Ecommerce sites that figure out what users are looking for tend to do better.
In fact, 39% of ecommerce purchases are influenced by how good the search is.
Say a user is looking for a winter jacket; showing them summer clothes won’t help.
This is where AI-based site search comes in—it’s all about connecting users with what they’re actually searching for.
Why should your ecommerce site search understand the users’ search intent?
These days users have finite levels of patience. Users use a search box as the fastest method to determine whether your store has a product they are looking for.
If a shopper types something in a search box, he expects you to deliver accurate results.
If your store returns a zero-result page, some users might try one more time to rephrase their search query. If they do not find what they came for, they will leave your website.
That is why you have just 1 (max 2) trial to satisfy users’ requests.
Using AI to nail the search results means:
- Users find what they want.
- There’s less clutter.
- More people buy things.
- Everyone has a better time on the site.
- Your brand looks good.
- It costs less to get new customers.
Users who use site search usually double the conversion rates compared with shoppers who do not use search. But there are no limits to your business success.
Businesses that introduced LupaSearch AI-based site search increased their conversion rates from 2.5X to 8X.
So, how can you better identify user intent and deliver a rewarding user experience?
How does an AI-based site search identify user intent?
An AI-based site search is one of the most effective existing and constantly improving methods to identify and satisfy user search intents.
Artificial Intelligence and Machine Learning
In such leading site search solutions like LupaSearch, users always receive custom-tailored search results. For example, when a user searches for Puma shoes but your store only has a few, AI starts doing its job.
A word2vec technology starts checking for any matches with similar words and phrases. That is why in the Puma case, your site search would return all relevant Puma sneakers and other similar-type sneakers of other brands.
Additionally, if the user previously browsed for Adidas and Nike sneakers, the site search would correspondingly rank those products higher on the search results page.
Recent studies have shown that understanding search intentions is essential for improving search results and query recommendations. Machine learning models can predict search intents based on behaviors performed during search processes [1].
Natural Language Processing
Another critical function that helps the site search to deliver user-relevant results is Natural Language Processing (NLP).
NLP translates complex user queries into computer-understandable pieces of searchable information. Therefore, if your website visitor is searching for black noise-canceling wireless headphones, your AI-based site search, powered by the NLP algorithm, should process this query and deliver products that match the request.
AI-driven systems utilize various techniques to understand user intent. For instance, the use of LLMs (Large Language Models) helps generate user intent taxonomies to analyze log data effectively, capturing diverse and dynamic user intents [2].
Take the most out of your ecommerce business with LupaSearch
Increase your ecommerce store’s conversion rates by introducing an AI-based site search solution. The LupaSearch is compatible with all the major ecommerce platforms and supports many languages.
The best part is that you do not need to have any coding, machine learning, or search knowledge. The LupaSearch team is here for you.
Contact us, request a free product demo (with your product catalog), and let’s discuss the opportunities for your business growth.
References
Zhu, M., Xu, R., Sun, M., & Zhang, Y. (2019). Identifying Behavioural Intents in a Complex Search Process Management System. Journal of Physics: Conference Series, 1302. https://doi.org/10.1088/1742-6596/1302/2/022076.
Shah, C., White, R., Andersen, R., Buscher, G., Counts, S., Das, S., Montazer, A., Manivannan, S., Neville, J., Ni, X., Rangan, N., Safavi, T., Suri, S., Wan, M., Wang, L., & Yang, L. (2023). Using Large Language Models to Generate, Validate, and Apply User Intent Taxonomies. ArXiv, abs/2309.13063. https://doi.org/10.48550/arXiv.2309.13063