Machine learning is inseparable from converting and scalable ecommerce businesses. It is especially critical to ecommerce search success.

Machine learning, running 24/7, earns money for the business while the owner sleeps.

So, what is the role of machine learning in ecommerce search? How can your business benefit from it?

What is machine learning?

Machine learning involves training algorithms to make predictions or decisions based on data.

For instance, ecommerce giants like Amazon or Asos use machine learning to improve search accuracy and increase sales. They use machine learning techniques for demand forecasting, product search, and review information extraction, with deep learning, probabilistic models, and tensor factorization algorithms used for question answering, product size recommendations, and fake review detection [1].

Machine learning is crucial for Amazon, enabling automated predictions of future data, with applications ranging from demand forecasting to computer vision and robotics [2].

In the context of ecommerce search, machine learning is often used to personalize search results, improve the accuracy of search predictions, and even provide more intelligent search suggestions.

The more data it gathers, the more accurate the predictions are. Of course, it does make mistakes, but it uses trial and error to improve the accuracy over time.

For example, 75% of all Netflix content recommendations are based on machine learning algorithms. That means no human sits and selects the show you should watch. The algorithm does it for you based on historical and real-time data [3].

What role does machine learning play in ecommerce search?

In a nutshell, machine learning makes ecommerce search faster, more efficient, and more accurate. Here’s how.

1. Personalized search results

Machine learning algorithms can analyze vast amounts of data, including users’ search history and real-time behavior, to offer personalized search results.

For example, a machine learning algorithm can use a customer’s previous search history, product clicks, and overall search trends to determine their preferences and suggest similar products.

For instance, if you frequently search for mystery novels in an online bookshop, the search engine can recommend other related books: crime thrillers, detective stories, and suspenseful literature, enhancing your reading experience.

This way, such an ecommerce search as LupaSearch can provide highly relevant search results that users cannot resist.

2. Improved accuracy

Machine learning understands the context and intent behind a search query, leading to more accurate search results.

In the case of LupaSearch, the Natural Language Processing technology behind the search engine ensures high search accuracy even for nonspecific search terms.

For example, if a user types “jacket” into the search bar, the algorithm can use the user’s search history and other relevant data to offer personalized suggestions such as “winter jackets,” “waterproof jackets,” or “leather jackets,” depending on what user might be interested the most.

Semantic query understanding, powered by personalization, aims to understand the intention behind a query, predicting possible intentions and using semantic information to rank search results [4].

3. Improved search ranking

Machine learning, a part of effective AI search, automatically analyzes many search-related factors such as relevance, popularity, and conversion rates to rank search results.

For example, it can analyze user behavior, such as click-through and conversion rates, to determine the relevance and popularity of search results.

It uses this data to arrange the products on the search results page, so the most demanding products are displayed the highest.

Deep learning techniques can improve search relevance in various industries, such as e-commerce, streaming services, and social networks [5].

4. Works in any language

Machine learning provides intelligent search suggestions regardless of the user’s language. For that, NLP (Natural Language Processing) comes into play.

As a user types their query into the search bar, the site search analyzes the text and deciphers complex phrases and long-tail keywords or understands the specific features.

For instance, a user might search for “something to snack on with wine.” The NLP-powered search engine deciphers the meaning of the search query and delivers that - snacks that go well with wine.

Also, NLP is crucial for understanding complex queries in different languages [6]. Supporting a variety of languages allows users to search for products using wording that feels native to them and still find what they need.

5. Autocompletes user’s queries

When shoppers start typing a query into the search box, autocomplete suggests possible search queries based on what has been written.

The algorithms analyze recent searches, product descriptions, and customer behavior to suggest relevant search queries. It also includes overall search trends across many different searchers.

The more a shopper interacts with the site, the more personalized the autocomplete results become.

Take your business to the next level with machine learning

Make the most out of machine learning. Introduce a site search that is powered by machine learning algorithms.

LupaSearch is here to assist you on this journey.

As a highly-converting ecommerce search solution, LupaSearch offers your business all the tools to deliver your customers an accurate and efficient site search.

By using machine learning to improve search accuracy, personalize search results, and increase conversion rates, ecommerce businesses can stay ahead of the competition and grow.

Schedule a demonstration with us, and let’s see your business grow.

References

1. Rastogi, R. (2017). Machine Learning @ Amazon. 2017 IEEE 24th International Conference on High Performance Computing (HiPC), 182-182. DOI: 10.1109/HiPC.2017.00029. https://ieeexplore.ieee.org/document/8287748.

2. Herbrich, R. (2017). Machine Learning at Amazon. Proceedings of the Tenth ACM International Conference on Web Search and Data Mining. DOI: 10.1145/3018661.3022764. https://dl.acm.org/doi/10.1145/3018661.3022764.

3. Hrnjica, B., Music, D., & Softic, S. (2020). Model-Based Recommender Systems. , 125-146. https://doi.org/10.1007/978-3-030-40037-8_8.

4. Baeza-Yates, R. (2017). Semantic Query Understanding. Proceedings of the 40th International ACM SIGIR Conference on Research and Development in Information Retrieval. https://doi.org/10.1145/3077136.3096472.

5. Pang, L., Liu, W., Chang, K., Li, X., Bhattacharya, M., Liu, X., & Guo, S. (2022). Deep Search Relevance Ranking in Practice. Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining. https://doi.org/10.1145/3534678.3542632.

6. Jain, D., Eyre, Y., Kumar, A., Gupta, B., & Kotecha, K. (2023). Knowledge-based Data Processing for Multilingual Natural Language Analysis. ACM Transactions on Asian and Low-Resource Language Information Processing. https://doi.org/10.1145/3583686.