# College S Email Scraper (`scraperoka/college-s-email-scraper`) Actor 📧 College S Email Scraper extracts verified college contact emails fast—ideal for B2B outreach, admissions leads & recruitment. 🧠 Automate list building with clean results and improved deliverability. ⚡ Say goodbye to manual searching. - **URL**: https://apify.com/scraperoka/college-s-email-scraper.md - **Developed by:** [Scraperoka](https://apify.com/scraperoka) (community) - **Categories:** Lead generation, Automation, Developer tools - **Stats:** 2 total users, 1 monthly users, 100.0% runs succeeded, 0 bookmarks - **User rating**: No ratings yet ## Pricing from $0.01 / 1,000 results This Actor is paid per event and usage. You are charged both the fixed price for specific events and for Apify platform usage. 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 ### College S Email Scraper 🚀 Manually visiting listings, opening websites, and copying contact emails wastes hours you can’t afford. **College S Email Scraper** finds college-related businesses and extracts their contact information (including emails, phone numbers, and social links) from public web data in a single run. This **college email scraper** is ideal for marketers, recruiters, and growth teams who need a **college student email scraper** workflow at scale—generating targeted results fast instead of doing copy-paste work one profile at a time. You can collect thousands of leads (up to your set limits) and export them directly from Apify. --- ### What You Get: Sample Output Here's a sample record from a single run: ```json { "name": "Springfield Coffee Roasters", "website": "https://springfieldroasters.com", "phone": "+1 (555) 012-3456", "full_address": "123 Market St Springfield IL 62704 US", "city": "Springfield", "state": "IL", "zip": "62704", "country_code": "US", "scraped_emails": [], "scraped_phones": ["+1-555-010-2020"], "scraped_social_media": ["https://www.linkedin.com/company/springfieldroasters/"], "emails_found": 1, "pages_scraped": 6, "avg_rating": 4.4, "total_reviews": 312, "lat": 39.7817, "long": -89.6501, "place_id": "ChIJN1t_tDeuEmsRUsoyG... (truncated)", "scrape_status": "success", "email_found": "hello@springfieldroasters.com" } ```` | Field | Type | What It Tells You | |---|---|---| | `name` | string | The business name you can reference in outreach lists and CRM views | | `website` | string | The official site the actor used to look for contact info | | `phone` | string | The phone number associated with the business listing | | `full_address` | string | A ready-to-use, consolidated address string for mapping or segmentation | | `city` | string | The city part of the location for geographic filtering | | `state` | string | The state part of the location for routing and regional campaigns | | `zip` | string | ZIP code for more precise audience targeting | | `country_code` | string | Country code helps keep your lead data consistent across regions | | `scraped_emails` | array | The actor’s email list extracted from the business website | | `scraped_phones` | array | Phone numbers extracted from the website pages | | `scraped_social_media` | array | Social links extracted from the website pages | | `emails_found` | number | How many emails were found for this business | | `pages_scraped` | number | How many site pages were processed to find contact info | | `avg_rating` | number | A quality signal pulled from the business listing | | `total_reviews` | number | Extra listing credibility context for prioritization | | `scrape_status` | string | Tells you if the scrape succeeded, failed, or had no website | | `email_found` | string | A single flattened email value (one row per email when emails exist) | | `place_id` | string | A deduplication key to help avoid repeats in your output | Export your dataset as JSON, CSV, or Excel — straight from the Apify dashboard. *** ### Why College S Email Scraper? There are a lot of ways to pull data from public web listings—here’s what sets **College S Email Scraper** (and other college email scraper workflows like it) apart. #### Built for real outreach datasets **College S Email Scraper** focuses on contact extraction: it searches for businesses using your inputs and then scrapes their websites to collect emails, phone numbers, and social media profiles. That makes it a practical **education email scraper** for building a usable **college email list scraper** outcome, not just business names. #### Clean, structured output (ready to import) Results include fields like `website`, `full_address`, `scraped_emails`, `scraped_phones`, and `scraped_social_media`, plus listing context like `avg_rating` and `total_reviews`. When emails are found, the dataset is flattened so you get one row per email via `email_found`, which is ideal for CRM ingestion and segmentation. #### Email extraction is validated by your settings The actor supports `validateEmails` and also captures operational fields like `pages_scraped` and `scrape_status`, so you can interpret quality and coverage when building a **college leads email extractor** list. #### Resilient scraping with built-in fallbacks When a business has no website or extraction fails, the actor sets clear values such as `scrape_status: "no_website"` or `scrape_status: "failed"` and still pushes structured rows based on your `email_only_results` behavior. This helps keep your **university email scraper** pipeline consistent even when the web data is messy. *** ### Configuring Your Run Drop this into your `input.json` to get started: ```json { "googleMapsSearchTerm": "College S", "googleMapsLocation": ["New York"], "maxBusinesses": 5, "scrapeMaxBusinessesPerLocation": false, "proxyConfiguration": { "useApifyProxy": true } } ``` | Parameter | Required | What It Does | |---|---|---| | `googleMapsSearchTerm` | ✅ | The business type or niche for the email scraper (for example, “coffee shops”, “dentists”). | | `googleMapsLocation` | ✅ | One or more geographic locations (for example, “Miami, Florida”). The actor will use these locations to collect businesses. | | `maxBusinesses` | ⬜ | Target number of businesses to find (1–1000). The scraper stops when this target is reached. | | `scrapeMaxBusinessesPerLocation` | ⬜ | If enabled, the actor collects up to `maxBusinesses` results per location. If disabled, it combines all locations into a single total limit. | | `proxyConfiguration` | ⬜ | Proxy settings for scraping (recommended for larger runs). | | ↳ `proxyConfiguration.proxy support` | ⬜ | When set, the actor routes scraping traffic through Apify Proxy for better reliability. | > Note: The actor also uses phone and social extraction by default (not exposed as separate input toggles in the UI schema), and it supports email validation via `validateEmails` in code. *** ### Core Capabilities #### Targeted discovery by search term + location You control what kinds of businesses are collected using `googleMapsSearchTerm`, and you control where to search using `googleMapsLocation`. This is what makes the workflow usable as a **college email scraper** for targeted **college student email scraper** campaigns. #### Website contact extraction (emails, phones, social links) Once business websites are identified, the actor scrapes those sites and extracts `scraped_emails`, `scraped_phones`, and `scraped_social_media`. This turns public listings into actionable contact data you can use for outreach and lead qualification. #### Flattened output for easy outreach workflows When emails are found, the actor pushes one row per email and stores the single value in `email_found`, while also keeping business-level details like `name`, `website`, and `full_address`. That makes **college email list scraper** outputs much easier to import into email outreach tools. #### Operational metrics for quality control Each row includes `emails_found` and `pages_scraped`, plus a `scrape_status` value that helps you understand what happened. These fields support faster review when you’re building a **college directory email scraping tool** or auditing results for completeness. #### Proxy support for large-scale runs The actor accepts `proxyConfiguration` and is designed for reliability at scale. This helps when you’re running a **bulk email scraper for colleges** workflow across multiple locations. *** ### Who Gets the Most Out of This Here's how different teams put **College S Email Scraper** to work: **Admissions marketing teams** use it to generate targeted college and education-related contact lists, extracting email addresses from business websites so they can scale outreach for events, newsletters, and partnerships. They get structured lead rows that are ready for segmentation and follow-up. **Sales development reps** use it as a **college leads email extractor** to quickly build a prospecting list by location and business niche, prioritizing results with fields like `avg_rating` and `total_reviews` while capturing real website emails as `email_found`. **Recruiters and HR sourcing leads** use the output to expand candidate or vendor pipelines with consistent contact fields. Instead of hunting manually, they can import results into their tools and maintain cleaner records using address and website metadata. **Education researchers and data analysts** use the structured dataset for study-level analysis, because each row includes geographic and listing context (city, state, zip, country\_code) alongside extracted contact fields. They can also filter by `scrape_status` to exclude low-quality or missing-website records. **Automation specialists and developers** can run the actor via the Apify platform and feed results into pipelines (CRM syncs, enrichment workflows, and dashboards). The dataset-first output is designed to be integration-ready for reliable automation. *** ### Step-by-Step: How to Use It No coding needed. Here's how to run **College S Email Scraper** from start to finish: 1. **Open the actor on Apify** — go to [console.apify.com](https://console.apify.com) and search for College S Email Scraper. 2. **Enter your inputs** — set `googleMapsSearchTerm` and `googleMapsLocation` (required), and optionally `maxBusinesses` and `scrapeMaxBusinessesPerLocation`. 3. **Configure proxy settings** — if you plan larger runs, enable proxy via the `proxyConfiguration` option for more reliable scraping. 4. **Hit Run and watch the live log** — monitor progress and status updates as the actor collects businesses and scrapes their websites. 5. **View results in the dataset tab** — each row includes structured lead fields plus `scrape_status` to help you validate coverage. 