Text matching capabilites

Feature	Description
Attribute indexing	Any attribute on the product can be indexed, and then, configured and scored independently.
Match types (exact partial, reverse, compound, fuzzy)	Loop54 use several match types and can find partial matches, reverse partial match, compounded words and fuzzy matches: - Partial match- users can search for a part of a word and still get a match Reverse partial match - users can search for the end of a word and still get a match - Compounded words - users can write two words together (that is separate on products) and still get matches - Fuzzy matches- users still get matches even though they have not typed the query exactly as it appears on the products. This is to accommodate for typos and small variations.
SKU/EAN search	Only show one product as result if search is for SKU or EAN number.
Content search	Search for things other than products (e.g. guides, blogs, articles, etc.). List of content results will be returned in a separate list from the product results and will be ranked default by relevance.
Suggested spelling corrections	When the query contains spelling errors that the engine can interpret with some confidence, it will return spelling suggestions. The engine normally auto-corrects minor misspellings, but if the query is severely misspelled, then the engine will return "best guess" results and provide suggested spelling corrections. The engine is able to determine the most relevant spelling corrections by looking at past queries by other users that led to a direct hit (direct hit = search phrase exists, exactly as typed, in a product's metadata)
Learning new words	If no results are found with regular methods, the engine will edit the search query significantly. The engine tries to break a word down into several words, traversing back through the query until it finds a word that has a match in the catalogue. If the engine still can not find a match from sub-words, then it will drastically edit the search phrase (e.g. by swapping letters, replacing letters, etc.). If users frequently interact with the results the engine will learn the meaning of that word and improve the relevance even further. If users don't interact with the results, the engine will eventually decide to return nothing instead.