Install
openclaw skills install ecomm-new-productsEcomm New Products
Provides end-to-end analysis and strategy for e-commerce new product development using category data, competition, market gaps, validation, and launch planning.
Skill: E-commerce New Product Research & Development
Overview
This skill provides an end-to-end workflow for new product research and development across e-commerce platforms (e.g., Amazon, Shopee, SHEIN). It covers:
- Category data analysis
- Target segment identification
- Competitive and feature analysis
- Market gap discovery
- Product definition
- Validation and launch strategy
This framework is category-agnostic and applicable to both private label and branded product development.
Inputs Required
| Input | Description | Example |
|---|---|---|
| Category export file | Product data export from research tools or platforms | .xlsx / .csv export |
| Target price range | Desired selling price band | $20 – $40 |
| Material / positioning | Target quality tier and audience | mid-tier / premium / budget |
| SKU count goal | Number of products to develop | 2–5 SKUs |
| Sample specs (optional) | Prototype dimensions and features | size, weight, components |
Supported Data Sources
This workflow supports export files from:
- Amazon tools: Sorftime, Helium10, Jungle Scout, SIF
- Shopee tools: Shopdora, platform exports
- SHEIN tools: SHEIN selection assistant
- Any structured dataset containing product-level metrics
Workflow Steps
Step 1 — Category Overview
Parse the export file and produce:
- Total monthly and annual unit volume
- Seasonality trends (monthly demand peaks)
- Price distribution (bucketed by intervals)
- Top brands / sellers by SKU count and sales
Typical fields to extract:
Monthly Sales, Annual Sales, Price,
Gross Profit, Margin,
Category Rank, Subcategory,
Listing Age (days),
Review Count, Rating,
Brand / Seller,
Bullet Points / Description,
Dimensions, Weight
Step 2 — Target Segment Deep Dive
Filter products by:
- Target price range
- Positioning (quality tier / material / use case)
Analyze:
- SKU count per sub-price band
- Average monthly sales per SKU
- Average margin per band
- Brand concentration per band
Decision rule:
Select the segment where:
- High average sales per SKU
- Low brand dominance / fragmentation
→ This indicates a potential opportunity zone
Step 3 — Competitor & Feature Analysis
Brand Landscape
- Identify top brands by SKU count
- Analyze their pricing patterns
- Understand positioning (budget vs premium vs niche)
Feature / Attribute Analysis
Extract recurring attributes from titles, bullets, or descriptions:
Group into:
- Functional attributes (performance, utility)
- Design attributes (style, form factor)
- Usage attributes (scenarios, convenience)
For each attribute:
- Count number of SKUs
- Calculate average sales
Interpretation:
- High sales + low SKU count → opportunity
- High sales + high SKU count → competitive but validated
- Low sales → weak demand signal
Step 4 — Market Gap Identification
Identify 3–5 opportunities using:
| Gap Type | Criteria | Signal |
|---|---|---|
| Attribute gap | High demand attribute with low SKU presence | Underdeveloped niche |
| Positioning gap | Missing tier (e.g. mid-tier between budget & premium) | Pricing imbalance |
| Format gap | Inefficient or outdated product formats | Optimization opportunity |
| Trend gap | Emerging trend not yet saturated | Early mover advantage |
Step 5 — Product Definition (per SKU)
For each recommended SKU:
Product Name (working title)
Suggested Price: $XX.XX
Why this opportunity exists:
[Data-backed reasoning]
Positioning:
[Target audience + tier]
Core Specifications:
- Dimensions:
- Weight:
- Key components:
- Structure / format:
Variants:
[Color / style / version options]
Key Differentiation:
[Clear advantage vs competitors]
Estimated Margin:
~$XX (~XX%)
Launch Timing:
[Quarter / season]
Step 6 — Sample / Prototype Validation
If prototype or spec sheet is available:
Dimension Validation
- Confirm size aligns with intended use case
- Benchmark against top competitors
- Check compatibility with primary usage scenarios
Feature Feasibility
- Validate physical feasibility of components
- Ensure no over-complexity or cost inefficiency
- Confirm manufacturability
Scoring (1–5 scale)
- Use-case fit
- Competitive differentiation
- Feature completeness
- Cost vs value balance
- Margin viability
Step 7 — Social Proof Validation
Validate demand using external signals:
Platforms
- Reddit (category discussions, sentiment)
- Google Trends (search demand over time)
- Media / blogs (trend mentions)
- Social platforms (indirect trend signals)
Validation Criteria
- At least one product with strong sales performance in niche
- Evidence of growing or stable search demand
- Community or media discussion indicating awareness
Step 8 — Keyword Advertising Framework
Tier 1 — Awareness
- Broad category and attribute keywords
- Goal: traffic discovery
Tier 2 — Conversion
- Specific long-tail keywords
- Use-case driven queries
- Goal: improve conversion rate
Tier 3 — Competitor Targeting
- Competitor brand + product type
- Goal: capture high-intent traffic
Campaign Structure
| Campaign | Type | Goal |
|---|---|---|
| Auto discovery | Automated ads | Keyword discovery |
| Core keywords | Manual broad | Ranking |
| Long-tail | Phrase / exact | Conversion |
| Competitor | Exact | Traffic capture |
| Retargeting | Display | Conversion recovery |
Step 9 — Launch Timeline
Phase 1: Initial launch (test demand)
Phase 2: Optimization (ads + listing improvements)
Phase 3: Expansion (variants / additional SKUs)
Pre-launch Checklist
- Supplier validation
- Sample approval (quality, usability)
- Listing assets (images, descriptions)
- Pricing and promotion strategy
- Advertising budget allocation
- Review acquisition process
Output Format
The workflow should produce:
- Market Overview
- Target Segment Analysis
- Competitive Landscape
- Market Gaps
- Product Recommendations
- Sample Validation (if applicable)
- Social Proof Summary
- Keyword Strategy
- Launch Plan