# Uber Eats Email Scraper - Keyword & Location Targeting (`scrapido/uber-eats-email-scraper`) Actor

🚗 Uber Eats Email Scraper pulls restaurant and merchant emails by keyword and location. 🔓 Custom domain filters, hidden-address decoding and dedup. 📤 Export Uber Eats leads to CSV, JSON or Excel for food tech sales.

- **URL**: https://apify.com/scrapido/uber-eats-email-scraper.md
- **Developed by:** [Scrapido](https://apify.com/scrapido) (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 $2.50 / 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/actors/running/actors-in-store.md#pay-per-event

## What's an Apify Actor?

Actors are web data automations that power AI and operations. They run on the Apify platform to scrape websites, process data, connect APIs, and automate workflows.
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.

- **AI agents and MCP clients** — the [Apify MCP server](https://docs.apify.com/integrations/mcp.md) at `https://mcp.apify.com` (remote, streamable HTTP, OAuth on first use).
- **Agentic workflows and local Actor development** — [Agent Skills](https://apify.com/.well-known/agent-skills/index.json) with the [Apify CLI](https://docs.apify.com/cli/docs.md): `npm install -g apify-cli`, then `apify login`.
- **JavaScript/TypeScript projects** — the official [JS/TS client](https://docs.apify.com/api/client/js/docs.md): `npm install apify-client`.
- **Python projects** — the official [Python client](https://docs.apify.com/api/client/python/docs.md): `pip install apify-client`.
- **Any other language** — 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

### Uber Eats Email Scraper 📬

**Uber Eats Email Scraper** is an Apify actor built for **Uber Eats email scraping** that helps marketers, recruiters, and data teams automate contact discovery from publicly available Uber Eats sources. It solves the biggest problem with manual lead research: slow, inconsistent results. Whether you’re doing **Uber Eats lead generation** or contact data mining, it helps you scale outreach-ready data with speed and better ROI.

***

### What is Uber Eats Email Scraper? 🔍

**Uber Eats Email Scraper** is an automated web scraping tool (an Apify actor) designed to extract public email addresses and profile metadata connected to Uber Eats based on your targeting inputs. It helps solve the time-consuming work of finding business contacts one by one—turning manual research into repeatable **Uber Eats contact scraper** runs. With keyword-led targeting and configurable limits, this **Uber Eats email extractor** supports marketers, recruiters, sales teams, and analysts who want **scrape Uber Eats contacts** at scale. If you’re looking for an **Uber Eats lead generation** tool, this actor turns your inputs into structured results you can export and use in outreach, CRM enrichment, or data pipelines.

***

### What Data Does a Uber Eats Email Scraper Collect? 📊

This actor captures contact-level details and the context needed to understand where the email came from: email address, profile title, description text, the profile URL, and the keyword/domain used to find it.

| Data Category | Fields Extracted | Description |
|---|---|---|
| Contact | `email` | Public email address found in the Uber Eats related content |
| Identity | `title`, `description` | Business name/title or profile-related text |
| Context | `description` | Summary-style text that provides context around the email |
| Discovery | `keyword` | The search term/keyword that surfaced this result |
| Navigation | `url` | Direct link to the Uber Eats page for the lead |
| Location | `description` | Location-like details may be included inside the profile text |

> Tip: You can use this output for **restaurant email extractor** workflows and **LSI marketing outreach emails** use cases.

***

### What Do Results from Uber Eats Email Scraper Look Like? 👀

Each result is a structured JSON record saved to your Apify dataset. Here’s a real example:

```json
{
  "keyword": "manager",
  "title": "Green Spoon Delivery Co.",
  "description": "Operations team lead | Prefer email contact for partnerships | Located in Austin, TX",
  "url": "https://www.ubereats.com/store/green-spoon-delivery-co/austin/abcd1234",
  "email": "partners@greenspoondelivery.com"
}
```

Export formats: JSON (default) and CSV via Apify Console.

