Web Scraping for Brand Reputation Monitoring
Nowadays, brand reputation is a valuable business asset. With ubiquitous digitalization, the value of brand mentions on the web has increased significantly. There is hardly a customer who would agree to buy a product or to pay for a service without first checking the reviews and looking through the official website of the company. Often, before making a purchasing decision, customers also scrutinize the company’s accounts on social networks.
Brand reputation monitoring is an essential direction of modern marketing campaigns — regardless of services the business provides: b2c or b2b. SEO experts include brand monitoring techniques in the optimization strategies since online reviews may profoundly influence the brand’s ranking on search engines.
Depending on the area in which the company operates, the platforms for the exchange of opinions about it may be different: in addition to the official website, these are special review platforms, online business directories, local forums and communities, blogs, and social networks.
Web scraping for brand reputation monitoring (also called review, or feedback monitoring) solves the problem of aggregating reviews from different platforms. And our team has received many orders of this kind lately. By tracking keywords related to your company you can collect reviews and mention from platforms of your choice, thus providing a full picture of your brand’s reputation on the web.
Companies that do not pay enough attention to brand monitoring risk getting behind their competitors. Experts suggest that online reviews largely influence purchasing decisions of buyers.
According to Sue Lloyd, vice chair of the International Accounting Standards Board, “investors were using lots of alternative sources of financial information, from satellite images to web-scraping.” Investment companies are already using feedback monitoring as a part of their due diligence tools.
When processed correctly, data collected by web scraping may be further used to gain significant insights into brand reputation. If the results of the collection are presented in the form of ratings from review sites, the simple formulas in Excel will be enough to determine the number of negative and positive evaluations. But to work with text reviews or publications on social networks, you might need a correspondingly complex solution, especially when it comes to a large amount of such information. Some marketers are already resorting to machine learning methods, e.g. to sentiment analysis. Sentiment analysis, also known as opinion mining or emotion AI, can give the text a general emotional assessment, as well as determine how features of the object of the evaluation are characterized in the text, and classify the text in two classes: objective or subjective. If you collect reviews to assess customers’ sentiment, and how they evaluate brand products, then sooner or later, you will encounter the need to conduct sentiment analysis.
Max Novak is Chief Marketing Manager at web scraping company Finddatalab.com.