Video Marketing Strategy — YouTube, TikTok & Short-Form Guide

class YouTubeStrategy:
    """Simulate YouTube content optimization."""

    def __init__(self):
        self.keywords = []
        self.videos = []

    def keyword_research(self, seed_terms):
        """Generate keyword clusters from seed terms."""
        clusters = []
        for seed in seed_terms:
            cluster = {
                "seed": seed,
                "head_term": seed,
                "long_tail": [
                    f"{seed} for beginners",
                    f"{seed} tutorial 2026",
                    f"how to {seed} step by step",
                    f"{seed} explained simply",
                    f"advanced {seed} techniques",
                ],
            }
            self.keywords.append(cluster)
        return self.keywords

    def optimize_video(self, title, description, tags):
        """Optimize a video for YouTube search."""
        optimization = {
            "title": title,
            "title_length": len(title),
            "title_score": "good" if 30 <= len(title) <= 60 else "needs work",
            "description_terms": len(description.split()),
            "tags": tags,
        }

        # Keyword density check
        for cluster in self.keywords:
            seed = cluster["seed"].lower()
            if seed in title.lower():
                optimization["primary_keyword"] = seed
                optimization["keyword_in_title"] = True
                break

        self.videos.append(optimization)
        return optimization

    def score_thumbnail(self, has_text, has_face, contrast_level, brightness):
        """Simple thumbnail quality scoring."""
        score = 0
        if has_text:
            score += 25
        if has_face:
            score += 20
        score += min(contrast_level, 30)
        score += min(brightness, 25)
        return {"score": score, "grade": "A" if score > 80 else "B" if score > 60 else "C"}

youtube = YouTubeStrategy()
kw = youtube.keyword_research(["data engineering", "python tutorial"])
print("Keyword clusters:")
for c in kw:
    print(f"  {c['head_term']}: {c['long_tail'][:2]}")

opt = youtube.optimize_video(
    "Data Engineering for Beginners — Complete 2026 Guide",
    "Learn data engineering from scratch with this complete beginner's guide covering ETL, pipelines, and tools.",
    ["data engineering", "etl", "data pipeline", "big data", "tutorial"],
)
print(f"\nVideo optimization: keyword='{opt.get('primary_keyword')}', "
      f"title_score={opt['title_score']}")

thumb = youtube.score_thumbnail(has_text=True, has_face=True, contrast_level=25, brightness=22)
print(f"Thumbnail score: {thumb['score']} ({thumb['grade']})")

class TikTokStrategy:
    """Simulate TikTok content optimization."""

    def __init__(self):
        self.hooks = []
        self.content = []

    def generate_hooks(self, topic):
        """Generate attention-grabbing hooks for the first 3 seconds."""
        hook_templates = [
            f"Stop scrolling if you want to learn {topic}",
            f"The #1 mistake people make with {topic}",
            f"I tried {topic} for 30 days and here's what happened",
            f"You're doing {topic} wrong. Here's the fix.",
            f"This {topic} trick will save you hours",
            f"POV: You just discovered the secret to {topic}",
        ]
        self.hooks = hook_templates
        return hook_templates[:3]

    def analyze_video(self, views, likes, comments, shares, save_rate):
        """Analyze TikTok performance metrics."""
        engagement_rate = ((likes + comments + shares) / views) * 100 if views > 0 else 0
        return {
            "views": views,
            "engagement_rate": round(engagement_rate, 2),
            "save_rate": round(save_rate, 2),
            "viral_potential": "high" if engagement_rate > 10 else "medium" if engagement_rate > 5 else "low",
        }

    def best_posting_time(self, audience_tz="US/Eastern"):
        """Suggest optimal posting times."""
        # TikTok recommends posting when target audience is most active
        times = {
            "US/Eastern": ["7:00 AM", "12:00 PM", "7:00 PM", "10:00 PM"],
            "Europe/London": ["8:00 AM", "1:00 PM", "6:00 PM", "9:00 PM"],
            "Asia/Tokyo": ["7:00 AM", "12:00 PM", "8:00 PM", "11:00 PM"],
        }
        return times.get(audience_tz, times["US/Eastern"])

tiktok = TikTokStrategy()
hooks = tiktok.generate_hooks("data engineering")
print("TikTok hooks:", hooks)

perf = tiktok.analyze_video(50000, 4500, 320, 1800, 12.5)
print(f"Performance: {perf['engagement_rate']}% engagement, viral={perf['viral_potential']}")
print(f"Best times: {tiktok.best_posting_time()}")

class VideoProduction:
    """Simulate video production workflow."""

    def __init__(self, title):
        self.title = title
        self.script_sections = []
        self.timeline_minutes = 0

    def add_section(self, name, duration_minutes, script):
        section = {
            "name": name,
            "duration": duration_minutes,
            "script": script,
        }
        self.script_sections.append(section)
        self.timeline_minutes += duration_minutes
        return section

    def calculate_retention(self):
        """Simulate audience retention curve."""
        retention = 100
        curve = []
        for i, section in enumerate(self.script_sections):
            retention *= 1 - (0.3 * (1 / (i + 2)))  # Decay slows over time
            curve.append({
                "section": section["name"],
                "retention_pct": round(retention, 1),
            })
        return curve

    def suggest_cta(self):
        """Suggest call to action based on content type."""
        if any("tutorial" in s["name"].lower() for s in self.script_sections):
            return "Subscribe for more tutorials"
        return "Like and share if you found this valuable"

