# Use Cases and Examples

Real-world applications of video intelligence with Memories.ai LVMM.

---

## Table of Contents

- [Competitor Content Intelligence](#competitor-content-intelligence)
- [Content Strategy Research](#content-strategy-research)
- [Meeting and Training Intelligence](#meeting-and-training-intelligence)
- [Social Media Monitoring](#social-media-monitoring)
- [Knowledge Base Management](#knowledge-base-management)
- [Creator and Influencer Research](#creator-and-influencer-research)

---

## Competitor Content Intelligence

### Use Case: Analyze Competitor Video Strategy

**Scenario:** You want to understand how Competitor X uses video content to drive conversions.

**Workflow:**
```python
# Stage 1: Discover their content
videos = search_social("youtube", "@competitor_x", count=50)

# Stage 2: Import their library
for video in videos:
    import_video(video['url'], tags=["competitor-x", "analysis-2026-q1"])

# Stage 3: Content pattern analysis
themes = chat_personal("""
Tags: competitor-x
Question: What are the main content themes and formats?
Break down by frequency and video type.
""")

# Stage 4: Messaging analysis
messaging = chat_personal("""
Tags: competitor-x
Question: What value propositions do they emphasize?
What pain points do they address?
""")

# Stage 5: Production insights
production = chat_personal("""
Tags: competitor-x
Question: What's their production quality level?
Average video length? Consistent branding elements?
""")

# Stage 6: Identify gaps
gaps = chat_personal("""
Compare competitor-x videos to our content library (tag: our-content).
What topics do they cover that we don't?
What angles are they using successfully?
""")
```

**Expected Output:**
- Content theme breakdown (60% product demos, 30% customer stories, 10% thought leadership)
- Key messaging pillars (ROI, ease of use, enterprise security)
- Production specs (3:24 avg length, professional editing, consistent intro/outro)
- Content gaps in your strategy

**ROI:** 20 hours of manual analysis → 2 hours automated

---

### Use Case: Competitive Pricing Intelligence

**Scenario:** Extract pricing information from competitor product videos.

**Workflow:**
```python
# Import competitor product demo videos
competitor_demos = search_social("youtube", "competitor pricing demo", count=20)
for video in competitor_demos[:10]:
    import_video(video['url'], tags=["competitor-pricing"])

# Extract pricing mentions
pricing_data = chat_personal("""
Tags: competitor-pricing
Question: Extract all pricing information mentioned.
Include: tiers, price points, billing cycles, discounts, enterprise pricing.
""")

# Analyze pricing strategy
strategy = chat_personal("""
Tags: competitor-pricing
Question: What pricing strategy are they using?
Value-based, cost-plus, competition-based, penetration?
How do they position their tiers?
""")
```

**Expected Output:**
- Pricing tier structure (Starter $49, Pro $99, Enterprise custom)
- Positioning strategy (value-based with ROI calculators)
- Competitive differentiation (monthly vs annual pricing emphasis)

---

## Content Strategy Research

### Use Case: Identify High-Performing Content Formats

**Scenario:** Research what video formats are working in your niche.

**Workflow:**
```python
# Search for top content in your niche
niche_videos = search_social("tiktok", "#SaaSmarketing", count=100)

# Import top performers (by engagement)
top_50 = sorted(niche_videos, key=lambda x: x['likes'] + x['views'], reverse=True)[:50]
for video in top_50:
    import_video(video['url'], tags=["niche-research", "top-performer"])

# Analyze successful patterns
format_analysis = chat_personal("""
Tags: top-performer
Question: What video formats are most successful?
Break down by: length, hook style, content structure, CTA approach.
""")

# Identify successful hooks
hooks = chat_personal("""
Tags: top-performer
Question: Extract the first 3 seconds (hook) from each video.
What patterns make them effective?
""")

# Production requirements
production = chat_personal("""
Tags: top-performer
Question: What's the production quality distribution?
Can successful content be made with smartphone + basic editing?
""")
```

**Expected Output:**
- Winning formats (60-second problem-solution, 15-second quick tips)
- Hook patterns ("Here's what nobody tells you about...", "3 mistakes I made...")
- Production level (70% smartphone-quality acceptable, 30% professional)

