Objective: Understand how call sentiment analysis identifies customer emotions and improves service quality at MTN Ghana.

  • Analyzing Customer Emotions:
    • Process: Convert call audio to text (speech-to-text) and use NLP to analyze sentiment (e.g., happy, frustrated, neutral).
    • Tools:
      • Speech-to-Text: Google Cloud Speech, AWS Transcribe.
      • Sentiment Classifiers: Pre-trained models (e.g., Hugging Face’s sentiment analysis models).
    • Telecom Example: Analyzing call center conversations to detect frustrated customers complaining about dropped calls.
    • MTN-Specific Example: MTN Ghana could use call sentiment analysis to identify dissatisfied Mobile Money users, offering follow-up support to prevent churn.
  • Benefits:
    • Identify Dissatisfied Customers: Flag negative sentiments for immediate action (e.g., escalating complaints).
    • Improve Service: Use insights to train agents or enhance network quality.
    • MTN Context: Sentiment analysis on calls about 5G issues could guide MTN Ghana’s network upgrades in urban areas.
  • Implementation:
    • Step 1: Record calls (with consent, per Ghana’s Data Protection Act).
    • Step 2: Convert audio to text using speech-to-text tools.
    • Step 3: Apply sentiment classifiers to categorize emotions.
    • Example: A customer says, “My calls keep dropping!” Sentiment analysis flags this as negative, prompting MTN to investigate network issues in the customer’s area.
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