Question 1

How does AI product recommendation work differently from "best sellers" lists?

Accepted Answer

Best seller lists show the same products to everyone - they're popularity-based, not personalised. AI recommendations analyse each individual customer's browsing history, purchase patterns, and behaviour to suggest products uniquely relevant to them. A customer who bought running shoes sees running socks and GPS watches, while a customer who bought dress shoes sees belt and tie recommendations. This 1:1 personalisation is why AI recommendations convert at 5× the rate of static "best seller" displays.

Question 2

What kind of revenue increase can we expect?

Accepted Answer

Australian e-commerce businesses implementing AI recommendations typically see a 15-35% increase in average order value and a 10-20% increase in overall conversion rate. The range depends on your catalogue size, traffic volume, and how personalised your current experience is. Businesses with large catalogues (1,000+ products) and no existing personalisation see the largest gains. We provide a revenue guarantee: if the AI doesn't generate at least 3× its monthly fee in additional revenue within 90 days, you pay nothing.

Question 3

How does "frequently bought together" actually get calculated?

Accepted Answer

The AI uses collaborative filtering - the same technique pioneered by Amazon - to analyse millions of purchase combinations across your order history. It identifies statistically significant co-purchase patterns that go far beyond what a human merchandiser would spot. For example, it might discover that customers who buy a specific brand of coffee also buy a particular type of biscuit 40% of the time. These patterns are recalculated daily as new orders come in, so recommendations stay fresh and seasonal.

Question 4

Does this work for businesses with small product catalogues?

Accepted Answer

Yes, though the approach differs. For businesses with fewer than 100 products, the AI focuses more on content-based recommendations (matching product attributes) rather than collaborative filtering (which needs volume). Even with 50 products, the AI can identify optimal cross-sell pairs, create "complete the look" bundles, and personalise the ordering of your catalogue page based on each visitor's interests. We've helped Australian businesses with as few as 30 products increase AOV by 18%.

Question 5

How do you integrate with our existing e-commerce platform?

Accepted Answer

We have plug-and-play integrations for Shopify, WooCommerce, BigCommerce, Magento, and Squarespace - installation takes under 30 minutes. For custom-built sites, we provide a JavaScript snippet and REST API. The recommendation widgets are fully customisable to match your brand styling. All data syncs in real time: when a customer adds an item to their cart, recommendations update instantly. We also integrate with email platforms (Klaviyo, Mailchimp, Campaign Monitor) to power personalised product recommendation emails.

Question 6

Will slow recommendations hurt our page load speed?

Accepted Answer

No. Our recommendation engine serves results from edge servers across Australia in under 50 milliseconds. Recommendations load asynchronously - your page renders first, then recommendations appear. This means zero impact on your Core Web Vitals scores. We use lazy loading so recommendations below the fold only load when the customer scrolls to them. Multiple A/B tests show that well-implemented AI recommendations actually improve engagement metrics like time-on-page and pages-per-session.

Question 7

How does AI handle new products with no purchase history?

Accepted Answer

This is called the "cold start problem" and we solve it with content-based filtering. When a new product is added to your catalogue, the AI analyses its attributes - category, price point, description, images - and matches it against similar products that already have purchase data. Within the first 24 hours, the new product starts appearing in relevant recommendations. After 50-100 purchases, collaborative filtering kicks in and recommendations become increasingly accurate.

Question 8

Is customer browsing data handled securely under Australian privacy law?

Accepted Answer

All data is processed in compliance with the Australian Privacy Act 1988 and Australian Privacy Principles (APPs). We use first-party data only - no third-party cookies or cross-site tracking. Customer behaviour data is anonymised for model training and encrypted at rest using AES-256. All processing happens on Australian servers located in Sydney. We provide full Data Processing Agreements and support your obligations under the Notifiable Data Breaches scheme. Our systems are audited annually for SOC 2 Type II compliance.

Boost Revenue 15-35% Per Customer

The Personalisation Revenue Gap

Revenue Lift Calculator

Your Store Metrics

Revenue Projection

From Browsing Data to Revenue in Under 30 Minutes

Plug Into Your Platform

AI Analyses Purchase Patterns

Personalised Widgets Go Live

Continuous A/B Testing & Optimisation

Seven Recommendation Types That Drive Revenue

Collaborative Filtering

Content-Based Matching

Real-Time Personalisation

Frequently Bought Together

Complete the Look / Set

Email Recommendations

In-Store Kiosk & POS

Wishlist & Save-for-Later Intelligence

Australian Support

Frequently Asked Questions