Sprint Engine / beseen.studio

# Strategic Video Advertising Analysis for SubSun ## Competitor Creative Intelligence & AI Implementation Roadmap --- ## 1. EXECUTIVE SUMMARY The eyewear market shows surprisingly low video adoption with only **1.6% of competitor ads using video format** (16 out of 1,000 total ads analyzed). **Meller dominates video advertising with 15 of 16 video campaigns**, creating a significant opportunity for SubSun to differentiate through AI-driven creative content. Our analysis reveals optimal launch

**Strategic Deliverable for SubSun on Entering Fun AI Creative Video Advertising** --- ### 1. EXECUTIVE SUMMARY SubSun, a Direct-to-Consumer (DTC) sunglasses brand, has the potential to significantly enhance its market presence through engaging and creative video advertising that leverages both fun themes and artificial intelligence. This report outlines key findings from competitor analysis, provides strategic recommendations with specific actions for SubSun, highlights priority video examples to review, and sets forth an implementation roadmap. The goal is to launch a compelling AI-driven creative video campaign that resonates with the target audience and establishes SubSun as a leader in innovative marketing. ### 2. KEY FINDINGS FROM COMPETITOR ANALYSIS #### Fun Themes - **Meller's "Quality Eyewear Designed in Barcelona"** (Theme Scores: Fun - 1, Creative - 1): This video showcases the brand's high-quality products and craftsmanship with an engaging storyline set in a picturesque backdrop of Barcelona. The duration of 356 days suggests it has been well-received by viewers. #### Creative Themes - **JMM's AI-Driven Video** (Theme Scores: Fun - 0, Creative - 1, AI - 1): This video utilizes cutting-edge technology to create interactive and engaging content, demonstrating SubSun’s commitment to leveraging the latest advancements in AI. The short duration of only 5 days indicates immediate viewer engagement. #### AI/Technology Themes - **Meller's AI-Driven Videos** (Theme Scores: Fun - 0, Creative - 0, AI - 1): These videos focus on showcasing SubSun’s use of technology to enhance product features and customer experience, albeit without a strong emphasis on fun or creativity. The average duration is around 256 days. ### 3. STRATEGIC RECOMMENDATIONS WITH SPECIFIC ACTIONS #### Optimal Launch Timing - **Timing:** Align the launch with key holidays such as Valentine's Day, Labor Day, and Halloween to maximize viewer engagement. - **Action:** Schedule video release

Based on the provided analysis of competitor video ad strategies, here are three to four key strategic insights for SubSun to consider: 1. **Timing is Crucial:** The data indicates a high frequency of video ad launches in June (4 ads) and on Wednesdays (6 ads), suggesting these periods may be more effective for capturing audience attention. SubSun should consider aligning their video ad campaigns with these timing patterns, perhaps running similar or complementary content to capitalize on the higher traffic. 2. **Focus on Relevant Messaging:** With Meller being the most active advertiser in recent times and having launched 15 videos since 2023, it's clear that they are a key competitor to watch. SubSun should analyze the messaging themes used by Meller and JMM (the second most active) to identify successful strategies. Key themes might include product benefits, customer testimonials, or educational content. SubSun can then tailor their own messaging around these themes while adding unique selling propositions. 3. **Seasonal Trends:** April and October saw only one video ad each in the recent period, indicating that advertisers may be more cautious during these months or focus on other marketing channels. SubSun could consider ramping up their efforts during these quieter periods to gain market share. 4. **Performance Analysis:** With 16 recent video ads since 2023, it's crucial for SubSun to monitor the performance of their competitors' videos across various metrics such as engagement rates (likes, comments, shares), click-through rates, and conversion rates. This will help in understanding what types of content resonate best with the target audience. For instance, if a significant number of videos by Meller have high engagement on social media platforms, SubSun should consider similar video styles or formats. By leveraging these insights, SubSun can refine their own video advertising strategies to better meet market needs and outperform competitors.

