AI Driven Sentiment Analysis Workflow for Customer Feedback

Sentiment Analysis for Customer Feedback and Support Calls

1. Data Collection

1.1 Sources of Data

• Customer feedback forms
• Support call recordings
• Social media interactions
• Email communications

1.2 Tools for Data Collection

• SurveyMonkey for feedback forms
• Zendesk for support ticket management
• Twilio for call recording and transcription

2. Data Preprocessing

2.1 Transcription

Utilize AI-driven transcription services to convert audio from support calls into text format.

• Example Tool: Google Cloud Speech-to-Text

2.2 Text Cleaning

Remove irrelevant content, such as filler words and background noise, from the transcribed text.

3. Sentiment Analysis

3.1 AI Model Selection

Select a suitable AI model for sentiment analysis, capable of detecting emotions and sentiments in text.

• Example Tool: IBM Watson Natural Language Understanding
• Example Tool: Microsoft Azure Text Analytics

3.2 Implementation of Sentiment Analysis

• Integrate the selected AI model with the preprocessed data.
• Run sentiment analysis to classify feedback and support call transcriptions as positive, negative, or neutral.

4. Data Interpretation

4.1 Reporting

Generate reports summarizing sentiment trends, highlighting areas of concern and customer satisfaction.

4.2 Visualization

Utilize data visualization tools to present findings in an accessible manner.

• Example Tool: Tableau for creating dashboards
• Example Tool: Power BI for reporting insights

5. Actionable Insights

5.1 Strategy Development

Develop strategies based on insights gained from sentiment analysis to improve customer service and product offerings.

5.2 Continuous Improvement

Implement feedback loops to continuously refine sentiment analysis processes and improve customer interaction based on insights.

6. Monitoring and Evaluation

6.1 Performance Metrics

Establish key performance indicators (KPIs) to evaluate the effectiveness of sentiment analysis initiatives.

6.2 Regular Reviews

Conduct regular reviews of sentiment analysis outcomes to ensure alignment with business objectives and customer expectations.