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Optimizing E-Commerce Search Functionalities

A Roadmap to Better Conversion and Retention Rates

Having an actionable search strategy roadmap can help you better understand your e-commerce search engine – its strengths, weaknesses, and a clear view of what’s needed to be done for short- and long-term improvements. An example roadmap is the e-commerce scorecard below. To see how the quadrant can be used to improve your site search and conversion rate, watch our video

e-commerce search optimization quadrant

 

E-Commerce Site Search Features - Defined

Below are the definitions for some of the functionalities mentioned in our e-commerce search optimization quadrant.

  • Breadcrumb negation - allows a customer to remove previous selections from the query without going back to the start. For example, a user searches for "Plasma TV," then selects "Sony," then selects price range "$400-$600." At this point they realize they can’t get a Sony TV within this price range. So if they negate the "Sony" selection (which is midway in this query), then it will keep the price range filter ($400-$600), but open up to brands like LG, Toshiba, Panasonic, etc.
  • Category facets - faceted classification gives the users the ability to find items based on more than one dimension, such as product facets, special feature facets, etc.
  • Category snapping - allows the search engine to understand the query better, resulting in a more satisfying user journey. For example, "Nike Sneaker red 12" probably means Brand:Nike ShoeType:Sneaker Color:Red Size:12.
  • “Did you mean?” - provides relevant alternative suggestions when the user may have misspelled a search term.
  • Facet negation – when a user clicks on the “negation” icon next to an option, the option will be removed from search results.
  • Intelligent query parsing - translates a search string into specific instructions for the search engine. Intelligent query parsing can take the context of the search query into account and produce more relevant search results. Read more about search query parsing. 
  • Personalization – uses big data and log analytics to provide relevant product recommendations based on online users’ search queries and click activities. Watch our case story on e-commerce personalization.
  • Phrase search - allows users to search for content containing an exact sentence or phrase rather than containing a set of keywords in random order.
  • Query completion - predicts the rest of a word or phrase that the user is typing into the search box, based on popular search queries or queries selected by the retailer.
  • Query fall backsallows a search engine to "step back" when it knows some combination of terms won’t find good results. For example, "little black summer dress" may produce good results, but falling back to "black summer dress" will.  
  • Query redirection - redirects a user's search query to relevant results when that specific query produces no results.
  • Relevancy ranking – uses predictive analytics to predict and place the items the user will most likely to buy at the top of the search results page. See how search engine scoring works to enhance search relevancy.
  • Results pagination – splits search results into multiple pages.
  • Social navigation - a user's navigation through the website is guided and structured by the behavior of other users.
  • Spell correction - uses dictionary documents to find possible misspellings for words entered by a user and suggests the correct spellings.
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