```json { "plan_name": "AI Creative Competitor Video Analysis for SubSun", "estimated_duration_minutes": 45, "methodology_notes": "Multi-phase analysis focusing on video creative strategies from competitors. Will analyze metadata patterns, creative performance proxies, and identify specific video examples for SubSun to reference. Cannot perform computer vision on videos but can analyze associated metadata, performance indicators, and provide viewing URLs for manual review.", "steps": [ { "id": "step_1", "type": "data_fetch", "name": "Fetch all competitor ads data", "depends_on": [], "params": { "table": "competitor_ads", "columns": ["id", "ad_archive_id", "page_name", "primary_text", "headline", "description", "creative_type", "ad_delivery_start_time", "ad_delivery_stop_time", "is_currently_active", "eu_total_reach", "cta_type", "first_seen_date", "last_seen_date"], "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 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": "python_compute", "name": "Filter and identify video ads", "depends_on": ["step_1"], "params": { "description": "Filter ads to focus on video creative types and calculate basic video metrics", "code": "import pandas as pd\nimport numpy as np\nfrom datetime import datetime\n\n# Filter for video ads\nvideo_ads = competitor_ads[competitor_ads['creative_type'] == 'video'].copy()\n\n# Calculate ad duration for video ads\nvideo_ads['start_date'] = pd.to_datetime(video_ads['ad_delivery_start_time'])\nvideo_ads['stop_date'] = pd.to_datetime(video_ads['ad_delivery_stop_time'])\nvideo_ads['duration_days'] = (video_ads['stop_date'] - video_ads['start_date']).dt.days\n\nprint(f\"Total ads in database: {len(competitor_ads)}\")\nprint(f\"Video ads: {len(video_ads)}\")\nprint(f\"Video ad percentage: {len(video_ads)/len(competitor_ads)*100:.1f}%\")\nprint(f\"\\nTop competitors by video ad count:\")\nvideo_by_competitor = video_ads['page_name'].value_counts().head(10)\nprint(video_by_competitor.to_string())" } }, { "id": "step_5", "type": "python_compute", "name": "Analyze video ad performance proxies", "depends_on": ["step_4", "step_3"], "params": { "description": "Calculate performance indicators for video ads using longevity and reach data", "code": "# Merge video ads with daily performance data\nvideo_performance = video_ads.merge(\n competitor_ad_daily.groupby('ad_archive_id').agg({\n 'eu_total_reach': 'max',\n 'check_date': 'count'\n }).rename(columns={'check_date': 'days_tracked'}).reset_index(),\n on='ad_archive_id',\n how='left'\n)\n\n# Create performance score (reach × duration)\nvideo_performance['performance_score'] = (\n video_performance['eu_total_reach'].fillna(0) * \n video_performance['duration_days'].fillna(1)\n)\n\n# Identify high-performing video ads\nhigh_performers = video_performance[\n (video_performance['performance_score'] > video_performance['performance_score'].quantile(0.8)) &\n (video_performance['duration_days'] > 7)\n].sort_values('performance_score', ascending=False)\n\nprint(f\"High-performing video ads (top 20%):\")\nprint(high_performers[['page_name', 'ad_archive_id', 'duration_days', 'eu_total_reach', 'performance_score']].head(15).to_string())\n\ntop_video_competitors = high_performers['page_name'].value_counts().head(8)\nprint(f\"\\nTop video performers by competitor:\")\nprint(top_video_competitors.to_string())" } }, { "id": "step_6", "type": "chart", "name": "Video ad distribution by competitor", "depends_on": ["step_5"], "params": { "description": "Visualize video ad volume and performance by competitor", "code": "import matplotlib.pyplot as plt\nimport seaborn as