Retail Merchandising Opportunity Agent

What does Retail Merchandising Opportunity Agent do?

Retail Merchandising Opportunity Agent turns public Shopify catalog and competitor data into workflow-ready merchandising actions. Instead of only exporting products, it reports catalog gaps, pricing checks, markdowns, stock risks, content fixes, blocked sources, and summary rows.

The Actor reads public Shopify products.json endpoints and public catalog fields. It does not use Shopify Admin APIs, private customer data, checkout data, or internal credentials.

Why use Retail Merchandising Opportunity Agent?

Generic Shopify scrapers can tell you what products exist. This Actor turns public catalog data into a prioritized task list for ecommerce operators, merchandisers, founders, and agencies.

Common use cases:

• Find competitor products or categories your store may not cover
• Check materially high or low prices against matched competitor products
• Review public markdown and compare-at signals
• Flag own-store products missing images or useful descriptions
• Spot stock risk where a competitor has availability and your matched product appears unavailable
• Schedule repeat runs in Apify and export actions as JSON, CSV, Excel, or through the API

What data can Retail Merchandising Opportunity Agent extract?

Field	Type	Description
recordType	string	catalog_gap, pricing_action, markdown_action, content_fix, stock_action, matched, blocked_or_unavailable, or summary
priority	string	Action priority: high, medium, or low
confidence	string	Deterministic confidence: high, medium, or low
actionTitle	string	Short title suitable for a task list or spreadsheet
actionSummary	string	Short explanation of the action
recommendedNextStep	string	Manual next step for a merchandiser
evidence	array	Structured evidence used to justify the row
deterministicScore	number	Score from 0 to 100
aiSummary / aiDraft	string	Optional AI enrichment when runMode is ai
sourceUrl	string	Primary product or source URL

How to use it

1. Enter your own Shopify storefront URL.
2. Add one or more competitor Shopify storefront URLs.
3. Add optional keywords and focus categories.
4. Set a maximum number of products per store.
5. Choose standard mode or AI-enriched mode.
6. Run the Actor and review the default dataset.

For the default NZ outdoor retail MVP, the reference store is https://www.bivouac.co.nz and competitors are https://dwights.co.nz and https://www.gearshop.co.nz.

Input

Important fields:

• ownStoreUrl: your reference Shopify storefront
• competitorStoreUrls: competitor Shopify storefronts
• keywords: optional product/category terms to highlight
• focusCategories: optional category terms for future focusing and reporting
• matchThreshold: score from 0 to 1; lower values mark more products as matched
• priceDeltaThresholdPct: minimum percent difference before matched products become pricing actions
• includeMatchedProducts: include non-action matched rows for auditing
• runMode: standard or ai
• aiApiKey, aiBaseUrl, aiModel, aiMaxRows: optional AI settings

Output

You can download the dataset in formats such as JSON, HTML, CSV, or Excel.

Example output:

[
  {
    "recordType": "catalog_gap",
    "priority": "high",
    "confidence": "medium",
    "actionTitle": "Review missing packs product: Example Hiking Pack",
    "recommendedNextStep": "Review the competitor product and decide whether to range, substitute, or ignore it.",
    "sourceUrl": "https://example-competitor.com/products/example-hiking-pack",
    "deterministicScore": 80,
    "evidence": [
      { "label": "Gap reason", "value": "missing_category" }
    ]
  },
  {
    "recordType": "summary",
    "productCount": 250,
    "actionCounts": {
      "catalog_gap": 42,
      "pricing_action": 5
    }
  }
]

AI mode

Standard mode is deterministic and does not require an AI key. AI mode is optional. When enabled, it enriches ready action rows with summaries and drafts using only the structured evidence already generated by the Actor.

AI output is nullable. If you do not provide AI settings, the Actor still produces the deterministic action rows.

Cost notes

Costs depend mainly on the number of stores and products scanned. The Actor uses simple HTTP requests against public Shopify JSON endpoints and does not launch a browser by default, so it should be cheaper than browser-based scraping for stores that expose products.json.

Start with a low maxProductsPerStore value such as 50 to inspect output quality, then increase it for fuller catalog comparisons.

FAQ, disclaimers, and support

This Actor only reads public Shopify catalog data. It does not extract private customer data, checkout data, or account data.

Use the results for legitimate competitive research, merchandising review, and catalog operations. Check source website terms and applicable laws before scraping at scale or using the output in commercial workflows.

For programmatic usage, use the Apify API tab after deployment. For feedback, open an issue on the Actor page.