How web data can help fuel your dynamic pricing strategy
Dynamic pricing is a great tool for businesses, especially those in the e-commerce field. A lot of major companies already use web extracted pricing data to formulate pricing strategies, adapt to price variations, spot MAP violations & analyze customer opinions. Adding dynamic pricing to that can add an array of benefits like following the competition, adjusting prices instantly, and easily capturing quantitative metrics about your products to boost revenue.
Using dynamic pricing makes perfect sense for the bottom line of your business.
If you’re looking to learn more about dynamic pricing and how to make the most of it, I am putting together this basic guide about dynamic pricing and how to unleash its maximum potential.
First things first...
What is dynamic pricing?
Dynamic pricing is a strategy that involves selling the same product at different prices to different groups of people and/or at different times. It is based on variable prices rather than fixed prices.
Dynamic pricing takes competitive intelligence to the next level by combining competitor pricing data with internal data to make automated pricing decisions. This allows companies to be proactive and regularly adjust their pricing in response to real-time demand, supply, and competitor benchmarks.
In simple terms, companies adjust their prices multiple times a day based on factors like changing market trends, competitor prices, and demand. This strategy gives companies the dual advantage of increasing sales and optimizing margins.
Now that you know what dynamic pricing is, here’s how to build it.
What do you need to build a dynamic pricing strategy to stay ahead of competitors?
To thrive in a fast-paced market, you’ll need to take a data-driven and agile approach to develop your pricing strategies allowing you to react quickly and stay ahead of the game.
However, there’s one thing you’ll need if you want to stay ahead of the curve - Data; in real-time, and at scale.
In an ever-changing market, you’re not going to manually monitor hundreds of competitors every few minutes. It would be too time-consuming, expensive, and completely unrealistic.
The solution is web-extracted product pricing data.
That way, all you have to do is identify your competition and set up web scrapers that collect pricing data every few minutes so that you can modify your strategy accordingly.
If you’re wondering how to build a web scraping project that allows you to extract real-time data from millions of price points regularly, here’s your answer!
How to extract pricing data from the web?
If you want to do it yourself, fortunately, multiple open-source and commercial tools and libraries are available to help you easily get the web data you need. Here are some of my favorites:
- Scrapy - An open-source and collaborative framework for building your scrapers and extracting the product and pricing data you need.
- Scrapy Cloud - Makes it easy to schedule and monitor regular crawls (there’s a forever free account option).
- Price-parser - An open-source library for price scraping used for extracting price and currency from raw text strings.
- Dateparser - An open-source library useful to parse the dates when the products you are tracking became available.
- Html-text - Another free tool that can be useful to parse rich-text product descriptions as plain text, especially useful for product matching
- Zyte API for product pricing data - The most complete and advanced AI-powered web scraping, anti-ban, and headless browser solution on the market. Useful to overcome antibot measures, especially in instances where websites intentionally show erroneous pricing to mislead price intelligence efforts. There's a free trial.
If you feel like you need a helping hand in your data extraction project or you’d prefer to leave the data extraction to experts and focus solely on making strategic pricing decisions, just give us a shout.
Here at Zyte, we specialize in delivering custom pricing data explicitly designed to make your revenue operations simple and efficient by providing product and pricing datasets from retailer sites and marketplaces of your choice. Quickly and reliably.