# Jcpenney Product Search Scraper (`stealth_mode/jcpenney-product-search-scraper`) Actor Scrape product listings from JCPenney search pages in bulk. Collect product names, pricing, SKU IDs, images, availability, and 40+ attributes per item — perfect for price monitoring, competitive analysis, and e-commerce automation. - **URL**: https://apify.com/stealth\_mode/jcpenney-product-search-scraper.md - **Developed by:** [Stealth mode](https://apify.com/stealth_mode) (community) - **Categories:** Automation, Developer tools, E-commerce - **Stats:** 2 total users, 1 monthly users, 100.0% runs succeeded, 0 bookmarks - **User rating**: No ratings yet ## Pricing from $2.00 / 1,000 results This Actor is paid per event. You are not charged for the Apify platform usage, but only a fixed price for specific events. Since this Actor supports Apify Store discounts, the price gets lower the higher subscription plan you have. Learn more: https://docs.apify.com/platform/actors/running/actors-in-store#pay-per-event ## What's an Apify Actor? Actors are a software tools running on the Apify platform, for all kinds of web data extraction and automation use cases. In Batch mode, an Actor accepts a well-defined JSON input, performs an action which can take anything from a few seconds to a few hours, and optionally produces a well-defined JSON output, datasets with results, or files in key-value store. In Standby mode, an Actor provides a web server which can be used as a website, API, or an MCP server. Actors are written with capital "A". ## How to integrate an Actor? If asked about integration, you help developers integrate Actors into their projects. You adapt to their stack and deliver integrations that are safe, well-documented, and production-ready. The best way to integrate Actors is as follows. In JavaScript/TypeScript projects, use official [JavaScript/TypeScript client](https://docs.apify.com/api/client/js.md): ```bash npm install apify-client ``` In Python projects, use official [Python client library](https://docs.apify.com/api/client/python.md): ```bash pip install apify-client ``` In shell scripts, use [Apify CLI](https://docs.apify.com/cli/docs.md): ````bash # MacOS / Linux curl -fsSL https://apify.com/install-cli.sh | bash # Windows irm https://apify.com/install-cli.ps1 | iex ```bash In AI frameworks, you might use the [Apify MCP server](https://docs.apify.com/platform/integrations/mcp.md). If your project is in a different language, use the [REST API](https://docs.apify.com/api/v2.md). For usage examples, see the [API](#api) section below. For more details, see Apify documentation as [Markdown index](https://docs.apify.com/llms.txt) and [Markdown full-text](https://docs.apify.com/llms-full.txt). # README ## JCPenney Product Search Scraper: Extract Fashion & Home Goods Data Instantly --- ### What Is JCPenney? JCPenney is a major American retail chain offering clothing, home furnishings, jewelry, and accessories across thousands of SKUs. Their online catalog features dynamic search results, promotional pricing, and real-time inventory status. Extracting this product data manually is inefficient — the **JCPenney Product Search Scraper** automates collection from search result pages, delivering structured product intelligence at scale. --- ### Overview The **JCPenney Product Search Scraper** extracts product cards from search list pages on JCPenney.com, transforming unstructured web content into clean, structured JSON records. It is ideal for: - **Price monitoring platforms** tracking JCPenney's dynamic pricing - **Competitive analysts** benchmarking product assortments and promotions - **E-commerce aggregators** building multi-retailer product catalogs - **Marketers** analyzing product trends by category and brand - **Retailers** monitoring competitor pricing and promotional activity Key features include bulk URL support, configurable item limits, flexible error handling, and extraction of 40+ product attributes including real-time pricing, availability, and promotional badges. --- ### Input Format The scraper accepts a JSON configuration with three simple parameters: ```json { "urls": [ "https://www.jcpenney.com/g/women/view-all-women?id=cat10011030002&page=2&mktTiles=2" ], "ignore_url_failures": true, "max_items_per_url": 