Sprint Engine / beseen.studio

```json { "plan_name": "AI Creative Video Analysis for SubSun Sunglasses", "estimated_duration_minutes": 45, "methodology_notes": "Multi-phase analysis focusing on video creative identification, competitor video strategy patterns, and AI-friendly creative frameworks. Limited by inability to analyze video content directly, but can correlate video usage with ad metadata, performance proxies, and timing patterns to identify successful video creative strategies.", "steps": [ { "id": "step_1", "type": "data_fetch", "name": "Fetch all competitor ads data", "depends_on": [], "params": { "table": "competitor_ads", "columns": ["ad_archive_id", "page_name", "primary_text", "headline", "description", "creative_type", "cta_type", "ad_delivery_start_time", "ad_delivery_stop_time", "is_currently_active", "eu_total_reach", "target_ages", "target_gender"], "filters": {}, "limit": 25000 } }, { "id": "step_2", "type": "data_fetch", "name": "Fetch competitor pages for context", "depends_on": [], "params": { "table": "competitor_pages", "columns": ["page_id", "page_name", "category", "country_code"], "filters": {}, "limit": 100 } }, { "id": "step_3", "type": "data_fetch", "name": "Fetch ad daily performance data", "depends_on": [], "params": { "table": "competitor_ad_daily", "columns": ["ad_archive_id", "check_date", "is_active", "eu_total_reach"], "filters": {}, "limit": 300000 } }, { "id": "step_4", "type": "storage_list", "name": "List all video creative files", "depends_on": [], "params": { "all_competitors": true, "video_only": true } }, { "id": "step_5", "type": "python_compute", "name": "Identify video ads and merge with metadata", "depends_on": ["step_1", "step_4"], "params": { "description": "Cross-reference video files with ad metadata to identify which ads have video creative", "code": "import pandas as pd\nimport re\nfrom collections import Counter\n\n# Extract ad_archive_id from video files\nvideo_ads = competitor_media[competitor_media['is_video'] == True].copy()\nvideo_ads['extracted_id'] = video_ads['ad_archive_id']\n\n# Merge video file data with ad metadata\nvideo_ad_data = competitor_ads.merge(video_ads[['ad_archive_id', 'public_url', 'size_bytes', 'folder']], \n left_on='ad_archive_id', right_on='ad_archive_id', how='inner')\n\nprint(f\"Total ads in database: {len(competitor_ads)}\")\nprint(f\"Video files found: {len(video_ads)}\")\nprint(f\"Ads with video creative matched: {len(video_ad_data)}\")\nprint(f\"\\nTop competitors by video ad count:\")\nvideo_counts = video_ad_data['page_name'].value_counts().head(10)\nprint(video_counts.to_string())\n\nvideo_percentage = len(video_ad_data) / len(competitor_ads) * 100\nprint(f\"\\nVideo adoption rate: {video_percentage:.1f}% of all ads\")" } }, { "id": "step_6", "type": "python_compute", "name": "Analyze video vs static ad performance proxies", "depends_on": ["step_1", "step_3", "step_5"], "params": { "description": "Compare longevity and reach patterns between video and static ads", "code": "import pandas as pd\nimport numpy as np\nfrom datetime import datetime\n\n# Calculate ad longevity for all ads\ncompetitor_ads['start_date'] = pd.to_datetime(competitor_ads['ad_delivery_start_time'])\ncompetitor_ads['stop_date'] = pd.to_datetime(competitor_ads['ad_delivery_stop_time'])\ncompetitor_ads['longevity_days'] = (competitor_ads['stop_date'] - competitor_ads['start_date']).dt.days\n\n# Mark ads as video or static\ncompetitor_ads['has_video'] = competitor_ads['ad_archive_id'].isin(video_ad_data['ad_archive_id'])\n\n# Compare performance proxies\nperformance_comparison = competitor_ads.groupby('has_video').agg({\n 'longevity_days': ['mean', 'median', 'count'],\n 'eu_total_reach': ['mean', 'median'],\n 'ad_archive_id': 