Product type search has emerged as a powerful tool that streamlines the online shopping experience.

How? It enables users to search for products based on their specific type or category. This blog post will explore the product type search, its significance, and how it enhances the online shopping journey.

What Is Product Type Search?

Product type search refers to the ability to search for items online based on their category, type, or classification.

Rather than relying solely on keywords or generic search terms, users can narrow their search results to specific product types, making the shopping process more efficient and effective.

That means they can type in such a search term as red male sneakers size 12, and the e-commerce search has to return category-relevant product suggestions.

At its core, product type search is similar to a faceted search. This functionality reduces product choice overload, speeds up shopping, and boosts conversion rates.

By organizing products into well-defined types or categories, ecommerce platforms provide a structured approach to browsing and searching for items, improving the overall user experience [1].

What Are the Benefits of Product Type Search?

1. Time and effort saving

By allowing users to search for products based on their specific type or category, product type search saves time and effort.

Instead of sifting through numerous search results, users can quickly narrow their options to the desired product type, streamlining the shopping process [2].

2. Enhanced user experience.

Product type search improves the browsing experience by providing users with a clear structure and navigation system.

By categorizing products into specific types, ecommerce platforms offer intuitive pathways for users to explore related items, compare options, and make informed purchase decisions.

This structured approach makes it easier for users to find what they are looking for and discover new products within their preferred category [3].

3. Reduction of information overload.

Online shopping platforms often feature many products, which can lead to information overload for users. Product type search mitigates this issue by presenting users with a focused selection of products within their desired category.

Instead of overwhelming users with endless options, product type search delivers relevant and targeted results, making the shopping experience more manageable and enjoyable [4].

Also, if you choose an e-commerce search like LupaSearch, you can additionally exploit dynamic filtering. This way, the filters will be automatically assigned based on the product type.

For instance, only book-relevant filters will be shown when searching for books, and all the others (like electronics, food, and travel-related filters) will be hidden.

4. Improved product discovery and comparison.

Product type search enables users to discover new products within a specific category or type. It allows users to explore alternative options and compare similar products easily.

This feature empowers users to make informed decisions based on their preferences, leading to higher customer satisfaction [5].

5. Customization and personalization.

Product type search lays the foundation for customization and search result personalization in the online shopping experience.

By understanding users’ preferences and search patterns, ecommerce platforms can recommend relevant products within specific types, enhancing the overall user experience and increasing the chances of successful conversions [6].

6. Simplified navigation.

With product type search, users can navigate through ecommerce platforms more efficiently. Instead of relying solely on generic search terms, users can leverage predefined product types to find what they need quickly.

It simplifies the navigation process and eliminates the need for complex searches or filters, making the overall user experience more seamless [7].

Implementation and Functionality

The implementation and functionality of product type search in ecommerce platforms involve several key aspects.

Here are some considerations:

• Classification and categorization: Ecommerce platforms need to establish a robust system for classifying and categorizing products. It involves assigning relevant attributes, such as size, color, brand, price range, or any other characteristics specific to the products being sold.

By organizing products into well-defined types or categories, platforms can ensure accurate search results based on user queries [8].

• Metadata and attributes: Metadata, tags, and attributes associated with each product are responsible for effective product type search. These include product names, descriptions, specifications, and other relevant information.

By structuring and organizing this data, platforms can provide users with relevant search results that match their desired product type [9].

• Search algorithms: Ecommerce platforms employ search algorithms to analyze user queries and match them with relevant product types. These algorithms consider keyword relevance, product attributes, popularity, and user behavior data.

By leveraging machine learning techniques, platforms can continuously refine and improve their search algorithms to provide accurate and personalized results to users [10].

• Filtering and sorting options: Product type search is often complemented by filtering and sorting options. These features allow users to refine their search results based on specific criteria such as price, brand, availability, customer ratings, or other attributes.

By offering flexible filtering and sorting options, platforms enhance the user experience by providing more control over the search results [11].

