Effective ecommerce search functionality is not just a convenience—it’s a necessity. In an industry where 43% of website visitors go straight to the search box, a well-optimized search can significantly drive sales and boost profits.
However, U.S. retailers lose an estimated $333 billion annually due to poorly performing search [1]. Enhancing the relevance and functionality of your ecommerce search can set your business ahead of the competition.
Personalization in ecommerce search has significantly improved search relevance and conversion rates. According to one study, combining content-based and content-agnostic features in search personalization drastically improves search ranking and conversion rates [2].
With so much competition in the market, set your business ahead. Make the most of your search engine.
Here’s how.
First, understand your users’ needs.
To truly optimize your ecommerce search, you must first deeply understand your users. This includes knowing what they are searching for, how they are searching, and what they expect to find.
There are several techniques you could apply to know that:
Identify the words and phrases that your target audience uses to find products like yours. You can then optimize your product descriptions, meta descriptions, and page titles to include these keywords. Optimizing these elements based on user behavior significantly improves search results and user experience [2].
Continuously analyze data from your ecommerce search provider. What products are trending? What products are your customers interested in (which you do not have)? Get used to gathering data and analyzing it.
Run market research, do focus groups, and talk to your customers. Customer feedback provides critical insights that can guide search optimization and improve personalization. Direct contact with customers provides endless insights you can later apply to make business decisions.
Based on the findings, adapt an ecommerce search solution that perfectly matches users’ expectations and offers the wanted products.
Then, apply well-tested sales-boosting tactics.
After identifying your customers’ needs and expectations, apply widely-used ecommerce search tactics that boost business sales and profit.
#1. Utilize faceted search
A faceted search is a powerful tool that allows customers to refine their search results based on specific criteria, such as price, color, size, and more.
Accurately extracting and presenting product attributes significantly enhances faceted search, improving the shopping experience [3].
By offering a faceted search option, you make it easier for customers to find the products they are looking for, which can lead to increased sales and profits. It improves user satisfaction by shortening the customer journey and making the search process more intuitive.
#2. Empower search personalization
Personalization is critical in enhancing the customer experience and driving sales. Personalized search experiences can significantly boost conversion rates [2]. By tracking what users are searching for, you can tailor the search experience individually for each user.
This may include personalized product recommendations, search results, and even auto-suggest options. Further studies indicate that personalization (when properly implemented) improves search relevance and enhances customer engagement [4].
#3. Use search merchandising
Search merchandising (also known as Searchandising) involves using data-driven strategies to optimize search results and product recommendations.
Effective ranking strategies (a form of searchandising) reduce search costs and increase sales [5].
You can prioritize the most relevant and profitable products in your search results, increasing the chances of a sale.
You can also run seasonal campaigns, launch special offers, boost categories, and sell more. Consider providing a specific example or a testimonial from a business that has successfully implemented these tactics.
#4. Implement typo-tolerance
People make mistakes when they search. Typo tolerance is a feature that allows customers to find what they are looking for despite typos in their search query. It improves user experience and increases conversion rates [4].
Ecommerce search should automatically convert “winter golves” into “winter gloves” and return relevant results. It can tremendously improve the customer experience and increase the chances of a sale.
#5. Turn on autocomplete
Autocomplete (also known as “search-as-you-type”) accurately predicts what a user is searching for while he types in the query. Research shows that autocomplete reduces zero-results pages and significantly boosts sales [4].
The ecommerce search automatically suggests relevant products to make it easier to find the right products and purchase them. Evidence suggests that autocomplete alone can increase sales by up to 24%, making it a powerful tool for maximizing ecommerce profits.
LupaSearch - your business scale partner
If you are looking for a reliable ecommerce search solution that always hits the mark, LupaSearch is one of the options you might consider. With its AI-based technology, LupaSearch provides an exceptional search experience.
This single tool can help you apply all profit-boosting tactics. It supports autocomplete, personalization, searchandising, faceted search, and corrects typos.
You can easily integrate LupaSearch into your ecommerce site regardless of your platform and coding experience. The LupaSearch team is always available to support your business growth.
Take advantage of this opportunity by reaching out to a LupaSearch product consultant for a free demonstration. Start your journey towards better search results and increased profits today.
References
Zhao, D., Fang, B., Li, H., & Ye, Q. (2018). Google Search Effect on Experience Product Sales and Users’ Motivation to Search: Empirical Evidence from the Hotel Industry. Journal of Electronic Commerce Research, 19, 357. (PDF) Google search effect on experience product sales and users’ motivation to search: Empirical evidence from the hotel industry (researchgate.net)
Aslanyan, G., Mandal, A., Kumar, P., Jaiswal, A., & Kannadasan, M. (2019). Personalized Ranking in eCommerce Search. Companion Proceedings of the Web Conference 2020. https://doi.org/10.1145/3366424.3382715.
Majumder, B., Subramanian, A., Krishnan, A., Gandhi, S., & More, A. (2018). Deep Recurrent Neural Networks for Product Attribute Extraction in eCommerce. ArXiv, abs/1803.11284. (PDF) Deep Recurrent Neural Networks for Product Attribute Extraction in eCommerce (researchgate.net)
Bi, K., Ai, Q., & Croft, W. (2020). A Transformer-based Embedding Model for Personalized Product Search. Proceedings of the 43rd International ACM SIGIR Conference on Research and Development in Information Retrieval. https://doi.org/10.1145/3397271.3401192.
Ursu, R. (2018). The Power of Rankings: Quantifying the Effect of Rankings on Online Consumer Search and Purchase Decisions. Mark. Sci., 37, 530-552. https://doi.org/10.1287/mksc.2017.1072.