200 } ```` | Field | Description | Type | |---|---|---| | `urls` | Array of JCPenney product search list page URLs | Array | | `ignore_url_failures` | If `true`, continues scraping even if some URLs fail | Boolean | | `max_items_per_url` | Maximum products to extract per URL (default: 20, range: 1–200+) | Integer | **Input Notes:** - Paste full search URLs including query parameters (e.g., `?id=cat10011030002&page=2`) - Multi-page scraping: add each page URL separately or use pagination logic - Supports category-filtered searches and keyword searches - Best practice: test with 1–2 URLs before running bulk operations *** ### Output Format **Sample output** ```json { "name": "Liz Claiborne Womens Midi V Neck Short Sleeve A-Line Dress", "pp_id": "ppr5008685829", "current_min": 55.5, "current_max": 55.5, "original_min": 74.0, "original_max": 74.0, "price_type": "SALE", "pre_order": false, "boosted_pp": null, "customer_favourite": false, "marketing_label": "FLASH SALE!", "bottom_badges": [ { "name": "Petite Available", "priority": 1, "url_param": "petite" } ], "upper_left_badge": [ { "name": "NEW", "priority": 1, "url_param": "new" }, { "name": "Only at JCP", "priority": 2, "url_param": "onlyatjcp" } ], "map_price": false, "original_price_label": "original", "sephora_in": false, "show_comparison": true, "bulleted_copy_attrs": [ "Features: Belted, Embroidered", "Closure Type: Belted", "Neckline: V Neck", "Pocket Types: 2 Interior Slip Pocket(s)", "Sleeve Length: Short Sleeve" ], "min_save_price": 0.0, "max_save_price": 0.0, "current_price_label": "SALE", "truck_item_in": false, "eco_rebate": false, "sku_id": "81404240034", "currency_code": "USD", "brand": "liz claiborne", "sku_swatch": [ { "color_name": "Chutney", "swatch_image_id": "DP0318202609045687S.tif", "colorized_image_id": "DP0318202609045583M.tif", "color_seq_num": 1, "sku_id": "81404240034", "default_swatch": false, "sku_alt_images": [ { "img_id": "DP0318202609045375M.tif", "color_seq": 0 }, { "img_id": "DP0318202609045480M.tif", "color_seq": 1 }, { "img_id": "DP0512202617061152M.tif", "color_seq": 2 } ] } ], "images_info": { "thumbnail_image_id": "DP0318202609045583M.tif", "alt_thumbnail_image_id": "DP0318202609045375M.tif" }, "pdp_url": "/p/liz-claiborne-womens-midi-v-neck-short-sleeve-a-line-dress/ppr5008685829?pTmplType=regular", "availability_status": [ { "message": "Unavailable for Pickup", "icon": "cross" }, { "message": "Available for Shipping", "icon": "check" } ], "bundle_status_badge": false, "sub_division": "814", "enable_mini_pdp": null, "fpac_price_max": 36.07, "fpac_price_min": 36.07, "fpac_coupon": "1BIGSALE", "bundle_indicator": false, "append_sku_id": null, "first_activation_date": "2026-05-23T01:49:58", "from_url": "https://www.jcpenney.com/g/women/view-all-women?id=cat10011030002&page=2&mktTiles=2" } ``` Each product record contains 40+ attributes covering identification, pricing, images, and availability: #### Product Identification | Field | Meaning | |---|---| | `Name` | Product name as displayed on search results | | `PP ID` | Internal JCPenney product identifier (unique per SKU) | | `SKU ID` | Stock-keeping unit identifier for inventory tracking | | `Brand` | Product brand or manufacturer name | | `PDP URL` | Direct link to the product detail page | | `Sub Division` | Product category or sub-category classification | #### Pricing & Promotions | Field | Meaning | |---|---| | `Current Min` | Lowest current price (may vary by size/color) | | `Current Max` | Highest current price (may vary by size/color) | | `Original Min` | Original/list price (lowest variant) | | `Original Max` | Original/list price (highest variant) | | `Price Type` | Pricing model (e.g., standard, sale, clearance) | | `Current Price Label` | Promotional text (e.g., "On Sale", "Regular Price") | | `Original Price Label` | Label for full retail price | | `Min Save Price` | Minimum discount amount | | `Max Save Price` | Maximum discount amount | | `MAP Price` | Manufacturer's advertised price (if applicable) | | `Currency Code` | Currency of prices (e.g., USD) | #### Promotions & Badges | Field | Meaning | |---|---| | `Marketing Label` | Promotional labels (e.g., "New Arrival", "Bestseller") | | `Bottom Badges` | Icons/badges at bottom of product card | | `Upper Left