What Is Data Scraping In Customer Feedback Analysis?

In today's digital age, businesses are sitting on a goldmine of data, but accessing and utilizing this data can be daunting without the right tools. This is where data scraping comes in, offering a way to extract valuable information from various online sources in an automated manner. From price comparison and market research to lead generation and content aggregation, the applications of data scraping are endless.

In this article, we will explore the world of data scraping, uncovering its real-world applications, common challenges, and how to overcome them, as well as its implications for data privacy and future trends. We'll also emphasize the scraping of online customer reviews. Read on for more!

Data Scraping: A New Way to Gather Information

Data scraping, a cutting-edge technique, automates the process of extracting valuable information from websites, databases, and other online sources. Scraping refers to the automated (or sometimes semi-automated) process of collecting, often large amounts of data from web pages. This powerful method has revolutionized the way businesses gather and analyze data, opening up a world of possibilities for market research, competitor analysis, lead generation, and more.

With advancements in technology, data scraping has become more accessible than ever, empowering businesses of all sizes to leverage the vast ocean of online data. Gone are the days of manual data collection, replaced by sophisticated software, such as Wonderflow's advanced analytics, that seamlessly gathers information from online sources. This data can include product prices, contact details, news articles, consumer reviews, and a plethora of other valuable insights. By harnessing the power of data scraping, businesses can gain a deeper understanding of their target audience, identify market trends, and stay ahead of the competition.

However, it's crucial to approach data scraping with legal and ethical considerations in mind. The source and nature of the data being scraped must be carefully evaluated to ensure compliance with relevant regulations and respect for privacy rights. Responsible data scraping practices are essential to maintaining trust and fostering a positive online environment.

Real-world Applications of Data Scraping

One common use is price comparison. By scraping data from multiple websites, businesses can compare prices and identify the best deals for their customers. This can be a valuable tool for consumers, especially when shopping for big-ticket items or comparing prices across multiple retailers. Another application of data scraping is market research. Businesses can use data scraping to collect information about their target market, such as demographics, interests, and buying habits. This information can then be used to develop targeted marketing campaigns and improve product offerings.

Data scraping can also be used for lead generation. By scraping data from websites and social media, businesses can identify potential customers and generate leads for their sales teams. This can be a cost-effective way to reach new customers and grow your business. Content aggregation is another common use of data scraping. Websites and apps can use data scraping to collect content from multiple sources and present it in a single, easy-to-use format. This can be a valuable tool for users who want to stay up-to-date on the latest news and information.

Finally, data scraping can be used for sentiment analysis. By scraping data from social media and other online sources, businesses can track public sentiment towards their brand or products. This information can be used to improve customer service, develop new products, and make informed business decisions.

Scraping Customer Reviews

At Wonderflow, customer ratings and reviews are our bread and butter. For a scraping process to be fulfilled, the right webpage sections must be indicated and selected. Next, the scraper should download the selected data; however, the scraping speed/frequency and accuracy depend on the channel you are scraping. When the scraping starts, it usually reaches the end.

However, there can be obstacles encountered that interrupt this process. This is mainly caused by external variables that cannot be overcome. These issues can influence the accuracy of the data. It is, therefore, essential to set a threshold for what is acceptable in terms of accuracy in the scraping process.

Collecting reviews across multiple channels is an extensive task. First, you would have to find the channels you want to collect reviews from, scroll through every review page on the product site, and organize this data properly for insight extraction. Would it not be better to have an application that collects all this data? The three main steps of scraping reviews are:

First, we must start by choosing the product page we want to collect data from.
Once we have the desired e-commerce platform we want to gather data from, we must indicate to the scraper which parts of the webpage we would like to gather data from. (E.g., ratings, reviews and images). This ensures that we collect only relevant data. This is done by using a so-called ad hoc parser build. (Note: Although the ad hoc parser builds work in an automated way, its setup is not automated. This build varies from web page to web page, given that the location of the data we want to scrape (such as reviews) varies greatly. In addition, if a web page updates and changes its structure (moving the placement of reviews, for example), the ad hoc parser build must be re-established to indicate the sections for the scraping process correctly.)
Once the correct sections of the webpage have been identified, the scraper can start going through these sections and collecting the data found.

It sounds relatively simple, right? This is, however, not the case when we try to gather data from hundreds of different e-commerce channels, as they vary in structure. Two main things to consider in a business' ratings and reviews scraping process are source type and accuracy.
Source or channel type

It turns out that not all web pages, sources, or channels are the same, and it can be more or less complicated to download customer reviews from one channel to another. For instance, there are channels where scraping is quick and accurate, and then there are channels that present restrictions. We can identify three types of channels:

The Good: The scraper downloads reviews without any human intervention.
The Bad: On these restricted channels, the scraper downloads the reviews but must be assisted by a human (E.g., solving complex captcha)
The Ugly: On these restricted channels, the scraper downloads the reviews, but it's heavily assisted by a human (E.g., A human must open the review pages one by one). Thus, some channels require a lot of manual effort to be scraped, which impacts how often we can refresh the data from that channel (and how much it costs).

