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Case-Study-Actowiz-Solutions-–-ML-Driven-Cart-Abandonment-Triggers-from-SKU-Trends-in-Zapp-UK

Introduction

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.

Client Objectives

  • Identify what SKUs are most frequently abandoned before checkout on Zapp
  • Understand behavioral triggers behind drop-offs (price, timing, stock)
  • Predict cart abandonment risk based on product type and time-of-day
  • Correlate discount patterns and abandonment likelihood
  • Generate alerts for high-risk product combinations in real time

Challenges Faced

Challenges-Faced
  • 🛒 Zapp doesn’t offer cart data publicly—behavior needed to be reverse-engineered
  • 📉 SKU trends changed hourly with dynamic pricing and availability
  • ⚡ Cart abandonment could spike due to delays, pricing, or stockouts
  • 📦 Identifying multi-SKU interactions (e.g., bundles abandoned) required clustering
  • 🧠 Client lacked predictive modeling for drop-off scenarios

Actowiz’s AI/ML & Data Scraping Strategy

Actowiz’s-AI-Powered-Data-Approach
🔍 1. Scraping User-Facing Signals on Zapp

Actowiz deployed a Zapp scraper capturing:

  • SKU availability, price, and discounts
  • “Low stock” tags, delivery ETA, and cart limits
  • Add-to-cart button status & changes
  • Repeat pattern tracking (e.g., items added repeatedly but never purchased)

Data was extracted across London, Manchester, Birmingham zones every 15–30 minutes.

📊 2. Data Points Extracted
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
🧠 3. Machine Learning Models Deployed
  • Logistic Regression – Classified SKUs by likelihood of abandonment
  • XGBoost Classifier – Scored risk levels based on discount, stock, and timing
  • K-Means Clustering – Grouped abandoned SKUs by behavioral signals
  • Sequence Pattern Mining – Detected repeat abandonment patterns by users

Top Abandonment Triggers Identified

🧾 Key Findings:
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

Cart Abandonment Heatmap by Time of Day (London)

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.

Real-World Example:

The-Client

SKU: Magnum Classic Ice Cream

  • 📍 London SW6
  • Regular Price: £3.50
  • Offered 5% discount → Drop rate: 29%
  • Offered 15% discount → Drop rate: 9%

Actowiz flagged this behavior for the client, prompting strategic discounting only after first drop-off detection.

Actowiz Dashboard Highlights

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

Geographic Coverage

Monitored regions across Zapp in the UK:

  • London (SW6, NW1, E14, EC1, SE10)
  • Manchester (M1–M16 zones)
  • Birmingham (B1–B33 regions)
  • Leeds, Bristol, Liverpool – added in Phase 2

Business Impact Delivered

📈 Results after 60 Days of Implementation:
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%

Client Testimonial

“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

Next Steps

  • Integrate behavioral email/SMS triggers tied to SKU abandonment
  • Expand insights to Amazon Fresh, GoPuff, and Deliveroo UK
  • Add price elasticity models by category
  • Build competitive benchmarking layer (e.g., Zapp vs Getir)

Conclusion

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.

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