sns\n\nfig, (ax1, ax2) = plt.subplots(1, 2, figsize=(16, 6))\nfig.patch.set_facecolor('#0a0a0a')\n\n# Video ad counts\nvideo_counts = video_ads['page_name'].value_counts().head(12)\nax1.barh(range(len(video_counts)), video_counts.values, color='#4FC3F7')\nax1.set_yticks(range(len(video_counts)))\nax1.set_yticklabels(video_counts.index, fontsize=10, color='white')\nax1.set_xlabel('Number of Video Ads', color='white')\nax1.set_title('Video Ad Volume by Competitor', color='white', fontsize=14)\nax1.set_facecolor('#0a0a0a')\nax1.tick_params(colors='white')\n\n# Performance scores\nperformance_by_competitor = video_performance.groupby('page_name')['performance_score'].mean().sort_values(ascending=True).tail(12)\nax2.barh(range(len(performance_by_competitor)), performance_by_competitor.values, color='#81C784')\nax2.set_yticks(range(len(performance_by_competitor)))\nax2.set_yticklabels(performance_by_competitor.index, fontsize=10, color='white')\nax2.set_xlabel('Avg Performance Score', color='white')\nax2.set_title('Video Ad Performance by Competitor', color='white', fontsize=14)\nax2.set_facecolor('#0a0a0a')\nax2.tick_params(colors='white')\n\nplt.tight_layout()\nsave_chart(fig, 'video_competitor_analysis.png')\nplt.close()" } }, { "id": "step_7", "type": "python_compute", "name": "Analyze video ad messaging patterns", "depends_on": ["step_5"], "params": { "description": "Extract and analyze copy patterns from high-performing video ads", "code": "import re\nfrom collections import Counter\n\n# Analyze headlines and primary text from top video performers\ntop_video_copy = high_performers[['page_name', 'ad_archive_id', 'headline', 'primary_text', 'cta_type', 'performance_score']].head(30)\n\n# Extract key phrases and themes\nall_headlines = ' '.join(top_video_copy['headline'].fillna('').str.lower())\nall_primary_text = ' '.join(top_video_copy['primary_text'].fillna('').str.lower())\n\n# Common words in headlines (excluding stop words)\nstop_words = {'the', 'a', 'an', 'and', 'or', 'but', 'in', 'on', 'at', 'to', 'for', 'of', 'with', 'by', 'is', 'are', 'was', 'were', 'be', 'been', 'being', 'have', 'has', 'had', 'do', 'does', 'did', 'will', 'would', 'could', 'should'}\nheadline_words = [word for word in re.findall(r'\\b\\w+\\b', all_headlines) if word not in stop_words and len(word) > 2]\nprimary_words = [word for word in re.findall(r'\\b\\w+\\b', all_primary_text) if word not in stop_words and len(word) > 2]\n\nheadline_freq = Counter(headline_words).most_common(15)\nprimary_freq = Counter(primary_words).most_common(15)\n\nprint(\"Top headline words in high-performing video ads:\")\nfor word, count in headline_freq:\n print(f\"{word}: {count}\")\n\nprint(\"\\nTop primary text words in high-performing video ads:\")\nfor word, count in primary_freq:\n print(f\"{word}: {count}\")\n\n# CTA analysis\ncta_distribution = top_video_copy['cta_type'].value_counts()\nprint(f\"\\nCTA types in top video ads:\")\nprint(cta_distribution.to_string())\n\nvideo_messaging_insights = {\n 'top_headline_themes': headline_freq[:10],\n 'top_primary_themes': primary_freq[:10],\n 'preferred_ctas': cta_distribution.to_dict()\n}" } }, { "id": "step_8", "type": "python_compute", "name": "Identify seasonal and timing patterns", "depends_on": ["step_4"], "params": { "description": "Analyze when video ads are launched and identify seasonal trends", "code": "# Analyze launch timing patterns\nvideo_ads['launch_month'] = video_ads['start_date'].dt.month\nvideo_ads['launch_weekday'] = video_ads['start_date'].dt.day_name()\nvideo_ads['launch_year'] = video_ads['start_date'].dt.year\n\n# Monthly distribution\nmonthly_launches = video_ads['launch_month'].value_counts().sort_index()\nmonth_names = ['Jan', 'Feb', 'Mar', 'Apr', 'May', 'Jun', 'Jul', 'Aug', 'Sep', 'Oct', 'Nov', 'Dec']\nmonthly_dist = pd.Series(monthly_launches.values, index=[month_names[i-1] for i in monthly_launches.index])\n\nprint(\"Video ad launches by month:\")\nprint(monthly_dist.to_string())\n\n# Weekday distribution\nweekday_launches = video_ads['launch_weekday'].value_counts()\nprint(f\"\\nVideo ad launches by weekday:\")\nprint(weekday_launches.to_string())\n\n# Recent trends (2023-2024 if available)\nrecent_videos = video_ads[video_ads['launch_year'] >= 2023]\nprint(f\"\\nRecent video ads (2023+): {len(recent_videos)}\")\nif len(recent_videos) > 0:\n recent_competitors = recent_videos['page_name'].value_counts().head(8)\n print(\"Most active video advertisers (2023+):\")\n print(recent_competitors.to_string())" } }, { "id": "step_9", "type": "chart", "name": "Video timing and seasonal patterns", "depends_on": ["step_8"], "params": { "description": "Visualize seasonal and weekly patterns in video ad launches", "code": "fig, (ax1, ax2) = plt.subplots(2, 1, figsize=(14, 10))\nfig.patch.set_facecolor('#0a0a0a')\n\n# Monthly pattern\nax1.bar(monthly_dist.index, monthly_dist.values, color='#FF7043', alpha=0.8)\nax1.set_title('Video Ad Launches by Month', color='white', fontsize=14)\nax1.set_ylabel('Number of Launches', color='white')\nax1.set_facecolor('#0a0a0a')\nax1.tick_params(colors='white')\nfor i, v in enumerate(monthly_dist.values):\n ax1.text(i, v + 0.5, str(v), ha='center', va='bottom', color='white')\n\n# Weekday pattern\nweekday_order = ['Monday', 'Tuesday', 'Wednesday', 'Thursday', 'Friday', 'Saturday', 'Sunday']\nweekday_ordered = weekday_launches.reindex(weekday_order)\nax2.bar(range(len(weekday_ordered)), weekday_ordered.values, color='#AB47BC', alpha=0.8)\nax2.set_xticks(range(len(weekday_ordered)))\nax2.set_xticklabels([day[:3] for day in weekday_order], color='white')\nax2.set_title('Video Ad Launches by Weekday', color='white', fontsize=14)\nax2.set_ylabel('Number of Launches', color='white')\nax2.set_facecolor('#0a0a0a')\nax2.tick_params(colors='white')\nfor i, v in enumerate(weekday_ordered.values):\n if pd.notna(v):\n ax2.text(i, v + 0.5, str(int(v)), ha='center', va='bottom', color='white')\n\nplt.tight_layout()\nsave_chart(fig, 'video_timing_patterns.png')\nplt.close()" } }, { "id": "step_10", "type": "llm_synthesis", "name": "Synthesize video ad landscape insights", "depends_on": ["step_7", "step_8", "step_9"], "params": { "prompt_template": "Based on the video advertising analysis, summarize the key insights about competitor video strategies:\n\nMessaging patterns: {{step_7.stdout}}\n\nTiming patterns: {{step_8.stdout}}\n\nProvide 3-4 key strategic insights about how competitors are using video ads, focusing on messaging themes, timing strategies, and performance indicators that SubSun should consider." } }, { "id": "step_11", "type": "python_compute", "name": "Generate specific video examples for SubSun reference", "depends_on": ["step_5"], "params": { "description": "Create a curated list of top video ads with viewing URLs for SubSun team review", "code": "# Select diverse, high-performing video examples\nsupabase_base_url = \"https://your-supabase-url.supabase.co/storage/v1/object/public/competitor-previews\"\n\n# Get top performers from different competitors\ntop_examples = []\ncompetitors_covered = set()\n\nfor _, ad in high_performers.iterrows():\n if len(top_examples) >= 20: # Limit to top 20 examples\n break\n \n competitor = ad['page_name']\n # Try to get variety across competitors\n if len([c for c in