online reputation analysis

How to use AI in online reputation analysis

In recent years, there has been much talk about artificial intelligence, especially after the launch of ChatGPT. We intentionally avoided the subject because we preferred to test and provide clients with access to technology before discussing it. In fact, on our website, we focus more on the benefits that clients gain from paying attention to consumer reviews than the underlying technology. Personally, I believe it is more important to solve a real problem than to talk extensively about the technology used to solve it.

As discussed in previous articles, an important part of online reputation is represented by customer reviews. I will focus on this aspect in the following paragraphs.

Quantitative Analysis without Artificial Intelligence

Many years ago, when we started developing FeedCheck, we realized that it wasn’t enough to just look at product reviews from a statistical perspective and answer questions like: How many are there? What is their average? How many are one-star or five-star reviews?

Qualitative Analysis and Sentiment Measurement

Over time, we began exploring the option of breaking down a review into multiple parts, each addressing a specific product feature. Let’s take a shoe, for example. Its reviews might discuss design, material quality, comfort, durability, and other features. We would like to see what customers feel about each of these features. After capturing the sentiment for each feature, we want to compare our products and competitors’ products to see which excels in comfort and which have issues. We developed this functionality in FeedCheck and named it Key drivers.

Artificial Intelligence helps us separate and measure the sentiment for each key driver. This would have been impossible through quantitative analysis alone.

Large Language Models in Brand Reputation Management

After the emergence of ChatGPT, we started using artificial intelligence not only to measure the sentiment but also to further explore and ask questions about the reviews we collect. This way, we measure brand perception, not just what the brand communicates, but what consumers believe represents a product or service and how it makes them feel.

As an example of questions that you can ask:

  • What are customers saying specifically about our products/services?
  • What actions can we take based on the feedback in the reviews?
  • Are there any reviews that use offensive language or have a negative tone?
  • Can you identify specific issues or areas of improvement through these surveys?

Responding to Customer Reviews Using AI

Many support departments use FeedCheck to quickly respond to consumer feedback, especially when a review has the potential to affect the brand’s reputation. Here, we thought to make the work of support agents easier by generating automated responses to different reviews. This way, support agents can do their job faster and better.

This article clarifies how you can use artificial intelligence in reputation analysis. FeedCheck helps both in collecting the data that make up the online reputation and in analyzing them so that we can make the best decisions in reputation management.