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216.73.216.35
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Walmart Grocery Data Scraping API – Web Scraping Walmart APIs

Actowiz Solutions offers the Walmart Grocery Data Scraping API, a powerful tool for businesses looking to scrape Walmart supermarket data efficiently. Web Scraping Walmart APIs enables you to gather comprehensive grocery data, including product listings, prices, and availability across multiple countries, including the USA, UK, India, UAE, Japan, Italy, Germany, Canada, Australia, China, Switzerland, Qatar, Singapore, Ireland, Macao SAR, Luxembourg, Austria, Denmark, and Norway. With our Grocery Data Scraping Services, you can effortlessly scrape Walmart product data and integrate it into your systems through seamless Walmart API Integration. Enhance your competitive edge with accurate, real-time grocery data at your fingertips.

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Key Features

Data-Accuracy

Data Accuracy

Ensure high accuracy in scraped product information and pricing.

Multi-Country-Support

Multi-Country Support

Access supermarket data from multiple countries effortlessly and efficiently.

Comprehensive-Listings

Comprehensive Listings

Retrieve detailed grocery product listings, including descriptions and images.

Price-Tracking

Price Tracking

Monitor price changes and promotional offers across various stores.

Custom-Extraction

Custom Extraction

Tailor data scraping to focus on specific product categories.

User-Friendly

User-Friendly API

Simple integration process with existing systems for seamless use.

Automated-Scraping

Automated Scraping

Set automated schedules for regular data collection without manual intervention.

Reliable-Performance

Reliable Performance

Consistent and dependable data extraction with minimal downtime or errors.

Use Cases

Market Analysis

Utilize Walmart Product Scraping to analyze competitor pricing strategies and adjust marketing plans accordingly for better market positioning.

Market-Analysis
Inventory-Management
Use Cases

Inventory Management

Integrate the Walmart Order Data API to streamline inventory management, ensuring stock levels meet customer demands effectively and efficiently.

Use Cases

Customer Insights

Leverage Walmart Customer Reviews Scraping to gather feedback and improve product offerings based on customer preferences and satisfaction levels.

Customer-Insights
Data-Integration
Use Cases

Data Integration

Employ the Walmart Product Data API to integrate product information into e-commerce platforms, enhancing the online shopping experience for customers.

Use Cases

Efficiency Boost

Implement Automated Walmart Data Extraction to save time and resources by regularly collecting data without manual effort, ensuring data freshness.

Efficiency-Boost

API Endpoints

/products

Description: Fetch detailed product information based on various criteria.

Parameteres:

keyword (string): Search term to find products.

category (string): Filter by specific product categories.

sort (string): Sorting options (e.g., price, popularity).

page (int): Pagination for large datasets.

Response: JSON object containing product name, ID, price, rating, and description.

/product/{product_id}

Description: Retrieve detailed information for a specific product using its unique ID.

Parameteres:

product_id (string): Unique ID for the product.

Response: JSON object with product details including title, price, features, and customer reviews.

/reviews

Description: Fetch customer reviews and ratings for a specific product.

Parameteres:

product_id (string): Unique ID for the product.

Response: JSON object containing reviewer names, ratings, review texts, and helpfulness votes.

/offers

Description: Retrieve current offers, discounts, and availability for a specific product.

Parameteres:

product_id (string): Unique ID for the product.

Response: JSON object with offer details, including price, discount percentages, and stock availability.

/related

Description: Get recommendations for related products based on a specific product.

Parameteres:

product_id (string): Unique ID for the product.

Response: JSON object containing a list of related product IDs and names.

/categories

Description: Retrieve a list of available product categories on Walmart.

Response: JSON object containing category names and IDs for filtering products.

/search

Description: Search for products based on various criteria like keywords and filters.

Parameteres:

query (string): Search term to find products.

sort (string): Sorting options (e.g., price, rating).

page (int): Pagination for large datasets.

Response: JSON object with a list of products matching the search criteria.

/price-history

Description: Retrieve historical pricing data for a specific product.

Parameteres:

product_id (string): Unique ID for the product.

Response: JSON object containing price changes over time, including dates and corresponding prices.

/store-locations

Description: Fetch store locations that offer delivery or pickup options.

Parameteres:

zipcode (string): ZIP code for location-based searching.

Response: JSON object containing store names, addresses, and operating hours.

/cart

Description: Manage the shopping cart for a user session.

Parameteres:

user_id (string): Unique identifier for the user.

action (string): Action to perform (add, remove, update).

product_id (string): Unique ID of the product.

Response: JSON object confirming cart status and updated totals.

/checkout

Description: Process the checkout for a user’s cart.

