# Sephora Qanda Spider (`getdataforme/sephora-qanda-spider`) Actor Scrape questions and answers from Sephora product pages to uncover customer insights and trends. Features accurate extraction, scalable processing, and JSON output for easy analysis in market research, competitive intelligence, and content aggregation. - **URL**: https://apify.com/getdataforme/sephora-qanda-spider.md - **Developed by:** [GetDataForMe](https://apify.com/getdataforme) (community) - **Categories:** AI, Automation, E-commerce - **Stats:** 2 total users, 1 monthly users, 100.0% runs succeeded, NaN bookmarks - **User rating**: No ratings yet ## Pricing from $9.00 / 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. 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 ### Description Scrape questions and answers from Sephora product pages to uncover customer insights and trends. Features accurate extraction, scalable processing, and JSON output for easy analysis in market research, competitive intelligence, and content aggregation. --- ## Sephora Qanda Spider The Sephora Qanda Spider is an Apify Actor designed to scrape questions and answers from Sephora product pages, providing valuable insights into customer inquiries and community discussions. It extracts detailed Q&A data, including user interactions, timestamps, and helpfulness ratings, enabling businesses and researchers to analyze consumer sentiment and product feedback. This tool streamlines data collection for market research, competitive analysis, and content aggregation. ### Features - **Comprehensive Q&A Extraction**: Scrapes all questions, answers, user nicknames, submission times, and interaction metrics from specified Sephora product URLs. - **High Data Accuracy**: Ensures reliable extraction of structured data, including brand answers and helpfulness votes, with minimal errors. - **Scalable Performance**: Handles multiple URLs efficiently, supporting batch processing for large-scale data needs. - **Flexible Output**: Delivers data in JSON format, easily integrable with downstream tools for analysis or visualization. - **User-Friendly Configuration**: Simple input schema with URL lists, requiring no advanced setup. - **Real-Time Insights**: Captures up-to-date community discussions, reflecting current customer trends. - **Error-Resilient**: Built-in handling for common web scraping challenges like dynamic content. ### Input Parameters | Parameter | Type | Required | Description | Example | |-----------|---------|----------|-----------------------------------------------------------------------------|---------| | Urls | array | No | A list of Sephora product URLs to scrape for questions and answers. Must include at least one valid URL starting with http or https. | ["https://www.sephora.com/product/lip-sleeping-mask-P420652"] | ### Example Usage #### Input Configuration ```json { "Urls": [ "https://www.sephora.com/product/lip-sleeping-mask-P420652" ] } ```` #### Example Output ```json [ { "Product_Id": "P420652", "Question_Id": "8367231", "Question": "Is it true these lip products contain artificial red 6 and yellow 5 dye coloring?", "User_Nickname": "kealaakapualani", "SubmissionTime": "2026-04-06T01:44:46.000+00:00", "Total_Answers": 2, "Answers": [ { "Answer_Id": "8781987", "Answer_Text": "yes", "User_Nickname": "StellaSLAYK", "SubmissionTime": "2026-04-08T00:18:45.000+00:00", "Is_Brand_Answer": false, "Helpful": 2, "Not_Helpful": 1 }, { "Answer_Id": "8782068", "Answer_Text": "Unfortunately it is true.", "User_Nickname": "miadesroches", "SubmissionTime": "2026-04-08T04:52:01.000+00:00", "Is_Brand_Answer": false, "Helpful": 0, "Not_Helpful": 0 } ], "actor_id": "Cg7HL8nJDx4OuVSFh", "run_id": "860szFEaXy4fHDKCf" }, { "Product_Id": "P420652", "Question_Id": "8351243", "Question": "Is this Product cruelty free?? Because I LOVE animals and I don't want to wear stuff that has hurt them", "User_Nickname": "Danie14", "SubmissionTime": "2026-03-24T21:42:26.000+00:00", "Total_Answers": 1, "Answers": [ { "Answer_Id": "8758379", "Answer_Text": "No it\u2019s not cruelty free. It is not vegan either probably.", "User_Nickname": "sarielll", "SubmissionTime": "2026-03-25T02:56:37.000+00:00", "Is_Brand_Answer": false, "Helpful": 2, "Not_Helpful": 2 } ], "actor_id": "Cg7HL8nJDx4OuVSFh", "run_id": "860szFEaXy4fHDKCf" }, { "Product_Id": "P420652", "Question_Id": "8341889", "Question": "Is it the product vegan?", "User_Nickname": null, "SubmissionTime": "2026-03-17T22:15:13.000+00:00", "Total_Answers": 3, "Answers": [ { "Answer_Id": "8753299", "Answer_Text": "I looked online and it says, \u201cNo, the Laneige Lip Sleeping Mask is generally not considered vegan or cruelty-free.