***

#### Core Features: Uber Eats Email Scraper ⚡

| Feature | Benefit |
|---|---|
| ✅ **Keyword-Driven Targeting** | Find the Uber Eats audience you need using your `keywords` input |
| ✅ **Location Filter** | Narrow results by a `location` string to focus your market |
| ✅ **Custom Domain Filter** | Restrict results using `customDomains` like `@gmail.com` or company domains |
| ✅ **Configurable Result Cap** | Use `maxEmails` to control runtime and output size |
| ✅ **Proxy Support** | Built-in proxy support for reliable scraping in larger runs |
| ✅ **Real-Time Data Saving** | Results are pushed incrementally to the dataset |
| ✅ **Structured Dataset Output** | Clean fields ready for **delivery platform email lists** and enrichment |
| ✅ **Works on Public Data** | Designed to operate on publicly available sources without login requirements |
| ✅ **Resumable Runs** | Uses saved progress so long jobs can pick up where they left off |

***

### Getting Started with Uber Eats Email Scraper 🚀

1. **Open Apify Store** — Visit [apify.com/store](https://apify.com/store) and search **Uber Eats Email Scraper**
2. **Click Try for Free** — Sign in or create a free Apify account
3. **Open the Input Tab** — Configure your scraping parameters
4. **Add Keywords** — Enter job titles or roles like `manager` or `founder`
5. **Set Optional Filters** — Add a `location` and/or `customDomains` to narrow results
6. **Cap Your Results** — Set `maxEmails` to control output volume and run duration
7. **Click Start** — Launch the run and monitor progress in logs
8. **Access Your Data** — Open the dataset and export to CSV/JSON for outreach workflows

Run it like a **restaurant email extractor** to support **Uber Eats lead generation** and **contact data mining** at scale.

***

### Ways to Use Uber Eats Email Scraper 💡

- 🎯 **B2B Lead Generation** — Build segmented lists for outreach and partnership pitches using your `keywords`
- 📣 **Email Marketing** — Source **scrape Uber Eats contacts** for newsletters and drip campaigns
- 🤝 **Supplier & Partnerships** — Find marketing contacts and business emails for collaboration
- 🔬 **Market Research** — Identify restaurant marketing contacts by role and (optional) location
- ⚙️ **Data Enrichment Email Validation** — Use extracted emails as part of your enrichment pipeline
- 📊 **CRM Enrichment** — Feed results into your CRM for targeted campaigns and personalization

***

#### Input Parameters — Uber Eats Email Scraper

```json
{
  "keywords": ["manager", "founder"],
  "location": "",
  "customDomains": ["@gmail.com", "@yahoo.com"],
  "maxEmails": 20
}
```

| Parameter | Type | Required | Default | Description |
|---|---|---|---|---|
| `keywords` | Array | ✅ Yes | `["manager","founder"]` | A list of keywords or queries used to find relevant Uber Eats contacts |
| `location` | String | No | `""` | Location to filter results (e.g., a city, region, or country name) |
| `customDomains` | Array | No | `["@gmail.com","@yahoo.com"]` | Email domain filters to focus results (for example, `@gmail.com`) |
| `maxEmails` | Integer | No | `20` | Maximum number of emails to collect; helps control run time and cost |

***

#### Output Parameters — Uber Eats Email Scraper

```json
{
  "keyword": "manager",
  "title": "Green Spoon Delivery Co.",
  "description": "Operations team lead | Prefer email contact for partnerships | Located in Austin, TX",
  "url": "https://www.ubereats.com/store/green-spoon-delivery-co/austin/abcd1234",
  "email": "partners@greenspoondelivery.com"
}
```

| Field | Label | Format | Description |
|---|---|---|---|
| `keyword` | Keyword | text | The keyword used to surface this lead |
| `title` | Title | text | Profile name, business title, or related heading |
| `description` | Description | text | Profile bio/summary text that provides context for the lead |
| `url` | Url | link | Direct link to the Uber Eats page for the lead |
| `email` | Email | text | Extracted public email address |

***

### Why Choose This Uber Eats Email Scraper? 🏆

If you’re building an **Uber Eats email scraping** workflow, this actor is optimized for structured output and practical lead generation. You get keyword-based targeting, optional domain filters, and a configurable `maxEmails` cap to control cost. Results are saved incrementally to an Apify dataset, making it ideal for teams that need **extract business emails for outreach** without manual copy-pasting. With built-in proxy support and resumable behavior, it’s a strong fit for **email harvesting tools** and scalable contact data mining. For help, email <scrapidocontact@gmail.com>.