Badge` | Icon/badge at top-left (e.g., "New", "Sale") | | `Customer Favourite` | Flag if marked as customer favorite | | `Boosted PP` | Whether product has paid promotional boost | | `Sephora In` | Flag if beauty product stocked in Sephora section | #### Inventory & Availability | Field | Meaning | |---|---| | `Availability Status` | Current stock status (in stock, low stock, out of stock) | | `Pre Order` | Whether the item is available for pre-order | | `Truck Item In` | Whether item can be delivered via truck (furniture/large items) | | `First Activation Date` | Date product first became available | #### Product Details & Media | Field | Meaning | |---|---| | `SKU Swatch` | Swatch image(s) for color/pattern variants | | `Images Info` | URLs and metadata for all product images | | `Bulleted Copy Attrs` | Key product features in bullet points | | `Bundle Indicator` | Flag if item is part of a bundle offer | | `Bundle Status Badge` | Visual indicator for bundle deals | #### Special Programs & Features | Field | Meaning | |---|---| | `FPAC Price Min` | Minimum price under JCPenney's price-lock program | | `FPAC Price Max` | Maximum price under JCPenney's price-lock program | | `FPAC Coupon` | Applicable coupon or promotional code | | `Eco Rebate` | Rebate available for eco-friendly products | | `Enable Mini PDP` | Whether quick-view popup is available | | `Show Comparison` | Whether product comparison feature is enabled | | `Append SKU ID` | Additional SKU identifiers (variants) | *** ### How to Use 1. **Find search URLs** — Navigate to JCPenney.com, perform a product search (by category, brand, or keyword), and copy the full URL including all query parameters. 2. **Add to scraper** — Paste URLs into the `urls` array, one per entry. 3. **Set limits** — Adjust `max_items_per_url` based on your needs (200 = all products on the page). 4. **Enable error handling** — Keep `ignore_url_failures: true` for reliable bulk runs. 5. **Execute & export** — Start the scraper and download results as JSON, CSV, or Excel. **Best Practices:** - Test with a single URL before bulk operations - Use consistent URL structure (avoid shortened URLs) - For multi-page results, add each page URL separately - Monitor pricing data collection during seasonal sales for complete snapshots **Troubleshooting:** - If images aren't captured, verify the page loaded fully (JavaScript-rendered pages may need delays) - For pricing discrepancies, cross-check with the PDP URL for real-time accuracy *** ### Use Cases & Business Value - **Price monitoring:** Track JCPenney's pricing strategies and promotional cadence - **Competitive intelligence:** Benchmark assortments against other retailers - **Product research:** Analyze product trends, brands, and category performance - **Dynamic pricing models:** Feed real-time pricing into algorithmic pricing systems - **Inventory insights:** Understand product availability patterns by category - **Marketing analysis:** Identify which products are flagged as "Customer Favourite" or promoted The JCPenney Product Search Scraper eliminates manual copy-paste work, enabling data-driven decision-making for retailers, marketers, and analysts. *** ### Conclusion The **JCPenney Product Search Scraper** simplifies collection of fashion and home goods data from one of America's largest retail platforms. With 40+ output fields covering pricing, promotions, images, and availability, it provides comprehensive product intelligence for competitive analysis, price monitoring, and e-commerce automation. Start scraping today and unlock actionable retail data. # Actor input Schema ## `urls` (type: `array`): Add the URLs of the product search list urls you want to scrape. You can paste URLs one by one, or use the Bulk edit section to add a prepared list. ## `ignore_url_failures` (type: `boolean`): If true, the scraper will continue running even if some URLs fail to be scraped. ## `max_items_per_url` (type: `integer`): The maximum number of items to scrape per URL. ## Actor input object example ```json { "urls": [ "https://www.jcpenney.com/g/women/view-all-women?id=cat10011030002&page=2&mktTiles=2" ], "ignore_url_failures": true, "max_items_per_url": 20 } ``` # API You can run this Actor programmatically using our API. Below are code