\u201d So that\u2019s your answer! Jesus \u2764\ufe0f You", "User_Nickname": "LOLONIKOLE", "SubmissionTime": "2026-03-19T21:22:49.000+00:00", "Is_Brand_Answer": false, "Helpful": 3, "Not_Helpful": 4 }, { "Answer_Id": "8754054", "Answer_Text": "Definitely not", "User_Nickname": "sarielll", "SubmissionTime": "2026-03-20T18:22:14.000+00:00", "Is_Brand_Answer": false, "Helpful": 0, "Not_Helpful": 1 }, { "Answer_Id": "8753438", "Answer_Text": "No it\u2019s definitely not.", "User_Nickname": "sarielll", "SubmissionTime": "2026-03-20T02:39:34.000+00:00", "Is_Brand_Answer": false, "Helpful": 0, "Not_Helpful": 1 } ], "actor_id": "Cg7HL8nJDx4OuVSFh", "run_id": "860szFEaXy4fHDKCf" } ] ``` ### Use Cases - **Market Research**: Analyze customer questions to identify trends in product concerns, such as ingredient safety or cruelty-free status. - **Competitive Intelligence**: Compare Q\&A data across similar products to gauge brand reputation and user engagement. - **Content Aggregation**: Collect community-driven insights for blog posts, reviews, or social media content. - **Academic Research**: Study consumer behavior and sentiment in beauty product discussions. - **Business Automation**: Automate data extraction for dashboards tracking product feedback over time. - **E-commerce Optimization**: Use insights to improve product descriptions and address common FAQs. ### Installation and Usage 1. Search for "Sephora Qanda Spider" in the Apify Store 2. Click "Try for free" or "Run" 3. Configure input parameters 4. Click "Start" to begin extraction 5. Monitor progress in the log 6. Export results in your preferred format (JSON, CSV, Excel) ### Output Format The output is a JSON array of objects, each representing a question and its associated answers. Key fields include: - **Product\_Id**: Unique identifier for the Sephora product. - **Question\_Id**: Unique ID for the question. - **Question**: The text of the user's question. - **User\_Nickname**: Nickname of the question asker (may be null). - **SubmissionTime**: ISO 8601 timestamp of question submission. - **Total\_Answers**: Number of answers provided. - **Answers**: Array of answer objects, each with Answer\_Id, Answer\_Text, User\_Nickname, SubmissionTime, Is\_Brand\_Answer (boolean), Helpful (count), and Not\_Helpful (count). - **actor\_id** and **run\_id**: Metadata for the Apify run. Data is structured for easy parsing and analysis, with timestamps in UTC. ### Support For custom/simplified outputs or bug reports, please contact: - Email: support@getdataforme.com - Subject line: "custom support" - Contact form: https://getdataforme.com/contact/ We're here to help you get the most out of this Actor! # Actor input Schema ## `Urls` (type: `array`): The urls for the spider. ## Actor input object example ```json { "Urls": [ "https://www.sephora.com/product/lip-sleeping-mask-P420652" ] } ``` # Actor output Schema ## `results` (type: `string`): Scraped data items from dataset # 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("getdataforme/sephora-qanda-spider").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("getdataforme/sephora-qanda-spider").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 getdataforme/sephora-qanda-spider --silent --output-dataset ``` ## MCP server setup ```json { "mcpServers": { "apify": { "command": "npx", "args": [ "mcp-remote", "https://mcp.apify.com/?tools=getdataforme/sephora-qanda-spider", "--header", "Authorization: Bearer " ] } } } ``` ## OpenAPI specification ```json { "openapi": "3.0.1", "info": { "title": "Sephora Qanda Spider", "description": "Scrape questions and answers from Sephora product pages to uncover customer insights and trends. Features accurate extraction, scalable processing, and JSON output for easy analysis in market research, competitive intelligence, and content aggregation.", "version": "0.0", "x-build-id": "Q5LtO2XG8ENhB6yM6" }, "servers": [ { "url": "https://api.apify.com/v2" } ], "paths": { "/acts/getdataforme~sephora-qanda-spider/run-sync-get-dataset-items": { "post": { "operationId": "run-sync-get-dataset-items-getdataforme-sephora-qanda-spider", "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": { "$ref": "#/components/schemas/inputSchema" } } } }, "parameters": [ { "name": "token", "in": "query", "required": true, "schema": { "type": "string" }, "description": "Enter your Apify token here" } ], "responses": { "200": { "description": "OK" } } } }, "/acts/getdataforme~sephora-qanda-spider/runs": { "post": { "operationId": "runs-sync-getdataforme-sephora-qanda-spider", "x-openai-isConsequential": false, "summary": "Executes an Actor and returns information about the initiated run in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$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": { "$ref": "#/components/schemas/runsResponseSchema" } } } } } } }, "/acts/getdataforme~sephora-qanda-spider/run-sync": { "post": { "operationId": "run-sync-getdataforme-sephora-qanda-spider", "x-openai-isConsequential": false, "summary": "Executes an Actor, waits for completion, and returns the OUTPUT from Key-value store in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$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", "minItems": 1, "type": "array", "description": "The urls for the spider.", "default": [ "https://www.sephora.com/product/lip-sleeping-mask-P420652" ], "items": { "type": "string" } } } }, "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 } } } } } } } } } } ```