Predictive analytics for Facebook Ads that flags risks before ROI drops.
Small businesses experience high churn and poor ROI on paid ad platforms like Facebook Ads Manager due to constant algorithm changes and steep learning curves.
FBAdPilot pulls your ad data, uses ML to predict performance dips from algorithm signals, and recommends preventive actions. Dashboards simplify insights into plain English, with scorecards rating campaign health. Owners avoid churn by acting on early warnings.
Small business owners managing their own paid ad campaigns on Facebook Ads Manager
Predictive scoring model using public FB signals + user data for 7-day ahead forecasts.
professional
Hourly pull from FB Ads.
0-100 score per campaign with risk breakdown.
Forecast dips & suggestions.
Weekly summaries in simple terms.
Charts for ROI, spend, conversions.
Test 'what-if' bid changes.
Manage 5+ accounts.
Connect to Google Sheets.
| Column | Type | Nullable |
|---|---|---|
| id | uuid | No |
| text | No | |
| created_at | timestamp | No |
| Column | Type | Nullable |
|---|---|---|
| id | uuid | No |
| user_id | uuid | No |
| fb_account_id | text | No |
| name | text | No |
Relationships:
| Column | Type | Nullable |
|---|---|---|
| id | uuid | No |
| account_id | uuid | No |
| score | int | No |
| forecast | jsonb | Yes |
| generated_at | timestamp | No |
Relationships:
| Column | Type | Nullable |
|---|---|---|
| id | uuid | No |
| prediction_id | uuid | No |
| data | jsonb | Yes |
Relationships:
/api/accountsConnect FB account
/api/predictionsLatest scores
/api/syncManual data pull
/api/reportsGenerate insights
/api/alertsConfigure notifications
Daily sync only
| Month | Users | Conversion | MRR | ARR |
|---|---|---|---|---|
| Month 1 | 180 | 1.2% | $76 | $912 |
| Month 6 | 1,200 | 3.5% | $1,470 | $17,640 |
Target r/FacebookAds with free prediction tool teaser; join SMB ad masterminds on FB & offer audits; cold outreach to underperformers via FB ad library analysis.
Optimization rules
Overwhelming UI
Predictive simplicity vs reactive
Fraud detection
No algo predictions
Broader risk coverage
Speed via lightweight ML on Supabase edge functions.
Post-ATT era with opaque algos heightens need for predictions.
ML accuracy degrades
Continuous retrain on user data
High API costs
Caching & tier limits
Success: 20 signups
Success: 40% mo2 retention
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