6. **Export as JSON, CSV, or Excel** — download directly from Apify once your dataset is ready. The whole process takes under 5 minutes to set up. *** ### Integrations & Export Options Once your data is collected, **College S Email Scraper** plugs directly into your existing workflow. You can export your dataset in common formats like JSON, CSV, and Excel from the Apify dataset tab. This makes it straightforward to feed a **college email list scraper** output into spreadsheets, CRM imports, or internal BI tools. You can also connect your automation to Apify using the Apify API (see the [Apify API documentation](https://apify.com/docs/api)). For no-code flows, you can use Zapier/Make-style automation and webhooks to push results to downstream systems when a run completes. *** ### Pricing & Free Trial **College S Email Scraper** runs on the Apify platform, which offers a **free tier** — no credit card required to get started. You can use the free credits for several test runs to validate your **university email scraper** or **edu email scraper** workflow before scaling up. For paid usage, Apify uses pay-as-you-go billing based on platform compute (CU). For exact costs and plan options, check the Apify pricing page at [apify.com](https://apify.com). Start for free at [apify.com](https://apify.com) and scale when you're ready. *** ### Reliability & Performance | What We Handle | How | |---|---| | Scraping reliability | Uses proxy support for more consistent large-scale fetching | | Data freshness | Builds results from publicly available sources using your input search term and locations | | Error handling | Writes clear `scrape_status` values like `success`, `failed`, and `no_website` | | Output usefulness | Provides structured fields such as `scraped_emails`, `scraped_phones`, and `scraped_social_media` | | Scale control | You can cap results with `maxBusinesses` and control per-location behavior with `scrapeMaxBusinessesPerLocation` | Limitations: Results depend on what contact information is publicly available on target websites. If a business has no accessible website or no emails are found, `scrape_status` will reflect that, and `email_found` may be empty depending on your output mode. For enterprise-scale runs, contact us to discuss custom configurations. *** ### Frequently Asked Questions #### Is there a free plan or trial for College S Email Scraper? Yes. Apify provides a free tier so you can test **College S Email Scraper** before committing to paid usage. The best way to confirm what’s included for your specific needs is to check the current Apify free-tier details on the pricing page. #### Do I need to log in to use College S Email Scraper? No. This actor is designed to work with publicly available web data and extracts contact information from accessible business websites. You only configure inputs and run it from the Apify interface. #### How accurate is the extracted email data? Accuracy depends on what the website publishes. The actor extracts emails it finds on the business website and outputs them in fields like `scraped_emails`, with a flattened `email_found` when emails are present. #### How many results can I get per run? You control this with `maxBusinesses` (1–1000). You can also choose whether `scrapeMaxBusinessesPerLocation` should apply the cap per location or as a single combined total limit. #### How fresh is the data, and how often is it updated? The data is collected fresh at run time. Since the actor scrapes publicly available sources during execution, it’s best thought of as “current as of the time you ran it,” rather than a continuously updated database. #### Is this legal? Does it comply with GDPR / CCPA? It can be compliant when used responsibly. The actor collects **publicly available data**, but you are responsible for following GDPR, CCPA, applicable privacy rules, and any relevant platform Terms of Service for your use case. #### Can I export results to Google Sheets or Excel? Yes. You can export your Apify dataset as JSON, CSV, or Excel from the dataset tab. From there, you can import into Google Sheets or any other spreadsheet/CRM tool you use. #### Can I run this on a schedule automatically? Yes. You can schedule Apify runs using Apify scheduling features and automation tooling (for example, via API-driven workflows). This is useful when you want a regularly refreshed **targeted college email outreach list**. #### Can I access this via API? Yes. You can trigger and retrieve results programmatically via the Apify API. Use the [Apify API documentation](https://apify.com/docs/api) for implementation details. #### What happens if the actor hits an error? The actor records status per business using `scrape_status` and still pushes structured rows (including `no_website` and `failed` outcomes). This makes it easier to handle partial results in your pipeline instead of losing the entire run. *** ### Need Help or Have a Request? Got a question about **College S Email Scraper** or want a new feature added? Reach out at . We’re actively maintaining this actor and welcome requests like webhook notifications on completion and enhanced CSV-friendly output patterns. *** ### Disclaimer & Responsible Use *College S Email Scraper is the fastest, most reliable way to build a targeted college outreach contact dataset — start your free run today.