Are your search and product discovery experiences not meeting expectations?
Here are some things you can do to take them to the next level:
At its simplest, autocomplete helps users search for products faster by estimating their queries and recommending completed queries. But at Constructor, we believe autocomplete can be used for much more than basic query estimation.
Great autocomplete corrects phonetic misspellings, keyboard proximity typos, punctuation nuances, and omitted character typos. Is it “blue ray”, “blue-ray” or “bluray?” It’s officially spelled “blu-ray”, but you shouldn’t require users to know this to get good results.
Great autocomplete can also detect synonyms within the query to serve autocomplete results. For instance, if a user types “pop” instead of “soda,” the autocomplete system should detect this.
As a hypothetical, if a user searches for “sleek red dresses for women,” the system should know that “sleek,” “red,” “dresses,” and “women” are the most important keywords in the query, and should serve recommendations based on those words.
Products in the Search Bar
On top of helping users search for products with autosuggest, products can also be embedded within the search bar to give a “visual autocomplete:”
Our own data tells us users are twice as likely to convert on products they’ve clicked from embedded product listings.
Relevance vs. Attractiveness
Traditionally, merchandisers and search architects were focused on ensuring their search results were “relevant.” However, this is not how companies like Amazon and Google evaluate their search.
The problem with relevance is its subjectivity. It’s not a clear, measurable goal the business can optimize for. For example…
If a user searches for “laptops” on an electronic retailers website, the user certainly wouldn’t benefit from seeing laptops from 2009 listed in the search results; however, those results are still technically relevant.
Here’s a more extreme example from our article covering “Relevance v.s. Attractiveness” in greater detail:
“If a user searches for chips on a grocery website, should the grocer show Cheetos? It’s not strictly relevant to the search because Cheetos are not chips, but what if users who search for chips are highly likely to buy Cheetos? Should the retailer pedantically disagree and only show potato chips, or should it show the user Cheetos, even if they aren’t strictly relevant?”
So, what do you optimize search results for if it isn’t relevance?
“Focus on the business metrics the company cares about: revenue, purchases, profit, or whatever else most needs to be increased. Then both evaluate the search, and drive up the probability that each customer has a successful buying journey using the clickstream data that happens after the search. In short, show each customer products for each search in the order that will most likely lead to an increase in the business metric.
To achieve this, retailers should use three key attributes balanced together: relevance, attractiveness, and personalized attractiveness. Relevance should prevent something like shoes showing up for that search for laptop. Attractiveness should ensure that the most attractive laptops (the ones most likely to be purchased from that search) should show up at the top, and personalized attractiveness should use what we know about each user, their individual clickstream data, to show the particular results most attractive to them.”
By optimizing your search for a particular KPI, you are able to generate search results that benefit both you and your users.
Custom, timed collections
Some merchant tools allow you to quickly create customized collections (ex. themed products around holidays or special events) you can show to users when they search for specific queries.
For instance, if a user searches for “birthday gifts” on your site, you could redirect them to a collection based around birthday gifts.
Boosting new or own-brand products
If you have a new product (or an own-brand product) you’d like to advertise to users, merchant tools should allow merchandisers to quickly slot those items in the top position for queries relating to the item.
Boosting high margin or low inventory products
In the same way merchant tools can be used to boost new products, they can also be used to boost products that generate high profit margins or products with high inventory you’re looking to get rid of.
While manually boosting these products can be great for driving impactful business metrics (like high-margin unit sales), it’s important to closely monitor user behavior after altering search results for any query. Some manually boosted products can negatively impact your search experiences leading to an opposite of the business metric you’re trying to drive.
Solving frustrated searches/zero-result pages
Your site will have search results users aren’t happy with — and when users aren’t happy, they’ll either leave the site or refine their search. We call these frustrated searches.
Intelligent search systems today record these frustrated searches and present them to merchandisers. For instance, if 500 users on your site search for “sun block” and 90% of them leave the page immediately after, that query will be presented to the merchandisers as a frustrated search. These systems also show merchandisers the queries that users refine their searches to and the queries they convert on (for instance, “sunscreen”). Merchandisers can then manually set synonyms and redirects to solve those frustrated searches.
Boost/Bury Rules Around Availability or Arrival Dates
Automatic boost and bury rules can be applied to those products based on their availability (i.e. they can boost in-stock or high-inventory products and bury out-of-stock products). You can also do the same with products that haven’t arrived yet; however, there is a case to be made for boosting products that haven’t yet arrived to increase pre-purchases or to promote an upcoming release.
We briefly discussed the idea of the benefits of ranking items based on business KPIs rather than relevance, but there’s still one point that needs to be addressed:
Users have preferences.
While a majority of users may convert on “Lays” chips for the “chips” query, that doesn’t mean everyone will. And that’s where personalized product re-ranking comes in.
Great product discovery systems take the idea of “relevance v.s. attractiveness” and apply it on a user-by-user basis. Once you have enough data about any specific user’s preferences, instead of continually re-ranking products on a broad basis, you can begin ranking products specifically for that user that will give you the best chance of increasing your business KPIs (like conversions).
This strategy can be applied in most places on your site, like category pages (and even in navigation facets):
Test Your Product Discovery Experience
How does your product discovery experience compare to internet giants like Amazon and Google? Take the 5-minute quiz to find out: