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Actowiz Solutions uses machine learning to analyze cart abandonment patterns from SKU-level trends on Zapp UK. Discover predictive triggers and behavioral insights.
Zapp, one of the fastest-growing Q-commerce platforms in the UK, delivers essentials within minutes. But with fierce competition from Getir, GoPuff, and Deliveroo Express, abandoned carts represent lost revenue—and missed insights.
Actowiz Solutions helped a major UK-based consumer goods brand integrate machine learning models and real-time scraping to detect and analyze SKU-level cart abandonment triggers on Zapp. The result: smarter campaign targeting, better pricing, and reduced drop-offs.
Actowiz deployed a Zapp scraper capturing:
Data was extracted across London, Manchester, Birmingham zones every 15–30 minutes.
| Timestamp | City | SKU Name | Price | Stock Status | Discount | Delivery ETA | Add-to-Cart Status |
|---|---|---|---|---|---|---|---|
| 2025-06-15 18:00 | London | Alpro Almond 1L | £2.10 | Low Stock | 10% | 22 mins | Enabled |
| 2025-06-15 18:00 | London | Cadbury Buttons | £1.80 | In Stock | 0% | 16 mins | Enabled |
| 2025-06-15 18:00 | London | Dettol Wipes 20ct | £2.50 | In Stock | 5% | 26 mins | Enabled |
| Abandonment Trigger | Impact Detected |
|---|---|
| Delivery ETA > 25 mins | +38% likelihood of cart drop-off |
| Discount < 5% | 2.3x more likely to be abandoned |
| Low Stock Tag | Increased hesitation on checkout |
| Multi-SKU Cart (3+ items) | Drop-off spike due to perceived complexity |
| Repeatedly Viewed SKU | Abandoned unless offered discount in 24 hrs |
| Time Slot | Avg Cart Abandonment Rate |
|---|---|
| 8 AM – 11 AM | 18% |
| 12 PM – 3 PM | 26% |
| 4 PM – 7 PM | 32% |
| 8 PM – 11 PM | 21% |
🔍 Insight: Evening hours saw highest drop-offs—often due to peak ETA delays or unavailable fast-moving SKUs.
SKU: Magnum Classic Ice Cream
Actowiz flagged this behavior for the client, prompting strategic discounting only after first drop-off detection.
| Feature | Description |
|---|---|
| SKU Drop-Off Risk Scoring | Visualize real-time cart abandonment likelihood per product |
| Time-Based Abandonment Patterns | Analyze hourly/day-wise cart abandonment trends |
| Trigger Alert System | Push alerts for high-risk SKU combos |
| Multi-SKU Cart Drop Analysis | Track how cart complexity affects purchase behavior |
| Promo Recommendation Engine | Suggest optimal discount % based on historic abandonment elasticity |
Monitored regions across Zapp in the UK:
| KPI | Before Actowiz | After Actowiz |
|---|---|---|
| Average Cart Abandonment Rate | 36% | 19% |
| Time to Detect Drop-Off Pattern | Manual (24h+) | Real-Time |
| Discount ROI (after AI-driven targeting) | - | +31% uplift |
| SKUs Recovered via Promo Alert | - | 800+ SKUs |
| Multi-SKU Cart Conversions | 41% | 63% |
“We never had real visibility into Zapp cart behavior. Actowiz gave us the triggers, patterns, and recommendations to turn drop-offs into conversions.”
– E-Commerce Lead, UK FMCG Brand Partnering with Zapp
In a Q-commerce ecosystem where consumers make split-second decisions, understanding why carts are abandoned can unlock substantial revenue.
Actowiz Solutions transformed SKU-level scraping and ML modeling into a high-ROI abandonment prediction tool—helping Zapp’s brand partners reclaim lost sales across the UK.
In a world where milliseconds matter, Actowiz lets brands act at the right moment.