A practical review for large Shopify catalogs
A large catalog can contain thousands of relevant products while shoppers repeatedly encounter the same narrow set. RecoMelon’s catalog discovery audit reviews the search, collection, PDP and basket journeys that determine how much of the range can realistically be found.
Search that understands what shoppers mean. Conversational and contextual search interprets style, color, attributes and intent. Dynamic merchandising balances commercial and catalog priorities, while inline Shop Similar and integrated LiveGrid™ UGC keep shoppers discovering without returning to a static results grid.
Shop Similar, directly from the product image. When a product is "almost right", Reveal™ offers visually aligned alternatives from within the primary product image, above the fold. Shoppers stay on the PDP and keep their intent while finding an alternative without the attribute stopping the purchase.
Turn “almost right” into another relevant option. Shop Similar keeps high-intent shoppers exploring with visually matched alternatives based on the product they are already considering - helping them refine their choice without starting their search again.
Turn one product into a complete look. Complementary recommendations create multiple looks around a chosen product, with configurable discounts and multi-buy offers to increase AOV. Bundle Rescue swaps unavailable or unsuitable items for visually similar alternatives without breaking the set. Integrated LiveGrid™ content helps shoppers visualize the combination.
Abandoned Cart Email Rescue. Sending shoppers back to rejected products repeats the problem. SecondLook™ restores the abandoned basket and offers visually matched alternatives for each item - helping shoppers find a better fit while preserving basket intent and cross-sell opportunities.
The discovery problem
A store can offer substantial choice while repeatedly exposing the same products. Search language, rigid collection paths and missing PDP alternatives can each limit the range that a shopper can realistically discover.
Bring examples of those dead ends, alongside the store URL, approximate SKU count, priority markets and categories. The aim is to identify an observable journey problem before proposing a new surface.
Fit and implementation
Review search, collection browsing, product alternatives and complementary choices against the existing storefront. Where relevant, consider The Index™, Smart Collections, Reveal™ or Bundles™ as possible responses to a specific gap.
Record what can remain in the current stack, which product data is needed and who owns implementation. The initial review is a starting point for scoping; any deeper technical or data audit is agreed separately.
Evaluation and evidence
Choose a first surface and establish its current product exposure, engagement and progression to purchase. Define the audience, comparison method and operational responsibilities before the test begins.
Assess whether the change expands useful catalog reach and improves the chosen journey. Include implementation effort and merchandising workload in the decision, rather than treating a larger number of interactions as sufficient proof of value.
The review focuses on the surfaces where product intent is created, refined or lost. This includes search language, filters, collection structure, long-tail exposure, PDP alternatives, zero-result recovery, complementary products and measurement.
Provide the store URL, approximate SKU count, priority markets, highest-value categories and the search or merchandising tools currently in use.
The initial review identifies the strongest discovery gaps, what can remain in the existing stack and a focused surface that could be tested first. Any deeper technical or data audit is agreed separately.
Request a review and we’ll map the smallest credible route to improving product discovery without assuming a full platform replacement.
Request a catalog review