E-commerce price monitoring

The problem

Retailers show different prices, currencies, promotions and delivery dates depending on the visitor's country, and many protect product pages against automated collection.

Reliable monitoring needs requests from residential addresses in each market, spread over time so that daily checks of thousands of products stay below anti-bot thresholds.

Configuration

Product

Rotating residential, billed per GB

Rotation

Per request

Targeting

-country-<cc> per storefront (de, fr, es, it…)

Scheduling

Spread checks over the day instead of one nightly burst

Payload

Prefer product JSON endpoints or structured data over full pages

Typical volume

A product page weighs 0.5–2 MB uncompressed; compression divides it several times

Example

import requests

LOGIN, PASSWORD = "px7h2k9m4q", "Rq4Tn8Vw2Lx6Bz9C"
STOREFRONTS = {
    "de": "https://shop.example.de/p/4011",
    "fr": "https://shop.example.fr/p/4011",
    "es": "https://shop.example.es/p/4011",
}

for country, url in STOREFRONTS.items():
    proxy = f"http://{LOGIN}-country-{country}:{PASSWORD}@resi.gw.proxuno.com:7000"
    r = requests.get(
        url,
        headers={"Accept-Encoding": "gzip, br"},
        proxies={"http": proxy, "https": proxy},
        timeout=30,
    )
    print(country, r.status_code, len(r.content))

Example credentials. Replace them with yours from the dashboard.

Best practices

• Target the storefront's own country; cross-border visits often trigger redirects or different prices.
• Fetch structured data (JSON-LD, product APIs used by the page) to cut traffic per product.
• Detect soft blocks — a 200 response with a CAPTCHA page — and count them as failures.
• Keep a per-domain request budget and adjust it to the observed success rate.