Understanding Search Engine Filtering of Customer Reviews

Understanding Search Engine Filtering of Customer Reviews

In the digital era, you see people dropping reviews online as customers. We can even see companies seeking a platform to provide them with online reviews to boost their business. You might have heard or read about online review filters and how it might have caused frustrations or confusion. Understanding search engine filtering of customer reviews can help you minimize this vagueness.

In the digital era, you see people dropping reviews online as customers. We can even see companies seeking a platform to provide them with online reviews to boost their business. You might have heard or read about online review filters and how it might have caused frustrations or confusion. Understanding search engine filtering of customer reviews can help you minimize this vagueness.

Why do we need to filter reviews?

We can see an obvious sky-rocket in searching among people in the past decade. People depend on their search results and the reviews they read about a particular thing.

Reviews can be an excellent scale to measure the popularity of a business. It also gives real-life experience to searchers for a specific kind of business or service or etc.

If reviews are so cool and helpful, why do we need an algorithm to filter them?

Since the development of technology, you see that robots also can drop reviews. Some companies even hire people who even might not be customers to give them good reviews. We can also see companies dropping reviews for themselves. So, there you have the reasons for the filtering of customer reviews. For instance, on social media platforms, Instagram uses the filtering algorithm for cleaning the purchased followers.

How does the filtering work?

The specific details of these kinds of algorithms have not been revealed, but the general sense of the mechanism is out there, which is the backbone of most filtering algorithms. Filtering gets triggered when one or more of the following happens:

• Sudden increase in the number of reviews in a short period of time

• Abnormal usage of keywords

• Overuse of complimenting adjectives or swearwords

• Link usage in the reviews

Some of the more advanced and cutting-edge filters do not only stop at the aforementioned points. They also check the characteristics of users, such as:

• The IP addresses

• Number of reviews written by a single user on a website

• How often a user leaves reviews on a particular site

Considering the points above, one could understand how probable it is for the fake reviewers to get stuck in the filter, all thanks to the smart algorithm.

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