# Coupon Promo Code Intelligence Tracker (`seeb/coupon-promo-code-intelligence-tracker`) Actor Track competitor coupon and promo-code offers with discount value, customer segment, minimum spend, expiry signal, and channel context. - **URL**: https://apify.com/seeb/coupon-promo-code-intelligence-tracker.md - **Developed by:** [Techionik](https://apify.com/seeb) (community) - **Categories:** E-commerce - **Stats:** 2 total users, 1 monthly users, 100.0% runs succeeded, 0 bookmarks - **User rating**: No ratings yet ## Pricing $20.00 / 1,000 promo intelligence rows This Actor is paid per event. You are not charged for the Apify platform usage, but only a fixed price for specific events. 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/docs.md): ```bash npm install apify-client ``` In Python projects, use official [Python client library](https://docs.apify.com/api/client/python/docs.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/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 ## Coupon Promo Code Intelligence Tracker Track competitor coupon and promo-code offers with discount value, customer segment, minimum spend, expiry signal, and channel context. Coupon Promo Code Intelligence Tracker is built for one focused job: turning public source pages or pasted research snippets into usable promo intelligence row records. It does not try to be a generic webpage scraper. The parser, fields, examples, and dataset schema are shaped around this actor's specific business question. Use it when you already have public pages, listings, announcements, reviews, directories, or copied snippets and want a repeatable way to extract the information that matters without manually filling a spreadsheet. ### Who Uses It Ecommerce operators, affiliate publishers, coupon sites, pricing analysts, and competitor intelligence teams. ### What It Finds The actor looks for signals connected to track competitor coupon and promo-code offers with discount value, customer segment, minimum spend, expiry signal, and channel context. It keeps the output focused on fields a buyer can review, filter, enrich, or send into a CRM, spreadsheet, product backlog, procurement workflow, or research database. The extraction method is specific to this actor: Coupon-code regex extraction, discount normalization, expiry detection, segment/minimum-spend parsing, and promotion-position classification. ### How It Works 1. The actor accepts public URLs or pasted snippets in `couponSources`. 2. Public URLs are fetched when possible and converted into visible text. 3. Pasted snippets are processed directly, which is useful for pages that block automated fetching or require manual browsing. 4. Actor-specific parsing logic extracts only the fields listed below. 5. Empty or low-information inputs are skipped safely, with clean logs instead of noisy failed rows. ### Input Use `couponSources` for the sources you want to process. Each item can be a public URL or a focused text snippet. ```json { "couponSources": [ "FitGear promo code SUMMER25 gives 25% off for new customers until August 1, 2026. Minimum spend $75.", "TravelNest newsletter offers free shipping plus code LUGGAGE15 for loyalty members this weekend.", "TaskFlow launch coupon STARTER40 gives 40% off first month for startups on annual plans." ], "maxResults": 3 } ```` ### Output Fields - `brandName` - Brand offering the promotion or affiliate program. - `promoCode` - Coupon or promo code found in the source. - `discountValue` - Discount percentage, amount, or free-shipping offer. - `offerType` - Promotion format such as coupon, free shipping, or launch discount. - `eligibleCustomer` - Customer segment eligible for the promotion. - `minimumSpend` - Minimum order value or spend requirement. - `expirySignal` - Expiry date, weekend-only note, or urgency wording. - `channelSignal` - Where the offer appears, such as newsletter, loyalty, or public promo. - `competitiveAngle` - Why the offer matters for competitive pricing research. - `sourceUrl` - Original source URL. ### Example Output ```json { "brandName": "FitGear", "promoCode": "SUMMER25", "discountValue": "25% off", "offerType": "discount code", "eligibleCustomer": "new customers", "minimumSpend": "$75", "expirySignal": "August 1, 2026", "channelSignal": "public promo", "competitiveAngle": "discount-depth monitoring", "sourceUrl": "https://example.com/coupon" } ``` ### Best Use Cases - Build a focused spreadsheet from repeated source pages. - Review public signals before sales, partnership, product, procurement, or market research work. - Monitor a category over time using the same output fields. - Turn copied notes from newsletters, communities, directories, review pages, or search results into a structured dataset. ### Tips For Better Results - Use source pages that visibly contain the signal you want this actor to extract. - For hard-to-fetch sites, paste the relevant public text snippet directly into the input. - Keep each input item focused on one page, listing, announcement, review, or profile. - Review extracted rows before making high-stakes