Ecommerce search is a highly effective sales-driving channel. As a rule of thumb, conversion rates can double when users find the products they need using a site search.

If an ecommerce store introduces a relevant search, the search can accumulate roughly 40% of total revenue.

And yet, the keyword here is relevant. An irrelevant search will fail to satisfy users’ requests and deliver request-matching products.

That’s why you have to overcome conversion blockers to succeed.

Here’s how.

First, opt for AI-driven, converting site search.

You should not expect a default search (that comes with your ecommerce platform) to deliver a satisfying user experience.

Often such default solutions lack customization and personalization options. Besides, they rarely allow monitoring of the search data and making decisions.

A dedicated ecommerce site search solution (like LupaSearch), powered with Artificial Intelligence and Machine Learning algorithms, gets to the core of the user’s search query, identifies the intent, and returns query-matching products [1].

A dedicated ecommerce site search solution (like LupaSearch), powered with Artificial Intelligence and Machine Learning algorithms, gets to the core of the user’s search query, identifies the intent, and returns query-matching products.

Besides, such site search provides clear-cut reports, so you can make data-based business decisions and continuously improve search relevance. A converting site search can help you exploit the tested strategies to overcome conversion blockers [2].

Now, improve search relevance.

One of the primary reasons for a high bounce rate or low conversion rate - is irrelevant search results.

Statistics suggest that 68% of shoppers that could not find what they search for do not return to that website again.

That’s why you cannot afford to lose shoppers because of a poor site search experience.

To solve this, you can optimize your search algorithm and ensure that it returns the most relevant results to the user query and overcomes all conversion blockers. Here’s how [3].

1. Exploit faceted search

Faceted search is a way to filter search results by attributes or facets such as price, brand, color, size, material, etc. By enabling faceted search, users can quickly narrow their search and find the products they are looking for.

A user who is looking for a summer midi-length green skirt in size M can immediately apply filters and, within seconds, find the product she wants.

Effective faceted search can increase your stores’ conversion rates by up to 20% [4].

2. Autocomplete

Autocomplete suggests search terms as users type in their query. If a user types in “phon,” the search engine can immediately suggest relevant results: mobile phones, phone cases, or other accessories you sell.

By implementing autocomplete, you can help users refine their search terms and find what they are looking for quickly.

Autocomplete alone can boost your site search conversion rates by 24% [5].

3. Synonyms and spelling correction

Users often make typos or use synonyms for the same product. Especially if you run a business worldwide, people will use many words to name the products you have (you will already notice a difference among UK and US shoppers).

Analyze search queries, identify trends, and add synonyms. The next time the user searches for a cooker, make sure you return the stoves.

By incorporating synonyms and spelling corrections, you can ensure that users find what they search for even if they make a mistake while typing their query [6].

4. Personalization

Another way to overcome conversion blockers is to personalize search results for each user based on their preferences and search history.

48% of shoppers spend more money on websites that offer personalized search experiences [7].

Personalizing search results allows you to offer products most relevant to the user. Such users enjoy a smoother and more efficient search experience. Besides, they convert at a higher rate.

5. Search merchandising

The last highly effective technique is to exploit search merchandising and product boosting. This feature allows you to manually curate search results and promote specific products, brands, deals, or categories.

By strategically placing certain products at the top of search results or adding banners or callouts, ecommerce businesses can guide users toward specific products or promotions. Additionally, it can help you promote new or underperforming products and drive sales [8].

For maximum effectiveness, analyze the dashboard, and notice search trends and seasonal changes. Use insights to plan future product campaigns according to what’s relevant to your users and grow revenue.

Implement changes to see your business grow.

Apply these strategies to overcome ecommerce conversion blockers. Start adjusting a site search and provide users with a better shopping experience.

It leads to higher customer satisfaction, increased loyalty, and, ultimately, increased conversions.

Start overcoming too-common conversion blockers today and stay ahead of the competition.

Contact LupaSearch product consultants and receive a free demo.

References

1. Harris, L. R. (2005). B2B Marketers Integrate Precision Search to Boost Profitability and Increase Satisfaction Across the eCommerce Value Chain. The Journal of Internet Banking and Commerce, 10(1), 1-4. B2B Marketers Integrate Precision Search to Boost Profitability and Increase Satisfaction Across the eCommerce Value Chain (researchgate.net).

2. 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.

3. Singh, S., Farfade, S., & Comar, P. (2023). Multi-Objective Ranking to Boost Navigational Suggestions in eCommerce AutoComplete. Companion Proceedings of the ACM Web Conference 2023. https://doi.org/10.1145/3543873.3584649.

4. Tsagkias, M., King, T., Kallumadi, S., Murdock, V., & Rijke, M. (2020). Challenges and research opportunities in eCommerce search and recommendations. ACM SIGIR Forum, 54, 1 - 23. https://doi.org/10.1145/3451964.3451966.

5. 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. https://doi.org/10.1145/2009916.2009930.

6. Vysotska, V. (2021). INFORMATION TECHNOLOGY FOR INTERNET RESOURCES PROMOTION IN SEARCH SYSTEMS BASED ON CONTENT ANALYSIS OF WEB-PAGE KEYWORDS. Radio Electronics, Computer Science, Control. https://doi.org/10.15588/1607-3274-2021-3-12.

7. Storto, C. (2013). Evaluating ecommerce websites cognitive efficiency: an integrative framework based on data envelopment analysis.. Applied ergonomics, 44 6, 1004-14 . https://doi.org/10.1016/j.apergo.2013.03.031.

8. Brenner, E., Zhao, J., Kutiyanawala, A., & Yan, Z. (2018). End-to-End Neural Ranking for eCommerce Product Search: an Application of Task Models and Textual Embeddings. ArXiv, abs/1806.07296. End-to-End Neural Ranking for eCommerce Product Search: an application of task models and textual embeddings | Request PDF (researchgate.net)