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Search technologies that power eCommerce stores are in the midst of a revolution. Thanks to the progression of technology, strides have been taken to bring site search optimization even further. With 43% of shoppers beginning at the search bar, and these consumers being more likely to convert and spend more, this has been a crucial leap forward.
In this article, we’ll take you through the top advanced tech that can help power your store’s search capabilities.
The Evolution of Search on Ecommerce Stores
Gone are the days of simply having a purely keyword-based search engine. It just won’t suffice for modern consumers. This is down to the fact that consumers have matured in the online space. They are accustomed to and very comfortable with using search engines to find what they’re looking for with barely any friction.
The widespread, frequent use of search engines like Google and their capabilities have changed what shoppers expect from searches on eCommerce stores.
Thanks to machine learning, these search engines are able to suggest and autocomplete based on what is inputted in the search bar with great precision. It’s also given consumers the ability to be more conversational with their search queries.
Long-tail keyword searches, abbreviations, and slang are a lot more commonplace. It’s been further propelled with enhancements in voice recognition leading to more voice searches. Aside from that, consumers view search engines as a discovery tool. They use them as a means to answer ambiguous questions.
So, what does this mean for site search on eCommerce stores? Put simply, shoppers expect the site search experience to mirror what they find on search engines.
They expect to be able to find relevant results no matter what they type, they want to see suggestions, and they want to feel known. We’re also seeing a larger appetite for visual-based searches as Millennials and Gen Z-ers take precedence in the e-retail landscape.
However, despite these shifts in behavior, it seems a lot of eCommerce sites are failing to keep up. A study by Baymard found that a massive 61% of all sites perform below an acceptable search performance and are misaligned with users’ actual search behavior and expectations.
(Source: Marketing Charts)
Technology in eCommerce, particularly related to search, is rapidly adapting. It’s making it possible for e-merchants to produce the site search experiences shoppers yearn for. Let’s take a look at the most promising advanced technologies for retail site search.
Advanced Search Technologies And Why They’re Great For E-commerce Stores
Artificial Intelligence (AI) and Machine Learning (ML)
AI and ML have been the darlings of technology for quite some time now. Their application is drummed into many aspects of modern technology and eCommerce. What we are seeing now, however, is an expansion in how these technologies are being used to bolster site search.
Natural Language Processing (NLP)
AI and ML are making it easier for search engines to understand the way we speak naturally in order to perform tasks.
Together, AI and ML allow search engines to process and analyze large amounts of natural language data in order to dissect what is being typed in the search box. It provides search engines with semantic understanding as well as the ability to comprehend long-tail keywords.
When we apply this technology to eCommerce site search, NLP morphs product tags into the language and context that shoppers use. This is achieved by employing two techniques:
- Syntactic Analysis - the analysis of basic grammar rules to glean how words relate to each other and how they’re organized. This involves removing stop words, tagging parts of speech, and downsizing words to their base form.
- Semantic Analysis - understanding the meaning of the text by studying each word, and then gaining context by looking at the word combination. In this process, it aims to dispel ambiguity and understand how words relate to each other.
In Red Dress’s search function powered by Boost AI Search & Discovery, we can see NLP come into action to relate tee to top.
NLP brings immense value as it surfaces as more relevant and accurate results when the specialized terminology found in product descriptions isn’t used in site search. It understands the conversational manner shoppers have when searching. With NLP, you can stamp out retail site searchers' top pet peeves.
Additionally, because it uses ML, NLP gives your site search engine the ability to determine which results best satisfy users under particular circumstances. Equipped with this vital data, the results that surface will become exponentially more relevant.
Vector-based search
This search method uses AI and ML to overcome ambiguity found in search terms. It works by replacing content such as keywords or images with a string of numbers known as vector embeddings.
Vector embeddings are pieces of data that represent the meaning of a search phrase. They also add on other information related to the query or the user. ML algorithms can then sift through other embeddings to find similar content, and compare the distances and similarities between their vector embeddings.