'count'\n}).round(2)\n\nprint(\"Performance Comparison - Video vs Static Ads:\")\nprint(performance_comparison.to_string())\n\n# Look at current active status\nactive_comparison = competitor_ads.groupby(['has_video', 'is_currently_active']).size().unstack(fill_value=0)\nprint(\"\\nActive Status Distribution:\")\nprint(active_comparison.to_string())\n\nvideo_stats = competitor_ads[competitor_ads['has_video'] == True]\nstatic_stats = competitor_ads[competitor_ads['has_video'] == False]\n\nprint(f\"\\nVideo ads average longevity: {video_stats['longevity_days'].mean():.1f} days\")\nprint(f\"Static ads average longevity: {static_stats['longevity_days'].mean():.1f} days\")" } }, { "id": "step_7", "type": "chart", "name": "Video adoption by competitor visualization", "depends_on": ["step_5"], "params": { "description": "Create bar chart showing video ad volume by top competitors", "code": "import matplotlib.pyplot as plt\nimport seaborn as sns\n\n# Get top 12 competitors by video count\ntop_video_competitors = video_ad_data['page_name'].value_counts().head(12)\n\nfig, ax = plt.subplots(figsize=(12, 8))\nbars = ax.bar(range(len(top_video_competitors)), top_video_competitors.values, \n color='#ff6b35', alpha=0.8)\n\n# Customize the chart\nax.set_xlabel('Competitors', fontsize=12, color='white')\nax.set_ylabel('Number of Video Ads', fontsize=12, color='white')\nax.set_title('Video Ad Volume by Competitor\\n(Eyewear/Fashion Brands)', fontsize=14, color='white', pad=20)\n\n# Set x-axis labels\nax.set_xticks(range(len(top_video_competitors)))\nax.set_xticklabels(top_video_competitors.index, rotation=45, ha='right', color='white')\nax.tick_params(colors='white')\n\n# Add value labels on bars\nfor i, bar in enumerate(bars):\n height = bar.get_height()\n ax.text(bar.get_x() + bar.get_width()/2., height + 1,\n f'{int(height)}', ha='center', va='bottom', color='white', fontsize=10)\n\n# Style the chart\nax.spines['top'].set_visible(False)\nax.spines['right'].set_visible(False)\nax.spines['bottom'].set_color('white')\nax.spines['left'].set_color('white')\nax.grid(True, alpha=0.3, color='white')\n\nplt.tight_layout()\nsave_chart(fig, 'video_adoption_by_competitor.png')\nplt.close()" } }, { "id": "step_8", "type": "python_compute", "name": "Analyze video ad copy patterns and themes", "depends_on": ["step_5"], "params": { "description": "Extract patterns from video ad headlines and primary text to identify 'fun AI creative' themes", "code": "import pandas as pd\nimport re\nfrom collections import Counter\n\n# Analyze headlines for patterns\nvideo_headlines = video_ad_data['headline'].dropna().str.lower()\nvideo_primary_text = video_ad_data['primary_text'].dropna().str.lower()\n\n# Keywords that suggest 'fun' or creative content\nfun_keywords = ['new', 'style', 'collection', 'fresh', 'cool', 'perfect', 'amazing', 'stunning', \n 'love', 'discover', 'explore', 'magic', 'transform', 'elevate', 'vibe', 'mood']\n\nai_creative_keywords = ['personalized', 'custom', 'unique', 'individual', 'personal', 'tailored',\n 'smart', 'intelligent', 'adaptive', 'dynamic', 'interactive']\n\n# Count keyword occurrences in headlines\nheadline_keywords = []\nfor headline in video_headlines:\n if pd.notna(headline):\n words = re.findall(r'\\b\\w+\\b', str(headline))\n headline_keywords.extend(words)\n\nheadline_counter = Counter(headline_keywords)\nprint(\"Top words in video ad headlines:\")\nprint(dict(headline_counter.most_common(20)))\n\n# Analyze fun keywords\nfun_matches = sum(1 for keyword in fun_keywords if keyword in ' '.join(headline_keywords))\nprint(f\"\\nFun-related keywords found: {fun_matches}\")\n\n# Look at CTAs\ncta_analysis = video_ad_data['cta_type'].value_counts()\nprint(\"\\nCall-to-Action types in video ads:\")\nprint(cta_analysis.to_string())\n\n# Sample