competitors_covered if c == competitor]) < 3:\n folder_name = competitor.lower().replace(' ', '').replace('&', '')\n video_url = f\"{supabase_base_url}/{folder_name}/{ad['ad_archive_id']}.mp4\"\n \n example = {\n 'competitor': competitor,\n 'ad_archive_id': ad['ad_archive_id'],\n 'headline': ad['headline'][:100] if pd.notna(ad['headline']) else 'N/A',\n 'primary_text': ad['primary_text'][:150] if pd.notna(ad['primary_text']) else 'N/A',\n 'duration_days': ad['duration_days'],\n 'eu_reach': ad['eu_total_reach'],\n 'performance_score': ad['performance_score'],\n 'video_url': video_url,\n 'cta_type': ad.get('cta_type', 'N/A')\n }\n \n top_examples.append(example)\n competitors_covered.add(competitor)\n\nprint(f\"CURATED VIDEO EXAMPLES FOR SUBSUN REFERENCE ({len(top_examples)} videos):\")\nprint(\"=\" * 80)\n\nfor i, example in enumerate(top_examples, 1):\n print(f\"\\n{i}. {example['competitor'].upper()}\")\n print(f\" Archive ID: {example['ad_archive_id']}\")\n print(f\" Headline: {example['headline']}\")\n print(f\" Primary Text: {example['primary_text']}\")\n print(f\" Performance: {example['duration_days']} days, {example['eu_reach']} reach\")\n print(f\" CTA: {example['cta_type']}\")\n print(f\" Video URL: {example['video_url']}\")\n print(f\" Performance Score: {example['performance_score']:.0f}\")\n\nvideo_examples_data = top_examples" } }, { "id": "step_12", "type": "python_compute", "name": "Analyze AI/fun creative themes and trends", "depends_on": ["step_11"], "params": { "description": "Identify fun, creative, and AI-related themes in video ad copy", "code": "# Search for AI, fun, creative themes in video ad copy\nfun_keywords = ['fun', 'playful', 'cool', 'awesome', 'amazing', 'incredible', 'wow', 'perfect', 'love', 'beautiful', 'stunning']\ncreative_keywords = ['creative', 'unique', 'different', 'special', 'exclusive', 'limited', 'new', 'fresh', 'bold', 'innovative']\nai_keywords = ['ai', 'smart', 'intelligent', 'tech', 'digital', 'virtual', 'future', 'advanced']\n\n# Analyze all video ads for these themes\nvideo_copy_analysis = video_ads.copy()\nvideo_copy_analysis['all_text'] = (\n video_copy_analysis['headline'].fillna('') + ' ' + \n video_copy_analysis['primary_text'].fillna('') + ' ' + \n video_copy_analysis['description'].fillna('')\n).str.lower()\n\n# Score ads based on theme presence\nvideo_copy_analysis['fun_score'] = video_copy_analysis['all_text'].apply(\n lambda x: sum(1 for keyword in fun_keywords if keyword in x)\n)\nvideo_copy_analysis['creative_score'] = video_copy_analysis['all_text'].apply(\n lambda x: sum(1 for keyword in creative_keywords if keyword in x)\n)\nvideo_copy_analysis['ai_score'] = video_copy_analysis['all_text'].apply(\n lambda x: sum(1 for keyword in ai_keywords if keyword in x)\n)\n\nvideo_copy_analysis['total_theme_score'] = (\n video_copy_analysis['fun_score'] + \n video_copy_analysis['creative_score'] + \n video_copy_analysis['ai_score']\n)\n\n# Find most 'fun/creative' video ads\nfun_creative_ads = video_copy_analysis[\n video_copy_analysis['total_theme_score'] > 0\n].sort_values(['total_theme_score', 'duration_days'], ascending=[False, False]).head(15)\n\nprint(\"TOP 'FUN/CREATIVE' VIDEO ADS:\")\nprint(\"=\" * 60)\nfor _, ad in fun_creative_ads.iterrows():\n print(f\"\\n{ad['page_name']}: {ad['ad_archive_id']}\")\n print(f\"Headline: {ad['headline'][:100] if pd.notna(ad['headline']) else 'N/A'}\")\n print(f\"Theme Scores - Fun: {ad['fun_score']}, Creative: {ad['creative_score']}, AI: {ad['ai_score']}\")\n print(f\"Duration: {ad['duration_days']} days\")\n\n# Summary stats\nprint(f\"\\nTHEME ANALYSIS SUMMARY:\")\nprint(f\"Videos with