Building an Intent Classification Model for Customer Support in Ghana Using NLP

Local Chapter Accra, Ghana Chapter

Coordinated byGhana ,

Status: Completed

Project Duration: 29 Jul 2023 - 16 Aug 2023

Open Source resources available from this project

Project background.

In Ghana, businesses and organizations often struggle with effectively handling customer support inquiries due to the high volume and diverse nature of the messages received. Support teams face challenges in promptly identifying the intent behind each inquiry, leading to delays and customer dissatisfaction.

The lack of an efficient intent classification system hinders businesses’ ability to provide timely and personalized customer support. Manual processing of customer inquiries is time-consuming and prone to errors, resulting in longer response times, misrouted tickets, and frustrated customers

By developing an intent classification model specifically designed for customer support in Ghana, we aim to revolutionize the way businesses handle customer inquiries. The automated classification system will accurately categorize intents such as product queries, technical issues, and account-related concerns, enabling faster ticket routing and response times. This will lead to improved customer satisfaction, increased operational efficiency, and enhanced brand reputation for businesses in Ghana.

The problem.

Customer support teams in Ghana struggle to effectively categorize and respond to customer inquiries, resulting in delays, confusion, and customer dissatisfaction.

Project goals.

- Develop an accurate intent classification model for customer support inquiries in Ghana. - Improve response times by automating the ticket routing process based on identified customer intents. - Enhance the efficiency of customer support operations through streamlined and optimized workflows. - Increase customer satisfaction and loyalty by providing personalized and relevant support solutions.

Project plan.

  • Week 1

    Project Setup and Data Collection

  • Week 2

    Data preparation and Annotation

  • Week 3

    Model Development and training

  • Week 4

    Model Optimization and Validation

  • Week 5


  • Week 6

    Model Deployment

Learning outcomes.

NLP techniques , Text processing , model training and evaluation, feature engineering , Pytorch

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