In recent years, artificial intelligence (AI) has been a hot topic, especially after the launch of ChatGPT.
At FeedCheck, we intentionally waited before diving into AI discussions because we preferred to test and provide you with access to technology, prioritizing solving real-world problems with technology, instead of simply talking extensively about it. Instead of focusing on the tech, we’ve been committed to delivering actionable insights from customer reviews – the cornerstone of online reputation management.
Now, let’s explore how AI enhances this process.
Quantitative Analysis without Artificial Intelligence
Many years ago, when we began developing FeedCheck, we realized that traditional statistical analysis – answering questions like How many reviews are there? or What’s the average star rating? – was insufficient for understanding the full picture of customer sentiment. Quantitative metrics alone couldn’t capture the complexities behind customer feedback.
Qualitative Analysis and Sentiment Measurement
Over time, we shifted towards qualitative analysis, breaking down reviews into key components and addressing specific product features.
Let’s take a shoe, for example. Its reviews might discuss design, material quality, comfort, durability, and other features. To truly understand what customers are saying, we needed a way to analyze each feature individually. That’s why we developed Key Drivers, a feature in FeedCheck that identifies and measures the sentiment associated with each product feature.
The Key Drivers feature can be used to measure sentiment for all your brand’s products, as well as competitor brands, getting a graph just like in the picture below:
AI has made this process even more precise. We can now separate and assess the sentiment behind each key driver, which would have been nearly impossible through quantitative analysis alone.
Large Language Models in Brand Reputation Management
After the emergence of ChatGPT, new possibilities opened up in AI. At FeedCheck, we started using AI not only to measure sentiment, but also to explore further and ask deeper questions about the reviews we collect.
This allows us to gain more nuanced insights into how the customers perceive your brand – not just through your brand’s messaging, but through what consumers believe and feel about your products and services.
Here are a few questions AI can help answer:
- 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?
But you can dig deeper and look for more specific and direct information, going beyond basic metrics and uncovering hidden patterns and sentiments, like the picture shows:
Responding to Customer Reviews Using AI
Many support departments use FeedCheck to quickly and effectively respond to consumer feedback, especially when a review has the potential to affect their brand reputation. With AI, we’ve made this process even more streamlined.
FeedCheck uses AI to generate automated responses to customer reviews, helping support agents save time while maintaining a high level of personalization and effectiveness. This ensures that negative feedback is addressed promptly, mitigating any potential damage of your brand’s reputation.
The Power of AI in Reputation Management
This article highlights how AI can be a game-changer in the analysis and monitoring of online reputation. With FeedCheck, we help businesses collect and analyze customer reviews across multiple platforms, using AI to provide deeper insights and actionable recommendations.
If you’re ready to unlock the power of AI to manage your online reputation more effectively, contact FeedCheck today for a demo and see how we can help your business grow.