some actual headlines for context\nprint(\"\\nSample video ad headlines:\")\nsample_headlines = video_ad_data['headline'].dropna().head(10)\nfor i, headline in enumerate(sample_headlines, 1):\n print(f\"{i}. {headline}\")" } }, { "id": "step_9", "type": "llm_synthesis", "name": "Synthesize video creative insights", "depends_on": ["step_5", "step_6", "step_8"], "params": { "prompt_template": "Based on the video ad analysis data, provide insights for SubSun about video creative opportunities:\n\nVideo Adoption Data:\n{{step_5.stdout}}\n\nPerformance Comparison:\n{{step_6.stdout}}\n\nContent Analysis:\n{{step_8.stdout}}\n\nWhat are the key takeaways about video creative strategy in the eyewear/sunglasses market? Focus on practical recommendations for SubSun's 'fun AI creative' initiative." } }, { "id": "step_10", "type": "python_compute", "name": "Identify seasonal and timing patterns in video ads", "depends_on": ["step_5"], "params": { "description": "Analyze when video ads are launched and their seasonal patterns", "code": "import pandas as pd\nimport matplotlib.pyplot as plt\nfrom datetime import datetime\n\n# Convert dates and extract timing info\nvideo_ad_data['launch_date'] = pd.to_datetime(video_ad_data['ad_delivery_start_time'])\nvideo_ad_data['launch_month'] = video_ad_data['launch_date'].dt.month\nvideo_ad_data['launch_year'] = video_ad_data['launch_date'].dt.year\nvideo_ad_data['launch_quarter'] = video_ad_data['launch_date'].dt.quarter\n\n# Monthly distribution\nmonthly_launches = video_ad_data['launch_month'].value_counts().sort_index()\nprint(\"Video ad launches by month:\")\nmonth_names = ['Jan', 'Feb', 'Mar', 'Apr', 'May', 'Jun', 'Jul', 'Aug', 'Sep', 'Oct', 'Nov', 'Dec']\nfor month, count in monthly_launches.items():\n print(f\"{month_names[month-1]}: {count}\")\n\n# Quarterly patterns\nquarterly_launches = video_ad_data['launch_quarter'].value_counts().sort_index()\nprint(\"\\nVideo ad launches by quarter:\")\nfor quarter, count in quarterly_launches.items():\n print(f\"Q{quarter}: {count}\")\n\n# Recent trends (2023-2024)\nrecent_video_ads = video_ad_data[video_ad_data['launch_year'] >= 2023]\nrecent_monthly = recent_video_ads['launch_month'].value_counts().sort_index()\nprint(\"\\nRecent video ad activity (2023+):\")\nfor month, count in recent_monthly.items():\n print(f\"{month_names[month-1]} 2023+: {count}\")\n\n# Peak seasons identification\nspring_summer = video_ad_data[video_ad_data['launch_month'].isin([3,4,5,6,7,8])]\nfall_winter = video_ad_data[video_ad_data['launch_month'].isin([9,10,11,12,1,2])]\n\nprint(f\"\\nSpring/Summer launches: {len(spring_summer)} ({len(spring_summer)/len(video_ad_data)*100:.1f}%)\")\nprint(f\"Fall/Winter launches: {len(fall_winter)} ({len(fall_winter)/len(video_ad_data)*100:.1f}%)\")" } }, { "id": "step_11", "type": "chart", "name": "Seasonal video launch patterns", "depends_on": ["step_10"], "params": { "description": "Create visualization of video ad seasonal launch patterns", "code": "import matplotlib.pyplot as plt\nimport numpy as np\n\n# Create the seasonal pattern chart\nfig, (ax1, ax2) = plt.subplots(1, 2, figsize=(15, 6))\n\n# Monthly distribution\nmonthly_data = video_ad_data['launch_month'].value_counts().sort_index()\nmonth_names = ['Jan', 'Feb', 'Mar', 'Apr', 'May', 'Jun', 'Jul', 'Aug', 'Sep', 'Oct', 'Nov', 'Dec']\n\nax1.bar(range(1, 13), [monthly_data.get(i, 0) for i in range(1, 13)], \n color='#35a8ff', alpha=0.8)\nax1.set_xlabel('Month', color='white')\nax1.set_ylabel('Video Ad Launches', color='white')\nax1.set_title('Video Ad Launches by Month', color='white', fontsize=12)\nax1.set_xticks(range(1, 13))\nax1.set_xticklabels([m[:3] for m in month_names], color='white')\nax1.tick_params(colors='white')\nax1.grid(True, alpha=0.3, color='white')\n\n# Quarterly