59% of online shoppers prefer buying items from websites that personalize their shopping experience, suggest custom products, and adapt suggestions based on previous interactions.
An AI-driven product recommender is becoming essential for successful ecommerce businesses. It leverages user data and AI algorithms to deliver personalized shopping experiences, significantly boosting customer satisfaction and business outcomes.
What Is a Product Recommender in Ecommerce Search?
An ecommerce search product recommender is a feature found in many advanced ecommerce search solutions that uses artificial intelligence (AI) to analyze customer behavior, preferences, and needs. In LupaSearch e-commerce search, for instance, this feature is called SellSence.
It provides personalized and relevant product suggestions, enhancing user satisfaction and encouraging customers to explore more products, often leading to increased purchases.
AI-powered recommendation systems, which analyze user preferences and behaviors through algorithms, have proven to significantly enhance customer engagement and satisfaction by delivering tailored product suggestions. This personalized approach increases both sales and customer retention [1, 2].
By helping users discover new products and switch to related or compatible items, product recommenders unlock new revenue opportunities from the same website visitors.
How Do They Work?
A product recommender is an AI-powered feature that interprets the user’s search query and considers a broader context, such as user behavior and past interests, to display the most relevant search results.
The product recommender typically works with these data sources:
- Product similarities: Based on categories and attributes;
- Session and user data: Including product clicks and add-to-cart actions;
- Cart/order combinations: Frequently bought together items.
This data is used with recommendation methods like:
- Content-based filtering: This method recommends products similar to those the user has previously viewed, liked, or searched for. It analyzes product features and customer history to suggest relevant items [3].
- Collaborative filtering: This method analyzes the preferences and purchases of other users with similar tastes. It recommends products that people with similar profiles have bought or interacted with [4].
How Can You Exploit a Product Recommender?
There are several strategies to creatively exploit a product recommender to maximize its potential:
Suggest Similar Products.
When a user buys an item, suggest related products that complement the purchase. For example, if a customer buys a winter beanie, recommend a matching scarf.
Such personalized recommendations increase user engagement and sales [5].
Promote Trending Products.
Suggest popular or trending items that many users have shown interest in. This tactic allows your customers to follow trends and stay engaged with your store.
Promoting trending products enhances conversion rates by offering personalized and timely product suggestions based on user behavior and purchase patterns.
Collaborative filtering algorithms in e-commerce can significantly improve product recommendation accuracy and lead to increased sales and customer engagement [6]. They also improve prediction precision, further enhancing user engagement and boosting conversion rates [7].
Present Bundled Products.
Use the “customers also bought” or “frequently bought together” features. Display these suggestions in the shopping cart or on the search results page to increase the average order value.
Bundling related products have been shown to boost revenue by creating a seamless shopping experience [4].
Highlight Products Viewed by Others:
Suggesting products that other customers have viewed when searching for the same item can significantly enhance user engagement and conversion rates.
Such collaborative filtering, by leveraging shared user preferences and behaviors, effectively boosts product recommendation accuracy and helps users find products they may not have initially considered, driving higher engagement and sales [8].
It also improves conversion rates by offering relevant product suggestions through peer insights [7].
As many successful business owners will affirm, implementing a product recommender system is a proven strategy for boosting revenue and enhancing user experience in online stores.
Why Can a Product Recommender Be a Game Changer for Your Ecommerce Business?
Introducing an AI-driven product recommender into your ecommerce search can drastically improve your store’s performance.
Businesses that leverage product recommendation systems report significant increases in average order value, click-through rates, and conversion rates.
AI-powered recommendations not only help customers find the products they want but also build long-term customer loyalty and satisfaction, leading to sustained business success.
Personalized recommendation systems can increase customer retention by up to 30%, making them a powerful tool for any online retailer [9]. By delivering customized recommendations, businesses can foster customer loyalty and create a shopping experience that is both relevant and satisfying.
LupaSearch: Your Next Lasting Ecommerce Search Partner
Exploit the opportunities that a product recommender system provides with a highly converting ecommerce search solution. LupaSearch is a trusted ecommerce search tool that delivers an intuitive, personalized shopping experience for stores like yours.
LupaSearch uses AI and machine learning to provide accurate and relevant search results, product recommendations, and analytics to help your business scale.
Request a free demo today and discover how a converting product recommender can drive growth and success for your ecommerce store.
References
Sangeetha, M., Kumar, B., Chokkanathan, K., Kumar, A., Prabha, S., Sattanathan, S., & Periasamy, J. (2023). Developing Algorithms for Personalized Recommendations Based on User Behavior. 2023 5th International Conference on Inventive Research in Computing Applications (ICIRCA), 1152-1158. https://doi.org/10.1109/ICIRCA57980.2023.10220711.
Sharma, A. (2023). Analyzing the Role of Artificial Intelligence in Predicting Customer Behavior and Personalizing the Shopping Experience in Ecommerce. INTERNATIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT. https://doi.org/10.55041/ijsrem17839.
Lv, Y., Zheng, Y., Wei, F., Wang, C., & Wang, C. (2020). AICF: Attention-based item collaborative filtering. Adv. Eng. Informatics, 44, 101090. https://doi.org/10.1016/j.aei.2020.101090.
Singh, A. (2022). CHATBOT MOVIE RECOMMENDER SYSTEM. INTERNATIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT. https://doi.org/10.55041/ijsrem11839.
Al-Hagery, M. (2020). A novel Based Approach Composed of Clustering Algorithm & Cosine Similarity for Products Recommendation. International Journal of Education, 14, 133-141. https://doi.org/10.46300/9109.2020.14.16.
Yu, Z. (2022). Precision Marketing Optimization Model of e-Commerce Platform Based on Collaborative Filtering Algorithm. Wireless Communications and Mobile Computing. https://doi.org/10.1155/2022/2906955.
Gong, S. (2021). Research on collaborative filtering based on user interest in the higher vocational e-commerce website development. Journal of Physics: Conference Series, 1982. https://doi.org/10.1088/1742-6596/1982/1/012171.
Iftikhar, A., Ghazanfar, M., Ayub, M., Mehmood, Z., & Maqsood, M. (2020). An Improved Product Recommendation Method for Collaborative Filtering. IEEE Access, 8, 123841-123857. https://doi.org/10.1109/ACCESS.2020.3005953.
Liu, L. (2022). e-Commerce Personalized Recommendation Based on Machine Learning Technology. Mobile Information Systems. https://doi.org/10.1155/2022/1761579.