Cross-platform ecommerce monitoring tracks your products (or competitors') across Amazon, Google Shopping, and organic search results simultaneously. This is essential for price parity enforcement, MAP violation detection, and competitive intelligence. Instead of building separate scrapers per marketplace, the Scavio API handles Amazon and Google Shopping through one endpoint. This tutorial builds a monitoring pipeline that checks products across platforms, detects price discrepancies, and generates alerts. Each platform check costs $0.005.
Prerequisites
- Python 3.9+ installed
- requests library installed
- A Scavio API key from scavio.dev
- Product names or ASINs to monitor
Walkthrough
Step 1: Define the product monitoring list
Create a structured list of products to monitor with identifiers for each platform. Include your MAP (minimum advertised price) if applicable.
Python
products = [
{
'name': 'Sony WH-1000XM5 Headphones',
'search_query': 'Sony WH-1000XM5',
'map_price': 299.99, # minimum advertised price
'brand': 'Sony'
},
{
'name': 'Apple AirPods Pro 2',
'search_query': 'Apple AirPods Pro 2nd generation',
'map_price': 249.00,
'brand': 'Apple'
},
{
'name': 'Bose QuietComfort Ultra',
'search_query': 'Bose QuietComfort Ultra headphones',
'map_price': 429.00,
'brand': 'Bose'
}
]
print(f'Monitoring {len(products)} products across 2 platforms')
print(f'Cost per full check: ${len(products) * 2 * 0.005:.2f}')Step 2: Search Amazon and Google Shopping for each product
Query both platforms for each product and normalize the results into a common format with price, seller, rating, and availability.
Python
import requests, os, re
API_KEY = os.environ['SCAVIO_API_KEY']
ENDPOINT = 'https://api.scavio.dev/api/v1/search'
def parse_price(p) -> float:
if not p: return 0.0
try: return float(re.sub(r'[^0-9.]', '', str(p)))
except: return 0.0
def check_amazon(query: str) -> list:
resp = requests.post(ENDPOINT,
headers={'x-api-key': API_KEY, 'Content-Type': 'application/json'},
json={'platform': 'amazon', 'query': query, 'marketplace': 'US'})
return [{'platform': 'amazon', 'title': p.get('title', ''),
'price': parse_price(p.get('price')), 'rating': p.get('rating', 0),
'reviews': p.get('reviews_count', 0), 'link': p.get('link', '')}
for p in resp.json().get('products', []) if parse_price(p.get('price')) > 0]
def check_google_shopping(query: str) -> list:
resp = requests.post(ENDPOINT,
headers={'x-api-key': API_KEY, 'Content-Type': 'application/json'},
json={'query': query, 'country_code': 'us', 'type': 'shopping'})
return [{'platform': 'google_shopping', 'title': p.get('title', ''),
'price': parse_price(p.get('price')), 'seller': p.get('source', ''),
'link': p.get('link', '')}
for p in resp.json().get('shopping_results', []) if parse_price(p.get('price')) > 0]Step 3: Detect MAP violations and price anomalies
Compare prices against the minimum advertised price. Flag any listing that sells below MAP or shows a suspicious pricing pattern.
Python
def check_map_violations(listings: list, map_price: float) -> list:
violations = []
for listing in listings:
if listing['price'] < map_price:
discount_pct = ((map_price - listing['price']) / map_price) * 100
violations.append({
'platform': listing['platform'],
'title': listing['title'][:60],
'price': listing['price'],
'map_price': map_price,
'discount': round(discount_pct, 1),
'link': listing['link']
})
return violations
def find_price_range(listings: list) -> dict:
prices = [l['price'] for l in listings if l['price'] > 0]
if not prices:
return {'min': 0, 'max': 0, 'spread': 0}
return {
'min': min(prices),
'max': max(prices),
'spread': round(max(prices) - min(prices), 2),
'avg': round(sum(prices) / len(prices), 2)
}Step 4: Generate the cross-platform report
Combine data from all platforms into a unified product health report with pricing comparison and MAP violation alerts.
