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E‑commerce: Predicting Customer Churn & Demand with AI

Client: Fashion E‑commerce • Objective: Reduce churn, forecast demand, and optimize inventory with predictive AI
AI analytics dashboard for e‑commerce churn and demand forecasting
0%
Churn Reduction
0%
Forecast Accuracy
0%
Stockouts Reduced
0%
CLTV Uplift

Project Overview

A mid-sized online fashion retailer faced rising churn and volatile product demand. We built a unified predictive system to identify at‑risk customers and forecast SKU-level demand, enabling targeted retention and smarter inventory decisions.

Challenges

  • No early indicator for churn across cohorts and seasons
  • Manual buying cycles caused overstock and stockouts
  • Fragmented data from web, CRM, and POS sources

Neuradigi Solution

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

1
Churn Prediction: RFM + behavioral features; calibrated probabilities; action lists for marketing.
2
Demand Forecasting: Multi-seasonal models at SKU x channel granularity.
3
MLOps & BI: Automated retraining, CI/CD, and dashboards for merchandising.

Business Outcomes

  • Targeted offers cut churn by 18% in priority segments
  • Forecast accuracy reached 92% for top movers
  • Stockouts reduced by 25%; dead stock lowered

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 Growth, Fashion E‑commerce
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