Predict outages. Schedule smarter. Market without interruption.
Businesses in Benin outside Cotonou lose hours weekly and miss campaign opportunities due to frequent internet outages and high latency that break real-time marketing automation, analytics, and social ads.
TimingGuard combines local signal monitoring with crowdsourced Benin outage patterns to predict optimal campaign windows. It automatically reschedules posts and ads around predicted outages and offers SMS fallback for critical messages when data networks fail completely.
Marketing teams and digital-first businesses in Benin operating outside Cotonou
The only platform that actively predicts internet availability using both local phone sensors and community data, combined with automatic fallback to SMS marketing channels popular in Benin.
friendly
Uses local signal strength and historical patterns to forecast connectivity
Automatically adjusts send times based on predictions
Converts failed social posts into SMS broadcasts when internet is unavailable
Visual schedule that works completely offline with prediction overlays
Anonymously contributes and benefits from regional outage intelligence
Handles Facebook, Instagram, WhatsApp, and SMS from one interface
Shows how accurate forecasts have been in your specific location
Runs tests during predicted high-reliability windows
Email summary of local network performance and recommendations
| Column | Type | Nullable |
|---|---|---|
| id | uuid | No |
| text | No | |
| phone | text | Yes |
| city | text | No |
| created_at | timestamp | No |
| Column | Type | Nullable |
|---|---|---|
| id | uuid | No |
| user_id | uuid | No |
| content | text | Yes |
| target_channels | text | No |
| desired_window_start | timestamp | Yes |
| status | text | No |
| used_fallback | bool | No |
| created_at | timestamp | No |
Relationships:
| Column | Type | Nullable |
|---|---|---|
| id | uuid | No |
| user_id | uuid | No |
| signal_strength | int | Yes |
| is_outage | bool | No |
| latitude | text | Yes |
| longitude | text | Yes |
| recorded_at | timestamp | No |
Relationships:
| Column | Type | Nullable |
|---|---|---|
| id | uuid | No |
| city | text | No |
| predicted_start | timestamp | No |
| confidence | int | No |
| reliability_score | int | No |
| created_at | timestamp | No |
/api/campaigns/scheduleSubmit campaign for smart timing optimization
/api/predictions/locationGet current outage predictions for user's city
/api/logs/connectivitySubmit anonymized connectivity reading
/api/sms/fallbackTrigger SMS fallback via integrated provider
No SMS fallback
None
None
| Month | Users | Conversion | MRR | ARR |
|---|---|---|---|---|
| Month 1 | 40 | 18% | $208 | $2,496 |
| Month 6 | 390 | 22% | $2,495 | $29,940 |
Predict when the network will be stable. Automatically schedule around outages. Switch to SMS when needed.
Engage with businesses in cities like Lokossa, Dogbo, and Kandi through targeted Facebook ads in French and local business associations. Offer lifetime 50% discount to first 10 Pro users who provide weekly prediction feedback. Create a 'Benin Network Heroes' community where early users share connectivity reports in exchange for free access.
Visual planning
No prediction or outage awareness
Proactive timing intelligence and SMS fallback
Direct platform access
No offline capability or prediction features
Cross-platform with outage intelligence and fallback channels
Growing proprietary dataset of Benin-specific connectivity patterns by city and time creates a defensible prediction model that improves weekly.
Increasing adoption of performance marketing in secondary cities combined with widespread dual-SIM smartphone usage makes predictive multi-channel scheduling both necessary and technically feasible.
Inaccurate predictions leading to lost trust
Start with conservative estimates, show confidence scores, and allow manual overrides
SMS fallback costs eating margins
Pass through Twilio costs with small margin or limit free tier usage
SMS compliance and privacy with user location data
Implement strict opt-in, anonymization, and consult Benin data protection rules
Success: 75% say they would change scheduling behavior with reliable predictions
Success: Achieve 75%+ accuracy on 2-week test in target cities
Success: 40 paid users within 45 days of launch
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