# Walmart Email Scraper - Bulk Keywords, Decodes Hidden Emails (`code-beat/walmart-email-scraper-ultra-fast-and-precise`) Actor

🏬 Walmart Email Scraper collects marketplace seller and supplier emails from bulk keyword searches. 🔓 Decodes obfuscated addresses with domain filtering. 📦 Perfect for retail vendors, CPG brands & B2B supplier outreach.

- **URL**: https://apify.com/code-beat/walmart-email-scraper-ultra-fast-and-precise.md
- **Developed by:** [Code Beat](https://apify.com/code-beat) (community)
- **Categories:** Lead generation, E-commerce, Automation
- **Stats:** 2 total users, 1 monthly users, 100.0% runs succeeded, 0 bookmarks
- **User rating**: No ratings yet

## Pricing

from $1.50 / 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

### Walmart Email Scraper

**Walmart Email Scraper** is a fast, practical way to collect public email addresses from Walmart using the keywords and filters you choose. It helps marketers, data analysts, and researchers build targeted lead lists with less manual work, making Walmart email extraction, Walmart contact scraper workflows, and Walmart lead generation much easier at scale. 🚀

### What is Walmart Email Scraper? 🔍

**Walmart Email Scraper** is an Apify actor that automatically collects email addresses from publicly available Walmart-related web data based on your chosen keywords. It is designed for teams that want to replace repetitive manual research with a structured Walmart email finder and Walmart business email finder process. Instead of checking pages one by one, you can run keyword-based searches, apply email-domain filters, and gather results in a clean dataset.

This makes it useful for marketers building outreach lists, researchers studying Walmart contact data collection, and analysts who need a Walmart directory scraper for faster discovery. In short, the Walmart Email Scraper helps you turn public web data into usable leads with much better speed and scale. 📬

### What Data Does Walmart Email Scraper Collect? 📊

The actor captures a focused set of fields that are useful for Walmart website email extraction and downstream lead workflows. Each result is stored as a structured record in the dataset, so you can review, filter, and export the data easily.

| Data Category | Fields Extracted | Description |
|---|---|---|
| Contact | `email` | Public email address found in the result |
| Identity | `title` | Title or page name associated with the result |
| Context | `description` | Supporting text from the result |
| Discovery | `keyword` | The keyword that surfaced the result |
| Navigation | `url` | Direct link to the source page |

### What Do Results from Walmart Email Scraper Look Like? 👀

Each result is saved as a structured JSON record in your Apify dataset. Here’s a realistic example of what a Walmart email address scraper output can look like:

```json
{
  "keyword": "manager",
  "title": "Jane Thompson",
  "description": "Store Operations Manager | Customer support and vendor inquiries",
  "url": "https://www.walmart.com/store/1234",
  "email": "jane.thompson@gmail.com"
}
```

You can export the results in JSON or CSV format from the Apify Console. This makes it easy to reuse the data in CRM systems, spreadsheets, or Walmart outreach contacts workflows. 📁

#### Core Features: Walmart Email Scraper ⚡

| Feature | Benefit |
|---|---|
| ✅ **Keyword-Driven Targeting** | Find relevant Walmart contacts using the terms that matter to your campaign |
| ✅ **Location Filter** | Narrow results by location when you want more relevant leads |
| ✅ **Custom Domain Filter** | Focus on specific email extensions like `@gmail.com` or `@yahoo.com` |
| ✅ **Configurable Result Cap** | Use `maxEmails` to control how many emails you collect |
| ✅ **Built-In Proxy Support** | Helps runs stay reliable on larger scraping jobs |
| ✅ **Real-Time Data Saving** | Results are saved as they are found |
| ✅ **Structured Dataset Output** | Clean records that are ready for analysis or export |
| ✅ **Public Data Only** | Works with publicly available Walmart data |

### Getting Started with Walmart Email Scraper 🚀

1. **Open the actor in Apify** — Find **Walmart Email Scraper** in the Apify Store.
2. **Open the Input tab** — Review the available fields and set your scraping options.
3. **Add keywords** — Enter the Walmart lead generation terms you want to target.
4. **Set optional filters** — Add a location or custom email domains if needed.
5. **Choose your result cap** — Set `maxEmails` to control volume and cost.
6. **Start the run** — Launch the actor and follow the live logs.
7. **Review the dataset** — Open the output dataset to preview results.
8. **Export your leads** — Download the data in JSON or CSV for further use.