**ROI:** Validate content strategy before investing in production

---

### Use Case: Topic Gap Analysis

**Scenario:** Find content opportunities your competitors aren't covering.

**Workflow:**
```python
# Import your content and competitor content
# (Assume already done with tags: "our-content", "competitor-a", "competitor-b")

# Identify covered topics
competitor_topics = chat_personal("""
Tags: competitor-a, competitor-b
Question: List all topics covered. Group by category.
""")

# Find gaps
gaps = chat_personal("""
Compare topics from competitors (tags: competitor-a, competitor-b)
vs audience questions (tag: customer-questions)
What topics are customers asking about that competitors haven't covered?
""")

# Opportunity sizing
opportunities = chat_personal("""
For each gap identified, search social platforms:
How many searches/hashtags exist for that topic?
Is there existing demand?
""")
```

**Expected Output:**
- 15 topic gaps with high demand, low competition
- Prioritized by search volume and strategic fit
- Content angle recommendations

---

## Meeting and Training Intelligence

### Use Case: Extract Action Items from Meetings

**Scenario:** Convert recorded meetings into structured action items.

**Workflow:**
```python
# Import meeting recording
meeting_id = import_video(
    "internal_recording.mp4",
    tags=["team-meeting", "product-planning", "2026-03-09"]
)

# Extract action items
action_items = query_video(meeting_id, """
Extract all action items mentioned in the meeting.
Format as:
- [ ] Action item description | Owner: Name | Due: Date | Context: Why needed
""")

# Extract decisions
decisions = query_video(meeting_id, """
List all decisions made during the meeting.
Format as:
DECISION: [Description]
RATIONALE: [Why]
OWNER: [Who's accountable]
IMPACT: [What changes]
""")

# Generate meeting summary
summary = query_video(meeting_id, """
Create executive summary:
1. Key topics discussed
2. Decisions made
3. Action items (grouped by owner)
4. Blockers identified
5. Next meeting agenda items
""")

# Store for future reference
create_memory(
    f"Meeting Summary {date}: {summary}",
    tags=["meeting-summary", "product-planning"]
)
```

**Expected Output:**
```
ACTION ITEMS:
- [ ] Update pricing page with new tier | Owner: Sarah | Due: 2026-03-15 | Context: Launch prep
- [ ] Schedule user interviews | Owner: Mike | Due: 2026-03-12 | Context: Validate feature priority

DECISIONS:
- Push mobile app launch to Q2 (Rationale: Backend infrastructure not ready)
- Focus Q1 on enterprise features (Rationale: 3 pilot customers waiting)
```

**ROI:** 30 minutes of manual note-taking → 2 minutes automated

---

### Use Case: Training Material Knowledge Base

**Scenario:** Build searchable library from training videos and courses.

**Workflow:**
```python
# Import all training videos
training_videos = [
    "onboarding_day1.mp4",
    "onboarding_day2.mp4",
    "product_training_basics.mp4",
    "product_training_advanced.mp4",
    "sales_process_training.mp4"
]

for video_url in training_videos:
    import_video(video_url, tags=["training", "onboarding"])

# Create searchable knowledge base
# New employees can now ask questions:
answer = chat_personal("How do I handle objections about pricing?")
answer = chat_personal("What's our product positioning vs competitors?")
answer = chat_personal("Walk me through the sales process step by step")
```

**Expected Output:**
- Instant answers to onboarding questions
- Reference to specific training video timestamps
- Consistent knowledge across team

**ROI:** Reduce onboarding time 40%, improve knowledge retention

---

## Social Media Monitoring

### Use Case: Track Brand Mentions Across Platforms

**Scenario:** Monitor videos mentioning your brand or product.

**Workflow:**
```python
# Search across platforms
tiktok_mentions = search_social("tiktok", "#YourBrand", count=50)
youtube_mentions = search_social("youtube", "YourBrand review", count=50)
instagram_mentions = search_social("instagram", "@yourbrand", count=50)

# Import for analysis
all_mentions = tiktok_mentions + youtube_mentions + instagram_mentions
for video in all_mentions:
    import_video(video['url'], tags=["brand-mention", video['platform']])

# Sentiment analysis
sentiment = chat_personal("""
Tags: brand-mention
Question: Analyze sentiment across all brand mentions.
Positive, neutral, negative breakdown.
Common praise points and complaints.
""")

# Feature requests
requests = chat_personal("""
Tags: brand-mention
Question: Extract all feature requests or improvement suggestions.
Rank by frequency mentioned.
""")

# Competitive comparisons
comparisons = chat_personal("""
Tags: brand-mention
Question: When creators compare us to competitors, what do they say?
What are our perceived strengths and weaknesses?
""")
```

**Expected Output:**
- Sentiment: 70% positive, 20% neutral, 10% negative
- Top feature requests: Mobile app (15 mentions), API access (12 mentions)
- Competitive position: "Easier to use than X, but lacks Y feature"

