custom products, unstable feed, sensitive category | analysis only |

Default uncertain SKUs to the more restrictive tier. Automation convenience is not evidence that a SKU is safe to automate.

### 4. Select and Validate Signals

For every signal, record source, freshness, coverage, failure mode, and fallback:

- **Competitor:** only normalized, comparable, available offers; reject mismatched packs, used items, suspicious sellers, and stale captures.
- **Demand:** use observed seller traffic and orders with timestamps; separate price effects from ads, content, seasonality, and stock.
- **Inventory:** use on-hand, age, sell-through, lead time, and replenishment risk; do not treat a feed error as surplus or scarcity.
- **Time or event:** use scheduled windows with explicit start, end, timezone, and promotion interaction.
- **Own promotion:** distinguish seller-funded from platform-funded incentives and confirm whether discounts stack.

Never use protected personal characteristics or opaque customer vulnerability to set individualized prices. Avoid price-gouging, collusion, and discriminatory outcomes.

### 5. Build the Rule Matrix

Each rule must specify:

| Field | Requirement |
|---|---|
| Scope | channel, market, SKU group, exclusions |
| Trigger | measurable condition and minimum duration |
| Evidence gate | freshness and completeness required |
| Action | hold, increase, decrease, or request approval |
| Step limit | maximum absolute and percentage change per action |
| Floor/ceiling | seller-approved hard bounds |
| Cooldown | minimum time before another change |
| Precedence | which rule wins when triggers conflict |
| Appro