E-commerce site search – understanding customer behaviour

By November 6, 2015General

home-carousel-03As someone who dabbles in online shopping more often than I should, I can say first hand that a pleasant user experience can easily turn a one time buyer into a regular, cart-filling devotee.

An important part of this experience is being able to find what you’re looking for quickly and easily. With major online retail events like Click Frenzy around the corner, you don’t want to have shoppers logging on to your site to make a purchase, only to be frustrated when they can’t find what they want. From experience, I can assure you the next tab opened will be one of your competitors.

Most of us associate online search with Google, which sets the bar pretty high for online retailers. Thanks Google! Regardless of your ecommerce platform—whether it’s MagentoWooCommerce or any of the various other platforms and shopping carts available—an effective search tool on your website leads to more conversions.

Gone are the days of invalid searches, irrelevant listings or the dreaded ‘no results returned’. Well, at least they should be gone. The technology exists that customers should no longer tolerate anything other than a perfect search experience on a retailer’s website. A good way to prevent these issues is to consider your typical customer’s behaviour and build your search function to fit. How do your customers use onsite search, and can your website match their expectations?

Before you invest in fancy new search technologies, investigate which user behaviours and query types your website should cater for.

Jamie Appleseed from Baymard has an interesting take on customer search query types and how you might address them. The following table of Jamie’s is an excellent overview, but it is well worth visiting his original post for a more detailed explanation of each.

Query TypeUser BehaviourHow you might support it
Exact Search:
“Keurig K45”
Searching for specific products by titleBasic keyword matching, along with support for multiple title variations and intelligent handling of misspellings
Product Type Search:
“Sandals”
Searching for groups or whole categories of productsSupport for synonyms as well as categories that aren’t part of the site’s navigation / hierarchy
Symptom Search:
“Stained rug”
Searching for products by querying for the problem they must solveSymptom database mapping “symptoms” to “cures” (i.e. problems to solutions)
Non-Product Search:
“Return policy”
Searching for help pages, company information, and other non-product pagesSearch engine must index the entire website, not just products
Feature Search:
“Waterproof cameras”
Searching for products with specific attributes or featuresIntelligent parsing of product specifications (i.e. structured product data)
Thematic Search:
“Living room rug”
Searching for categories or concepts that are vague in nature or have “fuzzy” boundariesInterpretive labelling of products and categories
Relational Search
“Movies starring Tom Hanks”
Searching for products by their affiliation with another objectAssociation data linking products and objects, ideally specifying the nature of the relationship too
Compatibility Search
“Lenses for Nikon D7000”
Searching for products by their compatibility with another itemCompatibility database mapping compatible products to one another
Subjective Search
“High-quality kettles”
Searching for products using non-objective qualifiersHandling of quantifiable single-attribute degrees (e.g. “cheap”), quantifiable but multi-attribute mix (“value for money”), and tasted-based (“delicious”) qualifiers
Slang, Abbreviation, and Symbol Search
“Sleeping bag -10 deg.”
Searching for products using various linguistic shortcutsSynonym mapping of slangs, abbreviations, and symbols, as well as interpretation of symbol intent (ranges, modifiers, etc)
Implicit Search
“[Women’s] Pants
Forgetting to include certain qualifiers in the search query due to one’s current frame of mindAll available environmental variables must be used to infer any implicit aspects of the user’s query
Natural Language Search
“Women’s shoes that are red and available in size 7.5”
Searching in full sentences rather than bundles of keywordsIntelligent parsing and deconstruction of the user’s query

Once you’ve identified which query types are relevant for your customers, work with your developers to address and support each one with the best possible experience.

Just as important as considering customer behaviour is what that search data can reveal to you. Having a solid and easy to use search function within your website is easy to justify when it helps you to achieve these common goals.

  • Improve conversion rate
  • Personalisation of results and lists
  • Reducing the customer effort for search queries

Plus, analysing how customers use your search function can reveal customer trends and popular terms that can inform future marketing and product strategies. This search data can help you to create a more pleasant experience, transforming casual customers into regularly returning advocates.

Speak to your developers about how you can work on improving your user experience with an effective search strategy.

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