**ROI:** Real-time feedback loop, inform product roadmap

---

### Use Case: Influencer Partnership Research

**Scenario:** Identify and vet potential influencer partners.

**Workflow:**
```python
# Find creators in your niche
creators = search_social("youtube", "SaaS founder", count=100)

# Filter to top performers
top_creators = sorted(creators, key=lambda x: x['views'], reverse=True)[:20]

# Import their content
for creator in top_creators:
    videos = search_social("youtube", f"@{creator['handle']}", count=10)
    for video in videos:
        import_video(video['url'], tags=["influencer-research", creator['handle']])

# Analyze each creator
for creator in top_creators:
    profile = chat_personal(f"""
    Tags: {creator['handle']}
    Question: Analyze this creator's content:
    - Main topics covered
    - Audience demographic (based on comments/content)
    - Brand alignment with our values
    - Engagement quality (comments depth)
    - Partnership potential (do they do sponsorships?)
    """)

    create_memory(profile, tags=["influencer-profile", creator['handle']])
```

**Expected Output:**
- Vetted list of 5 high-fit influencers
- Audience alignment scores
- Estimated reach and engagement
- Partnership readiness assessment

---

## Knowledge Base Management

### Use Case: Customer Research Repository

**Scenario:** Build searchable library of customer interviews and feedback videos.

**Workflow:**
```python
# Import customer interview recordings
interviews = [
    "customer_interview_acme_corp.mp4",
    "customer_interview_tech_startup.mp4",
    "user_testing_session_1.mp4"
]

for video_url in interviews:
    import_video(video_url, tags=["customer-research", "interview"])

# Import product feedback videos
feedback_videos = search_social("youtube", "ProductName feedback", count=30)
for video in feedback_videos:
    import_video(video['url'], tags=["customer-research", "feedback"])

# Cross-interview insights
pain_points = chat_personal("""
Tags: customer-research
Question: What are the top pain points mentioned across all interviews?
Rank by frequency and severity.
""")

feature_value = chat_personal("""
Tags: customer-research
Question: Which features do customers mention as most valuable?
What outcomes do they achieve?
""")

use_cases = chat_personal("""
Tags: customer-research
Question: What are the main use cases customers describe?
Group by industry or company size.
""")

# Store insights
create_memory(f"Customer Research Synthesis {date}: {pain_points}",
              tags=["research-insight", "product-roadmap"])
```

**Expected Output:**
- Top 10 pain points ranked
- Feature value hierarchy
- Use case taxonomy
- Product roadmap implications

**ROI:** Centralize customer knowledge, inform product decisions

---

### Use Case: Competitive Intelligence Database

**Scenario:** Maintain up-to-date competitive intelligence from video sources.

**Workflow:**
```python
# Weekly competitor monitoring (automate with cron)
competitors = ["@competitor_a", "@competitor_b", "@competitor_c"]

for competitor in competitors:
    # Search for new videos
    new_videos = search_social("youtube", competitor, count=10)

    # Import only videos from last 7 days
    recent = [v for v in new_videos if is_within_last_week(v['published'])]

    for video in recent:
        import_video(video['url'], tags=["competitive-intel", competitor, "2026-q1"])

# Weekly intelligence report
report = chat_personal("""
Tags: competitive-intel, 2026-q1
Filter: last 7 days
Question: Generate competitive intelligence summary:
1. New product announcements or features
2. Pricing changes
3. Marketing message shifts
4. Partnership announcements
5. Strategic moves (funding, acquisitions, etc.)
""")

# Send to stakeholders
create_memory(f"Weekly Competitive Intel {date}: {report}",
              tags=["intelligence-report", "weekly"])
```

**Expected Output:**
- Automated weekly competitive briefing
- Early detection of competitive moves
- Strategic planning inputs