No coding is required, and you can get started quickly. ⚙️

### Ways to Use Walmart Email Scraper 💡

- 🎯 **Walmart Lead Generation** — Build targeted contact lists for outreach campaigns.
- 📣 **Email Marketing** — Collect public emails for newsletters and follow-ups.
- 🔬 **Market Research** — Study Walmart contact data collection patterns by keyword.
- 📊 **CRM Enrichment** — Add Walmart-related contact details to your existing database.
- 🤝 **Vendor Outreach** — Use Walmart vendor email scraper workflows to find relevant business contacts.
- 🧩 **Research Projects** — Support investigations that need structured Walmart data scraping tool output.

#### Input Parameters — Walmart 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 matching Walmart results. |
| `location` | String | No | `""` | Optional location filter to narrow the results. |
| `customDomains` | Array | No | `["@gmail.com","@yahoo.com"]` | A list of email domains to include in the results. |
| `maxEmails` | Integer | No | `20` | Maximum number of emails to collect before the actor stops. |

#### Output Parameters — Walmart Email Scraper

| Field | Label | Format | Description |
|---|---|---|---|
| `keyword` | Keyword | text | The keyword that produced the result. |
| `title` | Title | text | The title associated with the result. |
| `description` | Description | text | Supporting text from the result. |
| `url` | Url | link | Direct link to the source page. |
| `email` | Email | text | The public email address extracted from the result. |

### Why Choose Walmart Email Scraper? 🏆

If you need Walmart email harvesting at scale, this actor gives you a simple, structured way to work with public contact data. It is designed for speed, consistency, and practical results, with built-in result capping and support for large batches. For teams comparing Walmart email extraction tools, the combination of keyword targeting, structured output, and proxy-backed reliability makes this a strong option. If you need help or want to discuss custom solutions, contact <codebeatapi@gmail.com>. ✉️

### How Many Results Can You Scrape? 📈

You can set `maxEmails` anywhere from 1 to 10,000. The actual number of results depends on how many public Walmart contacts match your keywords and filters. For larger Walmart contact data collection jobs, using broader keywords and more domain options can improve coverage. The dataset can store your results as they are collected, so you can export them whenever you’re ready.

### Legal Guidelines for Scraping Walmart ⚖️

This actor works with publicly available data only. It does not require logins or access private pages. Users are responsible for making sure their use of Walmart data complies with applicable laws, including privacy and spam rules, as well as any platform terms that may apply. Use the data only for legitimate business and research purposes. For data removal requests, contact <codebeatapi@gmail.com>.

### FAQ — Walmart Email Scraper ❓

#### How does Walmart Email Scraper find contacts?

The actor uses the keywords and filters you provide to find relevant public Walmart contact data, then collects email addresses from publicly available sources and saves them to a dataset.

#### What kind of Walmart profiles or pages can I scrape?

You can scrape public Walmart-related pages and records that include an email address in publicly available content. If no public email is available, there may be no result.

#### Why use Walmart Email Scraper for lead generation?

It saves time by turning manual searching into a structured Walmart lead generator workflow. That makes it easier to build outreach lists, research contacts, and organize data by keyword.

#### How does the location filter work?

The `location` field lets you narrow results by a place name if you want more targeted output. If you want broader coverage, you can leave it blank.

#### Can I limit how many emails are collected?

Yes. The `maxEmails` field controls the maximum number of emails the actor will collect in a run. This helps you manage scope and keep costs predictable.

#### How do I improve my Walmart email lookup results?

Try broader keywords, add similar terms, and include more email domains in `customDomains`. These adjustments can help you find more relevant public contacts.

#### Is this suitable for Walmart store email list building?

Yes. If your goal is to build a Walmart store email list from public web data, this actor is designed for exactly that kind of structured collection and export.

### Conclusion 🏁

**Walmart Email Scraper** is a reliable way to turn public Walmart-related data into usable contact lists. Whether you need Walmart website email extraction, Walmart outreach contacts, or a practical Walmart contact scraper for research, this actor gives you a fast and organized workflow. Start collecting smarter and save time on manual prospecting. 🚀

### 🆘 Support & Feedback

Have a question, bug report, or feature request for **Walmart Email Scraper**?

Please contact <codebeatapi@gmail.com> for support and feedback.

### Country & Time Targeting

Both filters are applied to the Google query itself, so they shape which pages
the dork is answered from rather than filtering after the fact.

