Before deciding to buy an item, do you shop around? Understandably, shoppers often want to get the best deal, and for consumers, price is one of the most important factors in deciding to buy. Not only consumers, but also companies in today's increasingly competitive e-commerce industry need to be kept abreast of price changes in the market. Price monitoring is an important business practice that can help companies understand competitors' pricing strategies, market trends, and consumer demand , unlocking new revenue . This article will explore why price monitoring is necessary, the importance of proxy residential in price monitoring, and how to build an e-commerce price monitoring system and analyze it.

What is Price Monitoring and Why do We Need It ?

Price monitoring is the ongoing tracking and analysis of price changes for a specific product or service in the marketplace , usually through the use of software tools or services . Price monitoring is a powerful tool for both consumers and businesses:

1.For consumers:

• Find the best time to buy: By monitoring changes in product prices, consumers can find the best time to buy and save money.
• Price History: Price monitoring tools can provide price history of products, helping consumers understand the price trend of a product and avoid making wrong purchasing decisions due to short-term price fluctuations.
• Promotion alerts: Some price monitoring tools can send alerts when product prices drop or there are promotions, helping consumers seize discounts in time.

2.For businesses:

• Enhance market insight : Real-time grasp of price fluctuations of similar products on the market, comprehensive analysis of price changes and market demand, and providing data support enable companies to better understand market demand and consumer behavior.
• Changing pricing strategies : Using the collected pricing data to identify pricing trends and patterns, and then predicting market demand, we can change product pricing and promotion strategies , such as setting dynamic pricing, offering discounts at strategic times, or adjusting prices based on demand fluctuations.
• Optimize inventory management : Control inventory levels by adjusting prices to avoid excessive overstocking or stockouts.

Taking sneaker sales as an example , you can use price monitoring tools to track sneaker prices at competitor retailers and record how the price of each model rises and falls over time. Based on the collected data , you can increase the price of certain models while lowering the price of other models to attract more customers and increase sales. This approach ensures that the fees charged are enough to make a profit from each item sold, but you will not be excluded from the market because of excessively high prices .

Challenges of Price Monitoring

Competitive price monitoring can help you gain more competitive intelligence to adjust your pricing strategy, but there are also some challenges in actual operation:

1.Bot blocking and CAPTCHA: In order to prevent access by automated scripts, many websites have set up Bot blocking mechanisms and CAPTCHA verification codes, which makes data crawling difficult.

2.Geo-blocking, product and price localization: Some e-commerce websites display different products and prices based on the user’s region, which increases the complexity of data collection.

3.Rendering JavaScript-heavy websites: Many modern websites use a lot of JavaScript to dynamically load content, making it more difficult to crawl and parse.

4.Website updates : E-commerce websites frequently update their layout and structure, which requires constant maintenance and updating of parsers. This is especially a huge workload when monitoring multiple websites, making it difficult to ensure the accuracy of data collection .

Why Use a Residential Proxy for Price Monitoring ?

Using proxy residential for price monitoring can effectively address the above challenges and improve the success rate and accuracy of data capture :

1.Bypass anti-crawler mechanism: The IP address of the residential proxy comes from a real user, simulating real traffic, and can bypass the Bot blocking mechanism and CAPTCHA verification of many websites , ensuring the smooth progress of data crawling tasks.

2.Multi-region data collection: Residential agents provide IP addresses in multiple places around the world. Through residential agents, data can be collected from multiple regions around the world to obtain more comprehensive price information and market insights.

3.Improve the success rate of data capture: Due to its high degree of concealment and diversity, proxy residential usually provide more stable and high-quality connections , which can effectively improve the success rate of data capture and reduce the risk of being banned.

4.Improve crawling speed and efficiency: By setting up multiple parallel crawling tasks and agent rotation, residential agents can significantly improve the speed and efficiency of data crawling.

How to Build an E-commerce Price Monitoring System?