business, legal, procurement, or security decisions. ### Limitations - The actor does not bypass logins, paywalls, private systems, CAPTCHA, or anti-bot walls. - The actor extracts from visible text and may miss information hidden behind scripts or user interactions. - Some fields may be empty when the source does not contain that information. - Output quality depends on source quality, so focused source text produces the strongest rows. # Actor input Schema ## `couponSources` (type: `array`): Add public URLs or paste focused text snippets. Use one source per row. The actor extracts only signals that match its specific purpose. ## `maxResults` (type: `integer`): Maximum number of useful dataset rows to save. ## Actor input object example ```json { "couponSources": [ "FitGear promo code SUMMER25 gives 25% off for new customers until August 1, 2026. Minimum spend $75.", "TravelNest newsletter offers free shipping plus code LUGGAGE15 for loyalty members this weekend.", "TaskFlow launch coupon STARTER40 gives 40% off first month for startups on annual plans." ], "maxResults": 25 } ``` # Actor output Schema ## `dataset` (type: `string`): No description ## `summary` (type: `string`): No description # API You can run this Actor programmatically using our API. Below are code examples in JavaScript, Python, and CLI, as well as the OpenAPI specification and MCP server setup. ## JavaScript example ```javascript import { ApifyClient } from 'apify-client'; // Initialize the ApifyClient with your Apify API token // Replace the '' with your token const client = new ApifyClient({ token: '', }); // Prepare Actor input const input = {}; // Run the Actor and wait for it to finish const run = await client.actor("seeb/coupon-promo-code-intelligence-tracker").call(input); // Fetch and print Actor results from the run's dataset (if any) console.log('Results from dataset'); console.log(`💾 Check your data here: https://console.apify.com/storage/datasets/${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 = {} # Run the Actor and wait for it to finish run = client.actor("seeb/coupon-promo-code-intelligence-tracker").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 '{}' | apify call seeb/coupon-promo-code-intelligence-tracker --silent --output-dataset ``` ## MCP server setup ```json { "mcpServers": { "apify": { "command": "npx", "args": [ "mcp-remote", "https://mcp.apify.com/?tools=seeb/coupon-promo-code-intelligence-tracker", "--header", "Authorization: Bearer " ] } } } ``` ## OpenAPI specification ```json { "openapi": "3.0.1", "info": { "title": "Coupon Promo Code Intelligence Tracker", "description": "Track competitor coupon and promo-code offers with discount value, customer segment, minimum spend, expiry signal, and channel context.", "version": "1.0", "x-build-id": "dgHfgTcLkdCZxATgZ" }, "servers": [ { "url": "https://api.apify.com/v2" } ], "paths": { "/acts/seeb~coupon-promo-code-intelligence-tracker/run-sync-get-dataset-items": { "post": { "operationId": "run-sync-get-dataset-items-seeb-coupon-promo-code-intelligence-tracker", "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": { "$ref": "#/components/schemas/inputSchema" } } } }, "parameters": [ { "name": "token", "in": "query", "required": true, "schema": { "type": "string" }, "description": "Enter your Apify token here" } ], "responses": { "200": { "description": "OK" } } } }, "/acts/seeb~coupon-promo-code-intelligence-tracker/runs": { "post": { "operationId": "runs-sync-seeb-coupon-promo-code-intelligence-tracker", "x-openai-isConsequential": false, "summary": "Executes an Actor and returns information about the initiated run in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$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": { "$ref": "#/components/schemas/runsResponseSchema" } } } } } } }, "/acts/seeb~coupon-promo-code-intelligence-tracker/run-sync": { "post": { "operationId": "run-sync-seeb-coupon-promo-code-intelligence-tracker", "x-openai-isConsequential": false, "summary": "Executes an Actor, waits for completion, and returns the OUTPUT from Key-value store in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$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", "required": [ "couponSources" ], "properties": { "couponSources": { "title": "Coupon pages, brand offers, newsletters, or promo snippets", "type": "array", "description": "Add public URLs or paste focused text snippets. Use one source per row. The actor extracts only signals that match its specific purpose.", "items": { "type": "string" }, "default": [ "FitGear promo code SUMMER25 gives 25% off for new customers until August 1, 2026. Minimum spend $75.", "TravelNest newsletter offers free shipping plus code LUGGAGE15 for loyalty members this weekend.", "TaskFlow launch coupon STARTER40 gives 40% off first month for startups on annual plans." ] }, "maxResults": { "title": "Maximum results", "minimum": 1, "maximum": 100, "type": "integer", "description": "Maximum number of useful dataset rows to save.", "default": 25 } } }, "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 } } } } } } } } } } ```