AI Driven Sentiment Analysis Workflow for Priority Support Solutions

Sentiment Analysis for Priority Support

1. Data Collection

1.1 Customer Interaction Data

Gather data from various customer interaction channels such as emails, chat logs, social media posts, and customer feedback surveys.

1.2 Tools for Data Collection

• Zendesk for customer support tickets
• Hootsuite for social media monitoring
• SurveyMonkey for customer feedback surveys

2. Data Preprocessing

2.1 Text Cleaning

Utilize natural language processing (NLP) techniques to clean the data by removing noise, such as irrelevant information, special characters, and stop words.

2.2 Tools for Data Preprocessing

• NLTK (Natural Language Toolkit) for Python
• spaCy for advanced NLP tasks

3. Sentiment Analysis

3.1 Model Selection

Select an appropriate sentiment analysis model that fits the business needs, such as binary classification (positive/negative) or multi-class classification (positive/neutral/negative).

3.2 Tools for Sentiment Analysis

• Google Cloud Natural Language API for sentiment analysis
• IBM Watson Natural Language Understanding for advanced sentiment insights

4. Data Interpretation

4.1 Sentiment Scoring

Assign sentiment scores to customer interactions based on the analysis results, categorizing them into priority levels for support.

4.2 Example of Scoring

• Positive: Score 1-0.5
• Neutral: Score 0.5
• Negative: Score 0-0.1

5. Prioritization of Support Tickets

5.1 Automated Ticket Routing

Implement an automated system to route high-priority tickets to the appropriate support teams based on sentiment scores.

5.2 Tools for Ticket Management

• Freshdesk for ticket management and automation
• ServiceNow for IT service management

6. Continuous Improvement

6.1 Feedback Loop

Establish a feedback loop to continually refine the sentiment analysis models and improve customer service strategies based on insights gathered.

6.2 Tools for Continuous Improvement

• Tableau for data visualization and reporting
• Google Analytics for tracking customer interaction metrics

7. Reporting and Analysis

7.1 Performance Metrics

Analyze the performance of the sentiment analysis implementation using KPIs such as customer satisfaction scores, response times, and resolution rates.

7.2 Tools for Reporting

• Power BI for business intelligence reporting
• Looker for data exploration and visualization