Building an efficient e-commerce price monitoring system involves the following steps:

Step 1: Data collection

Select the target website and determine the e-commerce platform and products that need to be monitored. Data collection is the foundation of the entire system. First, determine the target URL or keyword that needs to be monitored, and through preliminary analysis, evaluate the website's anti-crawler system, data acquisition method, and optimal proxy settings.

Step 2: Web crawling

• Setting up the proxy

Choose a data center or residential proxy based on the task requirements. Data center proxies are fast and suitable for large data volume crawling; proxy residential can avoid blocking and detection. Here I choose to use 911proxy , which has 90 million proxy residential covering more than 195 countries and regions around the world, helping me obtain accurate and timely market information.

import requests

# Setting up the proxy
proxies = {
'http': 'http://your_datacenter_proxy_ip:port',
'https': 'http://your_residential_proxy_ip:port'
}

url = 'https://www.example.com/product-page'

# Sending requests using a proxy
response = requests.get(url, proxies=proxies)

# Check the response status
if response.status_code == 200:
print("Request successful")
else:
print("Request failed, status code:", response.status_code)

• Create Fingerprint

Mimics real browser requests and creates organic HTTP headers.

headers = {
'User-Agent': 'Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/91.0.4472.124 Safari/537.36',
'Accept-Language': 'en-US,en;q=0.9',
}

# Send a request with HTTP headers
response = requests.get(url, headers=headers, proxies=proxies)

if response.status_code == 200:
print("Request successful")
else:
print("Request failed, status code:", response.status_code)

• Sending HTTP Request Use the requests library or a headless browser to send requests to the e-commerce website.

from selenium import webdriver
from selenium.webdriver.common.by import By
from selenium.webdriver.chrome.service import Service as ChromeService
from webdriver_manager.chrome import ChromeDriverManager

# Setting up a headless browser
options = webdriver.ChromeOptions()
options.add_argument('--headless')
options.add_argument('--disable-gpu')
driver = webdriver.Chrome(service=ChromeService(ChromeDriverManager().install()), options=options)

# Visit the website
driver.get(url)

# Get page content
page_content = driver.page_source

# Close the browser
driver.quit()

print(page_content)

Step 3 : Analyze the data

Structuring HTML into an easily readable format like JSON or CSV, parsing specific data elements like price, inventory, reviews, etc.

from bs4 import BeautifulSoup
import json

# Parsing HTML with BeautifulSoup
soup = BeautifulSoup(response.content, 'html.parser')

# Extract data
product_name = soup.find('h1', class_='product-title').text
price = soup.find('span', class_='price').text
stock_status = soup.find('div', class_='stock-status').text

# Structured Data
data = {
'product_name': product_name,
'price': price,
'stock_status': stock_status
}

# Convert to JSON format
data_json = json.dumps(data, ensure_ascii=False)
print(data_json)

Step 4 : Data cleaning and standardization

Clean and standardize data to ensure its accuracy and consistency in preparation for further analysis.

import pandas as pd

# Create data frame
df = pd.DataFrame([data])

# Data cleaning and standardization
df['price'] = df['price'].replace('[\$,]', '', regex=True).astype(float)
df['stock_status'] = df['stock_status'].str.strip()

print(df)

Step 5 : Data Analysis
Analyze the organized data to generate valuable business insights and help make strategic decisions.

import matplotlib.pyplot as plt

# Example Data Analysis: Price Trend Chart
df['date'] = pd.to_datetime('today')
df.set_index('date', inplace=True)

# Draw a price trend chart
df['price'].plot(kind='line')
plt.title('Price Trend Chart')
plt.xlabel('Date')
plt.ylabel('price')
plt.show()

Summarize

Price monitoring plays an important role in modern business, helping businesses and consumers understand market conditions, develop strategies, and make smarter purchasing decisions. At the same time, by making full use of the functions of residential agents and configuring an efficient price monitoring system, we can monitor prices more effectively , obtain accurate and timely market information, and thus improve competitiveness and obtain better business opportunities. I hope that through the above steps and techniques, you can also build an efficient e-commerce price monitoring system, obtain more competitive intelligence, and enhance business competitiveness.