🔍 1. Data Collection
Gather relevant data sources that reflect user feedback, performance metrics, or operational inefficiencies. These can include:
- Survey responses
- Customer reviews
- Internal reports
- Meeting transcripts
- Support tickets or chatbot logs
🤖 2. Apply AI-Assisted Topic Extraction
Use NLP (Natural Language Processing) techniques such as:
a. Keyword & Phrase Extraction
- Use tools like RAKE, YAKE, or spaCy to extract frequently occurring terms.
b. Topic Modeling
- Use Latent Dirichlet Allocation (LDA) or BERTopic to identify clusters of related terms.
c. Text Classification & Clustering
- Apply unsupervised ML models (e.g., K-Means, HDBSCAN) to group similar feedback into thematic categories.
d. Sentiment Analysis (Optional)
- Layer sentiment analysis to detect which topics are associated with negative or positive sentiments — helping prioritize.
🧠 3. Identify Enhancement Areas
From extracted topics, highlight areas where:
- Users express dissatisfaction
- Internal performance is poor
- Recurrent issues are mentioned
- There are gaps between expectations and delivery
Examples:
- “Lack of mobile responsiveness”
- “Slow response from support”
- “Confusing onboarding process”
🚦 4. Prioritize Using an AI-Assisted Scoring System
Create a prioritization matrix using factors like:
- Frequency (How often the issue is mentioned)
- Impact (Sentiment score or user segment affected)
- Feasibility (Ease of implementation)
Use AI to assign scores to each topic and visualize them in a priority matrix (e.g., High Impact/Low Effort → Do First).
📊 5. Output & Action Plan
Export topics and prioritization as a structured report or dashboard. Include:
- Top 10 enhancement areas
- Supporting quotes/data
- Recommended action steps
🛠️ Tools You Can Use:
- ChatGPT / SayPro GPT for summarization and clustering
- BERTopic, LDA for topic modeling
- MonkeyLearn, Lexalytics, or Google Cloud NLP for automated pipelines
- Power BI or Tableau for visualizing priorities
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