Text Analytics
Global Quick Service Restaurant
This client’s consumer insights and social listening teams needed a robust and flexible text analytics solution to uncover customer sentiment and context at scale.
Advanced Analytics
Architecture & Engineering
BI Reporting & Visualizations
Build & Migrations
Business Analytics
Business Intelligence & Insights
Data Management
Text Analytics & NLP
Amazon Comprehend
Amazon Redshift

Data analytics services case study

For McDonald’s, Improving Customer Satisfaction Begins with Superior Text Analytics

Overview: Limitations in feedback analytics left valuable insights undiscovered

The leading global food service retailer wanted to more quickly and accurately interpret customer experience data for improved decision-making. While unstructured text and natural language data can provide actionable insight, data from customer satisfaction surveys and social media comments were analyzed by different text analytics solutions, resulting in disparate outputs. Written comments were processed as a whole, leading to less accurate results than analyzing sub-comments that contain unique sentiments and tones. The QSR’s consumer Insights and social listening teams sought a more robust and flexible text analytics solution to effectively uncover sentiment and context at scale.


Solution: Digging deeper required an integrated approach for data capture, sentiment analysis, and powerful visualization

Wavicle implemented ActiveInsights™, a text analytics solution that integrates raw data from multiple sources, such as real-time social listening and customer satisfaction surveys, in an Amazon Redshift database. ActiveInsights™ captured the full scope of written feedback by first breaking complex sentences into sub-comments, then assigning a positive, negative, or neutral sentiment to each referenced topic, ensuring consistency across categories and themes. To preserve information and minimize information loss, the data model captured and replaced all previous references and co-references, offering tremendous visibility and insight into millions of surveys and comments already collected.


Approximately 95,000 user comments are processed daily by Talend cloud data integration software and Amazon Comprehend, a natural language processing service that uses machine learning to find insights and relationships in text specific to the QSR’s topics and sentiments. The output is transformed into dashboard intelligence and ad-hoc reporting by Tableau data visualization.


Outcome: Faster, more accurate text analytics drive enhanced insight and decisions

Wavicle’s ActiveInsights™ helped the global food service retailer to improve the speed, completeness, and accuracy of customer feedback analysis. The new text analytics solution processes unstructured text and natural language data 10x faster than before, with up to 70% greater accuracy.


With the ability to gauge the sentiment and tone of complex comments across myriad specific items, themes, and categories, the QSR’s consumer insights and social listening teams can quickly discover trends in customer experience with little to no lag time, driving meaningful action.


Original Text Analytics Solution


” I ordered a burger. It was good. The fries were cold. “

— Complex comment example from a customer satisfaction survey that was analyzed as a whole and categorized as neutral.


Improved Text Analytics Solution


— The sentence above is parsed into three sub-comments, each categorized with a unique sentiment.

“I ordered a burger” = neutral

“It was good” = positive

“The fries were cold” = negative


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