Customer feedback holds immense strategic value. Yet, without a focus on quality, the feedback data introduces noise, misguides decision-making, and undermines innovation. This whitepaper explores the hidden costs of low-quality feedback and presents a solution using Fusion Analytics, an AI-powered analytics framework that leverages Large Language Models (LLMs) to isolate high-value insights.
Our case study, based on 33,000+ McDonald’s reviews, demonstrates how organizations can unlock powerful insights by combining topic filtering, sentiment analysis, and quality scoring at scale.
The Problem with Low-Quality Feedback
Organizations often collect large volumes of customer reviews, surveys, and comments—but raw volume alone doesn’t generate value. Poor-quality feedback creates risk:
- Misguided Decisions: Noisy feedback leads to product or operational changes based on inaccurate assumptions.
- Missed Opportunities: Valuable customer insights are drowned out by irrelevant or vague commentary.
- Customer Trust Erosion: Acting on faulty feedback can worsen the customer experience.
- Increased Costs: Fixing issues caused by misinformed decisions requires time and resources.
- Innovation Delays: Insight extraction slows, and so does progress.
Simply put, low-quality feedback is expensive.
Why Manual Review Fails
Analyzing feedback by hand is labor-intensive, error-prone, and unscalable. For example, our case study uses a dataset of over 33,000 McDonald’s reviews, making a fast manual review infeasible. Organizations need automated tools that not only extract meaning from language but also evaluate the usefulness of the feedback itself.
Fusion Analytics: The Solution
Fusion Analytics integrates advanced LLM technology with data visualization and dashboard tools to:
- Classify Reviews by Topic (e.g., food quality, service, cleanliness)
- Score Sentiment using NLP models
- Assess Review Quality, flagging text that is coherent, offers suggestions, or contains useful information
- Filter and Prioritize the most actionable customer feedback
This system provides a dynamic way to interact with customer data, ensuring leaders focus only on insights that drive results.
Case Study: McDonald’s Review Analysis
Data Source
We utilized a public dataset from Kaggle containing over 33,000 anonymized reviews of McDonald’s U.S. locations. Each record included:
- Store address string
- geo-coordinates
- Review text
- star rating
Initial Dashboard
We built an interactive dashboard shown in Figure 1 that features:
- Bar plots of star ratings and review frequency
- A geographic heat map
- A table of reviews for drill-down analysis
Figure 1: Basic Data Exploration
Topic & Sentiment Filtering
LLM-enhanced filters were applied to segment reviews into categories like:
- Wait time
- Value for money
- Cleanliness
- Customer service
- Food quality
We used the latest LLM models from Meta to process each review for each categor – all automated with Fusion Analytics.
Widgets were added to the dashboard to explore reviews based on topic – see Figure 2.
Figure 2: Topic Exploration Widgets
Selecting the food quality category with low sentiment scores narrowed the dataset from 33,396 to 1,800 reviews in two clicks.
Feedback Quality Filtering
Focusing on feedback quality, we created dashboard widgets for the following – shown in Figure 3.
- Coherence
- Suggestions included
- Informational value
- Review quality metric
Each of these attributes were added to the dataset using an LLM driven by Fusion Analytics much like with the topic and sentiment filtering above.
Figure 3: Feedback Quality Widgets
Selecting high scoring reviews that were coherent, offer suggestions, and include useful information reduced the sample to 900 high-quality reviews, or 3% of the original dataset. These reviews were rich in actionable insights, revealing root causes of dissatisfaction tied to food quality. We can repeat the above techniques to dig further into sub-topics related to food quality to discover more insights.
Business Impact
Focusing on high-quality feedback enables:
- Faster, more accurate product improvement cycles
- More informed strategic decisions
- Reduced costs by minimizing wasteful fixes
- Higher customer retention and satisfaction
By narrowing scope to meaningful signals, businesses can prioritize improvements that have measurable impact on ROI.
Conclusion
The path to product and service improvement requires data collection directly from customers and filtering out low quality feedback. Fusion Analytics enables organizations to harness the true value of customer feedback by elevating only what is specific, useful, and actionable.
Next Steps
If your organization is struggling to extract meaningful insight from mountains of feedback—or simply wants to do it faster and smarter—Fusion Analytics can help.