How We Used NLP to Improve Customer Support for a Retail Client

In today’s retail landscape, customer expectations are shaped by instant service, accurate information, and consistent support across channels. As brands scale, managing this support at speed becomes challenging. Our retail client—a well-established player in the e-commerce space—was facing growing pressure to improve its ticket resolution time and overall support efficiency.

The solution we implemented was rooted in Natural Language Processing (NLP), a branch of artificial intelligence that interprets and processes human language. By deploying an NLP model that automatically categorized incoming support tickets, we helped the client reduce average ticket resolution time by 43%, streamline workflows, and improve customer satisfaction.

This blog breaks down the problem, our approach, and the impact we delivered.

The Challenge: Manual Ticket Handling at Scale

The client’s support team was handling thousands of customer interactions each week. Tickets arrived through email, chat, web forms, and social media, creating a diverse and unstructured data pool. The core issues included:

• Slow ticket triaging due to manual reading and categorisation
• Inconsistent tagging, leading to misrouted queries
• Delayed responses to high-priority issues
• Low visibility into recurring support themes and customer sentiment

With customer satisfaction scores declining and resolution times increasing, the client needed a technology-driven solution that could process and act on incoming support data quickly and intelligently.

Our Objective: Automate and Enhance Ticket Categorisation

Our primary goal was to develop an NLP-powered system that could:

• Automatically classify tickets into predefined categories such as shipping, returns, payments, technical issues, and product inquiries
• Detect urgency or negative sentiment to prioritise critical tickets
• Enable faster routing to the appropriate support agents
• Deliver real-time insights into support performance and issue trends

We aligned our solution to support both technical accuracy and operational usability for the client’s in-house support teams.

Step 1: Data Collection and Preprocessing

We started with a comprehensive review of six months of historical support tickets. This included structured fields (timestamps, channels, resolution time) and unstructured fields (customer message content).

Our preprocessing steps included:

• Text normalisation: Removing punctuation, correcting spellings, and standardising language
• Language detection: Filtering multilingual inputs
• Entity masking: Replacing sensitive data like order IDs, emails, and payment references
• Label mapping: Aligning tickets with high-level support categories based on agent resolutions

This clean dataset formed the foundation for supervised NLP model training.

Step 2: Building the NLP Classification Model

We designed a multi-label text classification model using transformer-based architectures such as BERT and DistilBERT. These models are well-suited for understanding context in short text inputs—ideal for support queries.

Our model performed the following tasks:

• Intent Detection: Understanding what the customer wanted—whether it was a refund, complaint, or inquiry
• Category Prediction: Assigning tickets to one or more relevant categories
• Sentiment Analysis: Flagging tickets that carried a negative tone or frustration cues
• Named Entity Recognition (NER): Identifying key attributes like product names, order numbers, or delivery dates

We used stratified k-fold cross-validation to fine-tune the model’s hyperparameters. Our final model achieved over 92% accuracy and a high precision-recall balance across all categories.

Step 3: Integration with the Support Platform

The model was deployed through an API and integrated directly into the client’s existing helpdesk system. As tickets came in, the model:

• Automatically categorised them
• Flagged high-priority or urgent queries
• Suggested agent response templates (for common issues)
• Tagged each ticket with confidence scores and metadata for internal tracking

Agents could override classifications, and their feedback was looped back to improve model performance over time.

Step 4: Monitoring, Evaluation, and Fine-Tuning

We closely monitored model performance over the first eight weeks. Key performance indicators (KPIs) included:

• Average Resolution Time
• First Response Time
• CSAT Scores (Customer Satisfaction)
• Routing Accuracy (measured against agent overrides)

The results were compelling:

• Resolution time dropped by 43%, from 18 hours to under 10 hours on average
• Routing accuracy reached 94%, reducing manual reassignment
• CSAT scores improved by 21% within the first quarter of deployment
• Agent workload reduced by 30%, enabling more focus on high-complexity cases

Key Features That Drove Impact

Our NLP solution was built with scalability and usability in mind. Notable features included:

• Real-Time Processing: Tickets were categorized and routed within milliseconds
• Confidence Scoring: Each prediction came with a confidence level for agent review
• Multilingual Support: The model was trained on both English and Hindi data
• Agent Assist Suggestions: Contextual response recommendations were displayed for common query types
• Analytics Dashboard: We provided a visualisation layer showing trends, volumes, categories, and emerging issues

Lessons Learned

This implementation highlighted a few critical learnings:

1. Domain-Specific Fine-Tuning Is Essential: Off-the-shelf NLP models lacked the accuracy needed for the retail context until they were fine-tuned on relevant datasets.
2. Human-in-the-Loop Is Valuable: Allowing agents to review and correct predictions helped the model learn continuously and improved trust.
3. Integration Should Be Seamless: For adoption to succeed, the solution had to fit naturally into the existing workflow without introducing friction.

What This Means for Retail Businesses

For retailers aiming to scale support operations, NLP offers a powerful way to automate and improve service without sacrificing personalization. It enables faster responses, improves issue resolution accuracy, and provides valuable data-driven insights into customer concerns.

As more customer interactions move to digital, using language models for real-time support categorization is not just efficient—it’s essential.

Final Thoughts

At IT IDOL Technologies our mission is to deliver practical AI solutions that drive measurable impact. This NLP implementation is a clear example of how advanced language models, when designed with a user-centric approach, can revolutionise everyday business operations.

Whether you’re a retail brand, fintech company, or logistics provider, NLP has the potential to enhance your customer experience and reduce operational costs. If you’re ready to explore what’s possible, we’re here to help you design, deploy, and scale intelligent systems tailored to your unique business needs.