E-commerce Personalisation Engine: Recommendations That Actually Convert
Your "customers also bought" widgets treat every visitor the same. Rising CAC means every storefront visit has to work harder, yet browse history and purchase patterns sit unused while generic carousels burn attention.
We build the personalisation engine that serves the right products in session.
Sound Familiar?
These are the exact issues ecommerce teams face before a real personalisation engine:
- Your "customers also bought" widgets show the same bestsellers to everyone, regardless of what they browsed
- Merchandisers spend hours a week hand-curating carousels that go stale within days
- Returning visitors see generic grids while their purchase history sits unused in Shopify or WooCommerce
- AOV stays flat because complementary products never appear at the right moment on PDP, cart, or homepage
- Ad costs keep climbing, so every storefront visit has to convert harder, and irrelevant recommendations waste that traffic
Ecommerce CAC rose 40–60% from 2023 to 2025, with median acquisition now above R1,400 per customer. Waiting for ad costs to fall is not a plan. Personalisation that lifts conversion and AOV on traffic you already buy is how storefronts claw margin back.
What the Personalisation Engine Actually Does
Shopper browses → signals scored → relevant products served → AOV and conversion rise. No weekly carousel rebuilds.
Signals Captured
Browse history, cart adds, and purchase patterns stream from your storefront in session
Relevance Scored
The engine ranks products by affinity, inventory, and merchandising rules
Recommendations Served
Relevant products appear on homepage, PDP, cart, and post-purchase slots
Revenue Attributed
Conversion and AOV lifts tracked by placement so you optimise what earns
Everything You Need for Ecommerce AI Personalisation
Browse-History Recommendations
Surface products related to what each shopper just viewed, so the next click feels intentional rather than random.
Purchase-Pattern Affinity
Recommend complementary and higher-value items from real order history, not a static "also bought" list.
Inventory-Aware Serving
Recommendations respect live stock levels so you never push sold-out SKUs or dead-end journeys.
On-Site Placement Rules
Wire the engine into homepage, PDP, cart, and post-purchase slots with fallbacks when profile data is thin.
Merchandising Overrides
Marketing keeps campaign pins and brand rules while the AI fills the rest, so control and relevance coexist.
Revenue Attribution
Track click-through, add-to-cart, and revenue lift by widget so you double down on placements that earn.
Platforms & Engines We've Connected
From 8 Hours/Week of Merchandising to 2
How a mid-market fashion retailer lifted conversion 12% and AOV 18% with on-site product recommendations wired to browse and purchase history.
The Generic Widgets
- Same "customers also bought" bestsellers on every PDP, ignoring browse context
- Merchandiser rebuilt homepage and cart carousels by hand every week
- Sold-out SKUs still appeared in static recommendation blocks
- Paid traffic landed on pages that pushed irrelevant products
- No attribution on which recommendation placements earned revenue
The Personalised Storefront
- Browse and purchase signals drive PDP, cart, and homepage recommendations in session
- Merchandiser reviews campaign pins and brand rules, not every product tile
- Inventory filters stop sold-out recommendations at source
- Complementary and higher-value items lift basket size on the same traffic
- Placement-level revenue reporting shows which widgets pay for themselves
Before vs After Personalisation
How It Works
From first conversation to live personalisation in 2–4 weeks.
Audit Your Storefront
Where recommendations live today, what data you already capture, and which pages leak the most revenue.
Free Scoping Call
30-minute call to pick the highest-ROI placements: PDP, cart, homepage, and post-purchase.
Build & Test
We wire browse and purchase signals, inventory filters, and A/B the new widgets against your current carousels.
Go Live & Optimise
Ship the personalisation engine, watch conversion and AOV by placement, then iterate on the winners.
Frequently Asked Questions
How is a personalisation engine different from Shopify's built-in product recommendations?
Native Shopify and similar platform widgets are a solid starting point, but they rarely combine deep browse history, purchase affinity, inventory constraints, and merchandising overrides in one model. We build engines that score relevance from your real behavioural data and serve it wherever shoppers decide: homepage, PDP, cart, and post-purchase.
Which ecommerce platforms can you connect?
We build personalisation engines for Shopify, WooCommerce, BigCommerce, Magento / Adobe Commerce, and custom headless storefronts. We also wire Algolia Recommend, Nosto, and similar recommendation APIs when that is the right fit for your stack.
Will this replace our merchandising team?
No. Merchandisers keep campaign pins, brand rules, and seasonal overrides. The engine removes the weekly grind of hand-building generic carousels and surfaces relevant products automatically between those campaigns.
How long does an ecommerce personalisation engine take to launch?
A focused PDP and cart recommendation build typically takes 2 to 4 weeks from scoping to go-live. Broader programmes with homepage, post-purchase, inventory-aware rules, and attribution reporting sit closer to 4 to 6 weeks.
How do you measure whether product recommendations are working?
We instrument click-through, add-to-cart, conversion, and revenue attributed to each recommendation placement, then A/B against your current widgets. Clients typically watch conversion and AOV lifts within the first 30 to 90 days.
How much does an ecommerce personalisation engine cost?
Focused on-site recommendation builds start from around R35,000. Full engines with browse and purchase affinity, inventory filters, multi-placement rules, and revenue attribution typically range from R45,000 to R90,000. Most mid-market stores see payback inside one to three months once conversion and AOV lifts compound across paid and organic traffic.
Stop Paying for Traffic That Sees Irrelevant Products
If your storefront still pushes the same bestsellers to every visitor, you are leaving conversion and AOV on the table while CAC keeps rising.
Tell us which platform you run, where recommendations live today, and which pages waste the most paid traffic. We will show you exactly how a personalisation engine would work for your catalogue.