Ecommerce and technology go hand-in-hand when it comes to providing the best user experiences. Search personalization using machine learning is a part of artificial intelligence technology that plays a vital role in creating good experiences for customers. At the same time, ecommerce organizations save a lot of money, effort, and time implementing it. According to G2, a software marketplace, Netflix saved $1 billion due to its machine-learning algorithm for the combined effect of personalization and content recommendations. Learn 5 ways to improve search personalization using machine learning in this blog.
5 Ways to Improve Search Personalization Using Machine Learning
Here are 5 ways machine learning adds personalization to your ecommerce search platform:
Smarter Pattern Detection
There are multiple benefits of pattern detection for search personalization using machine learning. Here are two major benefits:
- Venture Beat, a tech news reporter, suggests that 57% of attacks on ecommerce websites are done by bots. Ecommerce organizations that implement machine learning in their search engines improve the number of spam and duplicate queries they receive. These help their website avoid cyber attacks.
- Data that machine learning gathers from customer search patterns also help personalize the product recommendation process.
Improved Understanding of Search Intent
Natural language processing (NLP) is part of the machine learning process. According to a report by Statista, the NLP market may reach over $40 billion in revenue in 2025. This will result in 14x growth since 2017. Understanding the context behind any search query is beneficial when it comes to search personalization using machine learning. The following are the benefits of implementing natural language processing in ecommerce search:
- Natural language processing gives search terms genuine context instead of artificial meaning. This allows the search engine of a platform to serve relevant results.
- Voice search and chatbots on an ecommerce site benefit the most from NLP.
- Social listening becomes easier once NLP is incorporated with social platforms and your ecommerce website. It gives businesses an idea about popular search terms being used to find their products and business. Search results on specific keywords can be closely monitored to identify trends and find target audiences.
Looking for an efficient way to structure your data and improve the search journey for customers? Read the blog Clotho Search: Right Item at Right Time with Clotho to learn more.
Better Search Functions to Find Products
Search personalization using machine learning improves the customer’s product discovery journey. According to a survey conducted by Nosto, an ecommerce service provider, 70 percent of consumers want brands to provide them with a personalized experience.
- Sometimes, customers are looking for a product and cannot remember the correct name, make spelling mistakes, or are searching for one thing but typing a search term with a synonym. Improved search functionality can fix this problem by understanding the customer’s search intent.
- Machine learning improves basic algorithms of the search functionality to provide personalized experiences. Ecommerce search engines understand what the customer really wants and then retrieve the correct results.
Streamlined Information
Information is everything. According to an analysis by G2, a software review marketplace, data-driven organizations are 23 times more likely to get new clients. Search personalization that uses machine learning allows ecommerce businesses to collect search data and convert it into useful information. Then, they develop customer recommendations, understanding patterns, and proper next actions from this information. One of the biggest benefits of streamlined information is:
- Similar products are placed near each other based on data collected from customers. For example, “other products you may like ” categories on product pages. This makes it easy for customers to find information about similar products they might be interested in.
Increased User Classification
According to a blog post by Tech Jury, a tech reviewing platform, poor data quality costs the US economy up to $3.1T annually.
Different customers can have similar buying patterns. Search personalization utilizes machine learning to allow ecommerce businesses to collect this data and turn it into useful information. The data classifies different users on the basis of their similar search patterns. Ecommerce platforms benefit from better user classification because:
- They can easily recommend similar products with seamless customer data databases. This saves cost, time, and effort.
- The improved accuracy in user classification helps convert consumers and significantly increases their chances of becoming loyal customers.
Read 5 Ways Smart Search Technology Maximizes Profit to understand the importance of smart search.
Clotho and Machine Learning
At UpStart Commerce, we understand the importance of incorporating the latest technology in our features, services, and products. Clotho, our smart search platform, is the perfect solution when it comes to search personalization using machine learning. Clotho quickly retrieves the relevant results from databases using open-source APIs and NLPs. Our clients have the ability to pick and choose or add and remove features from our services to best suit their needs. Learn about Clotho in this video.
Learn more: Check out our glossary of ecommerce keywords containing machine learning and other important topics.