examples in JavaScript, Python, and CLI, as well as the OpenAPI specification and MCP server setup. ## JavaScript example ```javascript import { ApifyClient } from 'apify-client'; // Initialize the ApifyClient with your Apify API token // Replace the '' with your token const client = new ApifyClient({ token: '', }); // Prepare Actor input const input = { "urls": [ "https://www.jcpenney.com/g/women/view-all-women?id=cat10011030002&page=2&mktTiles=2" ], "ignore_url_failures": true, "max_items_per_url": 20 }; // Run the Actor and wait for it to finish const run = await client.actor("stealth_mode/jcpenney-product-search-scraper").call(input); // Fetch and print Actor results from the run's dataset (if any) console.log('Results from dataset'); console.log(`💾 Check your data here: https://console.apify.com/storage/datasets/${run.defaultDatasetId}`); const { items } = await client.dataset(run.defaultDatasetId).listItems(); items.forEach((item) => { console.dir(item); }); // 📚 Want to learn more 📖? Go to → https://docs.apify.com/api/client/js/docs ``` ## Python example ```python from apify_client import ApifyClient # Initialize the ApifyClient with your Apify API token # Replace '' with your token. client = ApifyClient("") # Prepare the Actor input run_input = { "urls": ["https://www.jcpenney.com/g/women/view-all-women?id=cat10011030002&page=2&mktTiles=2"], "ignore_url_failures": True, "max_items_per_url": 20, } # Run the Actor and wait for it to finish run = client.actor("stealth_mode/jcpenney-product-search-scraper").call(run_input=run_input) # Fetch and print Actor results from the run's dataset (if there are any) print("💾 Check your data here: https://console.apify.com/storage/datasets/" + run["defaultDatasetId"]) for item in client.dataset(run["defaultDatasetId"]).iterate_items(): print(item) # 📚 Want to learn more 📖? Go to → https://docs.apify.com/api/client/python/docs/quick-start ``` ## CLI example ```bash echo '{ "urls": [ "https://www.jcpenney.com/g/women/view-all-women?id=cat10011030002&page=2&mktTiles=2" ], "ignore_url_failures": true, "max_items_per_url": 20 }' | apify call stealth_mode/jcpenney-product-search-scraper --silent --output-dataset ``` ## MCP server setup ```json { "mcpServers": { "apify": { "command": "npx", "args": [ "mcp-remote", "https://mcp.apify.com/?tools=stealth_mode/jcpenney-product-search-scraper", "--header", "Authorization: Bearer " ] } } } ``` ## OpenAPI specification ```json { "openapi": "3.0.1", "info": { "title": "Jcpenney Product Search Scraper", "description": "Scrape product listings from JCPenney search pages in bulk. Collect product names, pricing, SKU IDs, images, availability, and 40+ attributes per item — perfect for price monitoring, competitive analysis, and e-commerce automation.", "version": "0.0", "x-build-id": "zvshtPhSmuFtw7rkM" }, "servers": [ { "url": "https://api.apify.com/v2" } ], "paths": { "/acts/stealth_mode~jcpenney-product-search-scraper/run-sync-get-dataset-items": { "post": { "operationId": "run-sync-get-dataset-items-stealth_mode-jcpenney-product-search-scraper", "x-openai-isConsequential": false, "summary": "Executes an Actor, waits for its completion, and returns Actor's dataset items in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$ref": "#/components/schemas/inputSchema" } } } }, "parameters": [ { "name": "token", "in": "query", "required": true, "schema": { "type": "string" }, "description": "Enter your Apify token here" } ], "responses": { "200": { "description": "OK" } } } }, "/acts/stealth_mode~jcpenney-product-search-scraper/runs": { "post": { "operationId": "runs-sync-stealth_mode-jcpenney-product-search-scraper", "x-openai-isConsequential": false, "summary": "Executes an Actor and returns information about the initiated run in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$ref": "#/components/schemas/inputSchema" } } } }, "parameters": [ { "name": "token", "in": "query", "required": true, "schema": { "type": "string" }, "description": "Enter your Apify token here" } ], "responses": { "200": { "description": "OK", "content": { "application/json": { "schema": { "$ref": "#/components/schemas/runsResponseSchema" } } } } } } }, "/acts/stealth_mode~jcpenney-product-search-scraper/run-sync": { "post": { "operationId": "run-sync-stealth_mode-jcpenney-product-search-scraper", "x-openai-isConsequential": false, "summary": "Executes