(Source: Google)
This helps search engines quickly find results with high relevance as they can understand subtle meanings and nuances. It can accurately identify synonyms, thereby eliminating the chances of relevant content being omitted because a specific keyword was absent. It also gives site search engines better knowledge of shoppers’ intent.
Another application of this technology in eCommerce is powering visual search. A neural learning network can be deployed to extract vector embeddings from all product images in a store. This will then allow the site search engine to find images similar to one uploaded by a visitor.
Personalized search and recommendations
A machine learning model can be used to provide a personalized site search experience. One that is completely in tune with each unique visitor. The ML techniques that are utilized to bring this to life are recommender systems and personalized search.
Recommender systems allow shoppers to discover relevant products when searching. They may use a content-based method that looks at similarities through product attributes, a collaborative filtering method that determines similarities through customers’ interactions, or a combination of the two. This can then be used to power as-you-type search suggestions and autocomplete.
Camilla uses Boost Product Filter & Search to provide as-you-type search suggestions based on popular searches.
Personalized search analyzes a customer’s browsing history, previous purchases, demographic, and other customer data to provide search results that are uniquely relevant to them.
These techniques can also be used to rank results in a way that resonates with the individual shopper. By analyzing the similarities between users and a user’s history, the site search engine can predict the items of greater and lesser preference and rank them accordingly.
Personalization is no longer a nice-to-have in eCommerce, it’s a must. Visitors to your site will face massive frustration if you give them irrelevant results and search cues. By adopting a personalized search model, you’ll completely sidestep this issue.
Related reads:
- All you need to know about online product recommendations and how to optimize it
- eCommerce Personalization: Top Low-Cost Strategies for SMBs
Cloud-based solutions for search
Cloud computing has completely changed the way businesses share, store, and access information. It’s become an ever-important component of businesses worldwide. In fact, 94% of businesses are using the cloud.
Having a cloud infrastructure comes with a host of benefits such as unlimited storage capacity, easy scalability, and greater efficiency in deploying new design application architecture. These capabilities of cloud technology are being used to bolster search engines.
In site search engines, cloud-based infrastructure can ensure speed and reliability even with high traffic, in addition to fast deployment of new features. Amazon Elastic Compute Cloud (Amazon EC2) is a cloud solution used right here at Boost to bring these capabilities to our users.
The cloud infrastructure that enables high availability and elasticity (Source: VTI Cloud)
Amazon EC2 enables high performance and stability despite increased traffic thanks to its infrastructure.
It uses a distributed network of Availability Zones (aka data centers) that each uses its own power and network connectivity. This allows instances to be launched in multiple locations and insulates Availability Zone failures from each other.
This means that your site search engine is extremely guarded against breakdowns. Should a data center fail or lose data, the site search application is still able to run.
Amazon EC2 also ensures elasticity - the ability for the cloud infrastructure to scale up or down with ease. This is the key to accommodating multifold increases in traffic.
Amazon EC2 is able to monitor demand and match it by adding or decreasing resources on demand. It’s able to do this rapidly as having multiple, distributed data centers make resources highly available. They’re so highly available that a 99% or higher uptime is guaranteed! This results in a highly reliable search function even when faced with the extremely high traffic of sales seasons.
Find out more: AWS Global Infrastructure Components and Its Benefits For eCommerce
Make Your Store’s Site Search Work Smarter
The shifting behaviors of shoppers are making it necessary for eCommerce merchants to have a search function that goes above and beyond the basics. Fortunately, advancements in technology like AI, ML, and cloud computing have trickled down into eCommerce. Advanced search capabilities are now readily available for the average e-merchant.
If you’re looking for a way to make your store’s site search work smarter and meet the needs of the modern consumer, we’ve got you covered. Boost Product Filter and Search leverages cloud infrastructure and machine learning to give your Shopify store advanced search capabilities. Get started with a free trial today!
