Viral Scout
Public demo with sample data: two example businesses, real public YouTube posts. Read-only. Source on GitHub
← home-services

Your performance

Your own videos and ads, from official APIs and your own exports, against your own medians, not industry averages. Updated Nov 3, 3:59 AM · last 28 days · goal sales.

No performance data yet.

Add your own keys on Setup (Instagram insights, YouTube Analytics, Meta ads, TikTok ads) and run

python -m viral_scout perf home-services

or import a CSV export (below).

Platform definitions differ
    Import a CSV (no keys needed)

    Export from the platform, then run on your PC (the published site is read-only):

    python -m viral_scout perf import home-services export.csv --source meta|tiktok|youtube|generic
    • Meta Ads Manager: Ads tab → Columns: add Video engagement + Conversions (3-second video plays, ThruPlays, Video plays at 25–100%, Link clicks, Landing page views, Purchases, Purchases conversion value) → Reports → Export table data (.csv). Break down by Day for fatigue.
    • TikTok Ads Manager: Ads → Custom columns: Video views, 2-second / 6-second video views, Video views at 25–100%, Average play time per video view, Clicks (destination), Conversions → Export. Add “By Day” for fatigue.
    • YouTube Studio: Analytics → Advanced mode → Content → add Impressions, Impressions CTR, Average percentage viewed, Stayed to watch, Shares, Subscribers gained → Export → Comma-separated values (Table data.csv).
    • Generic: one row per video/ad with columns named like the schema (impressions, views_3s, spend, purchases, revenue, …).

    Column mappings and every formula: docs → performance.