The Ups and Downs of AI-Powered Site Search: Opportunities and Challenges for Ecommerce Businesses

Let’s first draw a line between conventional site search and AI search. The primary purpose of the conventional site search is to help visitors find the products they want. To deliver search results, it utilized the keywords associated with the goods. More advanced site search tools offer additional features like autocomplete, auto-suggest, and synonyms to enhance the search experience.

Intelligent site search shares the same primary function of helping visitors find products but approaches it differently. AI search takes in vast amounts of data, learns from it, and adapts accordingly in real-time, aiming to provide more accurate and relevant site search experiences.

To better understand how AI search works in e-stores, let’s examine it through three essential layers: natural language processing, machine learning, and personalization.

Machines face a significant challenge in understanding and effectively responding to human speech because of their unstructured nature and diverse variations. We often use synonyms and ambiguous terms or rely heavily on the context of communication.

Natural Language Processing (NLP) enables computers to detect language patterns and establish connections between words, allowing them to grasp the true intent behind human expressions. NLP serves as the foundation for voice assistants like Alexa and Siri and allows users to communicate with computers the same way they would talk to other human being.

In e-commerce, NLP can be used to extract facets from search queries. For example, a person searching for "white sneakers under $100" will receive search results that match this query.

While NLP has proven highly effective for voice assistants and is frequently used for general search queries on Google and other search engines, such as “What is the best camera for portraits”, its application in the e-commerce site search is currently limited.

When searching on an e-commerce website, users rarely use natural language search queries such as "white sneakers under $100". Most likely, they will just search for “sneakers” and then narrow down the search output using filters. The trends could change in the future, but at the moment, NLP is not a must for e-stores.

Machine learning (ML) empowers AI to replicate how people process information and learn from it. It leverages data and algorithms to enhance the accuracy of AI.

Speaking about site search, the primary function of machine learning is to refine and optimize search results without human intervention automatically. To do so, it analyzes information from different sources, such as past search data, search behavior, and product data from the store. This information is used to adapt and improve the underlying algorithm continuously, in particular, to detect and generate synonyms.

For instance, if a customer searches for “plus-size jeans” listed as “curvy jeans” on the website, ML algorithms should recognize that the meaning of these search queries is the same and deliver the relevant search outcome.

When adequately trained, machine learning can understand the user's intent behind the search query and provide relevant search results, resulting in better search experience. However, if they are not properly trained, the outcome can be highly unsatisfactory. In the following abstract about the hidden challenges of the AI search bar, we will examine the specific issues that can arise and the reasons behind them.

Another use case of the AI-managed ecommerce site search is personalized search results. Artificial intelligence algorithms analyze the customers' behavior on the website, such as product views, add-to-cart actions, purchases, and tailors search output for each user. For example, if the customer buys iPhone 14 and then searches for “cases”, the cases for iPhone 14 will be displayed higher in the search results.

Personalization can significantly improve the search experience by helping customers discover products tailored to their needs. However, if users' intent changes at some point, personalization will need some time to adjust to the new behavior. For example, a customer shopping for kids' clothes decides to pick a t-shirt for themselves and prints “t-shirt” in the search bar. The first response of personalized search will be to show kids t-shirts higher to the top. But personalization will adjust the search results once the user’s intent becomes clear to the algorithms.

Each component of the AI search has certain peculiarities that should be taken into account:

• NLP is useful for general search engines and voice assistants, but not for e-commerce, at least at the moment.
• Machine learning requires proper training, otherwise it may generate incorrect or offensive results.
• Personalization needs time to adjust to the user’s intent if it changes during shopping.

Our team has been studying the implementation of AI for site search and has reached the conclusion that the most beneficial feature is personalized site search. This conclusion is based on our experience of over 10 years in developing site search for e-commerce stores and more than 14k happy customers. By incorporating personalization, the potential drawbacks and risks are minimized, while the benefits become evident and significant: personalized search results elevate the user experience to a higher level.

Our team has recently implemented AI personalization into our search and filter app for Shopify stores. Equipping your store with personalized AI search has never been simpler: just install the app, and your customers will enjoy tailored search results right away!

AI site search can become a game-changer in e-commerce, offering significant advantages for online stores:

• More accurate and relevant search results

Unlike conventional search engines, AI-driven algorithms can understand user intent. This means that customers are more likely to find what they are looking for faster and easier.

• Time-saving

With its advanced capabilities, the technology can anticipate user intent and provide instant suggestions or auto-complete options as users begin typing their queries. By minimizing the time and effort required to find desired products or information, free AI site search enhances overall efficiency and convenience for customers, encouraging them to explore further and engage more deeply with the website.

• Personalization

The technology can tailor search results specifically to the interests of individual customers. This level of customization creates a highly personalized experience, making customers feel valued and understood. Consequently, they are more likely to stay engaged, browse a more comprehensive range of goods, and ultimately make a purchase.

• Increased conversions

When users have a positive and seamless experience while navigating the website, they are more likely to stay engaged, explore products, and ultimately make a purchase. By delivering accurate search output, saving users' time, and providing personalized recommendations, AI site search becomes a powerful tool in driving conversions and boosting revenue for e-commerce businesses.

Powered by AI site search can bring numerous benefits to e-commerce businesses. Still, it is essential to acknowledge the challenges they may face.

One notable drawback is the high cost associated with implementing and maintaining an AI search engine. Developing sophisticated algorithms, training models, and acquiring the necessary computing resources can be expensive, especially for small or medium-sized businesses. Furthermore, the expenses associated with implementing AI solutions may always yield a positive return on investment.

AI requires access to an extensive amount of customer data to function effectively. That is why privacy concern comes to light, as customers may feel uneasy about their personal information being gathered and scrutinized.

E-commerce companies utilizing AI must be transparent regarding their data collection methods and take measures to keep customers’ data safe.

If algorithms are trained on partial data, they can be biased, leading to skewed results. In the worst-case scenario, it can perpetuate discrimination or inequality in search results, alienating certain user groups.

Businesses must take proactive measures to address these issues, ensuring transparency, fairness, and inclusivity in their AI site search implementations.