In the highly competitive world of online retail, having a good search functionality has become a critical necessity. Imagine walking into a massive library without a catalog or a librarian to guide you. That’s what online shopping feels like if your website does not have a robust search tool. Not long ago, Nosto, one of the global leaders specializing in e-commerce personalization and search functionalities for eCommerce stores, conducted a study and found out that more than 40% of its users rely on the search bar to find what they need quickly. This shows that a complex search capability is key for creating that unique shopping experience.

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The latest developments in Artificial Intelligence (AI) are revolutionizing product search and discovery by making it more intuitive and personalized. Nowadays, AI algorithms can analyze user behavior and preferences to deliver highly relevant search results. For example, there are many AI-driven search platforms that can handle voice queries, recognize product images to suggest similar items and personalize recommendations based on past interactions. These advancements make shopping more convenient and efficient with a huge role in increasing conversion rates and customer satisfaction.

The Importance of efficient product search and discovery

A good search works in two ways. First, search impacts the user’s journey, making it more streamlined.

There is nothing more frustrating for a shopper than trying to search for a specific product and getting in return tens of wrong products. A good search function provides quick and accurate access to desired products. 

More than that, enhanced search features, such as auto-suggestions and filters, further refine the process, enabling users to find exactly what they’re looking for with minimal effort. This efficiency not only saves time but it also leads to higher satisfaction and the likelihood of repeat visits.

Second, there is a direct correlation between search efficiency and sales. An efficient search functionality reduces frustration and abandonment rates. Moreover, relevant search results and personalized recommendations facilitate additional sales by introducing users to products they might not have otherwise discovered.

Traditional search methods and their limitations

To understand how far technology has come nowadays, let’s have a look at the humble beginnings. Traditional search methods relied on matching user-inputted keywords with the text available in product listings. This approach used simple algorithms to find exact matches, ranking results based on keyword frequency and placement. But the approach failed to handle synonyms effectively, meaning a search for “sofa” might not yield results for “couch.” Additionally, keyword-based search struggled with spelling errors. The lack of contextual understanding also meant it could not interpret user intent, often leading to irrelevant results.

Native Search for Magento

At the beginning, Magento’s native solution employed this basic keyword matching to retrieve product results. While it covered fundamental search needs, it soon became inadequate for meeting the demands of modern e-commerce.  That’s why, starting with Magento 2.4, ElasticSearch was introduced as the default search engine. With its advanced algorithms, the tool significantly improved search functionality, leading to higher conversion rates and customer satisfaction.

Welcome to the future – the introduction of AI in Product Search

In recent years, AI has made its way into e-commerce platforms’ search functionality and its help is tremendous –  it makes product discovery easier and more accurate. The advantage of AI is that it uses natural language processing (NLP) to understand and interpret user queries, providing relevant and personalized search results.

Rather than merely matching keywords, NLP enables the search engine to interpret natural, conversational language, allowing users to find what they are looking for without needing to use specific keywords or technical jargon. For instance, a search for “red running shoes under $100” will yield precise results tailored to the user’s intent rather than a random assortment of products containing the words “red,” “running,” and “shoes”.

At the same time, Machine learning algorithms analyze user behavior and preferences, enabling the platform to offer tailored product recommendations and predictive search suggestions. A good example is Amazon. Its ML engine is the ‘culprit’ for Amazon’s huge success, because it is able to suggest products based on previous searches and purchases, which in turn has significantly boosted sales.

Not last, image recognition technology allows users to search for products using photos, improving the visual search experience. For instance, a user can upload a picture of a dress they like, and the search engine will identify and suggest similar items available on the platform. This technology is particularly useful for retailers where visuals play a crucial role in purchasing decisions, like fashion or home and decor.

As you can see, these tools make product discovery more intuitive and efficient​. Natural Language Processing improves accuracy in search results and Machine Learning (ML) algorithms can offer personalized recommendations and therefore a unique experience for each user. Not to mention that AI excels at managing and interpreting complex or vague queries, which traditional search methods often struggle with.

Implementing AI in Product Search

The online shops in our portfolio realized that having an efficient search function will boost their sales and over the last few years, we concentrated our efforts for 

For example, one of the projects that we implemented successfully was enhancing Magento’s ElasticSearch with a layer of complexity by installing various third-party extensions like Search Ultimate from Mirasvit, a great tool that corrects typing errors, is responsive to both desktop and mobile and returns relevant results within milliseconds.

Another option for ecommerce systems powered by Magento OS is integrating third party platforms like Algolia, Kelvu or Nosto. Each have different sets of features, but their AI driven engines offer enhanced search accuracy by understanding natural language queries, correcting spelling mistakes, providing contextually relevant results, and personalized search experiences by analyzing user behavior and preferences.

They all work through powerful APIs and  integrate seamlessly with major eCommerce platforms.

Lacoste, for example, uses Algolia to improve search relevancy and speed on their platform. This AI-powered integration helps customers find products more efficiently and is able to personalize product recommendations and predict upcoming fashion trends.

Implementing AI in Product Search

Adobe Commerce probably has one of the most powerful Search functions among major eCommerce platforms. In its mid-quarter release in June 2021, Adobe launched the powerful Live Search, looking to enhance the online shopping experience and create unique online experiences.

Live Search is powered by Adobe Sensei, the machine learning engine from Adobe and uses various AI algorithms, paired with real-time ecommerce data, to create superior online search experience and deliver context-specific results live, as the user types in the query.

This means that Live Search offers you deep insights about online search queries entered by shoppers  . This information is helpful to fine tune your offerings and marketing strategies to showcase personalized product recommendations that are most relevant for each shopper.

For eCommerce stores powered by Adobe Commerce, Live Search can be downloaded from the Adobe/Magento marketplace. Live Search is pretty intuitive to set up and Admins can seamlessly customise the search features along with the search facets, synonyms, and merchandising rules to suit their business model.

It is worth mentioning Adobe’s Sensei AI engine which is capable of analyzing large volumes of data, including user behavior, preferences, and historical interactions. This powerful AI engine can understand the context of user queries, correct spelling errors, and recognize synonyms, ensuring that users find exactly what they are looking for. Additionally, Sensei AI enables Adobe Commerce to offer personalized product recommendations, improving user engagement and conversion rates. Sensei is the powerful brain that drives the whole Adobe ecosystem, from integrations with Adobe Experience Cloud, to business specific tools that help commerce teams to reach new audiences and sell.

What we might see in the future

AI has found a perfect place for growing in product discovery. With developments speeding at such a fast pace, we will definitely see search platforms become more intuitive, personalized, and efficient. One area worth observing is Predictive search, where AI anticipates user needs by analyzing past behavior, preferences, and contextual data. This means users might see personalized product suggestions even before they start typing, making the shopping experience seamless and highly tailored to individual needs.

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