Sale season is finally here! You decide to go online to make the most of it. Type in “fur coat” and hit the search bar. Uh-Oh. “But they showed us different products when my friend searched for it the previous day! How?” “How does search know I want women coats when I just mentioned coats? Could it be stalking?”
Well, my dear, we call it “analytics” in fancier terms. Let’s look at how a computer understands what you want.
Search algorithm takes into account various factors to provide a smooth user experience. Let’s look at an analogy.
You have been wanting to adopt a healthier lifestyle for some time now. The very same day, you meet a friend who also shares similar concerns and further, shows you a new pair of Nike running shoes he purchased, which inspires you to work towards a healthier lifestyle yourself. (Check it out for yourself!)
You go to the mall next day and walk into a Nike store. Upon being greeted by the store manager, you tell him you are looking for a pair of running shoes. He points at a variety of available options and inquires if there is something particular you’d like and you tell him about your friend’s shoes which you liked. He brings them to you. Since it is specifically what you wanted, you proceed with the buying flow (try the shoes on, ask for the price, check for any other color/size/best fit, pay and leave satisfied on reaching the first stepping stone to a healthier lifestyle).
The above scenario describes what we call a “specific user” of eCommerce. In this case, the user knows specifically what he wants and “makes it clear” in the first interaction with the platform. Similar to the analogy, the user types in “Nike Running Shoes *Model/Style*”. It is the search algorithm’s (developer of the algorithm) responsibility to “understand” the user query (algorithm identifies brand as Nike, category as Shoes and checks for the particular style) and show the most relevant product to the user. Since the user sees the specific product he wanted, he selects the product and proceeds with the checkout process. “Personalization” and “context search” also comes into play. Since the store manager asked you if you wanted it for yourself, he was in a better state to show you men/women shoes. However, the algorithm uses your personal information(called personalization) to identify whether you want it for a guy or girl unless explicitly mentioned.
You walk into the Nike store, hoping for the store manager to help you find the best fit for you. The store manager tries to gather your requirements through a series of questions such as, “what kind of shoes? Are you a beginner or an athlete? How much do you plan to use them? What is your body target?” All your answers enable him to put forward the best-fit pair of shoes. The store manager tried to incorporate all your requirements and addressed your query accordingly. However, it is now your discretion to buy or not to buy the shoes. The store manager can only hope he showed you what you wanted.
Users demonstrating such behavior are categorized as “explorers” on an eCommerce platform. The platform uses intelligence and user information to “anticipate” what the user might want/like and shows it to them, thereby, acting as a store manager. This is ensured due to several factors such as user profile information, user demographics, user previous interaction with the website, the device the user is operating on, trends in the locality, the performance of related products, weather, mood, reviews, and ratings, etc. The algorithm “tries” to put forth the best fit for you, addressing your query but buying it is again your discretion.
Turns out, makers of search put in more effort than you thought to anticipate what you want in order to provide a seamless user experience. Surprised to see how we skip details and assume the site will take care of them? Being “taken for granted” is what makes a product popular and easy to use!
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