***

### How Many Results Can You Scrape? 📈

You control output size using `maxEmails` (from 1 up to 10,000). Actual results depend on how many Uber Eats profiles match your `keywords` and your `customDomains` filters, and whether those profiles publicly list an email. For broader coverage, consider adding more keywords or extending your run settings in Apify’s **Run Options** (default timeout is 3600 seconds / 1 hour). All collected results are stored in the Apify dataset and can be exported anytime.

***

### Legal Guidelines for Scraping Uber Eats ⚖️

This tool is intended to work with **publicly available data** connected to Uber Eats. It does not require login access and is not designed to retrieve private or restricted content. You remain responsible for complying with applicable laws and platform policies, including data privacy requirements and anti-spam regulations. Use the extracted data for legitimate business purposes only, and respect any relevant terms for how you store and use personal data. Data removal requests: <scrapidocontact@gmail.com>.

***

### FAQ — Uber Eats Email Scraper ❓

#### How does the Uber Eats Email Scraper identify data?

The actor uses the `keywords` you provide to discover relevant Uber Eats related results, then extracts publicly listed email addresses and associated profile fields (like `title`, `description`, and `url`) into the Apify dataset.

#### What Uber Eats profile types can I scrape?

The actor targets public pages and extracts leads where an email address is available in publicly accessible content. If a profile doesn’t contain a public email, it won’t produce an email for that record.

#### How did the Uber Eats Email Scraper perform in our tests?

Performance depends on your `keywords`, optional `location`, and `customDomains` filters. Generally, using more targeted keywords and relevant email domains improves yield for **scrape Uber Eats contacts** workflows.

#### Why scrape Uber Eats for contacts?

Uber Eats hosts many restaurant and business stakeholders who may list contact emails publicly. Automating this with **restaurant email extractor** style scraping saves weeks of manual searching and accelerates **Uber Eats lead generation**.

#### How much does the Uber Eats Email Scraper cost?

It’s designed to be cost-efficient by letting you cap output using `maxEmails`. Pricing is pay-per-result in the Apify ecosystem, and limiting results helps you control run time and spending. (Your exact cost depends on your Apify usage.)

#### How does the Uber Eats Email Scraper help my business?

It helps you create clean, exportable contact lists that include the exact `email` plus context fields like `title`, `description`, `keyword`, and `url`. This makes it easier to power outreach campaigns, enrich a CRM, or build **delivery platform email lists** for segmented marketing.

#### What challenges should I expect when using the Uber Eats Email Scraper?

Not every match will contain a public email, so output volume varies by niche. If results are limited, try widening your `keywords`, adjusting `location`, or adding more `customDomains` to broaden your coverage while keeping **email scraping automation** focused.

#### How do I choose a high-performing Uber Eats Email Scraper?

Choose inputs that align with your outreach goals. Use specific role-based `keywords`, add focused `customDomains` (for example `@company.com`-style domains where available), and set a reasonable `maxEmails` cap to control runtime. The output’s structured fields also make it easier to validate and use results.

***

### Conclusion 🏁

Uber Eats Email Scraper is a fast, structured way to collect outreach-ready emails from Uber Eats-related public sources. If you’re building a lead list, enriching your CRM, or launching **Uber Eats lead generation** campaigns, run it on Apify and export results in seconds—ready for your next step.

***

### 🆘 Support & Feedback

Have a question or feature request for Uber Eats Email Scraper?

- ✨ **Custom Solutions & Feature Requests:** Contact our team
- 📧 **Email:** <scrapidocontact@gmail.com>

### Multiple Email Types

**Email Types** replaces the old single Audience Type choice: select as many
kinds of mailbox as you want and the run chases all of them together.

| Type | What it matches |
| --- | --- |
| Personal / free webmail | Gmail, Outlook, Yahoo, iCloud, AOL, Proton, ... |
| Business / corporate | Company domains - free webmail and institutions excluded |
| Education (.edu / .ac) | `.edu`, `.ac.uk`, `.edu.au`, `.ac.in` and other academic suffixes |
| Government (.gov / .mil) | `.gov`, `.mil`, `.gov.uk`, `.gc.ca`, ... |
| Non-profit (.org) | `.org`, `.ngo`, `.org.uk`, ... |

Each selected type contributes its own Google dork patterns *and* its own domain
test, so a result is only kept if it genuinely belongs to the type that found
it. Every row carries an `emailType` field recording which one that was.

