Enhancing Customer Experience with Sentiment Analysis at a Leading Retail Company

Use Cases

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A prominent retail company faced the challenge of effectively analyzing customer sentiments, focused on the anger emotion of callers, from their call center interactions to improve customer experience and service quality. With a vast volume of customer calls and diverse feedback, their internal manual analysis was time-consuming, expensive and prone to human errors. To address this challenge, the retail company partnered with to implement a sentiment analysis machine learning (ML) model trained on call center data.

They initially tried to manage it internally only to find out that it takes many hours and is tedious, exhausting and frustrating. When they tried to outsource it through BPOs, they found it expensive, time consuming and the quality wasn’t that great either.


The primary objective of the project was to develop and deploy a sentiment analysis ML model capable of accurately categorizing customer sentiments expressed during call center interactions, in the English language. The model aimed to streamline the analysis process, identify trends and patterns in customer feedback, and enable proactive measures to address customer concerns and enhance overall satisfaction.

Solution collaborated with the retail company to develop a tailored solution that leveraged their extensive call center data for sentiment analysis. The process involved the following steps:


  • Input Data Validity: The first step of the project workflow ensures the validity and relevance of the input data. In valid conversation for the sentiment analysis, there should be only a single speaker and it should be a real person. This step filters out mistakes in the data generation process by the client, which includes a speaker separation component (the sentiment to analyze should be that of the client and not affected by the call center assistant).  
  • Data Annotation: facilitated the annotation of a diverse dataset of call center interactions, including mainly customer calls in the average length of ~30-45 seconds, suspected to be of ‘angry nature’. Human annotators from’s global networks and internal teams labeled the data with sentiment categories, focused on anger, to train the ML model. A Ground Truth dataset including examples of various  emotion calls was created by & the customer to assist in training the annotators and to monitor the quality of the annotations through the process (hidden qualification questions). Multiple dynamic judgements were implemented to receive high confidence scores. A dedicated professional QA team reviewed samples including mainly low confidence scores data to approve/take final decisions. 
  • Model Training, Calibration, and Ongoing Improvement: For automatic and rapid emotion analysis,’s data scientists trained and optimized an audio classification model. The optimization entailed training different state-of-the-art models and finding the best hyper-parameters for optimal results. All our classification models are calibrated to enable clients to choose how to route conversations in an intuitive way. For example, a client that chooses to route conversations according to a “95% precision” criterion ensures that the human operators that specialize in angry customers get calls of which only 5% are not really angry. In addition, as the built workflow is used over time, more QAed data accumulate and our system auto-updates the model that improves over time.
  • Integration and Deployment: Upon successful training and validation, the sentiment analysis model was seamlessly integrated into the retail company’s call center infrastructure. provided technical support and guidance throughout the deployment process to ensure smooth implementation and alignment with the company’s operational requirements.


The implementation of the sentiment analysis ML model yielded significant benefits for the retail company:


  • Efficiency Gains: The automated sentiment analysis process significantly reduced the time and resources required for manual data analysis, enabling call center agents to focus on addressing customer needs and resolving issues promptly.


  • Insightful Analytics: The ML model provided valuable insights into customer sentiments, trends, empowering the retail company to proactively identify and address customer concerns, improve service quality, and enhance overall customer experience.


  • Enhanced Decision-Making: Armed with actionable data and insights from the sentiment analysis model, the retail company’s management made informed decisions to optimize call center operations, allocate resources effectively, and implement targeted initiatives to boost customer satisfaction and loyalty.

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By leveraging’s expertise in data annotation & machine learning, the retail company successfully implemented a sentiment analysis solution that revolutionized their call center operations. The partnership with enabled the retail company to harness the power of AI to gain deeper insights into customer sentiments, drive operational efficiencies, and deliver superior customer experiences in an increasingly competitive market landscape.  Currently the retail company & are working on additional models for more specific and complex tasks such as customer buying intent, product return intent and more. For more information, please contact:

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