SayPro Analysis techniques for user feedback

1. Thematic Analysis

Purpose: Identify recurring themes, patterns, and issues in qualitative feedback (e.g., open-text responses, interviews).

Steps:

• Familiarize with data
• Generate initial codes
• Group codes into themes
• Review and refine themes
• Report findings with evidence

Tool Examples: NVivo, Dedoose, manual coding with spreadsheets

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🔹 2. Sentiment Analysis

Purpose: Determine emotional tone (positive, negative, neutral) in user feedback.

Use Cases:

• Analyze satisfaction levels
• Monitor brand perception
• Detect early signs of dissatisfaction

Tool Examples: MonkeyLearn, Lexalytics, Python NLP libraries (e.g., TextBlob, spaCy)

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🔹 3. Quantitative Analysis

Purpose: Analyze numerical feedback data (e.g., ratings, multiple-choice survey responses).

Common Methods:

• Descriptive statistics (mean, median, mode)
• Cross-tabulation
• Correlation and regression analysis

Tool Examples: Excel, SPSS, R, Python (Pandas)

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🔹 4. Text Mining / Natural Language Processing (NLP)

Purpose: Automatically analyze large volumes of textual feedback for trends and topics.

Techniques:

• Keyword extraction
• Topic modeling (e.g., LDA)
• Named Entity Recognition (NER)

Tool Examples: RapidMiner, Orange, Python (NLTK, Gensim)

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🔹 5. Cluster Analysis

Purpose: Group users based on similar feedback patterns or sentiments.

Benefits:

• Identify user segments
• Tailor responses or improvements to specific clusters

Tool Examples: Python (Scikit-learn), R, Tableau

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🔹 6. Root Cause Analysis

Purpose: Understand underlying causes of common complaints or feedback.

Methods:

• 5 Whys
• Fishbone Diagram (Ishikawa)
• Fault Tree Analysis

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🔹 7. Comparative Analysis

Purpose: Compare user feedback across different segments (e.g., age groups, regions, time periods).

Approach:

• Split data into subgroups
• Analyze trends and discrepancies
• Identify segment-specific needs

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🔹 8. Voice of Customer (VoC) Framework

Purpose: Organize user feedback into actionable insights aligned with service goals.

Components:

• Collection (surveys, interviews, social media)
• Analysis (quantitative + qualitative)
• Action (recommendations and changes)

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🔹 9. Net Promoter Score (NPS) Analysis

Purpose: Measure customer loyalty and likelihood of recommendation.

NPS Score:

• Promoters (9–10)
• Passives (7–8)
• Detractors (0–6)

Action: Analyze open-ended NPS comments to understand scores

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🔹 10. Gap Analysis

Purpose: Identify gaps between user expectations and experiences.

Steps:

• Collect expected vs actual feedback
• Calculate gaps
• Prioritize fixes based on impact