The internet has given consumers the tools needed to get the best prices – faster than at any other time in modern human history. As a result, online businesses are responding with strategies that enable them to set competitive prices to gain maximum market share.
There are numerous ways to obtain pricing data, including manual collection and purchased data sets. While most methods provide the information required, price monitoring architecture that leverages web scraping tops the list by providing the automation needed to collect massive data volumes at scale – in seconds.
Price monitoring helps enterprises stay agile, giving them the flexibility needed to pivot their strategy and quickly adapt to changing market conditions. In addition, pricing intelligence via web scraping catalyzes dynamic pricing strategies, fuels competition analysis, and enables companies to monitor Minimum Advertised Price (MAP) policies.
How web scraping works
Web scraping obtains information via specially-programmed scripts (also known as “bots”) that crawl ecommerce shops, marketplaces, and other online public spaces to extract data. Besides pricing intelligence, web scraping has numerous alternative uses that include cybersecurity testing, illegal content detection, extracting information for databases, obtaining alternative financial data, and many more.
4 ways online businesses leverage price intelligence
Pricing intelligence has been fundamental to businesses since humans began buying and selling products and services. However, unlike traditional marketplaces, web scraping amplifies the process exponentially by enabling enterprises to extract thousands of data points in seconds. Some applications of scraped data for product and pricing intelligence include:
1. Digital Marketing
Digital marketing comprises a set of practices designed to target your ideal customers and guide them through the buying process. Successful strategies depend significantly on the ability to collect timely, accurate data to enhance marketing practices.
Some digital marketing applications of data include:
- Profit-maximizing pricing strategies.
- Customer avatar creation.
- SEO-optimized content marketing.
- Email marketing.
- Sales funnel optimization.
Public sources of product, service, sales, and marketing data include online stores, marketplaces, search engines, social media platforms, and forums.
Some types of data available to online enterprises from these sources include:
- Product titles.
- Current and previous prices.
- Product descriptions.
- Image URLs .
- Product IDs from URLs.
- Currency information.
- Consumer sentiment.
- Brand mentions.
Digital marketing strategies vary significantly from sector to sector, however, success greatly depends on the quality of data extracted and insights obtained. Web scraping provides a targeted method for acquiring that information, customized for your business.
2. Competition Analysis
Competition analysis is fundamental to online sales success. Scraped data from public websites gives businesses the vital information required to pivot their marketing strategy to outperform the competition and gain a greater market share.
Web scraping can be used to obtain competitor information that includes:
- Historical pricing data
- Detailed product and service information
- Complete product catalogs
- Inventory/stock information
- Shipping policies
- Anonymized reviews from competitor websites and marketplaces
Competition analysis is essential to any ecommerce strategy. Web scraping provides the data required to refine your product catalog, pricing, branding strategy, and email marketing to beat competitors and adapt to ever-changing market conditions.
3. Dynamic pricing strategies
Dynamic pricing refers to the strategy of shifting prices according to product or service demand. Most consumers are familiar with the practice from transacting with travel websites to book flights and hotel rooms.
Price monitoring via web scraping has amplified the practice via process automation. As a result, enterprises across additional sectors can leverage dynamic pricing to quickly adjust prices based on real-time supply and demand data.
4. Minimum Advertised Price Monitoring
Minimum Advertised Price (MAP), MSRP (Manufacturer’s Suggested Retail Price), or RRP (Recommended Retail Price) refer to the lowest price allowed for a retailer to advertise a product.
MAP policies are implemented to protect a brand by preventing retailers from excessively lowering the price and reducing consumer confidence in a product. Price monitoring architecture is used to crawl the internet to collect pricing data and identify online businesses that may be violating MAP policies.
Web scraping challenges while collecting pricing intelligence
Web scraping is a complex process that requires expertise to select the most relevant target websites, effectively program scripts, and choose the most appropriate proxies to distribute requests and prevent server issues.
As mentioned previously, extracting large volumes of data via web scraping requires automation to collect data at scale. The process requires consistent monitoring because web scraping algorithms must be adjusted to account for numerous challenges that include:
- Failure to differentiate same or similar products – even if product titles and images don’t match.
- Constantly changing website layouts and HTML structure.
- Server issues such as blocking and captchas
How web scraping works within price monitoring architecture
Price monitoring is based on an entire architecture that includes price tracking, monitoring, and analysis. The process requires four main steps that include:
Step 1: Collecting target URLs
The first step is to analyze competitors and identify target URLs. Following URL selection, a database containing the URLs is created either by manual collection or automated web crawling.
Step 2: Web scraping
Configuring the web scraper is the next part of the process, requiring three steps that include:
Selecting and configuring proxies – intermediaries between the scraper and server to provide anonymity and prevent blocks.
Creating a browser “fingerprint” – configuring identification data that relays information to the server, allowing a scraper to submit requests and extract data successfully. Sending HTTP requests – the actual data requests sent to the server to scrape the desired information.
Step 3: Data parsing
Data parsing transforms extracted raw HTML data into a readable format that can be analyzed for insights. Learn more about the process by listening to episode 3 of the OxyCast – Data Parsing: The Basic, the Easy, and the Difficult.
Step 4: Data cleaning and normalization
Data cleaning and normalization is an optional step that refines the scraped data by removing inaccurate or corrupt records, converting currencies, and translating foreign language text.
Get an inside look at price monitoring architecture
This article is a valuable introduction for anyone interested in price monitoring architecture. To get a detailed explanation of how it works, download our free white paper Real-Time Price Monitoring System Architecture.
Here’s what you’ll learn:
- Detailed pricing architecture concepts.
- More technical steps and sub-steps to configure and operate price monitoring architecture.
- Different proxy types and how to choose them.
- Different proxy types and how to choose them.
- Overcoming price monitoring challenges.
- Next steps to get started.
Price is the critical factor that can make or break your online business. Download Real-Time Price Monitoring System Architecture to discover how to unlock the power of data for creating pricing strategies that outperform the competition.