CASE STUDY
Transforming Customer Service Using AI
Automated support ticket classification using ML, increasing accuracy to 98% and reducing time spent reclassifying by 70%.
Problem Solved
The project involved developing an AI-driven solution to classify customer support tickets for an international client. Previously, the manual process of category selection by customers led to frequent errors and misclassifications, causing delays, inefficiencies and customer frustration. The objective was to create a machine learning model that accurately classifies tickets into predefined categories (e.g., shipping issues, refund issues, account settings issues) based on their content. This approach aimed to reduce misrouting, improve response times, and enhance overall customer satisfaction.
Problem Solving Approach
First, a custom text classification model was implemented to automate support ticket categorization. By automatically classifying tickets based on their content using NLP and ML techniques, the need for customers to manually select categories was eliminated.
This streamlined the user interface allowing users to report issues quickly without the hassle of selecting the correct category. This approach enhanced the overall user experience and improved the efficiency of the support system.
The data team conducted Exploratory Data Analysis (EDA) to understand data distribution and key patterns. Insights from EDA guided feature engineering, converting raw text into a format suitable for ML algorithms. The approach was refined through EDA and feature engineering, then selected and deployed to production. This approach significantly improved accuracy, saved time, and boosted customer satisfaction, demonstrating our commitment and expertise.
The final model was constructed and the classifier deployed to the production environment. Applying the classifier to new customer support tickets involves detecting the language of the incoming text and translating it if necessary. Text preprocessing is performed, followed by vectorization to convert the text into a suitable form for the classifier. The classifier then categorizes the customer support ticket based on its content.
Outcome
Increased classification accuracy from 80% to 98% in production, significantly improving reliability. Reduced reclassification time by 70%, enabling faster responses and more efficient support operations.
Accuracy achieved
Less time spent recategorizing misclassified tickets
Streamlined operational flows
Increased team productivity
Achieved faster and more accurate responses to customer inquiries
Increased customer satisfaction
Key Features Implemented
ML-based text classification model created and integrated with the UI customer service form
Technologies
Development
Project Timeline and Team Structure
The project spanned three months, from defining the problem to deploying the classifier to production. The team consisted of three members, including a Software Engineer, DevOps engineer and a Data Scientist, delivering Data Science & Analytics and Software Development services.
Software Engineer
Data Scientist
DevOps Engineer
AI/ML Software Engineer
Methodology
A labeled dataset was collected from the ticketing system, including ticket texts and their categories. Initially, a rule-based approach was tried but proved inadequate for the complexity of ticket content. Collaboration with the data team led to the application of Machine Learning.
Implementing MLOps practices ensured continuous integration, deployment, and monitoring of classifier performance. Defining and agreeing on the frequency of retraining was essential to maintain the model’s accuracy and relevance. This collaborative effort and adherence to best practices were key to achieving sustainable and efficient ML-based solutions in real-world applications.
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