# Build a tutorial video
production = VideoProduction("Data Engineering for Beginners")
production.add_section("Hook", 0.5, "Stop scrolling if you want to break into data engineering")
production.add_section("Problem", 1.0, "Most beginners don't know where to start")
production.add_section("Solution", 3.0, "Here are the 3 skills you need: SQL, Python, and pipelines")
production.add_section("Demo", 2.5, "Let me show you a real pipeline in action")
production.add_section("Outro", 0.5, "Subscribe for more and comment your questions")

print(f"Total duration: {production.timeline_minutes} minutes")
print(f"CTA: {production.suggest_cta()}")
print("\nRetention curve:")
for c in production.calculate_retention():
    print(f"  {c['section']:<20} {c['retention_pct']}% retained")

class YouTubeSEO:
    """Optimize YouTube metadata for search."""

    def __init__(self):
        self.rankings = {}

    def optimize_title(self, keyword, power_words=None):
        """Generate SEO-optimized title variations."""
        pw = power_words or ["Complete", "Ultimate", "Step-by-Step", "2026"]
        variations = [
            f"{keyword} — {pw[0]} Guide for Beginners",
            f"{pw[1]} {keyword} Tutorial ({pw[0]} Course)",
            f"Learn {keyword}: {pw[2]} {pw[0]} Guide 2026",
            f"{keyword} Explained — {pw[0]} Beginner's Guide",
        ]
        return variations[:3]

    def score_video(self, title, description_keywords, avg_view_duration, ctr):
        """Score video for YouTube ranking potential."""
        score = 0
        if 30 <= len(title) <= 60:
            score += 20
        if avg_view_duration > 0.5:  # 50%+ retention
            score += 35
        if ctr > 5:
            score += 25
        if len(description_keywords) >= 5:
            score += 10
        if "2026" in title or "Guide" in title:
            score += 10

        return {"score": score, "ranking_potential": "high" if score > 70 else "medium" if score > 50 else "low"}

seo = YouTubeSEO()
titles = seo.optimize_title("data engineering tutorial", ["Complete", "Ultimate", "Step-by-Step"])
print("Optimized titles:", titles)

score = seo.score_video(
    "Data Engineering Tutorial — Complete Beginner's Guide 2026",
    ["data engineering", "etl", "pipeline", "sql", "python", "big data"],
    0.62, 8.2,
)
print(f"SEO score: {score['score']}/100 — {score['ranking_potential']}")

class VideoAnalytics:
    """Track video performance across platforms."""

    def __init__(self):
        self.metrics = {}

    def record_platform(self, platform, video_name, views, engagement, conversions):
        """Record metrics for a video on a platform."""
        key = f"{platform}:{video_name}"
        self.metrics[key] = {
            "platform": platform,
            "video": video_name,
            "views": views,
            "engagement_rate": round((engagement / views) * 100, 2) if views > 0 else 0,
            "conversion_rate": round((conversions / views) * 100, 3) if views > 0 else 0,
            "cost_per_view": 0,
        }
        return self.metrics[key]

    def calculate_roi(self, total_cost, attributed_revenue):
        """Calculate ROI of video marketing campaign."""
        roi = ((attributed_revenue - total_cost) / total_cost) * 100
        return {"cost": total_cost, "revenue": attributed_revenue, "roi_pct": round(roi, 1)}

    def best_performing(self, metric="conversion_rate"):
        """Find the best performing video by a metric."""
        best = max(self.metrics.items(), key=lambda x: x[1].get(metric, 0))
        return best[1]

analytics = VideoAnalytics()
analytics.record_platform("YouTube", "Data Eng 101", 15000, 1200, 45)
analytics.record_platform("TikTok", "SQL in 60s", 85000, 12000, 12)
analytics.record_platform("LinkedIn", "Pipeline Tips", 5000, 350, 8)

best_convert = analytics.best_performing("conversion_rate")
print(f"Best converting: {best_convert['platform']} - {best_convert['video']} "
      f"({best_convert['conversion_rate']}%)")

roi = analytics.calculate_roi(5000, 22500)
print(f"Campaign ROI: {roi['roi_pct']}%")

# video_calendar.py
# Generate a 30-day video content calendar

class VideoContentCalendar:
    def __init__(self, topic, platforms):
        self.topic = topic
        self.platforms = platforms
        self.schedule = []

    def generate(self, days=30):
        import random
        content_types = ["tutorial", "tip", "behind_scenes", "case study", 
                         "comparison", "FAQ", "trend", "story"]
        for day in range(1, days + 1):
            platform = random.choice(self.platforms)
            content = random.choice(content_types)
            self.schedule.append({
                "day": day,
                "platform": platform,
                "content_type": content,
                "title": f"{self.topic} {content} #{day}",
            })
        return self.schedule

    def report(self):
        from collections import Counter
        platform_counts = Counter(s["platform"] for s in self.schedule)
        type_counts = Counter(s["content_type"] for s in self.schedule)
        print(f"30-Day Video Calendar: {self.topic}")
        print(f"Platforms: {dict(platform_counts)}")
        print(f"Content mix: {dict(type_counts)}")

cal = VideoContentCalendar("Data Engineering", ["YouTube", "TikTok", "LinkedIn"])
cal.generate()
cal.report()