Can predictive ML actually reduce churn for a D2C brand?
Yes. Churn ML predicts which customers will stop buying 60-90 days ahead. Your retention team gets a ranked list, focuses win-back offers on savable customers, and stops wasting discounts on people who would have bought anyway. Typical outcome: 15-25% reduction in effective churn rate on the intervened segment. Works best when combined with a WhatsApp win-back bot.
How does customer LTV prediction help pricing decisions?
CLV forecasts tell you what each new customer is worth over 12-24 months. This changes how you bid on paid acquisition (higher CPA is fine for high-LTV cohorts), which channels to double down on, and whether to offer aggressive first-order discounts. Marketing teams typically see 20-40% improvement in ROAS after CLV-informed bidding kicks in.
What's RTO fraud detection?
Return-to-origin fraud is a huge problem in Indian COD e-commerce. Customers place orders they never intend to accept, costing you shipping and reverse logistics. Our RTO model scores every COD order at checkout for RTO probability. High-risk orders get: prepaid nudges, WhatsApp confirmation flows, or (for very high risk) manual verification calls. Typical RTO reduction: 25-40%.
Can it forecast demand for inventory planning?
Yes. SKU-level demand forecasts at daily, weekly, or monthly granularity depending on your planning cycle. Handles seasonality, promotions, festivals, holidays, weather effects. Typical accuracy: MAPE 10-20% for stable SKUs, 20-35% for new or highly-seasonal SKUs. Feeds directly into your warehouse and purchase-order workflows.
How does it integrate with Shopify or WooCommerce?
Shopify: we pull orders, customers, and products via Shopify Admin API, push scores back via Shopify Metafields, and inject checkout logic via Shopify Functions or Script Editor. WooCommerce: standard REST API for reads, custom plugin for score writes. Custom storefronts: any REST or GraphQL API. Scores available in your admin, CRM, and via webhook for real-time actions.
What data do we need to start?
12-18 months of orders (minimum 20,000 orders across at least 5,000 unique customers) for churn and LTV. 24 months of daily/weekly SKU sales for demand forecasting. Historical RTO flags for RTO models (even a few thousand cases work). Product catalogue for recommendation engines. If you have less, we start rule-based and add ML as data grows.
How much does it cost?
Prototype: ₹1.5-3 lakh (2-3 weeks). Full production build: ₹6-15 lakh depending on integration complexity (4-8 weeks). Ongoing ops: ₹40,000/month covering drift detection, quarterly retraining, dashboard maintenance. Scoping is free — start via the contact form.