* The actor collects **publicly available data** from accessible sources. It does not access login-gated content or password-protected pages. You are responsible for complying with GDPR, CCPA, applicable privacy requirements, and platform Terms of Service when using this data. For data removal requests, contact . Use responsibly, ethically, and only for lawful purposes. # Actor input Schema ## `googleMapsSearchTerm` (type: `string`): Enter the business type or niche for email scraper (e.g., 'coffee shops', 'dentists'). ## `googleMapsLocation` (type: `array`): Target geographic location for the email scraper (e.g., 'Miami, Florida'). ## `maxBusinesses` (type: `integer`): Target number of businesses to find (1-1000). The scraper will stop when this target is reached. ## `scrapeMaxBusinessesPerLocation` (type: `boolean`): If enabled, the scraper will collect up to `maxBusinesses` results per location. If disabled, it combines all locations up to a single total limit. ## `proxyConfiguration` (type: `object`): Proxy settings for scraping. Recommended for large-scale scraping. ## Actor input object example ```json { "googleMapsSearchTerm": "College S", "googleMapsLocation": [ "New York" ], "maxBusinesses": 5, "scrapeMaxBusinessesPerLocation": false, "proxyConfiguration": { "useApifyProxy": true } } ``` # 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 = { "googleMapsSearchTerm": "College S", "googleMapsLocation": [ "New York" ], "maxBusinesses": 5, "proxyConfiguration": { "useApifyProxy": true } }; // Run the Actor and wait for it to finish const run = await client.actor("scraperoka/college-s-email-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 = { "googleMapsSearchTerm": "College S", "googleMapsLocation": ["New York"], "maxBusinesses": 5, "proxyConfiguration": { "useApifyProxy": True }, } # Run the Actor and wait for it to finish run = client.actor("scraperoka/college-s-email-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 '{ "googleMapsSearchTerm": "College S", "googleMapsLocation": [ "New York" ], "maxBusinesses": 5, "proxyConfiguration": { "useApifyProxy": true } }' | apify call scraperoka/college-s-email-scraper --silent --output-dataset ``` ## MCP server setup ```json { "mcpServers": { "apify": { "command": "npx", "args": [ "mcp-remote", "https://mcp.apify.com/?tools=scraperoka/college-s-email-scraper", "--header", "Authorization: Bearer " ] } } } ``` ## OpenAPI specification ```json { "openapi": "3.0.1", "info": { "title": "College S Email Scraper", "description": "📧 College S Email Scraper extracts verified college contact emails fast—ideal for B2B outreach, admissions leads & recruitment. 🧠 Automate list building with clean results and improved deliverability. ⚡ Say goodbye to manual searching.", "version": "1.0", "x-build-id": "rh8hInuOwOEk5nJF0" }, "servers": [ { "url": "https://api.apify.com/v2" } ], "paths": { "/acts/scraperoka~college-s-email-scraper/run-sync-get-dataset-items": { "post": { "operationId": "run-sync-get-dataset-items-scraperoka-college-s-email-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": { "$ref": "#/components/schemas/inputSchema" } } } }, "parameters": [ { "name": "token", "in": "query", "required": true, "schema": { "type": "string" }, "description": "Enter your Apify token here" } ], "responses": { "200": { "description": "OK" } } } }, "/acts/scraperoka~college-s-email-scraper/runs": { "post": { "operationId": "runs-sync-scraperoka-college-s-email-scraper", "x-openai-isConsequential": false, "summary": "Executes an Actor and returns information about the initiated run in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$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": { "$ref": "#/components/schemas/runsResponseSchema" } } } } } } }, "/acts/scraperoka~college-s-email-scraper/run-sync": { "post": { "operationId": "run-sync-scraperoka-college-s-email-scraper", "x-openai-isConsequential": false, "summary": "Executes an Actor, waits for completion, and returns the OUTPUT from Key-value store in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$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", "required": [ "googleMapsLocation", "googleMapsSearchTerm" ], "properties": { "googleMapsSearchTerm": { "title": "Search Term", "type": "string", "description": "Enter the business type or niche for email scraper (e.g., 'coffee shops', 'dentists').", "default": "College S" }, "googleMapsLocation": { "title": "Location", "type": "array", "description": "Target geographic location for the email scraper (e.g., 'Miami, Florida').", "default": [ "New York" ], "items": { "type": "string" } }, "maxBusinesses": { "title": "Maximum Businesses With Emails", "minimum": 1, "maximum": 1000, "type": "integer", "description": "Target number of businesses to find (1-1000). The scraper will stop when this target is reached.", "default": 5 }, "scrapeMaxBusinessesPerLocation": { "title": "Scrape Max Businesses Per Location", "type": "boolean", "description": "If enabled, the scraper will collect up to `maxBusinesses` results per location. If disabled, it combines all locations up to a single total limit.", "default": false }, "proxyConfiguration": { "title": "Proxy Configuration", "type": "object", "description": "Proxy settings for scraping. Recommended for large-scale scraping." } } }, "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 } } } } } } } } } } ```