Web scraping is a technique for automating data extraction from web pages. It involves virtual machines with Python scripts crawling web-page HTML to extract data.
The data from web scraping can serve many purposes. Essentially, digital professionals will want to use the information to answer a series of questions, such as how to increase sales, reduce costs, improve customer satisfaction, and reap other business benefits. In this article, we refer to web scraping specifically in the online retailer space, generating insights around performance on third-party affiliate sites, however the opportunities for leveraging web scraping are endless.
Why web scraping is important
It’s easy to see why web scraping is essential to brands in our data-driven, online world. It’s a reliable way to gain insights that help optimise decision-making across product development, product placement, pricing, promotions, development, and more.
Any doubts about web scraping’s value can be dispelled with a quick look at the market for web scraping software. Driven by the continued growth of e-commerce, the market for web scraping software is expected to grow from $1.1 billion in 2024 to $2.49 billion by 2032.
The different types of web scraping
In the online retailer space, there are numerous types of data that can be harvested. These can include everything from brand visibility and banner usage to search terms, pricing, and customer reviews. It’s a long list. The critical point is that brands use a combination of web scraping techniques and decide which ones to focus on based on the questions they want answered.
To make things easier, we’ve divided the techniques into several broad categories, with a few sub-categories for extracting data from specific web page features.
Product listing page scraping
Product listing pages (PLPs) list products under various categories on a website. They are a vital part of any e-commerce site, providing search engine visibility and a better online shopping experience for customers.
PLPs, also known as category pages, contain valuable information on product visibility. When scraped, they can reveal insights into your products’ popularity compared to competitors’ and the characteristics of the most popular products.
Filter scraping
This is more of an extension of standard PLP scrapes. Rather than scrape category pages, marketers scrape filters on a PLP web page. For instance, in the case of TVs, brands can scrape for filters such as screen size or price and see what share of visibility their products gain under these terms.
Banner scraping
Again, this builds on PLP scraping, delivering an additional key performance indicator (KPI). It allows marketers to track daily banner changes to see brand share on key pages. Brands also use it to check their banners are appearing on web pages per their campaign plan.
Product description page (PDP) scraping
This kind of scraping takes place on the page where a product is listed. It’s more taxing than PLP and takes longer because of all the insights available. While PLP scraping might be daily, PDP scraping could be weekly.
Brands can gather information like the number of product reviews, reviewer ratings, and product images or videos available. Other scrapable data includes product price, discounting, and stock information. They can also see the current product description.
Search scraping
Here, marketers are scraping data on different search terms. This shows you what products are visible using which search terms. In practice, a marketer could scrap 5-10 generic search terms on a product to obtain an average visibility score.
Typical use cases for web scraping
There are many use cases for web scraping, and these are our top 5:
- Consumer sentiment analysis – Essentially, web scraping allows you to filter out the noise and gain direct feedback and sentiment from your target audience. Using PDP scraping, you can analyse user-generated text from reviews to evaluate product performance.
- Lead generation – One way to overcome the challenges of generating leads is to use web scraping, a low-cost form of collecting relevant information on potential customers. It can provide information such as email addresses, job titles or company names.
- Content strategy monitoring – Data from web scraping can provide insights into competing brands’ content strategies and search engine optimisation tactics. It can help brands refine their approaches most effectively based on the latest market trends.
- Price comparison – Optimised pricing is key to success in any competitive market. With web scraping, brands can access up-to-date information on competitors’ pricing to improve the effectiveness of their pricing strategies.
- Supply chain and inventory monitoring – Web scraping can reveal e-commerce retailers’ stock for a brand’s product. Likewise, it can monitor the price of the raw materials, among other things, used in the manufacturing process. As such, brands can use the insights to identify a shortage of products in stores and potential supply chain issues.
What are the business benefits of web scraping?
It’s easy to see the business benefits of web scraping from the use cases. Again, a rapid online search would provide you with a long list, but to save time, we’re focusing on the main ones:
- Greater revenues – With granular detail on a product’s market performance and competitors’ performance, brands can identify gaps they can look to fill. Furthermore, with deep product insights updated daily, they can optimise product development, their go-to-market strategy, and the product’s lifetime performance to maximise sales.
- Cost savings – Web scraping is a software-driven, automated process that can be operated 24/7. Therefore, it offers a highly cost-effective way to obtain data that can help determine the success of a product or a business.
- New markets – Brands can practice web scraping across many websites and web pages in multiple regions. Theoretically, this can provide data on the viability of launching products in new markets or developing products to fill a noticeable gap. The same web scraping can increase revenues in existing markets and help maximise sales and revenues among first-time audiences.
How to ace web scraping in 10 simple steps
- Choose the business questions that you want to answer
- Complete a data audit to understand what information you already have
- Identify the gaps in your data and the possible online sources
- Ask yourself how granular you want the data to go
- Define what your KPIs are going to be
- Begin creating the Python scripts to scrap the data you need
- Create a process for extracting, managing and storing the data
- Ensure your processes are compliant with current data regulations
- Decide what data will go into your Power BI dashboard reports
- Determine who gets access to what dashboard reports
How to deal with the technical side of web scraping
Web scraping is simple, but it comes with specific technical requirements. There’s no getting around the fact you’ll need some Python expertise. And that your processes for extracting, managing and storing the data should be well-polished.
Often, companies prefer to focus their resources on other things rather than creating Python scripts, managing scraped data, and hosting data. While they may love the idea of dashboards populated with insights that are easy to grasp and share with colleagues, they question whether spending time and money teaching staff how to convert raw data into a Power BI-friendly format is the best option.
All of this is entirely reasonable.
That’s why, at Ipsos Jarmany, we’re helping an increasing number of clients get the maximum value out of web scraping. With our expertise and experience, we’re helping them formulate their strategies and execute their campaigns to deliver insights that are helping improve the bottom line and identify new business opportunities. It’s delivering outstanding results for them and can do the same for you.
Start a conversation on web scraping at its best by contacting us today.
