Smart autosuggest is essential for retail

Business problem

Rising customer expectations and increasing impatience means that search experiences must be seamless without any issues or frustrations. Even typing search queries is now considered a tedious task, especially on mobile devices. Therefore, smart typeahead or autocomplete suggestions are essential features that can make a huge difference in customer engagement with digital search.

Relevant suggestions not only reduce typing time, but also help customers formulate longer, more detailed queries for the product search engine, leading to more relevant results and better conversion rates. On top of that, good suggestions can inspire the customer to further explore the catalog, resulting in additional sales.

What to expect from smart suggestions?
What to expect from smart suggestions?

A modern suggestion generation system should be able to tap into all available data sources including: catalog data, customer search logs and customer behavior signals.

Signal-based ranking:
An autocomplete system should be able to blend customer's signals, like concept popularity and trendiness, with business signals, such as conversion rates associated with particular suggestions.

Suggestions should take into account geolocation, customer preferences and session history.

Merchandisers should be able to effect suggestion ranking by promoting particular concepts, or blacklisting undesirable suggestions.

Match explain:
The suggestion matching engine should provide a match explanation so that it is possible to highlight differences between suggestions.

Approaches to generating relevant suggestions

Smart autosuggest commandments

Ensures high quality search results

Removes search experiences with zero results, partial matches or irrelevant results.

Reveals breadth and depth of the catalog

Uses suggestions to initiate customer exploration of catalog brands, styles, sizes, colors and combinations thereof.

Natural looking suggestions

Suggestions are spelled correctly and have proper grammar, word order and prepositions. For example, "table lamp", not "lamp table".

No unnecessary repetition

Provides semantically unique suggestions. For example, it does not suggest both "table lamp" and "desk lamp" when lamp is typed.

Search intent is understood

Suggestions recognize misspellings, slang, abbreviations and domain-specific terminology. For example, it understands that "ck"= Calvin Klein.

Experiences are personalized

Suggestions are ranked according to personal preferences and shopping history, providing a unique search experience for each customer.

Smart autosuggest system blueprint

Read more

How to implement autocomplete search for large-scale e-commerce catalogs
We implemented a smart autocomplete suggestion service for a large online retailer based on Solr and a catalog-based suggestion generator.
Read more
Smart autocomplete best practices: improve search relevance and sales
Learn how suggestions improve the user’s search experience, increasing both online conversion rates and average online cart value.
Read more

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