Minimum Advertised Price Monitoring with Mrscraper

In the digital era, price competition among online sellers has become increasingly intense. Many brands implement a Minimum Advertised Price (MAP) policy to protect product value, maintain profit margins, and ensure consistent pricing across the market. However, manually monitoring hundreds or even thousands of product pages is clearly inefficient.
This is where MAP Monitoring plays a critical role, and Mrscraper can serve as an effective automation solution.

What Is Minimum Advertised Price (MAP)?

Minimum Advertised Price (MAP) is the lowest price that resellers or retailers are allowed to advertise publicly, whether on websites, marketplaces, or digital advertisements.

The primary objectives of MAP include:

• Preserving brand image and perceived value
• Preventing price wars among resellers
• Stabilizing profit margins
• Promoting healthy and fair competition

It is important to note that MAP does not represent the final selling price, but rather the price that is publicly displayed to consumers.

Role of Mrscraper

Mrscraper acts as the scraping engine with the following capabilities:

• Fetching public product pages
• Supporting JavaScript-rendered content
• Handling dynamic pricing
• Extracting structured data using flexible selectors

With Mrscraper, there is no need to manage browser automation, proxy rotation, or anti-bot mechanisms manually.

Technical Flow of MAP Monitoring

1. A scheduler triggers the monitoring job
2. Mrscraper fetches the product page
3. The system extracts the advertised price
4. The price is normalized into a numeric value
5. The price is compared against the MAP value
6. If a violation is detected, an alert is triggered

Python Implementation Example

Dependencies

The requests library is used to send HTTP requests to the Mrscraper API, while python-dotenv allows sensitive configuration such as API keys to be stored securely in environment variables.

Environment Variables

The API key is stored as an environment variable to avoid hardcoding sensitive credentials and to support safer deployment in different environments.

Mrscraper Client

The MrscraperClient class acts as an abstraction layer between the application and the Mrscraper API.It encapsulates all scraping-related logic, including JavaScript rendering and structured data extraction.By centralizing this functionality, the code becomes more modular, reusable, and easier to maintain or test.

Any future changes to the scraping configuration or API endpoint can be handled in one place.

Price Normalization

| def extract_price(scrape_result: dict) -> float | None: try: raw_price = scrape_result["data"]["price"] normalized = ( raw_price .replace("Rp", "") .replace(".", "") .replace(",", "") .strip() ) return float(normalized) except Exception: return None | | :---- |

The extract_price function is responsible for converting raw price strings into numeric values.Product prices on websites often include currency symbols, thousands separators, or locale-specific formatting.Normalizing the price ensures accurate and consistent comparisons against the predefined MAP value.The function safely returns None when parsing fails, allowing the system to handle errors gracefully.

MAP Monitoring Logic

The MAPMonitor class encapsulates the core business logic of MAP Monitoring by comparing The MAPMonitor class contains the core business logic for MAP Monitoring.It coordinates the scraping process, price normalization, and comparison against the defined MAP threshold.Separating this logic from the scraping client improves code clarity and supports easier future extensions.

The output clearly indicates whether a product is compliant or in violation, along with a timestamp for auditing.

Main Execution

This section serves as the application’s entry point and demonstrates how all components are connected.It initializes the Mrscraper client and the MAPMonitor, then executes a monitoring check for a sample product.This structure can easily be extended to support multiple products or batch processing workflows.

It also provides a clear foundation for integrating scheduling or alerting systems.

Scheduling

A cron job is used to run the MAP Monitoring script automatically at regular intervals.Scheduled execution ensures continuous enforcement of MAP compliance without manual intervention.The monitoring frequency can be adjusted based on product volume and violation risk.This approach enables scalable and reliable price monitoring as the business grows.

Conclusion

Minimum Advertised Price (MAP) Monitoring is a critical practice for brands operating in highly competitive digital marketplaces. As online sellers continuously adjust prices, relying on manual checks is no longer practical or reliable. An automated approach ensures that advertised prices remain compliant with MAP policies, helping brands protect their perceived value, avoid destructive price wars, and maintain stable profit margins across distribution channels.

By leveraging Mrscraper as the scraping engine, MAP Monitoring becomes significantly more efficient and resilient. Mrscraper’s ability to handle JavaScript-rendered pages, dynamic pricing, and structured data extraction allows brands to monitor real-world advertised prices exactly as consumers see them. This removes the complexity of managing browser automation, proxies, and anti-bot mechanisms, enabling teams to focus on enforcement and strategic decision-making rather than infrastructure maintenance.

Combined with a Python-based monitoring workflow, this approach provides a scalable and extensible foundation for MAP compliance. Automated scheduling, price normalization, and validation logic make it possible to detect violations in near real time and respond quickly through alerts or reports. Overall, implementing MAP Monitoring with Mrscraper empowers brands to enforce pricing policies consistently, efficiently, and at scale in an increasingly dynamic digital market.