distribution\nquarterly_data = video_ad_data['launch_quarter'].value_counts().sort_index()\ncolors = ['#ff6b35', '#35a8ff', '#4ecdc4', '#45b7d1']\nax2.pie([quarterly_data.get(i, 0) for i in range(1, 5)], \n labels=['Q1', 'Q2', 'Q3', 'Q4'],\n colors=colors, autopct='%1.1f%%', startangle=90,\n textprops={'color': 'white'})\nax2.set_title('Video Ad Distribution by Quarter', color='white', fontsize=12)\n\n# Style both subplots\nfor ax in [ax1, ax2]:\n ax.spines['top'].set_visible(False)\n ax.spines['right'].set_visible(False)\n ax.spines['bottom'].set_color('white')\n ax.spines['left'].set_color('white')\n\nplt.tight_layout()\nsave_chart(fig, 'seasonal_video_patterns.png')\nplt.close()" } }, { "id": "step_12", "type": "python_compute", "name": "Analyze top performing video competitors in detail", "depends_on": ["step_5", "step_6"], "params": { "description": "Deep dive into top 5 video-using competitors and their strategies", "code": "import pandas as pd\nfrom collections import Counter\n\n# Get top 5 video competitors\ntop_5_video = video_ad_data['page_name'].value_counts().head(5).index.tolist()\n\nprint(\"DETAILED ANALYSIS - TOP 5 VIDEO COMPETITORS:\")\nprint(\"=\" * 50)\n\nfor competitor in top_5_video:\n comp_videos = video_ad_data[video_ad_data['page_name'] == competitor]\n \n print(f\"\\n{competitor.upper()}:\")\n print(f\"- Total video ads: {len(comp_videos)}\")\n print(f\"- Average longevity: {comp_videos['longevity_days'].mean():.1f} days\")\n print(f\"- Average EU reach: {comp_videos['eu_total_reach'].mean():.0f}\")\n print(f\"- Currently active: {comp_videos['is_currently_active'].sum()}\")\n \n # CTA analysis\n ctas = comp_videos['cta_type'].value_counts().head(3)\n print(f\"- Top CTAs: {', '.join([f'{cta} ({count})' for cta, count in ctas.items()])}\")\n \n # Sample headlines\n sample_headlines = comp_videos['headline'].dropna().head(3).tolist()\n print(f\"- Sample headlines:\")\n for i, headline in enumerate(sample_headlines, 1):\n print(f\" {i}. {headline}\")\n \n # File size analysis (proxy for video complexity)\n avg_size_mb = comp_videos['size_bytes'].mean() / (1024*1024)\n print(f\"- Average video size: {avg_size_mb:.1f} MB\")\n \n print(\"-\" * 30)\n\n# Generate public URLs for top examples\nprint(\"\\nTOP VIDEO EXAMPLES FOR REVIEW:\")\nprint(\"=\" * 40)\n\n# Get best performing videos (by longevity * reach proxy)\nvideo_ad_data['performance_score'] = video_ad_data['longevity_days'] * (video_ad_data['eu_total_reach'].fillna(1))\ntop_videos = video_ad_data.nlargest(10, 'performance_score')\n\nfor idx, row in top_videos.iterrows():\n print(f\"\\n{row['page_name']} - Performance Score: {row['performance_score']:.0f}\")\n print(f\"Headline: {row['headline']}\")\n print(f\"Video URL: {row['public_url']}\")\n print(f\"Duration: {row['longevity_days']} days | Reach: {row['eu_total_reach']}\")" } }, { "id": "step_13", "type": "python_compute", "name": "Create AI creative framework recommendations", "depends_on": ["step_8", "step_12"], "params": { "description": "Synthesize findings into actionable AI creative framework for SubSun", "code": "import pandas as pd\nfrom collections import Counter\n\n# Analyze successful patterns for AI creative framework\nprint(\"AI CREATIVE FRAMEWORK FOR SUBSUN\")\nprint(\"=\" * 40)\n\n# 1. Format Strategy\nvideo_adoption_rate = len(video_ad_data) / len(competitor_ads) * 100\nprint(f\"\\n1. VIDEO OPPORTUNITY ASSESSMENT:\")\nprint(f\"- Current market video adoption: {video_adoption_rate:.1f}%\")\nprint(f\"- This represents a differentiation opportunity for SubSun\")\nprint(f\"- Video ads show {video_ad_data['longevity_days'].mean():.1f} day average lifespan\")\n\n# 2. Content Themes\nprint(f\"\\n2. SUCCESSFUL CONTENT THEMES:\")\nall_headlines = ' '.join(video_ad_data['headline'].dropna().str.lower())\ntheme_keywords = {\n 'lifestyle': ['style', 'look', 'vibe', 'mood', 'feel'],\n 'product_focus': ['sunglasses', 'eyewear', 'glasses', 'frames'],\n 'emotional': ['love', 'perfect', 'amazing', 'stunning', 'beautiful'],\n 'action': ['discover', 'explore', 'find', 'get', 'shop', 'try'],\n 'seasonal': ['summer', 'sun', 'beach', 'vacation', 'outdoor']\n}\n\nfor theme, keywords in theme_keywords.items():\n count = sum(all_headlines.count(keyword) for keyword in keywords)\n print(f\"- {theme.replace('_', ' ').title()}: {count} mentions\")\n\n# 3. CTA Strategy\nprint(f\"\\n3. CALL-TO-ACTION STRATEGY:\")\ntop_ctas = video_ad_data['cta_type'].value_counts().head(5)\nfor cta, count in top_ctas.items():\n percentage = (count / len(video_ad_data)) * 100\n print(f\"- {cta}: {count} uses ({percentage:.1f}%)\")\n\n# 4. Timing Strategy\nprint(f\"\\n4. OPTIMAL TIMING PATTERNS:\")\nspring_summer_count = len(video_ad_data[video_ad_data['launch_month'].isin([3,4,5,6,7,8])])\nfall_winter_count = len(video_ad_data[video_ad_data['launch_month'].isin([9,10,11,12,1,2])])\nprint(f\"- Spring/Summer launches: {spring_summer_count} ({spring_summer_count/len(video_ad_data)*100:.1f}%)\")\nprint(f\"- Fall/Winter launches: {fall_winter_count} ({fall_winter_count/len(video_ad_data)*100:.1f}%)\")\n\n# 5. AI Implementation Suggestions\nprint(f\"\\n5. AI CREATIVE IMPLEMENTATION IDEAS:\")\nprint(f\"- Personalized frame try-on experiences\")\nprint(f\"- Dynamic lifestyle scene matching (beach, city, etc.)\")\nprint(f\"- Seasonal color/style adaptation\")\nprint(f\"- User-generated content amplification\")\nprint(f\"- Virtual styling assistant demos\")\n\n# 6. Competitor Benchmarks\nprint(f\"\\n6. KEY COMPETITORS TO WATCH:\")\ntop_performers = video_ad_data.groupby('page_name').agg({\n 'performance_score': 'mean',\n 'ad_archive_id': 'count'\n}).sort_values('performance_score', ascending=False).head(5)\n\nfor competitor, data in top_performers.iterrows():\n print(f\"- {competitor}: Avg Performance {data['performance_score']:.0f}, {data['ad_archive_id']} videos\")" } }, { "id": "step_14", "type": "chart", "name": "AI creative opportunity matrix", "depends_on": ["step_13"], "params": { "description": "Create visual framework showing video adoption vs performance for competitor positioning", "code": "import matplotlib.pyplot as plt\nimport numpy as np\n\n# Create competitor performance matrix\ncompetitor_stats = video_ad_data.groupby('page_name').agg({\n 'longevity_days': 'mean',\n 'eu_total_reach': 'mean',\n 'ad_archive_id': 'count',\n 'size_bytes': 'mean'\n}).fillna(0)\n\n# Filter to competitors with at least 5 videos\nsignificant_competitors = competitor_stats[competitor_stats['ad_archive_id'] >= 5]\n\nfig, ax = plt.subplots(figsize=(12, 8))\n\n# Create bubble chart\nfor idx, (competitor, data) in enumerate(significant_competitors.iterrows()):\n x = data['longevity_days']\n y = data['eu_total_reach']\n size = data['ad_archive_id'] * 20 # Scale bubble size\n \n ax.scatter(x, y, s=size, alpha=0.6, \n color=plt.cm.viridis(idx / len(significant_competitors)))\n \n # Label points\n ax.annotate(competitor, (x, y), xytext=(5, 5), \n textcoords='offset points', fontsize=9, color='white')\n\nax.set_xlabel('Average Ad Longevity (Days)', fontsize=12, color='white')\nax.set_ylabel('Average EU Reach', fontsize=12, color='white')\nax.set_title('Video Creative Performance Matrix\\n(Bubble size = Number of Videos)', \n fontsize=14, color='white', pad=20)\n\n# Add quadrant lines\nmedian_longevity = significant_competitors['longevity_days'].median()\nmedian_reach = significant_competitors['eu_total_reach'].median()\n\nax.axvline(median_longevity, color='white', alpha=0.3, linestyle='--')\nax.axhline(median_reach, color='white', alpha=0.3, linestyle='--')\n\n# Add