[00] Case study
How an AI recommendation engine raised e-commerce conversion rates by 35%, with personalised product suggestions that lifted order value and retention.
published ·updated
results
From our published case study: results within six months.
input the challenge
High traffic but low conversion: generic recommendations, poor product discovery, high cart abandonment and few repeat purchases.
output what we delivered
A custom machine learning recommendation engine that personalises products in real time from browsing, search, wishlist and purchase history.
the full story
Shoppers expect a store to show them products that match their interests, browsing and purchase history. This case study explains how a custom AI recommendation engine helped an e-commerce business raise its conversion rate by 35%, improve engagement and increase revenue.
The client is a mid-sized e-commerce company selling fashion and lifestyle products across several regions. The site attracted plenty of visitors but struggled to turn them into paying customers.
The company wanted a recommendation system that could personalise the shopping experience in real time.
A custom AI recommendation engine was built around the client’s e-commerce operations. It combined:
The system learned from each customer’s interactions:
From these signals it chose the most relevant products to show each shopper.
Products suggested from each customer’s interests and shopping behaviour.
Recommendations changed as the shopper browsed, rather than being refreshed overnight.
The engine identified related and complementary products to increase average order value.
Search results improved using AI predictions of what the shopper was likely looking for.
Shoppers were grouped automatically by preferences and buying behaviour.
The system forecast customers’ future interests and trending products.
Within six months of launch, the company recorded:
| Metric | Before AI | After AI | Change |
|---|---|---|---|
| Conversion rate | 2.8% | 3.78% | +35% |
| Average order value | $68 | $92 | +35% |
| Customer retention | 22% | 41% | +86% |
| Cart abandonment | 71% | 49% | Reduced significantly |
| Product engagement | Moderate | High | +60% |
A good recommendation engine helps a store increase conversions, improve customer satisfaction, reduce bounce rates, encourage repeat purchases and build loyalty. The results depend on clean product and behaviour data, so the work starts with understanding what data the store already collects.
A custom AI recommendation engine raised this retailer’s conversion rate by 35% and lifted order value and retention at the same time. If you run an online store with traffic that does not convert, see how we approach recommendation engine development.
A system that uses machine learning to suggest products or services based on each customer’s behaviour and preferences.
By showing personalised product recommendations that match the customer’s interests and buying intent.
E-commerce, healthcare, entertainment, education, travel and finance all use recommendation engines.
Common choices include machine learning frameworks such as TensorFlow, Python, big data analytics and cloud computing.
Recommendation engines help increase sales, improve the customer experience and raise retention.
more proof
Every figure is taken from the published case study.
All case studies70%lower content production costs
inputRising content costs, slow production cycles and a heavy reliance on freelancers and agencies made a steady publishing schedule hard to keep.
3×increase in customer engagement
inputSupport replies took 12 to 24 hours, repetitive questions tied up agents, support costs were high and customer engagement was low.
40%lower operational costs
inputManual, repetitive processes, high operating costs, little data to base decisions on and systems that could not keep up with growth.
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