Customer Care Dataset

Project Overview

Objective

Our objective was to curate a comprehensive dataset to empower advanced customer care solutions. This dataset plays a pivotal role in training machine learning models to revolutionize customer support services, ensuring efficiency, and customer satisfaction.

Scope

We undertook a significant data collection and annotation project to create the Customer Care Dataset. This dataset focuses on improving customer interactions, understanding customer needs, and enhancing support services using a diverse range of data types, including text, audio, and video.

Sources

Customer Interactions: We collected real customer interactions from a variety of industries, including e-commerce, telecommunications, and healthcare, to ensure the dataset’s diversity.
Publicly Available Data: To enrich the dataset, we included publicly available customer service interactions from various sources, providing a broader context.
Annotated Data: Our team meticulously annotated customer interactions, including sentiment analysis, issue classification, and resolution status, to make the dataset machine-learning-ready.

Data Collection Metrics

Total Data Collected: 15000 customer interactions
Data Annotated for ML Training: 25000 interactions

Annotation Process

Stages

Sentiment Analysis: We classified customer sentiments into categories such as positive, negative, or neutral, providing insights into customer satisfaction.
Issue Categorization: Each interaction was categorized based on the customer’s issue, whether it’s a billing problem, technical support request, or general inquiry.
Resolution Status: We tagged interactions with information on whether the issue was resolved or required further action, aiding in improving support workflows.

Annotation Metrics

Annotated Interactions: 15000
Sentiment Analysis Labels: 10000
Issue Categorization Labels: 20000
Resolution Status Labels: 15000

Quality Assurance

Continuous Model Evaluation: We regularly evaluated the machine learning models trained on this dataset to maintain high accuracy in sentiment analysis, issue categorization, and resolution prediction.
Privacy Compliance: Ensured that all data collected adhered to privacy regulations, with any sensitive information appropriately anonymized or excluded.
Feedback Loop: We established a feedback mechanism where customer support agents provided insights to improve the model’s performance, resulting in higher customer satisfaction rates.

QA Metrics:

Sentiment Analysis Accuracy: 95%
Issue Categorization Accuracy: 92%
Resolution Prediction Accuracy: 88%
Privacy Compliance: 100%

Conclusion

The Customer Care Dataset has significantly transformed customer support services. With accurate sentiment analysis, issue categorization, and resolution prediction. Customer interactions are streamlined, resulting in improved efficiency and higher customer satisfaction. This dataset empowers machine learning models to deliver better customer care. Making it an invaluable resource for businesses aiming to enhance their customer support operations.