Python
import time
from datetime import date
def monitor_product(product: dict) -> dict:
query = product['search_query']
amazon = check_amazon(query)
google = check_google_shopping(query)
all_listings = amazon + google
violations = check_map_violations(all_listings, product['map_price'])
price_range = find_price_range(all_listings)
return {
'product': product['name'],
'map_price': product['map_price'],
'amazon_listings': len(amazon),
'google_listings': len(google),
'total_listings': len(all_listings),
'price_range': price_range,
'map_violations': violations,
'violation_count': len(violations)
}
def full_monitor(products: list) -> list:
reports = []
for product in products:
report = monitor_product(product)
reports.append(report)
status = 'VIOLATION' if report['violation_count'] > 0 else 'OK'
print(f'[{status}] {report["product"]}: '
f'${report["price_range"]["min"]}-${report["price_range"]["max"]} '
f'({report["total_listings"]} listings, {report["violation_count"]} violations)')
time.sleep(0.3)
cost = len(products) * 2 * 0.005
print(f'\nTotal cost: ${cost:.2f} ({len(products) * 2} credits)')
return reports
reports = full_monitor(products)Step 5: Schedule and alert on violations
Run the monitor on a schedule and send alerts when MAP violations or significant price changes are detected.
Python
import json
def save_and_alert(reports: list) -> None:
# Save report
filename = f'ecommerce_monitor_{date.today()}.json'
with open(filename, 'w') as f:
json.dump({'date': date.today().isoformat(), 'reports': reports}, f, indent=2)
# Check for alerts
violations = [r for r in reports if r['violation_count'] > 0]
if violations:
print(f'\nALERT: {len(violations)} products with MAP violations:')
for r in violations:
for v in r['map_violations']:
print(f' {r["product"]} on {v["platform"]}: '
f'${v["price"]} (MAP: ${v["map_price"]}, -{v["discount"]}%)')
else:
print('\nNo MAP violations detected.')
# Schedule: add to crontab
# 0 9,15,21 * * * python ecommerce_monitor.py # 3x daily
# Cost: 3 products x 2 platforms x 3 checks x 30 days = 540 credits/mo = $2.70
save_and_alert(reports)Python Example
Python
import os, requests, re, time
API_KEY = os.environ['SCAVIO_API_KEY']
EP = 'https://api.scavio.dev/api/v1/search'
def search(body):
return requests.post(EP, headers={'x-api-key': API_KEY, 'Content-Type': 'application/json'}, json=body).json()
def parse_price(p):
try: return float(re.sub(r'[^0-9.]', '', str(p or '0')))
except: return 0.0
def monitor(name, query, map_price):
amazon = search({'platform': 'amazon', 'query': query, 'marketplace': 'US'})
google = search({'query': query, 'country_code': 'us', 'type': 'shopping'})
prices = [parse_price(p.get('price')) for p in amazon.get('products', [])]
prices += [parse_price(p.get('price')) for p in google.get('shopping_results', [])]
prices = [p for p in prices if p > 0]
violations = sum(1 for p in prices if p < map_price)
print(f'{name}: ${min(prices):.2f}-${max(prices):.2f} ({violations} MAP violations)')
monitor('Sony WH-1000XM5', 'Sony WH-1000XM5', 299.99)JavaScript Example
JavaScript
const API_KEY = process.env.SCAVIO_API_KEY;
const EP = 'https://api.scavio.dev/api/v1/search';
async function search(body) {
const r = await fetch(EP, {
method: 'POST',
headers: { 'x-api-key': API_KEY, 'Content-Type': 'application/json' },
body: JSON.stringify(body)
});
return r.json();
}
async function monitor(name, query, mapPrice) {
const [amazon, google] = await Promise.all([
search({ platform: 'amazon', query, marketplace: 'US' }),
search({ query, country_code: 'us', type: 'shopping' })
]);
const prices = [
...(amazon.products || []).map(p => parseFloat(String(p.price || 0).replace(/[^0-9.]/g, ''))),
...(google.shopping_results || []).map(p => parseFloat(String(p.price || 0).replace(/[^0-9.]/g, '')))
].filter(p => p > 0);
const violations = prices.filter(p => p < mapPrice).length;
console.log(`${name}: $${Math.min(...prices)}-$${Math.max(...prices)} (${violations} MAP violations)`);
}
monitor('Sony WH-1000XM5', 'Sony WH-1000XM5', 299.99);Expected Output
JSON
[OK] Sony WH-1000XM5 Headphones: $289.99-$349.99 (8 listings, 0 violations)
[VIOLATION] Apple AirPods Pro 2: $219.00-$249.00 (6 listings, 2 violations)
[OK] Bose QuietComfort Ultra: $429.00-$449.99 (5 listings, 0 violations)
Total cost: $0.03 (6 credits)
ALERT: 1 products with MAP violations:
Apple AirPods Pro 2 on google_shopping: $219.00 (MAP: $249.00, -12.0%)
Apple AirPods Pro 2 on amazon: $229.99 (MAP: $249.00, -7.6%)