Your site search dashboard is critical for understanding customer behavior and optimizing your ecommerce store’s performance. Data-driven decisions based on site search analytics can significantly boost sales and customer satisfaction [1].

Analyzing the data from your site search dashboard allows businesses to gain insights into customer behavior, preferences, and pain points, ultimately leading to more effective decision-making and higher conversion rates [2].

So, what metrics should you prioritize? How to interpret them to improve your ecommerce results?

Keep on reading to find out.

Search Dashboard - the most accurate indication of your search performance

Introducing an AI-driven site search solution is a great start, but more is needed to drive significant business sales. Continuous monitoring and optimization are key to ensuring the search engine remains effective.

Businesses that regularly optimize their AI algorithms see better search accuracy and customer engagement [3].

Therefore, choosing an ecommerce search provider that offers an easy-to-read dashboard is crucial.

A well-designed dashboard, like the one provided by LupaSearch, not only helps analyze data about user interactions but also allows you to configure synonyms, boost product positions, and test various search query settings.

So, how to interpret the search dashboard data to improve your ecommerce search results further?

Here are the steps you should take.

How to read your site search dashboard data?

Try using your search bar as if you were the user.

Look at the search results displayed for each search term. Are the results relevant and accurate? Would customers find what they came for?

If not, you may need to update the search algorithm and improve product descriptions. Improving search relevance through such optimizations can significantly increase sales and customer retention [4].

Look at the search terms used most frequently on your site. They can give you valuable data about what customers are looking for and help ensure your products and content are optimized for these terms.

Case studies have demonstrated that focusing on popular search terms can directly correlate with improved product visibility and sales [5].

Examine the search terms most frequently suggested to your users. These suggestions are prioritized in the suggestions list based on their relevance to the user.

Reviewing and updating these suggestions can enhance the user experience and potentially increase conversion rates.

Check for misspellings and synonyms

Identify frequent misspellings or synonyms that can prevent the search engine from delivering expected results. While AI-driven site search engines support autocorrection, manual reviews are sometimes necessary.

For instance, a case study on an ecommerce platform showed that implementing a robust synonym strategy led to a 15% increase in successful searches [3].

Analyze CTR metrics

CTR (or click-through rate) is a metric that can help you understand which search results were most relevant to your users. If some search results receive very little CTR, they may be irrelevant to your users.

Case studies have shown that optimizing search results based on CTR can significantly impact user engagement and sales [6].

Look at search terms with no results

Some search terms might lead a user to a no-result page. This scenario indicates missed opportunities.

Addressing these gaps can not only enhance user satisfaction but also open up new business avenues. Effective no-result page management can drive product expansion and improve ecommerce performance [7].

Turn dashboard analysis into your weekly habit

As a business owner or shop administrator, focused on growth, you should revisit your dashboard at least once a week.

Regular analysis of search dashboard data has been linked to sustained ecommerce growth and improved customer loyalty. Businesses that commit to weekly data reviews outperform their competitors in customer retention and revenue generation [8].

Analyzing data from your site search dashboard can help you better understand the needs and interests of your users and allow you to make informed decisions about optimizing your ecommerce site to better meet those needs.

If you need consultation regarding your site search improvement, LupaSearch dedicated team is always here for you. Reach out to us, and let’s grow your ecommerce business together.

References

  1. Tontini, G. (2016). Identifying opportunities for improvement in online shopping sites. Journal of Retailing and Consumer Services, 31, 228-238. https://doi.org/10.1016/J.JRETCONSER.2016.02.012.

  2. Drivas, I., Sakas, D., Giannakopoulos, G., & Kyriaki-Manessi, D. (2020). Big Data Analytics for Search Engine Optimization. Big Data Cogn. Comput., 4, 5. https://doi.org/10.3390/bdcc4020005 .

  3. 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. [1803.11284] Deep Recurrent Neural Networks for Product Attribute Extraction in eCommerce (arxiv.org)

  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. Yakubu, H., & Kwong, C. (2020). Using Online Big Data for Determining the Importance of Product Attributes. 2020 IEEE International Conference on Industrial Engineering and Engineering Management (IEEM), 691-695. https://doi.org/10.1109/IEEM45057.2020.9309746.

  6. Agichtein, E., Brill, E., & Dumais, S. (2019). Improving Web Search Ranking by Incorporating User Behavior Information. ACM SIGIR Forum, 52, 11 - 18. https://doi.org/10.1145/3308774.3308778.

  7. Monfared, A., & Avizheh, A. (2023). Re-rank Search Engine Results via Machine Learning. 2023 9th International Conference on Web Research (ICWR), 253-258. https://doi.org/10.1109/ICWR57742.2023.10139285.

  8. 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.