Data accuracy
In the process of extracting reviews, we also need to take into account the efficacy of the scraper. For example, if you have a review page with 100 reviews, how many will the scraper be able to download? The scraping accuracy varies from channel to channel and is independent of the type of channel, meaning that we may have a fully automatic channel that can show a very low accuracy level or a fully manual channel that instead has super high accuracy.
Why is it important to understand accuracy in terms of data scraping? The accuracy can impact your expectations. Therefore, knowing some of the possible causes linked to accuracy issues is good. Here are some possible causes of discrepancies your business may run into when scraping reviews:

Channel does not display all the reviews: Some channels print a number of reviews but do not allow you to see more than a capped number of feedback (e.g. on Amazon, you cannot go over Page 500). In other words, you cannot scrap those hidden reviews beyond the 500th page of Amazon.
The number of available reviews is false: Some channels are simply printing a made-up number of reviews on a product page, such as this example.
"Page Not Found": The webpage no longer exists.

In the next section, let's review more common challenges in overall data scraping, including how to overcome them.Common Data Scraping Challenges & How to Overcome ThemData scraping is a powerful method, but it can also be challenging. Some common challenges that data scrapers face include:

Dealing with dynamic content: Many websites use dynamic content, which means that the content of the page changes frequently. This can make it difficult to scrape data from these websites, as the scraper may not be able to keep up with the changes.
Handling CAPTCHAs: Many websites use CAPTCHAs to prevent bots from scraping their data. CAPTCHAs are tests that require users to identify objects or solve puzzles in order to prove that they are human.
Dealing with rate limits: Some websites impose rate limits on how often a scraper can access their data. This can make it difficult to scrape data from these websites, as the scraper may be unable to collect all of the data that it needs.
Handling authentication: Some websites require users to authenticate themselves before they can access their data. This can make it difficult to scrape data from these websites, as the scraper may not be able to provide the necessary credentials.
Avoiding detection: Many websites have measures in place to detect and block scrapers. This can make it difficult to scrape data from these websites, as the scraper may be blocked from accessing the data.

There are a number of ways to overcome these challenges. Some common solutions include:

Using a headless browser: A headless browser is a web browser that runs without a graphical user interface (GUI). This can be used to bypass CAPTCHAs and rate limits, as the browser does not need to display the CAPTCHA or wait for the rate limit to reset.
Using a proxy server: A proxy server is a server that acts as an intermediary between the scraper and the website. This can be used to avoid detection, as the scraper's IP address will be hidden from the website.
Using a data scraping service: A data scraping service is a company that provides data scraping services to businesses. These services can be used to overcome the challenges of data scraping, as they have the expertise and resources to handle these challenges.

By overcoming these challenges, businesses can use data scraping to extract valuable data from websites, databases, and other online sources. This data can be used for a variety of purposes, such as price comparison, market research, lead generation, content aggregation, and sentiment analysis.Future Trends in Data ScrapingThe future of data scraping holds numerous advancements that will reshape the way data is collected and utilized. One significant trend is the integration of artificial intelligence (AI) and machine learning (ML) into data scraping tools like Wonderflow's technology. AI-powered scrapers can analyze vast amounts of data, identify patterns, and extract insights with unprecedented accuracy and speed. This enables businesses to gain deeper insights into customer behavior, market trends, and competitive landscapes.
Another emerging trend in data scraping is the growing regulation of the practice. Governments worldwide are enacting stricter laws to protect consumer privacy and data security. This regulatory landscape requires data scrapers to adhere to ethical and legal standards, such as obtaining consent from individuals before collecting their data and ensuring data security measures are in place. Organizations must stay informed about these regulations to avoid legal consequences and maintain their reputation.

Blockchain technology is also making its way into the realm of data scraping. Blockchain's decentralized and immutable nature can enhance data security and transparency, ensuring that scraped data remains accurate and trustworthy. This technology holds promise for industries that handle sensitive information, such as healthcare or financial services.

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Furthermore, the accessibility of data scraping tools and techniques is democratizing data collection. With the availability of user-friendly software and online tutorials, individuals and small businesses can now perform data scraping tasks without extensive technical expertise. This trend empowers more organizations to leverage data-driven insights for decision-making and competitive advantage.

Final words

Data scraping has emerged as a transformative tool in the digital landscape, offering endless possibilities for businesses to extract valuable insights from online sources efficiently. From price comparison and market research to lead generation and sentiment analysis, the applications of data scraping are diverse and impactful. However, it is crucial for businesses to approach data scraping with ethical considerations, ensuring compliance with regulations and respect for privacy rights.

By understanding the challenges associated with data scraping and implementing appropriate solutions, businesses can harness the power of data scraping to stay ahead of the competition, make informed decisions, and drive success in an increasingly data-driven world.