In recent years, there has been much talk about artificial intelligence, especially after the launch of ChatGPT.
We intentionally waited to approach the subject because we preferred to test and provide you with access to technology before discussing it. In fact, on our website, we focus more on the benefits that you gain from paying attention to consumer reviews than the underlying technology. And that’s because we 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 customer reviews. We will focus on this topic in the following article.
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 1-star or 5-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 to the competitors’ and 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 in a way where we don’t look at just what the brand communicates, but what consumers believe represents a product or service and how it makes them feel.
A few examples of questions you can ask are:
- 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 their brand reputation.
To make the work of support agents easier, FeedCheck generates automated responses to customer reviews. This way, support agents can do their job faster and better.
This article clarifies how you can use artificial intelligence in online reputation analysis. FeedCheck helps both in collecting the data that make up your brand reputation on digital platforms and in analyzing them so that we can make the best decisions when it comes to reputation management.