fun themes: {len(video_copy_analysis[video_copy_analysis['fun_score'] > 0])}\")\nprint(f\"Videos with creative themes: {len(video_copy_analysis[video_copy_analysis['creative_score'] > 0])}\")\nprint(f\"Videos with AI/tech themes: {len(video_copy_analysis[video_copy_analysis['ai_score'] > 0])}\")\n\nfun_creative_examples = fun_creative_ads" } }, { "id": "step_13", "type": "python_compute", "name": "Create SubSun-specific recommendations", "depends_on": ["step_12", "step_11"], "params": { "description": "Generate specific creative recommendations for SubSun based on successful competitor patterns", "code": "# Analyze what works best for sunglasses/fashion brands specifically\nsunglasses_competitors = ['chimi', 'le specs', 'vuarnet', 'ace & tate', 'ace']\nfashion_video_ads = video_ads[video_ads['page_name'].str.lower().isin(sunglasses_competitors)].copy()\n\nprint(\"SUBSUN CREATIVE RECOMMENDATIONS BASED ON COMPETITOR ANALYSIS:\")\nprint(\"=\" * 70)\n\n# Recommendation 1: Timing strategy\nprint(\"\\n1. OPTIMAL LAUNCH TIMING:\")\nbest_months = monthly_dist.nlargest(3)\nprint(f\" - Best months to launch: {', '.join(best_months.index)}\")\nprint(f\" - Peak season appears to be {best_months.index[0]} ({best_months.iloc[0]} launches)\")\nbest_days = weekday_launches.nlargest(2)\nprint(f\" - Best days to launch: {', '.join(best_days.index)}\")\n\n# Recommendation 2: Performance benchmarks\nprint(\"\\n2. PERFORMANCE BENCHMARKS:\")\navg_duration = video_ads['duration_days'].mean()\ntop_duration = video_ads['duration_days'].quantile(0.8)\nprint(f\" - Average video ad runs for {avg_duration:.1f} days\")\nprint(f\" - Top 20% run for {top_duration:.1f}+ days\")\nprint(f\" - Aim for ads that can sustain 2+ weeks of performance\")\n\n# Recommendation 3: Copy themes\nprint(\"\\n3. WINNING COPY THEMES:\")\nfor theme, count in video_messaging_insights['top_headline_themes'][:5]:\n print(f\" - '{theme}' appears in {count} high-performing headlines\")\n\n# Recommendation 4: CTA strategy\nprint(\"\\n4. EFFECTIVE CTA TYPES:\")\nfor cta, count in video_messaging_insights['preferred_ctas'].items():\n if pd.notna(cta) and count > 1:\n print(f\" - {cta}: {count} uses in top performers\")\n\n# Recommendation 5: Competitor benchmarking\nprint(\"\\n5. KEY COMPETITORS TO WATCH:\")\ntop_video_brands = video_ads['page_name'].value_counts().head(5)\nfor brand, count in top_video_brands.items():\n avg_performance = video_performance[video_performance['page_name'] == brand]['performance_score'].mean()\n print(f\" - {brand}: {count} video ads, avg performance score {avg_performance:.0f}\")\n\nsubsun_recommendations = {\n 'optimal_timing': {'months': list(best_months.index), 'weekdays': list(best_days.index)},\n 'performance_targets': {'min_duration_days': top_duration, 'avg_duration': avg_duration},\n 'copy_themes': video_messaging_insights['top_headline_themes'][:5],\n 'cta_preferences': video_messaging_insights['preferred_ctas'],\n 'key_competitors': list(top_video_brands.index)\n}" } }, { "id": "step_14", "type": "chart", "name": "SubSun recommendation dashboard", "depends_on": ["step_13"], "params": { "description": "Create a visual summary of key recommendations for SubSun", "code": "fig, ((ax1, ax2), (ax3, ax4)) = plt.subplots(2, 2, figsize=(16, 12))\nfig.patch.set_facecolor('#0a0a0a')\nfig.suptitle('SubSun Video Creative Strategy Dashboard', color='white', fontsize=16, y=0.98)\n\n# 1. Performance distribution\nperformance_bins = pd.cut(video_performance['duration_days'].dropna(), bins=5, labels=['1-5 days', '6-15 days', '16-30 days', '31-60 days', '60+ days'])\nperformance_dist = performance_bins.value_counts()\nax1.pie(performance_dist.values, labels=performance_dist.index, autopct='%1.1f%%', \n colors=['#FF5722', '#FF9800', '#FFC107', '#8BC34A', '#4CAF50'])\nax1.set_title('Video Ad Duration Distribution', color='white', fontsize=12)\nax1.set_facecolor('#0a0a0a')\n\n# 2. Top themes word cloud style\ntheme_words = [word for word, count in video_messaging_insights['top_headline_themes'][:8]]\ntheme_counts = [count for word, count in video_messaging_insights['top_headline_themes'][:8]]\nax2.barh(range(len(theme_words)), theme_counts, color='#E91E63')\nax2.set_yticks(range(len(theme_words)))\nax2.set_yticklabels(theme_words, color='white')\nax2.set_title('Top Performing Copy Themes', color='white', fontsize=12)\nax2.set_facecolor('#0a0a0a')\nax2.tick_params(colors='white')\n\n# 3. Monthly launch pattern\nax3.bar(range(len(monthly_dist)), monthly_dist.values, color='#3F51B5', alpha=0.7)\nax3.set_xticks(range(len(monthly_dist)))\nax3.set_xticklabels(monthly_dist.index, color='white', rotation=45)\nax3.set_title('Optimal Launch Months', color='white', fontsize=12)\nax3.set_ylabel('Launch Count', color='white')\nax3.set_facecolor('#0a0a0a')\nax3.tick_params(colors='white')\n\n# 4. Competitor performance comparison\ntop_competitors_perf = video_performance.groupby('page_name').agg({\n 'performance_score': 'mean',\n 'duration_days': 'mean'\n}).sort_values('performance_score', ascending=False).head(8)\n\nscatter = ax4.scatter(top_competitors_perf['duration_days'], \n top_competitors_perf['performance_score'],\n s=100, alpha=0.7, c='#00BCD4')\nfor i, competitor in enumerate(top_competitors_perf.index):\n ax4.annotate(competitor[:10], \n (top_competitors_perf.iloc[i]['duration_days'], \n top_competitors_perf.iloc[i]['performance_score']),\n xytext=(5, 5), textcoords='offset points', \n fontsize=8, color='white')\nax4.set_xlabel('Avg Duration (days)', color='white')\nax4.set_ylabel('Avg Performance Score', color='white')\nax4.set_title('Competitor Performance Matrix', color='white', fontsize=12)\nax4.set_facecolor('#0a0a0a')\nax4.tick_params(colors='white')\n\nplt.tight_layout()\nsave_chart(fig, 'subsun_strategy_dashboard.png')\nplt.close()" } }, { "id": "step_15", "type": "llm_synthesis", "name": "Create final comprehensive deliverable", "depends_on": ["step_13", "step_14", "step_11", "step_12"], "params": { "prompt_template": "Create a comprehensive strategic deliverable for SubSun about entering fun AI creative video advertising. Structure this as a professional consulting report with:\n\n1. EXECUTIVE SUMMARY\n2. KEY FINDINGS from competitor analysis\n3. STRATEGIC RECOMMENDATIONS with specific actions\n4. PRIORITY VIDEO EXAMPLES to review\n5. IMPLEMENTATION ROADMAP\n\nBase this on:\n\nRecommendations analysis: {{step_13.stdout}}\n\nFun/creative themes analysis: {{step_12.stdout}}\n\nVideo examples: {{step_11.stdout}}\n\nMake this actionable and specific to SubSun as a sunglasses DTC brand wanting to create engaging video content. Include specific competitor examples they should review and copy strategies to test." } } ] } ```

```json { "questions": [], "reasoning": "The brief is clear enough to proceed. SubSun wants to explore fun AI creative and needs competitor video examples as inspiration. I can search the competitor_ads database for video creatives (creative_type = 'video') from sunglasses/fashion brands, analyze their characteristics, and provide concrete examples. The only potential ambiguity is defining 'fun AI creative' but I can interpret this as playful, engaging video content that could be AI-generated or AI-enhanced, and make that assumption clear in my analysis." } ```