There are two main aspects to the Personalized Search ecommerce tool - search accuracy, and personalized ranking.
Search accuracy is all about precision and recall - making the search as smart as possible so that it can return relevant results to the shopper putting in the query. This includes an autocomplete which predicts what the shopper will type as they’re typing - meaning fewer keystrokes for the shopper and fewer barriers to conducting a successful search.
Other aspects include Natural Language Processing - the search being able to tell the difference between products, like if it's a t-shirt or a dress, and attributes, like the colour red.
An effective search should also include spelling tolerance, ensuring shoppers get relevant results even if they make a typo; a zero results workaround - ensuring relevant products come up even if the exact query searched for is not present; and filtering options enabling shoppers to filter down results quickly and easily.
Personalization, then, is all about the AI algorithm which analyzes the behaviour of the shopper and learns, in real time, what their preferences are - and search results are ranked in order of preference. So, if the algorithm learns the shopper frequently buys only vegan-friendly products, or has a preference for a certain colour or pattern, this is what they will see more of.
The machine-learning algorithm constantly re-ranks products based on how the shopper behaves in order to deliver a continuously tailored experience where products most likely to convert are boosted to the top.
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Updated about a month ago