Test product recommendations with your catalogue

A best-seller list uses an overall ranking. A recommendation workflow may use approved catalogue attributes, basket contents or measured purchase patterns to rank eligible products. The chosen inputs, exclusions and fallback need testing for the actual store. This page does not claim a conversion advantage over static lists.

No revenue increase is promised. Measure eligible sessions, recommendation views, clicks, completed orders, returns and gross order value using an agreed experiment. The scenario calculator compares your independent conversion and order-value assumptions; it is not a forecast or attribution model.

One option is to count products that appear together in completed orders over a defined period. Set minimum sample sizes, exclude returns and test whether the relationship persists. Catalogue compatibility, stock, margin, promotions and merchandising rules still need review. The calculation method and refresh schedule depend on the scoped implementation.

A small catalogue may use reviewed compatibility or merchandising rules instead of behaviour-based ranking. Suitability depends on product relationships, traffic, order history and the outcome being measured. Start with a fictional or test catalogue and compare against the current presentation before using customer data.

Shopify, WooCommerce, BigCommerce, Magento, Squarespace and custom stores are examples to assess, not guaranteed connectors. Confirm the exact plan, API or theme access, catalogue and order fields, write operations, rate limits and failure handling. Email use needs separate consent, platform and campaign review. Timing is quoted after scoping.

Any widget can affect loading, layout stability and responsiveness. Set a performance budget, test representative devices and network conditions, and provide a stable fallback when the recommendation service is slow or unavailable. Measure Core Web Vitals and business outcomes on the actual implementation; no zero-impact claim is made.

New products can begin with approved catalogue attributes, compatibility rules or a merchandiser-selected fallback. Check stock, availability, price, exclusions and the quality of product data. Behaviour-based ranking needs enough relevant observations and should not be assigned a fixed accuracy or activation threshold without evidence.

Agree how customer behaviour and purchase records may be used before connecting any systems. Review each selected provider, the locations of storage and AI processing, retention, access permissions and any onward transfers. Australian-only handling must be confirmed for the complete data flow in the project agreement. Check the relevant provider security evidence and test the configured controls before live use.