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Understanding The Science Behind E-Commerce Search

These days, most people take technology for granted, especially when it comes to e-commerce platforms. This is pretty understandable, as most people with a working computer (or a mobile device) and access to the internet have the world at their fingertips. Thanks to global marketplaces like Amazon and Google, consumers can almost instantaneously buy products ranging from groceries to automotive parts. 

Most people, however, do not understand how e-commerce search technologies function. This is fairly ironic, as consumers can easily identify a weak on-site search as well as poor search results. Which in turn, results in them abandoning the site looking for other sites with a better shopping experience. The chances of these people coming back will be close to zero.

On-site search is one of the most utilized but most misunderstood pieces of technology in the e-commerce world.

According to a study conducted by the Baymard Institute, most people jump straight to a website’s on-site search bar when browsing products, but most lack knowledge of how this technology functions. This raises an important question for e-commerce companies:  How can companies cater search practices to their client’s needs if they do not have a fundamental understanding of how search works?

On-site search can be broken down into three components: Language Processing, Relevance and Ranking. To give a rundown of the three:

Language Processing

Language processing is a technology that processes search queries. In most instances, language processing takes the form of a keyword search, which provides a blanket list of search results based on important keywords.

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For example, if a user queries the question “How much does the new iPhone cost, ” a traditional keyword search will focus on the term “New iPhone” and will give a broad list of search results which will likely include collateral results – phone cases, chargers, headphones, etc. Natural language processing, which is more modern technology, can process everyday conversational language and can interpret search queries as a whole. If a user queries the same question, “How much does the new iPhone cost,” a search engine using natural language processing will analyze and interpret the entire search query and will give a direct answer, or a more concise list of results at the very least. Thanks to task managers like Siri, Cortana and Alexa, the need to process conversational language queries is in high demand. 

Relevance

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Search relevance, to put it simply, is how closely search results align with their original query. The process of providing consumers with relevant results, however, is anything but simple. Most websites will follow a guide that determines important search factors – this is called a site index. Indexes typically include site pages and data that are evaluated when querying. Many algorithms can be evaluated with a good index, which will lead to more relevant results. It is very important for e-commerce to maximize relevance, as 96 percent of users will abandon a website if they receive poor results.  

Ranking

Search ranking is the order in which search results appear. Similar to relevance, this process is seemingly simple but requires a lot of maintenance. In most instances, digital specialists can handpick which results/product listings they want to appear based on previous data/consumer research. This, of course, requires a lot of legwork; it is no wonder Amazon hires over 1,500 people on their search team to manage ranking. Companies with a more forward-thinking approach can allow machine learning to boost relevant results on their behalf. 

In all three instances, personalization is an overarching theme. Most personalization begins with the collection of data. The more information retailers have regarding their customers, the more accurate their personalization can be. According to a study conducted by Accenture, 91 percent of consumers expect some level of personalization when shopping online. 

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When language processing, relevance and search ranking are all working in tandem, a high level of personalization can be achieved. However, maximizing this trio is very difficult. Luckily, artificial intelligence (AI) will soon be shouldering the weight and will hopefully construct an almost perfect digital journey. 

AI can process all forms of language: misspellings, ambiguous search terms and conversational search queries. Additionally, AI helps rank relevant products faster and more accurately than a human can without having to hire massive teams of search engineers and data scientists. 

E-commerce professionals should understand the science behind site search because the industry is constantly in flux. The old methods are going by the wayside in favor of new technology, like artificial intelligence, that improves the search experience for the customer. On-site search engines should deliver fast and accurate results that help brands and retailers convert queries into sales. 

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