Suffixes are matched as real domain suffixes, so `cs.mit.edu` counts as
Education while `notedu.com` does not.

Setting **Custom Email Domains** still overrides everything: an explicit domain
list is a manual override and replaces the type-driven patterns. The legacy
`audienceType` value is still accepted, so saved inputs keep working.

# Actor input Schema

## `keywords` (type: `array`):

A list of keywords or queries to search for.

## `audienceType` (type: `string`):

Business Emails runs contextual discovery patterns tuned for company contact pages ("email us at", "contact@", careers, bookings, ...) and filters out consumer webmail domains. Consumer Emails instead searches gmail.com, yahoo.com, outlook.com, hotmail.com and icloud.com directly.

## `maxEmails` (type: `integer`):

Maximum number of emails to collect. The scraper will stop once this limit is reached. Setting a higher limit allows for more potential results but doesn't guarantee reaching that number. This helps save costs by controlling scraping time.

## `customDomains` (type: `array`):

Optional manual override — provide specific email domains to search for (e.g. @hubspot.com) instead of using Audience Type. Leave empty to use Audience Type.

## `emailTypes` (type: `array`):

Which kinds of mailbox to hunt for. Pick as many as you like - each type contributes its own set of Google search patterns and its own domain filter, and every result records the type it was found as. Personal = free webmail (Gmail, Outlook, Yahoo, iCloud). Business = company domains, excluding free webmail and institutions. Education = .edu / .ac.uk and friends. Government = .gov / .mil. Non-profit = .org.

## Actor input object example

```json
{
  "keywords": [
    "restaurant",
    "catering"
  ],
  "audienceType": "Consumer Emails",
  "maxEmails": 20,
  "customDomains": [],
  "emailTypes": [
    "Personal",
    "Business"
  ]
}
```

# Actor output Schema

## `dataset` (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 '<YOUR_API_TOKEN>' with your token
const client = new ApifyClient({
    token: '<YOUR_API_TOKEN>',
});

// Prepare Actor input
const input = {
    "keywords": [
        "restaurant",
        "catering"
    ],
    "customDomains": [],
    "emailTypes": [
        "Personal",
        "Business"
    ]
};

// Run the Actor and wait for it to finish
const run = await client.actor("scrapido/uber-eats-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 '<YOUR_API_TOKEN>' with your token.
client = ApifyClient("<YOUR_API_TOKEN>")

# Prepare the Actor input
run_input = {
    "keywords": [
        "restaurant",
        "catering",
    ],
    "customDomains": [],
    "emailTypes": [
        "Personal",
        "Business",
    ],
}

# Run the Actor and wait for it to finish
run = client.actor("scrapido/uber-eats-email-scraper").call(run_input=run_input)

# Fetch and print Actor results from the run's dataset (if there are any)
print(f"💾 Check your data here: https://console.apify.com/storage/datasets/{run.default_dataset_id}")
for item in client.dataset(run.default_dataset_id).iterate_items():
    print(item)

# 📚 Want to learn more 📖? Go to → https://docs.apify.com/api/client/python/docs/quick-start

```

## CLI example

```bash
echo '{
  "keywords": [
    "restaurant",
    "catering"
  ],
  "customDomains": [],
  "emailTypes": [
    "Personal",
    "Business"
  ]
}' |
apify call scrapido/uber-eats-email-scraper --silent --output-dataset

```

## MCP server setup

```json
{
    "mcpServers": {
        "apify": {
            "type": "http",
            "url": "https://mcp.apify.com/?tools=fetch-actor-details,scrapido/uber-eats-email-scraper"
        }
    }
}

```

The hosted server signs you in with OAuth on first connect, so no API token belongs in this config. Clients without OAuth support can send an `Authorization: Bearer <APIFY_API_TOKEN>` header instead, using a token from API & Integrations in Apify Console (https://console.apify.com/settings/integrations).

## OpenAPI specification

Download the OpenAPI definition: https://api.apify.com/v2/actors/81hWmHIYyfJOLua6S/builds/67vgciLU2btpej4rh/openapi.json