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Faceted search is not just a feature; it’s a game-changer for ecommerce websites. It transforms the way users interact with your site, enhancing user experience and driving conversions.

If you are still considering whether or not to invest in providing faceted search functionality on your website, this article is for you.

Let’s discuss the benefits of implementing faceted search on your ecommerce site.

What is faceted search, and how does it work?

Faceted search combines traditional search methods with a dynamic navigation system, allowing users to refine their search based on specific attributes like color, size, brand, and price range.

Amazon.com example of faceted search

Amazon.com example of faceted search

This powerful feature is more than just a convenience; it’s a catalyst for customer satisfaction and engagement:

1. Enhanced engagement through satisfaction and trust:

Faceted search on e-commerce sites can boost customer engagement by increasing satisfaction and trust.

By providing customers with an efficient and tailored browsing experience, e-commerce sites can significantly enhance the user experience, leading to higher satisfaction levels [1].

2. Influence on customer loyalty:

Faceted search can influence engagement behaviors, which in turn affect the perceived benefits like social, entertainment, and economic benefits on e-commerce sites.

This enhanced perception can increase customer loyalty and satisfaction, critical metrics for e-commerce success [2].

3. Positive attitudes and loyalty:

For e-commerce, engaged customers who have a positive interaction with faceted search interfaces are likely to develop positive attitudes towards the brand.

This can lead to increased future loyalty and a better perception of product value, drastically boosting customer retention [3].

4. User satisfaction with preference-based interfaces:

Preference-based search interfaces directly relate to e-commerce success. Implementing faceted search that caters to customer preferences in product selection can significantly improve user satisfaction, potentially leading to better conversion rates and customer loyalty [4].

Faceted search is widely used in the top ecommerce stores. Why should you implement this functionality on your website?

1. Refined search results to enhance the shopping experience:

The automated algorithm optimizes facet ranking based on user preferences, streamlining the product discovery process and enhancing user satisfaction [5].

2. Discovery of new products and increased order value:

With faceted search, users discover more alternatives that meet their preferences than standard search. As a result, the faceted search can lead to the discovery of products users hadn’t initially considered, potentially increasing average order value [6].

3. Enhanced website performance and improved SEO:

Faceted search, by efficiently organizing and presenting information, can contribute to improved SEO and website performance [7].

Research exploring the importance of SEO in marketing demonstrates that efficient search functionality, like faceted search, can enhance a website’s SEO performance and effectiveness of online advertisement [8].

How to implement faceted search in your store?

When it comes to implementation, user-friendly integration should be your main priority.

The filters and options should be clear and easy to understand, with an option to easily clear selections. Navigation should be simple, making it easy for users to move between filters, and it should be easily visible on the page.

You can choose a custom integration or an already-tested and perfected ecommerce search solution like LupaSearch.

LupaSearch is an AI-driven ecommerce search that gets to the core of the users’ search queries and delivers highly-matching, personalized results. This site search engine integrates faceted search functionality to improve product discoverability and boost sales.

You do not need coding knowledge to power your site search with facets. The LupaSearch dedicated team of search professionals is here for your needs.

Contact us today, and receive a free demo.

References

  1. Santini, F., Ladeira, W., Pinto, D., Herter, M., Sampaio, C., & Babin, B. (2020). Customer engagement in social media: a framework and meta-analysis. Journal of the Academy of Marketing Science, 1-18. https://doi.org/10.1007/s11747-020-00731-5.

  2. Gummerus, J., Liljander, V., Weman, E., & Pihlström, M. (2012). Customer engagement in a Facebook brand community. Management Research Review, 35, 857-877. https://doi.org/10.1108/01409171211256578.

  3. Bergel, M., Frank, P., & Brock, C. (2019). The role of customer engagement facets on the formation of attitude, loyalty and price perception. Journal of Services Marketing, 31, 890-903. https://doi.org/10.1108/jsm-01-2019-0024.

  4. Kern, D., Hoek, W., & Hienert, D. (2018). Evaluation of a search interface for preference-based ranking: measuring user satisfaction and system performance. Proceedings of the 10th Nordic Conference on Human-Computer Interaction. https://doi.org/10.1145/3240167.3240170.

  5. Vandic, D., Aanen, S., Frasincar, F., & Kaymak, U. (2017). Dynamic Facet Ordering for Faceted Product Search Engines. IEEE Transactions on Knowledge and Data Engineering, 29, 1004-1016. https://doi.org/10.1109/TKDE.2017.2652461.

  6. Kern, D., Hoek, W., & Hienert, D. (2018). Evaluation of a search interface for preference-based ranking: measuring user satisfaction and system performance. Proceedings of the 10th Nordic Conference on Human-Computer Interaction. https://doi.org/10.1145/3240167.3240170.

  7. Tsuei, H., Tsai, W., Pan, F., & Tzeng, G. (2018). Improving search engine optimization (SEO) by using hybrid modified MCDM models. Artificial Intelligence Review, 53, 1-16. https://doi.org/10.1007/s10462-018-9644-0.

  8. Khraim, H. (2015). The Impact of Search Engine Optimization on Online Advertisement: The Case of Companies using E-Marketing in Jordan. American Journal of Business and Management, 4, 76-84. https://doi.org/10.11634/216796061706676.

  9. Kharlamov, E., Giacomelli, L., Sherkhonov, E., Grau, B., Kostylev, E., & Horrocks, I. (2017). SemFacet: Making Hard Faceted Search Easier. Proceedings of the 2017 ACM on Conference on Information and Knowledge Management. https://doi.org/10.1145/3132847.3133192.

  10. Dang, D., Nguyen, H., Nguyen, B., & Duong, T. (2015). A Framework of Faceted Search for Unstructured Documents Using Wiki Disambiguation. , 502-511. https://doi.org/10.1007/978-3-319-24306-1_49.

  11. Baeza-Yates, R. (2006). Algorithmic Challenges in Web Search Engines. , 277-278. https://doi.org/10.1007/11764298_25.

  12. Mahdi, M., Ahmad, A., Ismail, R., & Subhi, M. (2020). Review of Techniques in Faceted Search Applications. 2020 International Symposium on Networks, Computers and Communications (ISNCC), 1-5. https://doi.org/10.1109/ISNCC49221.2020.9297275.

  13. Koren, J., Zhang, Y., & Liu, X. (2008). Personalized interactive faceted search. , 477-486. https://doi.org/10.1145/1367497.1367562.