Parameteres:

user_id (string): Unique identifier for the user.

payment_info (object): Payment details for transaction processing.

Response: JSON object containing order confirmation details and estimated delivery time.

API Response Format

All responses are returned in JSON format for easy integration into your application.

from flask import Flask, jsonify, request

app = Flask(__name__)

# Sample data for demonstration purposes
products = [
    {"id": "1", "name": "Apple", "price": 1.00, "rating": 4.5, "description": "Fresh apples"},
    {"id": "2", "name": "Banana", "price": 0.50, "rating": 4.0, "description": "Ripe bananas"},
    # Add more products as needed
]

# Sample data for reviews
reviews = {
    "1": [
        {"reviewer": "Alice", "rating": 5, "text": "Great apples!", "helpfulness": 10},
        {"reviewer": "Bob", "rating": 4, "text": "Very fresh.", "helpfulness": 5}
    ],
    "2": [
        {"reviewer": "Charlie", "rating": 4, "text": "Tasty bananas.", "helpfulness": 3}
    ]
}

@app.route('/products', methods=['GET'])
def get_products():
    keyword = request.args.get('keyword', '')
    category = request.args.get('category', '')
    sort = request.args.get('sort', 'name')
    page = int(request.args.get('page', 1))

    # Filter and sort products (basic implementation)
    filtered_products = [p for p in products if keyword.lower() in p['name'].lower()]
    sorted_products = sorted(filtered_products, key=lambda x: x[sort])

    return jsonify(sorted_products)

@app.route('/product/', methods=['GET'])
def get_product(product_id):
    product = next((p for p in products if p['id'] == product_id), None)
    if product:
        return jsonify(product)
    return jsonify({"error": "Product not found"}), 404

@app.route('/reviews', methods=['GET'])
def get_reviews():
    product_id = request.args.get('product_id')
    product_reviews = reviews.get(product_id, [])
    return jsonify(product_reviews)

@app.route('/offers', methods=['GET'])
def get_offers():
    product_id = request.args.get('product_id')
    # This is a placeholder; implement offer logic as needed
    offers = {"product_id": product_id, "offers": [{"discount": "10%", "availability": "In Stock"}]}
    return jsonify(offers)

@app.route('/related', methods=['GET'])
def get_related():
    product_id = request.args.get('product_id')
    # This is a placeholder; implement related product logic as needed
    related_products = [{"id": "3", "name": "Orange"}, {"id": "4", "name": "Grapes"}]
    return jsonify(related_products)

@app.route('/categories', methods=['GET'])
def get_categories():
    categories = [{"id": "1", "name": "Fruits"}, {"id": "2", "name": "Vegetables"}]
    return jsonify(categories)

@app.route('/search', methods=['GET'])
def search_products():
    query = request.args.get('query', '')
    sort = request.args.get('sort', 'name')
    page = int(request.args.get('page', 1))

    # Implement search logic (basic implementation)
    searched_products = [p for p in products if query.lower() in p['name'].lower()]
    sorted_products = sorted(searched_products, key=lambda x: x[sort])

    return jsonify(sorted_products)

@app.route('/price-history', methods=['GET'])
def get_price_history():
    product_id = request.args.get('product_id')
    # This is a placeholder; implement price history logic as needed
    price_history = [{"date": "2023-10-01", "price": 1.00}, {"date": "2023-10-10", "price": 1.20}]
    return jsonify(price_history)

@app.route('/store-locations', methods=['GET'])
def get_store_locations():
    zipcode = request.args.get('zipcode')
    # This is a placeholder; implement location logic as needed
    locations = [{"name": "Store A", "address": "123 Main St", "hours": "9am-9pm"}]
    return jsonify(locations)

@app.route('/cart', methods=['POST'])
def manage_cart():
    user_id = request.json.get('user_id')
    action = request.json.get('action')
    product_id = request.json.get('product_id')
    # Implement cart management logic here
    return jsonify({"status": "success", "message": f"Product {action} to cart for user {user_id}."})

@app.route('/checkout', methods=['POST'])
def checkout():
    user_id = request.json.get('user_id')
    payment_info = request.json.get('payment_info')
    # Implement checkout logic here
    return jsonify({"status": "success", "message": "Checkout completed successfully."})

if __name__ == '__main__':
    app.run(debug=True)

Optimize Product Data with Our Walmart Scraping API

Optimize your product data with our Walmart Grocery Data Scraping API. Our powerful Walmart Scraping API allows businesses to efficiently scrape Walmart product data from various supermarkets, ensuring you stay updated with the latest product information. With our comprehensive Grocery Data Scraping Services, you can access critical insights into pricing, availability, and promotions across multiple regions. This enables informed decision-making and enhances competitive advantage in the ever-evolving market. Trust our reliable solutions to streamline your data extraction processes, ensuring you have the most accurate and timely data at your fingertips. Experience the difference with our Walmart data solutions today!

