What Customers Really Feel: How Sentiment Scoring Is Changing Retail Decisions

By Usman Niazi SEO

Feedback plays a crucial role in the success and goodwill of companies interacting with direct customers. When it comes to retailers, they have long relied on numbers such sales, returns, click-through rates and others to understand how their business is doing. But in a market where customer expectations are rising and competition is stiff, those numbers no longer tell the full story. This is one of the reasons why retailers now want to know how their customers feel, and they are turning to machine learning (ML) to find out.

A seasoned Data Specialist, Ravi Kiran Alluri, has focused on using ML to assist organisations read between the lines of customer feedback. He built a system using AWS Comprehend that automatically scans through thousands of customer comments like, product reviews, service interactions, surveys, and classifies them as ‘Positive’, ‘Negative’, ‘Neutral’, or ‘Mixed’. The system also pulls out key phrases to help companies understand exactly what topics are driving each emotion.

Alluri argued that this kind of insight is becoming more important. Feedback is coming in faster and in larger volumes than ever before. Businesses need ways to process it quickly, without losing depth or accuracy. This system does just that. He added, “The platform provides real-time analytics, empowering product managers and marketers with immediate insights into user behavior.”

What makes it more effective is that it goes beyond sentiment alone. The data engine behind it also tracks click behavior and other digital actions, combining those signals with feedback to form a more complete view of the customer. The platform integrates smoothly into tools already used across teams, and it supports self-serve reporting, so decision-makers can access the insights they need without waiting on analysts.

Discussing his recent work, he mentioned, “I successfully employed a Naive Bayes algorithm to accurately test customer sentiment, achieving results remarkably close to those provided by large language models (LLMs).” He further noted, “This foundational analysis allowed me to effectively track customer sentiment across different user segments.” The system was expanded to include dashboards for tracking customer pain points and understanding where people dropped off in their journey. These additions helped teams act faster and more precisely, especially in areas like customer support, product design, and retention.

Additionally, he explored customer sentiment using the Amazon Fine Food Reviews dataset. This hands-on project laid the foundation for his later work in large-scale sentiment analysis, giving him practical insights into how algorithms interpret real-world feedback.

As the professional pointed out, there were some technical challenges faced along the way. After all, processing real-time data across multiple languages and global regions isn’t easy. The system had to be fast, scalable, and secure. He shared that the usage of AWS Comprehend made it possible to support multiple languages while keeping the data safe and compliant. Role-based permissions were put in place to control who had access to what information.

As is evident in the experience of Alluri, sentiment scoring is increasingly being used across retail, not just as an analytics tool, but as part of everyday decision-making. Teams can now see how customers are reacting to new features, updates, or campaigns as they happen. And they can respond more quickly when something doesn’t land as expected.

In conclusion, understanding how customers feel is just as important as knowing what they bought. Sentiment scoring gives firms that extra layer of insight and helps turn feedback into action. As more businesses adopt this approach, the goal isn’t just better data. It’s better decisions, shaped by a clearer view of what really matters to the people they serve.