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Intelligent AI Product Recommendations – E-commerce Personalization

AI product recommendation system based on purchase history and user behaviour. AOV growth of 15–35% and higher returning customer rate.

SERVICE DETAILS

I design and implement AI product recommendation systems for Shopify and WooCommerce stores — from simple similar-product mechanisms through collaborative filtering (customers like you also bought...) to advanced hybrid models combining purchase data with real-time session behaviour. The system integrates directly with the store, displaying recommendations on the product page, in the cart, and in post-purchase emails. Results from e-commerce deployments: Average Order Value (AOV) growth of 15-35% and returning customer rate growth of 20-40%.

> INVESTMENT:

from €2,500
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Key Benefits

AOV growth of 15-35% through accurate cross-sell and up-sell recommendations displayed at the right moment in the purchase journey.

Personalisation based on real purchase data from your store — not generic bestsellers, but recommendations matched to each customer's history.

Recommendations at multiple touchpoints: product page, cart, post-purchase email, thank-you page — each an opportunity to grow order value.

Returning customer rate growth of 20-40% — personalisation builds purchase habits and preference for your store over competitors.

A/B testing built into the system — you measure the real impact of recommendations on conversion and AOV, not estimates.

The Process

1

Historical data analysis

I export order data (minimum 6 months history for meaningful results) and conduct exploratory analysis: AOV distribution, frequently co-purchased products, customer segments.

2

Algorithm selection and architecture

Based on catalogue size and order volume I choose the method: collaborative filtering for large stores, content-based for niche catalogues, hybrid for mid-size. I design the platform integration.

3

Deployment and calibration

I build the recommendation engine, integrate with Shopify Storefront API or WooCommerce hooks, deploy recommendation widgets in the store UI, and configure learning on new orders.

4

Measurement and optimisation

I launch an A/B test (50% of users with recommendations, 50% without), measure AOV and conversion growth over 4 weeks, and optimise widget placement and the algorithm based on results.

Frequently Asked Questions

How much order history do I need for the system to work well?

Minimum 1,000 orders and 6 months of history for meaningful collaborative filtering results. For smaller stores — I start with content-based (similar product attributes) and migrate to hybrid as data grows.

Does the system work with Shopify Plus and standard Shopify?

Yes — I integrate via Shopify Storefront API and Theme App Extensions, which work on all Shopify plans. For WooCommerce I use REST API and a custom plugin. Shopify Plus is not required.

How long until recommendations improve AOV?

First measurable results after 4-6 weeks of A/B testing. Full algorithm calibration on your data takes 2-3 months — the system becomes increasingly accurate with every new order.

Do AI recommendations replace plugins like LimeSpot or Frequently Bought Together?

SaaS plugins (€50-300/month) offer a quick start but are generic and expensive at scale. A dedicated recommendation system is cheaper to maintain at volumes above 500 orders/month and gives full control over the algorithm.

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All modules of E-commerce AI

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BIAŁYSTOK, PL
+48 732 022 086pawel.wiszniewski95@gmail.com