• User interface and navigation: The user interface of an ecommerce platform should incorporate intuitive navigation elements that facilitate product type search. It includes clear category menus, subcategories, and navigation bars that allow users to browse through different product types seamlessly.

A well-designed and user-friendly interface enhances the overall experience and ensures that users can easily find the desired product types [12].

• Integration with other search features: Product type search can be integrated with other features, such as keyword search, to provide a comprehensive search experience. It allows users to combine different search criteria, such as a specific product type and keyword, to further refine their search results [13].

• Continuous maintenance and updates: Ecommerce platforms must regularly maintain and update their product databases to ensure accurate classification and categorization. New products, changes in attributes, and updates to product types should be updated in the search system to provide users with the most up-to-date and relevant results [14].

By implementing these functionalities effectively, ecommerce platforms can optimize the product type search experience, making it easier for users to find desired products and enhancing their online shopping journey.

Ready to boost your product discoverability and conversion rates? Integrate LupaSearch e-commerce search into your e-shop, and unlock the benefits of AI-powered search that gets to the core of users’ search queries.

Request a free product demo, and let’s grow your business revenue together.

References

1. Mityko, D. S. V. (2012). The search experience credence product classification paradigm in the eyes of the electronic consumer. Management and Marketing, 7, 449.

2. Gao, Y., Reddy, M. C., & Jansen, B. (2017). ShopWithMe!: Collaborative Information Searching and Shopping for Online Retail. Proceedings of the 50th Hawaii International Conference on System Sciences, 1-10.

3. Singh, G., Parikh, N., & Sundaresan, N. (2011). User behavior in zero-recall ecommerce queries. Proceedings of the 34th International ACM SIGIR Conference on Research and Development in Information Retrieval.

4. Antipov, E. A., & Pokryshevskaya, E. B. (2018). Product evaluations online: Search vs. experience goods. Microeconomics: Search; Learning; Information Costs & Specific Knowledge; Expectation & Speculation eJournal.

5. Bae, S., & Lee, T. (2011). Product type and consumers’ perception of online consumer reviews. Electronic Markets, 21, 255-266.

6. Liu, J., Dou, Z., Zhu, Q., & Wen, J. (2022). A Category-aware Multi-interest Model for Personalized Product Search. Proceedings of the ACM Web Conference 2022.

7. Xu, X., Qian, Y., & Yuan, H. (2018). Exploring User Group Behavior in Reviewing Online Product. 2018 15th International Conference on Service Systems and Service Management (ICSSSM).

8. Hafernik, C., Cheng, B., Francis, P., & Jansen, B. (2011). Mapping user search queries to product categories.

9. Pawłowski, M. (2021). Machine Learning Based Product Classification for eCommerce. Journal of Computer Information Systems, 62, 730-739.

10. Cheng, X., Bowden, M., Bhange, B. R., Goyal, P., Packer, T., & Javed, F. (2020). An End-to-End Solution for Named Entity Recognition in eCommerce Search. Proceedings of the 35th AAAI Conference on Artificial Intelligence, 15098-15106.

11. Pu, P., Chen, L., & Kumar, P. (2008). Evaluating product search and recommender systems for E-commerce environments. Electronic Commerce Research, 8, 1-27.

12. Ziaei, H., Wobcke, W., & Wong, A. (2012). User-Oriented Product Search Based on Consumer Values and Lifestyles. Lecture Notes in Computer Science, 313-327.

13. Tsagkias, M., King, T. H., Kallumadi, S., Murdock, V., & de Rijke, M. (2020). Challenges and research opportunities in eCommerce search and recommendations. ACM SIGIR Forum, 54, 1-23.

14. Hong, Y., Chen, P.-y., & Hitt, L. (2014). Measuring Product Type with Dynamics of Online Product Review Variances: A Theoretical Model and the Empirical Applications. IO: Empirical Studies of Firms & Markets eJournal.