quadrant labels\nax.text(ax.get_xlim()[1]*0.95, ax.get_ylim()[1]*0.95, 'High Reach\\nLong Duration', \n ha='right', va='top', color='white', alpha=0.7)\nax.text(ax.get_xlim()[0]*1.05, ax.get_ylim()[0]*1.05, 'Low Reach\\nShort Duration', \n ha='left', va='bottom', color='white', alpha=0.7)\n\nax.tick_params(colors='white')\nax.spines['top'].set_visible(False)\nax.spines['right'].set_visible(False)\nax.spines['bottom'].set_color('white')\nax.spines['left'].set_color('white')\nax.grid(True, alpha=0.3, color='white')\n\nplt.tight_layout()\nsave_chart(fig, 'video_performance_matrix.png')\nplt.close()" } }, { "id": "step_15", "type": "python_compute", "name": "Generate specific video examples for SubSun", "depends_on": ["step_12", "step_13"], "params": { "description": "Create specific video URLs and examples SubSun should review for inspiration", "code": "import pandas as pd\n\nprint(\"CURATED VIDEO EXAMPLES FOR SUBSUN REVIEW\")\nprint(\"=\" * 45)\n\n# Categories of videos to showcase\ncategories = {\n 'High Performance': video_ad_data.nlargest(5, 'performance_score'),\n 'Long Running': video_ad_data.nlargest(5, 'longevity_days'),\n 'Recent Launches': video_ad_data[video_ad_data['launch_date'] >= '2024-01-01'].head(5),\n 'Fun/Lifestyle Focus': video_ad_data[video_ad_data['headline'].str.contains('style|vibe|look|cool|amazing', case=False, na=False)].head(5)\n}\n\nfor category, videos in categories.items():\n if len(videos) > 0:\n print(f\"\\n{category.upper()} EXAMPLES:\")\n print(\"-\" * 30)\n \n for idx, (_, video) in enumerate(videos.iterrows(), 1):\n print(f\"{idx}. {video['page_name']}\")\n print(f\" Headline: {video['headline']}\")\n print(f\" Duration: {video['longevity_days']} days | Reach: {video['eu_total_reach']}\")\n print(f\" Video URL: {video['public_url']}\")\n print(f\" File Size: {video['size_bytes']/(1024*1024):.1f} MB\")\n \n # Add AI creative suggestions based on this example\n if 'style' in str(video['headline']).lower():\n print(f\" 💡 AI Idea: Personalized style matching\")\n elif 'new' in str(video['headline']).lower():\n print(f\" 💡 AI Idea: Dynamic product reveals\")\n elif 'perfect' in str(video['headline']).lower():\n print(f\" 💡 AI Idea: Face-shape matching algorithm\")\n else:\n print(f\" 💡 AI Idea: Context-aware lifestyle scenarios\")\n print()\n\n# Summary recommendations\nprint(\"\\nSTARTER VIDEO CONCEPTS FOR SUBSUN:\")\nprint(\"=\" * 35)\nprint(\"1. 'Perfect Frame Finder' - AI-powered face shape analysis\")\nprint(\"2. 'Style Chameleon' - Dynamic background/lifestyle matching\")\nprint(\"3. 'Seasonal Adaptive' - Auto-adjusting color palettes\")\nprint(\"4. 'Virtual Try-On Journey' - Progressive style exploration\")\nprint(\"5. 'Sun Story Generator' - Personalized adventure narratives\")\n\n# Technical specs based on successful videos\navg_size = video_ad_data['size_bytes'].mean() / (1024*1024)\nprint(f\"\\nTECHNICAL RECOMMENDATIONS:\")\nprint(f\"- Target video size: {avg_size:.1f} MB average

```json { "questions": [], "reasoning": "The brief is clear enough to proceed. The objective is to identify 'fun AI creative' video examples from competitors in the sunglasses/fashion space that match a similar vibe for SubSun. I have access to the competitor_ads table with 22k+ ads including creative_type, preview_url, and other metadata needed to filter for video content. I can analyze competitor video ads in the fashion/sunglasses vertical and provide actionable examples. The term 'fun AI creative' may be subjective but I can infer this means engaging, creative video content that could be AI-generated or AI-enhanced. I'll make reasonable assumptions about what constitutes 'similar vibe' based on the data patterns." } ```