an Actor, waits for completion, and returns the OUTPUT from Key-value store in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$ref": "#/components/schemas/inputSchema" } } } }, "parameters": [ { "name": "token", "in": "query", "required": true, "schema": { "type": "string" }, "description": "Enter your Apify token here" } ], "responses": { "200": { "description": "OK" } } } } }, "components": { "schemas": { "inputSchema": { "type": "object", "properties": { "urls": { "title": "URLs of the product search list urls to scrape", "type": "array", "description": "Add the URLs of the product search list urls you want to scrape. You can paste URLs one by one, or use the Bulk edit section to add a prepared list.", "items": { "type": "string" } }, "ignore_url_failures": { "title": "Continue running even if some URLs fail to be scraped", "type": "boolean", "description": "If true, the scraper will continue running even if some URLs fail to be scraped." }, "max_items_per_url": { "title": "Max items per URL", "type": "integer", "description": "The maximum number of items to scrape per URL." } } }, "runsResponseSchema": { "type": "object", "properties": { "data": { "type": "object", "properties": { "id": { "type": "string" }, "actId": { "type": "string" }, "userId": { "type": "string" }, "startedAt": { "type": "string", "format": "date-time", "example": "2025-01-08T00:00:00.000Z" }, "finishedAt": { "type": "string", "format": "date-time", "example": "2025-01-08T00:00:00.000Z" }, "status": { "type": "string", "example": "READY" }, "meta": { "type": "object", "properties": { "origin": { "type": "string", "example": "API" }, "userAgent": { "type": "string" } } }, "stats": { "type": "object", "properties": { "inputBodyLen": { "type": "integer", "example": 2000 }, "rebootCount": { "type": "integer", "example": 0 }, "restartCount": { "type": "integer", "example": 0 }, "resurrectCount": { "type": "integer", "example": 0 }, "computeUnits": { "type": "integer", "example": 0 } } }, "options": { "type": "object", "properties": { "build": { "type": "string", "example": "latest" }, "timeoutSecs": { "type": "integer", "example": 300 }, "memoryMbytes": { "type": "integer", "example": 1024 }, "diskMbytes": { "type": "integer", "example": 2048 } } }, "buildId": { "type": "string" }, "defaultKeyValueStoreId": { "type": "string" }, "defaultDatasetId": { "type": "string" }, "defaultRequestQueueId": { "type": "string" }, "buildNumber": { "type": "string", "example": "1.0.0" }, "containerUrl": { "type": "string" }, "usage": { "type": "object", "properties": { "ACTOR_COMPUTE_UNITS": { "type": "integer", "example": 0 }, "DATASET_READS": { "type": "integer", "example": 0 }, "DATASET_WRITES": { "type": "integer", "example": 0 }, "KEY_VALUE_STORE_READS": { "type": "integer", "example": 0 }, "KEY_VALUE_STORE_WRITES": { "type": "integer", "example": 1 }, "KEY_VALUE_STORE_LISTS": { "type": "integer", "example": 0 }, "REQUEST_QUEUE_READS": { "type": "integer", "example": 0 }, "REQUEST_QUEUE_WRITES": { "type": "integer", "example": 0 }, "DATA_TRANSFER_INTERNAL_GBYTES": { "type": "integer", "example": 0 }, "DATA_TRANSFER_EXTERNAL_GBYTES": { "type": "integer", "example": 0 }, "PROXY_RESIDENTIAL_TRANSFER_GBYTES": { "type": "integer", "example": 0 }, "PROXY_SERPS": { "type": "integer", "example": 0 } } }, "usageTotalUsd": { "type": "number", "example": 0.00005 }, "usageUsd": { "type": "object", "properties": { "ACTOR_COMPUTE_UNITS": { "type": "integer", "example": 0 }, "DATASET_READS": { "type": "integer", "example": 0 }, "DATASET_WRITES": { "type": "integer", "example": 0 }, "KEY_VALUE_STORE_READS": { "type": "integer", "example": 0 }, "KEY_VALUE_STORE_WRITES": { "type": "number", "example": 0.00005 }, "KEY_VALUE_STORE_LISTS": { "type": "integer", "example": 0 }, "REQUEST_QUEUE_READS": { "type": "integer", "example": 0 }, "REQUEST_QUEUE_WRITES": { "type": "integer", "example": 0 }, "DATA_TRANSFER_INTERNAL_GBYTES": { "type": "integer", "example": 0 }, "DATA_TRANSFER_EXTERNAL_GBYTES": { "type": "integer", "example": 0 }, "PROXY_RESIDENTIAL_TRANSFER_GBYTES": { "type": "integer", "example": 0 }, "PROXY_SERPS": { "type": "integer", "example": 0 } } } } } } } } } } ```