AI for User Research: How to Extract Insights from Support Tickets, Reviews, and Session Data at Scale

The User Research Problem

You have:

• 10,000 support tickets
• 5,000 app reviews
• 1M user sessions
• 200 customer interviews

You can't read all of it. You're making decisions blind.

AI can analyze everything and surface actionable insights.

Use Case 1: Support Ticket Analysis

Extract themes and pain points automatically.

from openai import OpenAI
import pandas as pd

def analyze_support_tickets(tickets):
    """
    Cluster tickets by topic and sentiment
    """
    client = OpenAI()
    
    # Batch analyze
    themes = []
    
    for ticket in tickets:
        prompt = f"""
        Analyze this support ticket:
        
        "{ticket['message']}"
        
        Extract:
        1. Primary issue category
        2. Sentiment (frustrated/neutral/happy)
        3. Urgency (low/medium/high)
        4. Feature request (yes/no)
        
        Return JSON.
        """
        
        response = client.chat.completions.create(
            model="gpt-4",
            messages=[{"role": "user", "content": prompt}],
            response_format={"type": "json_object"}
        )
        
        analysis = json.loads(response.choices[0].message.content)
        themes.append(analysis)
    
    # Aggregate
    df = pd.DataFrame(themes)
    
    return {
        'top_issues': df['category'].value_counts().head(10),
        'frustrated_users': len(df[df['sentiment'] == 'frustrated']),
        'feature_requests': df[df['feature_request'] == 'yes']['category'].value_counts()
    }

Use Case 2: Review Mining

Find what users love and hate.

def analyze_app_reviews(reviews):
    """
    Extract sentiment + specific feedback from reviews
    """
    positive_features = []
    negative_features = []
    
    for review in reviews:
        # Sentiment analysis
        sentiment = analyze_sentiment(review['text'])
        
        # Extract mentioned features
        features = extract_features(review['text'])
        
        if sentiment > 0.5:
            positive_features.extend(features)
        elif sentiment < -0.5:
            negative_features.extend(features)
    
    return {
        'most_loved': Counter(positive_features).most_common(10),
        'most_hated': Counter(negative_features).most_common(10),
        'feature_sentiment': calculate_feature_sentiment(reviews)
    }

Use Case 3: Session Replay Analysis

AI watches user sessions and finds friction.

def analyze_session_patterns(sessions):
    """
    Identify common drop-off points and confusion patterns
    """
    from collections import defaultdict
    
    drop_offs = defaultdict(int)
    confusion_signals = defaultdict(list)
    
    for session in sessions:
        # Detect drop-off points
        if session['completed'] == False:
            last_page = session['page_path'][-1]
            drop_offs[last_page] += 1
        
        # Detect confusion (rapid back/forth, rage clicks)
        if has_confusion_pattern(session):
            page = session['page_path'][-1]
            confusion_signals[page].append({
                'session_id': session['id'],
                'pattern': detect_confusion_type(session)
            })
    
    return {
        'drop_off_pages': sorted(drop_offs.items(), key=lambda x: x[1], reverse=True)[:10],
        'confusion_hotspots': {k: len(v) for k, v in confusion_signals.items()}
    }

Use Case 4: Interview Synthesis

Turn hours of interviews into actionable insights.

def synthesize_interviews(transcripts):
    """
    Extract themes from customer interviews
    """
    client = OpenAI()
    
    prompt = f"""
    Synthesize these {len(transcripts)} customer interviews.
    
    Transcripts:
    {json.dumps(transcripts)}
    
    Extract:
    1. Top 5 pain points mentioned
    2. Feature requests with frequency
    3. Workflow patterns described
    4. Competitive mentions
    5. Buying criteria
    
    Return structured summary.
    """
    
    response = client.chat.completions.create(
        model="gpt-4",
        messages=[{"role": "user", "content": prompt}],
        max_tokens=2000
    )
    
    return response.choices[0].message.content

Use Case 5: Feature Prioritization

AI-powered scoring based on user feedback.

def prioritize_features(feedback_sources):
    """
    Score features by impact and demand
    """
    features = {}
    
    # Aggregate from multiple sources
    support_requests = count_feature_requests(feedback_sources['support'])
    reviews_mentions = count_feature_mentions(feedback_sources['reviews'])
    interview_signals = extract_feature_signals(feedback_sources['interviews'])
    
    # Calculate scores
    all_features = set(support_requests.keys()) | set(reviews_mentions.keys()) | set(interview_signals.keys())
    
    for feature in all_features:
        score = (
            support_requests.get(feature, 0) * 0.3 +
            reviews_mentions.get(feature, 0) * 0.4 +
            interview_signals.get(feature, 0) * 0.3
        )
        
        features[feature] = {
            'score': score,
            'sources': {
                'support': support_requests.get(feature, 0),
                'reviews': reviews_mentions.get(feature, 0),
                'interviews': interview_signals.get(feature, 0)
            }
        }
    
    return sorted(features.items(), key=lambda x: x[1]['score'], reverse=True)

Use Case 6: Churn Reason Classification

Automatically categorize why users churn.

def classify_churn_reasons(churned_users):
    """
    Identify patterns in churn behavior
    """
    reasons = {
        'price': 0,
        'missing_feature': 0,
        'poor_onboarding': 0,
        'competitor_switch': 0,
        'usage_decline': 0,
        'support_issues': 0
    }
    
    for user in churned_users:
        # Check for signals
        if check_price_sensitivity(user):
            reasons['price'] += 1
        
        if check_feature_requests(user):
            reasons['missing_feature'] += 1
        
        if not user['activated']:
            reasons['poor_onboarding'] += 1
        
        if check_competitor_mentions(user):
            reasons['competitor_switch'] += 1
        
        if user['usage_trend'] < -0.5:
            reasons['usage_decline'] += 1
        
        if user['support_tickets'] > 5:
            reasons['support_issues'] += 1
    
    return reasons

Automated Insight Generation

Daily digest of what's happening.

def generate_daily_insights():
    """
    AI-powered product insights digest
    """
    today = datetime.now()
    yesterday = today - timedelta(days=1)
    
    # Gather data
    new_support_tickets = get_tickets(since=yesterday)
    new_reviews = get_reviews(since=yesterday)
    session_data = get_sessions(since=yesterday)
    
    # Analyze
    ticket_themes = analyze_support_tickets(new_support_tickets)
    review_sentiment = analyze_app_reviews(new_reviews)
    drop_offs = analyze_session_patterns(session_data)
    
    # Generate report
    client = OpenAI()
    
    prompt = f"""
    Generate a product insights summary:
    
    Support tickets: {ticket_themes}
    Review sentiment: {review_sentiment}
    Session drop-offs: {drop_offs}
    
    Write:
    1. Top 3 issues to address
    2. New patterns or trends
    3. Recommended actions
    
    Keep it concise and actionable.
    """
    
    response = client.chat.completions.create(
        model="gpt-4",
        messages=[{"role": "user", "content": prompt}]
    )
    
    return response.choices[0].message.content

Real Examples

Airbnb: AI analyzes millions of reviews to identify hosting issues
Intercom: Automated support ticket categorization and routing
Notion: Session analysis to find UX friction points

Implementation

Week 1-2: Set up data pipelines (support, reviews, sessions)
Week 3-4: Build analysis scripts for each source
Week 5-6: Automate daily insight generation
Week 7-8: Integrate with product roadmap process

AI-powered user research scales infinitely. Start analyzing.

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