x-twitter-stats-analyzer

# X (Twitter/X) Analytics Analyze social media data to extract insights and generate strategic recommendations. ## Analysis Framework ### 1. Data Ingestion & Validation Read the uploaded file and identify available metrics. Common columns: | Category | Metrics | |----------|---------| | Reach | Impressions, Reach, Views, Profile visits | | Engagement | Likes, Comments, Replies, Shares, Reposts, Bookmarks, Saves | | Growth | New followers, Unfollows, Net followers | | Content | Posts created, Video views, Media views | Validate data completeness. Note any missing or zero-value columns. ### 2. Calculate Key Performance Indicators ``` Engagement Rate = (Total Engagements / Total Impressions) × 100 Follow Conversion = (New Followers / Profile Visits) × 100 Net Growth = New Followers - Unfollows Likes per Post = Total Likes / Posts Created Impressions per Post = Total Impressions / Posts Created ``` ### 3. Temporal Analysis Identify patterns across time periods: - **Best performing days**: Highest impressions, engagement rate, follower growth - **Worst performing days**: Lowest metrics, potential issues - **Posting frequency correlation**: Compare posts/day vs engagement/post - **Viral content detection**: Days with 2x+ average performance ### 4. Engagement Composition Analysis Break down total engagements by type: - Likes (passive appreciation) - Bookmarks/Saves (high-intent, reference value) - Replies/Comments (active conversation) - Shares/Reposts (amplification) High bookmark rates suggest educational/reference content resonates. High reply rates indicate conversation-driving content. ### 5. Quality vs Quantity Assessment Analyze the relationship between posting volume and performance: ```python for each day: likes_per_post = likes / posts impressions_per_post = impressions / posts # Compare high-volume vs low-volume days # Often: fewer high-quality posts > many low-quality posts ``` ### 6. Growth Funnel Analysis Track the conversion funnel: ``` Impressions → Engagements → Profile Visits → New Followers ``` Calculate conversion rates at each stage. Identify bottlenecks. ## Visualization Guidelines Create an interactive React dashboard with tabs: 1. **Overview**: Key metrics cards, impressions/engagement trend, engagement breakdown pie chart 2. **Engagement**: Daily engagement rate bar chart, likes per post analysis 3. **Growth**: Follower gains/losses, profile visit to follower conversion 4. **Insights**: Key findings cards with actionable recommendations Use Recharts for charts: - ComposedChart for dual-axis (impressions + engagements) - BarChart for daily comparisons - PieChart for engagement breakdown ## Recommendation Categories ### Posting Schedule Optimization - Identify best/worst performing days - Suggest optimal posting frequency based on quality vs quantity analysis - Recommend content scheduling strategy ### Content Strategy - Analyze which content types drive bookmarks (save-worthy) - Identify viral content patterns - Suggest content pillars based on engagement composition ### Growth Tactics - Profile optimization suggestions based on follow conversion rate - Engagement strategy (when to be active in replies) - Audience retention insights from unfollow patterns ## Output Structure 1. **Summary metrics**: Total impressions, engagements, engagement rate, net followers 2. **Key findings**: 3-5 bullet points of critical insights 3. **Interactive dashboard**: React component with tabbed views 4. **Strategic recommendations**: Prioritized action items with rationale ## Example Insights - "Monday drove 24% of weekly impressions—analyze what made it viral" - "Tuesday underperformed despite 13 posts—consider reducing output" - "30 posts yielded 99 likes/post vs 7 posts with 309 likes/post—quality over quantity" - "5,312 bookmarks (6.8% of engagements) suggests save-worthy content resonates" - "10.3% of profile visitors convert to followers—optimize bio for higher conversion"