D2C · Product Recommendations

AI Product Recommendation Engine for D2C India

Amazon's recommendation engine drives 35% of their revenue. XPndAI builds the same technology for Indian D2C brands — personalised on-site recommendations, WhatsApp-powered cross-sells, and "complete the look" flows. Not a Shopify plugin. A custom engine trained on your data.

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35%Revenue from recommendations
2.4xAverage order value
15%Conversion lift
4 wksBuild time

Why Shopify's default recommendations don't work for Indian D2C

How XPndAI Builds This

Fixed price · 3–4 week delivery · You own all code · No monthly SaaS fees

Step 1

Train on Your Purchase Data

AI analyses your store's purchase history — which products are bought together, which sequence indicates high LTV, and which product combinations drive repeat orders. Trained on your data, not generic retail patterns.

Step 2

On-Site Personalisation

Each visitor sees personalised recommendations on product pages, cart page, and post-purchase page — based on their browsing history, purchase history (if returning), and real-time session behaviour.

Step 3

WhatsApp Personalised Campaigns

Weekly AI-curated WhatsApp messages to each customer segment — not bulk blasts. "Based on your purchase, you might love this new arrival" — relevant, personalised, high-open-rate.

Step 4

Post-Purchase Upsell Automation

Order confirmation page shows an AI-curated upsell ("Add this for ₹X, dispatched with your order"). 15–20% of buyers add an item when it's contextually relevant at this moment.

TECH STACK

Shopify Storefront APIPython (collaborative filtering + content-based)Claude 3.5 (WhatsApp message generation)WhatsApp Business APIRedis (real-time session)PostgreSQL (purchase graph)

Pricing

All XPndAI projects are fixed price — no per-seat fees, no usage billing, no surprises.

On-site Only
₹5L
Product page, cart, post-purchase recommendations
MOST POPULAR
Full Engine
₹11L
On-site + WhatsApp campaigns + upsell flows
Enterprise
₹22L
Real-time personalisation, A/B testing, seasonal logic

Frequently Asked Questions

How is this different from Shopify's recommendation feature or Beeketing?

Shopify's native feature uses simple co-purchase data. Beeketing and similar apps use generic models not trained on your specific data and charge monthly SaaS fees. XPndAI builds a custom recommendation model trained exclusively on your purchase graph and customer behaviour — you own the model, no monthly fees.

How many product SKUs does the recommendation engine work for?

Effectively from 30 SKUs to 10,000+ SKUs. For smaller catalogues (under 100 SKUs), the engine uses collaborative filtering (customer-to-customer patterns). For larger catalogues, it combines collaborative filtering with content-based matching (product attribute similarity).

Can the recommendations be seasonally adjusted for Indian festivals and trends?

Yes. XPndAI builds a seasonal override layer — Diwali gifting, summer skincare, Navratri (if relevant), year-end sales — where the recommendation weights shift to contextually relevant products. These seasonal rules are configurable by your team without code changes.

Does the AI recommendation engine work for new visitors with no purchase history?

Yes. For new visitors, the engine uses real-time session signals (which pages they view, search terms, category browsing) plus your bestsellers and trending products to make relevant recommendations — before any purchase history exists.

Let AI recommend. Let humans buy.

Free demo. We'll show you recommendations trained on your store's data. Fixed price.

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