Semantic search for ecommerce site search – do you need it?
Semantic search as per Wikipedia seeks to improve search accuracy by understanding the searcher’s intent and the contextual meaning of terms as they appear in the searchable database, whether on the Web or within a closed system, to generate more relevant results.
Some of the examples of how this may translate in the eCommerce site search for a fashion retailer are search queries that read like “Dresses for spring” or “Bestselling moccasins in 2016 April” or ” Trendiest shirt for a cocktail party”. You get the drift.
So will it make an impact on your conversion rate, if your search engine has the potential to show accurate search results to these search queries?
In our cumulative analysis of search query data across clients across verticals, less than 0.07%, meaning 7 out of 10000 search queries, was semantic in nature.
Semantic search in eCommerce becomes relevant if the revenue generated from the x% who convert from the 0.07% is considered as significant to your business and also offsets the increase in cost for this added ability.
Even sites like Amazon, Walmart, and Target where 1% of their search volume could lead to millions of search queries, do not place a heavy emphasis on Semantic search for many reasons. Some of which could be ROI, customer experience or latency in the display of search results, that could increase search bounce rate. Convinced and want to SIGNUP NOW?
2) Frictionless experience
As suggested in our article best practices in site search, consumers expect to be fed. They are spoilt and prefer to have predictive suggestions that match the intent. Hence almost 99.07% of your audience is more likely to click on a suggestion or type a short query, vs. a fully type a semantic query.
If you notice a trend on certain contextual search queries or terms (e.g., cheap, under, newest, etc.,) from the database, that can be managed without a semantic system. But creating a rule set or synonyms libraries to tell your search index what products are potentially relevant for that query.
3) Consumer search behavior
When it comes to visitors who choose the search funnel, most of them already show intent or interest in purchase. Hence search queries are typically tied to products (dresses, shoes, beds, etc.,) or combinations of products and tags (Maxi dress, Nike Shoes, Single beds, etc.,).
Semantic search in eCommerce is usually more common in a contextual database vs. a product database. Most visitors who visit your online store are looking to buy products. Most of your visitors know what to search for unless you are selling something that is not easily understood.
5) Performance vs Price
Let’s say that over 1% of your search volume consists of queries that are semantic in nature and you need a site search engine to deliver results. As a product manager, this leads to leveraging an NLP system than can process or cache results for a semantic query in less than 50 ms. Anything longer than that is going to slow your results page and that can lead to the bad search experience.
Secondly, there is an increase in cost to build and maintain a system that can not only process these queries with accurate results but also show these results to your visitors like any other search query. Is that increase in overall cost, going to be offset by an increase in revenue driven by semantic searches?
Tagalys maximizes conversion rate & gives merchants visual control of products displayed in Site Search, Category Pages & Product Recommendations at their online store. To know more about our solutions and features, get in touch with us now.