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Next Best Action Machine Learning Pipeline for Global QCommerce Customer Engagement

netguru.com

eCommerce

Retail

Food & Beverage

Logistics

Customer Churn Reduction Challenge

The client faces significant customer churn due to ineffective engagement strategies. Current communication methods risk irritating customers rather than fostering platform loyalty. The business needs intelligent decision-making capabilities to determine optimal customer interactions (messages, vouchers, discounts) that drive engagement without causing disaffection.

About the Client

Multinational quick-commerce platform connecting customers with stores, supermarkets, and restaurants via apps and couriers across 70+ countries

Key Project Goals

Implement machine learning-driven customer behavior analysis
Create automated next-best-action recommendation system
Reduce customer churn through personalized engagement
Enable seamless integration with existing communication channels
Establish continuous learning mechanism for engagement optimization

Core System Capabilities

Customer behavior tracking and data aggregation
Machine learning model for action prediction
Feature engineering pipeline for customer segmentation
Reward calculation system for action effectiveness measurement
Multi-channel communication integration (APIs for CRM, push notifications)
Extensible action library (vouchers, discounts, messages)
Performance reporting dashboard

Technology Stack Requirements

Machine learning frameworks (TensorFlow/PyTorch)

Data processing pipelines (Apache Spark)

Cloud infrastructure (AWS/GCP)

Real-time prediction services

Data warehousing solutions

System Integration Needs

Customer Relationship Management (CRM) systems
Incentive management APIs
Push notification services
Existing data lakes/warehouses
User analytics platforms

Operational Requirements

High scalability for 2M+ customer base
Real-time prediction capabilities
Data security and privacy compliance
System availability >99.9%
Modular architecture for future expansion

Expected Business Outcomes

Implementation of the NBA system will enable automated, personalized customer engagement at scale, reducing churn through optimized communication strategies. The machine learning system will continuously improve engagement effectiveness through trial-and-error learning, with extensible capabilities for new incentive types and communication channels. Expected outcomes include increased customer lifetime value, higher platform usage frequency, and reduced marketing costs through targeted interventions.