**Target Country** - runs the search as if from that country (`gl`). Turn on
**Strict country filter** to additionally restrict results to pages Google
attributes to it (`cr=countryXX`); that is much tighter and returns noticeably
fewer results. Leave the country on *Global (no country filter)* for worldwide
results.

**Result Language** - restricts results to a single language (`hl` + `lr`).

**Time Range** - limits results to a publication window: past hour, 24 hours,
week, month, year, or an explicit *Custom range* using **Custom range: from** /
**to** in `YYYY-MM-DD` form. A page Google indexed last week is far more likely
to carry a live mailbox than one it last saw five years ago.

Selecting *Custom range* without either date falls back to no time filter rather
than searching all of time by accident.

# Actor input Schema

## `scrapeMode` (type: `string`):

Find New Emails (default) searches Airbnb by keyword. Clean My List skips searching entirely and instead runs the emails you paste into Email List (below) through the same deliverability checks.

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

A list of keywords or queries to search for.

## `location` (type: `string`):

Location to filter search results.

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

List of custom email domains

## `emailList` (type: `array`):

Paste the emails you want checked for deliverability. Only used when Scrape Mode is set to "Clean My List".

## `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.

## `verificationMode` (type: `string`):

Quick Check (default) validates syntax and MX records only - fast, no added latency. Deep Check additionally flags disposable-domain addresses and checks each domain against a public spam blocklist (Spamhaus DBL). Off skips validation entirely.

## `minQualityScore` (type: `number`):

Drop any contact whose quality score (0.0-1.0) falls below this. Leave at 0 for no filter.

## `excludeCatchAll` (type: `boolean`):

Drop contacts on domains confirmed to accept mail for any address (only applies when that signal is available).

## `requireMxValid` (type: `boolean`):

Drop any contact whose domain has no valid mail server (MX record).

## `targetCountry` (type: `string`):

Ask Google to answer as if searching from this country. Leave on "Global (no country filter)" for worldwide results.

## `strictCountry` (type: `boolean`):

Restrict results to pages Google attributes to the target country (cr=countryXX), instead of only preferring them. Much tighter targeting, noticeably fewer results.

## `searchLanguage` (type: `string`):

Restrict results to one language. Leave on "Any language" for no filter.

## `timeRange` (type: `string`):

Only return pages Google indexed within this window. Recent pages are more likely to hold a mailbox that still works.

## `customDateFrom` (type: `string`):

Only used when Time Range is "Custom range". Format: YYYY-MM-DD.

## `customDateTo` (type: `string`):

Only used when Time Range is "Custom range". Format: YYYY-MM-DD.

## Actor input object example

```json
{
  "scrapeMode": "Find New Emails",
  "keywords": [
    "manager",
    "founder"
  ],
  "location": "",
  "customDomains": [
    "@gmail.com",
    "@yahoo.com"
  ],
  "emailList": [],
  "maxEmails": 20,
  "verificationMode": "Quick Check",
  "minQualityScore": 0,
  "excludeCatchAll": false,
  "requireMxValid": false,
  "targetCountry": "Global (no country filter)",
  "strictCountry": false,
  "searchLanguage": "",
  "timeRange": "Any time",
  "customDateFrom": "",
  "customDateTo": ""
}
```

# 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": [
        "manager",
        "founder"
    ],
    "location": "",
    "customDomains": [
        "@gmail.com",
        "@yahoo.com"
    ],
    "emailList": []
};

// Run the Actor and wait for it to finish
const run = await client.actor("code-beat/walmart-email-scraper-ultra-fast-and-precise").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": [
        "manager",
        "founder",
    ],
    "location": "",
    "customDomains": [
        "@gmail.com",
        "@yahoo.com",
    ],
    "emailList": [],
}

# Run the Actor and wait for it to finish
run = client.actor("code-beat/walmart-email-scraper-ultra-fast-and-precise").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": [
    "manager",
    "founder"
  ],
  "location": "",
  "customDomains": [
    "@gmail.com",
    "@yahoo.com"
  ],
  "emailList": []
}' |
apify call code-beat/walmart-email-scraper-ultra-fast-and-precise --silent --output-dataset

```

## MCP server setup

```json
{
    "mcpServers": {
        "apify": {
            "type": "http",
            "url": "https://mcp.apify.com/?tools=fetch-actor-details,code-beat/walmart-email-scraper-ultra-fast-and-precise"
        }
    }
}

```

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/bJTrWjjfGzxVWLEly/builds/eZDNAN1qzVAcbyJ91/openapi.json