Optimize-Product-Data-with-Our-Scraping-API

Maximize your competitive advantage with our Walmart Scraping API—Start your data journey now!

216.73.216.35
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    [lat] => 39.9625
    [lon] => -83.0061
    [timezone] => America/New_York
    [isp] => Amazon.com
    [org] => Anthropic, PBC
    [as] => AS16509 Amazon.com, Inc.
    [query] => 216.73.216.35
)

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From Raw Data to Real-Time Decisions

All in One Pipeline

Scrape Structure Analyze Visualize

Look Back Analyze historical data to discover patterns, anomalies, and shifts in customer behavior.

Find Insights Use AI to connect data points and uncover market changes. Meanwhile.

Move Forward Predict demand, price shifts, and future opportunities across geographies.

Industry:

Coffee / Beverage / D2C

Result

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Smarter product targeting

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“Actowiz Solutions has been instrumental in optimizing our data scraping processes. Their services have provided us with valuable insights into our customer preferences, helping us stay ahead of the competition.”

Operations Manager, Beanly Coffee

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Real Estate

Result

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Real-time RERA insights for 20+ states

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“Actowiz Solutions provided exceptional RERA Website Data Scraping Solution Service across PAN India, ensuring we received accurate and up-to-date real estate data for our analysis.”

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✓ Boosted data acquisition speed by 3×

Industry:

Organic Grocery / FMCG

Result

Improved

competitive benchmarking

★★★★★

“With Actowiz Solutions' data scraping, we’ve gained a clear edge in tracking product availability and pricing across various platforms. Their service has been a key to improving our market intelligence.”

Product Manager, 24Mantra Organic

✓ Real-time SKU-level tracking

Industry:

Quick Commerce

Result

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“Actowiz Solutions has greatly helped us monitor product availability from top three Quick Commerce brands. Their real-time data and accurate insights have streamlined our inventory management and decision-making process. Highly recommended!”

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✓ 28% product availability accuracy

✓ Reduced OOS by 34% in 3 weeks

Industry:

Quick Commerce

Result

3x Faster

improvement in operational efficiency

★★★★★

“Actowiz Solutions' data scraping services have helped streamline our processes and improve our operational efficiency. Their expertise has provided us with actionable data to enhance our market positioning.”

Business Development Lead,Organic Tattva

✓ Weekly competitor pricing feeds

Industry:

Beverage / D2C

Result

Faster

Trend Detection

★★★★★

“The data scraping services offered by Actowiz Solutions have been crucial in refining our strategies. They have significantly improved our ability to analyze and respond to market trends quickly.”

Marketing Director, Sleepyowl Coffee

Boosted marketing responsiveness

Industry:

Quick Commerce

Result

Enhanced

stock tracking across SKUs

★★★★★

“Actowiz Solutions provided accurate Product Availability and Ranking Data Collection from 3 Quick Commerce Applications, improving our product visibility and stock management.”

Growth Analyst, TheBakersDozen.in

✓ Improved rank visibility of top products

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Real results from real businesses using Actowiz Solutions

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“I strongly recommend Actowiz Solutions for their outstanding web scraping services. Their team delivered impeccable results with a nice price, ensuring data on time.”
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CEO / Datacy.es
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See Actowiz in Action – Real-Time Scraping Dashboard + Success Insights

Blinkit (Delhi NCR)

In Stock
₹524

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Price Drop + 12 min
in 6 hrs across Lel.6

Appzon AirPdos Pro

Price
Drop −12 thr

Zepto (Mumbai)

Improved inventory
visibility & palniring

Monitor Prices, Availability & Trends -Live Across Regions

Actowiz's real-time scraping dashboard helps you monitor stock levels, delivery times, and price drops across Blinkit, Amazon: Zepto & more.

✔ Scraped Data: Price inights Top-slling SKUs

Our Data Drives Impact - Real Client Stories

Blinkit | India (Relail Partner)

"Actow's helped us reduce out of ststack incidents by 23% within 6 weeks"

✔ Scraped Data, SKU availability, delivery time

US Electronics Seller (Amazon - Walmart)

With hourly price monitoring, we aligned promotions with competitors, drove 17%

✔ Scraped Data, SKU availability, delivery time

Zepto Q Commerce Brand

"Actow's helped us reduce out of ststack incidents by 23% within 6 weeks"

✔ Scraped Data, SKU availability, delivery time

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