Case Study

E‑commerce: Predicting Churn & Demand

Helping a fashion retailer reduce churn and optimize inventory with predictive AI

Project Overview
AI-powered analytics dashboard for e-commerce

A mid-sized online fashion retailer was losing customers and struggling with inventory chaos. We built a unified AI system that predicts customer churn and forecasts product demand, enabling proactive retention and smarter inventory decisions.

Industry
E-commerce & Retail
Duration
12 weeks
Engagement
Build & Deploy
Team
Data, ML, FE, MLOps
Tech Stack
PythonTensorFlowscikit-learnAWS SageMakerAirflowReact
The Challenge

Growing Pains of a Scaling Retailer

Despite strong brand recognition, the retailer faced critical operational challenges that threatened their growth trajectory.

  • No early warning system for customer churn—by the time they noticed, customers were already gone
  • Manual buying cycles resulted in costly overstocks during slow periods and stockouts during peak demand
  • Customer data scattered across web analytics, CRM, and POS systems with no unified view
Our Approach

A Unified Predictive Intelligence System

We engineered two complementary AI engines: a Customer Churn Scoring model and a Demand Forecasting engine. Together they power proactive retention campaigns and optimal inventory planning.

1

Data Unification

Integrated web, CRM, order, and catalog data into a unified feature store with real-time entity resolution.

PythonApache SparkAWS Glue
2

Churn Prediction Engine

Built RFM + behavioral feature models with calibrated probability outputs, feeding automated action lists to marketing.

XGBoostTensorFlowscikit-learn
3

Demand Forecasting

Developed multi-seasonal models at SKU × channel granularity for accurate inventory planning.

ProphetTemporal CNNAWS SageMaker
4

MLOps & Deployment

Implemented automated retraining pipelines, CI/CD, and real-time dashboards for merchandising teams.

AirflowMLflowReact
The Results

Measurable Business Impact

Within 3 months of deployment, the system delivered transformative results across all key metrics.

0% Churn Reduction
0% Forecast Accuracy
0% Stockouts Reduced
0% CLTV Uplift
  • Targeted retention offers reduced churn by 18% in priority customer segments
  • Demand forecasting achieved 92% accuracy for top-moving SKUs
  • Dead stock inventory reduced by 30%, freeing up warehouse capacity
How We Built It

Approach & Architecture

Data Layer:Unified web, CRM, order, and catalog data; entity resolution; feature store.
Models:Churn: XGBoost/NN with calibration; Demand: Prophet/Temporal CNN per SKU x channel.
MLOps:Airflow pipelines, SageMaker training endpoints, automated retraining.
BI & Actions:Retention lists to CRM; buy plans to purchasing; real‑time dashboards.
High-level architecture for churn and demand forecasting

Neuradigi helped us move from guesswork to precision. We now know who might churn and what to stock—our campaigns and margins both improved. Highly recommended.

VP of GrowthFashion E-commerce

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