---

## Creator and Influencer Research

### Use Case: Content Creator Trend Analysis

**Scenario:** Identify emerging content trends in your industry.

**Workflow:**
```python
# Search across platforms for industry hashtags
hashtags = ["#SaaSmarketing", "#ProductManagement", "#StartupTips"]
all_videos = []

for tag in hashtags:
    tiktok = search_social("tiktok", tag, count=100)
    youtube = search_social("youtube", tag.replace("#", ""), count=100)
    all_videos.extend(tiktok + youtube)

# Import recent content (last 30 days)
recent_videos = [v for v in all_videos if is_recent(v['published'], days=30)]
for video in recent_videos:
    import_video(video['url'], tags=["trend-research", "2026-q1"])

# Trend analysis
trends = chat_personal("""
Tags: trend-research, 2026-q1
Question: What are the emerging content trends?
Look for:
- Topics gaining traction (mentioned in 5+ videos)
- Format innovations (new video structures)
- Messaging shifts (new angles on old topics)
- Platform-specific trends (what works on TikTok vs YouTube)
""")

# Validate trend strength
validation = chat_personal("""
Tags: trend-research
Question: For each identified trend, assess:
- Growth trajectory (increasing or peak?)
- Audience engagement (comments, shares)
- Creator adoption (how many creators using this trend?)
- Longevity prediction (fad or sustainable?)
""")
```

**Expected Output:**
- 5-10 emerging trends with growth metrics
- Format innovations to test
- Timing recommendations (early mover vs wait and see)

---

## Advanced Workflows

### Multi-Stage Research Pipeline

**Complete competitive research workflow:**

```python
# Stage 1: Discovery
print("🔍 Stage 1: Discovering competitor content...")
competitors = ["@competitor_a", "@competitor_b"]
all_videos = []

for comp in competitors:
    videos = search_social("youtube", comp, count=50)
    all_videos.extend([(v, comp) for v in videos])

print(f"Found {len(all_videos)} videos")

# Stage 2: Import top performers
print("📥 Stage 2: Importing top performers...")
top_videos = sorted(all_videos, key=lambda x: x[0]['views'], reverse=True)[:30]

for video, comp in top_videos:
    import_video(video['url'], tags=["competitor", comp, "top-performer"])

# Stage 3: Content analysis
print("🔬 Stage 3: Analyzing content patterns...")
content_analysis = chat_personal("""
Tags: competitor, top-performer
Question: Comprehensive content analysis:
1. Content themes (with % breakdown)
2. Average video length by theme
3. Hook patterns (first 5 seconds)
4. CTA strategies
5. Production quality levels
6. Posting frequency
""")

# Stage 4: Messaging extraction
print("💬 Stage 4: Extracting messaging...")
messaging = chat_personal("""
Tags: competitor, top-performer
Question: What are their core messaging pillars?
What customer pain points do they address?
What value propositions do they emphasize?
What proof/credibility elements do they use?
""")

# Stage 5: Gap identification
print("🎯 Stage 5: Identifying opportunities...")
gaps = chat_personal("""
Tags: competitor, top-performer
Question: Based on their content coverage, identify:
1. Topics they're NOT covering (search-demand exists)
2. Angles they're missing on covered topics
3. Audience questions unanswered
4. Format opportunities (they use X, but Y format might work)
""")

# Stage 6: Actionable recommendations
print("📋 Stage 6: Generating recommendations...")
recommendations = chat_personal("""
Based on the competitive analysis (tags: competitor, top-performer),
generate actionable content strategy recommendations:

1. QUICK WINS: What can we do in next 2 weeks?
2. STRATEGIC BETS: What should we invest in next quarter?
3. AVOID: What are they doing that's not working?
4. DIFFERENTIATION: How can we stand out?

Format with specific video ideas and rationale.
""")

# Stage 7: Report generation
print("📊 Stage 7: Compiling final report...")
final_report = f"""
COMPETITIVE CONTENT INTELLIGENCE REPORT
Date: {current_date}
Scope: {len(all_videos)} videos analyzed from {len(competitors)} competitors

{content_analysis}

{messaging}

{gaps}

{recommendations}
"""

create_memory(final_report, tags=["competitive-report", "strategy"])
print("✅ Complete! Report stored in knowledge base.")
```

**Timeline:** 40 hours manual → 3 hours automated
**Output:** Comprehensive competitive intelligence report with actionable recommendations

---

## ROI Summary

| Use Case | Manual Time | Automated Time | Time Saved | Quality Improvement |
|----------|-------------|----------------|------------|---------------------|
| Competitor Analysis | 40 hours | 3 hours | 37 hours | +50% depth |
| Content Research | 20 hours | 2 hours | 18 hours | +70% coverage |
| Meeting Notes | 30 min/meeting | 2 min/meeting | 28 min | +90% completeness |
| Brand Monitoring | 10 hours/week | 1 hour/week | 9 hours | Real-time vs weekly |
| Training KB | N/A | 3 hours setup | N/A | Instant access |
| Influencer Research | 15 hours | 2 hours | 13 hours | +60% data depth |

**Average ROI:** 40x time savings, 60% quality improvement
