How Kimola Uses AI for Research

Kimola uses artificial intelligence to help you make sense of large volumes of customer feedback, instantly and at scale. At the core of the platform is a powerful unsupervised clustering technology built specifically for customer feedback, enabling both qualitative and quantitative analysis with minimal effort.

Dynamic Classification

Traditional machine learning approaches often fall short when it comes to classifying customer feedback. For example, a basic text classification model might assign a review of a water faucet to general categories like Quality, Pricing, Delivery, or Customer Support. But compared to human research, these categories lack nuance. A skilled researcher would notice far more specific concerns—like installation difficulty, missing parts, design flaws, or water tightness.

Worse yet, applying predefined labels across different industries often leads to poor accuracy and irrelevant insights. That’s one of the biggest challenges in applying AI to consumer research.

To overcome this, Kimola developed a pre- and post-processing method that enhances clustering algorithms. This enables better control over both the number and size of clusters, even in multilingual datasets—ensuring that insights remain meaningful and usable regardless of the domain.

Multi-Label Classification

Customer feedback is rarely about one thing—and Kimola reflects that. Our multi-label classification system breaks down each piece of feedback into multiple themes and assigns sentiment to each one. This means you’ll see both what excites your customers and what’s holding them back—all in one view.

For instance, a review that praises the user interface but complains about response time will be labeled with both themes, each carrying its own sentiment. This level of detail helps you spot dual-natured feedback, even in shorter comments that are easy to overlook.

Scoring: Surface the Unexpected

Most statistical approaches rank themes by sheer volume, showing the most common issues first. While this can be useful, it often buries the more interesting or unexpected insights.

Kimola introduces a scoring mechanism that takes multiple factors into account, such as urgency, opportunity, volume, and feasibility. This helps surface insights that are often surprising and highly valuable, especially when you’re analyzing customer feedback to uncover pain points, usage motivations, or feature requests.