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Ecommerce internal search is the process of customers searching for products and services within an online store. It is an essential part of the customer experience, as it helps customers find what they need quickly and efficiently.

If implemented correctly, an internal search can improve the customer experience and increase sales.[1]

In this article, we will explore how to use internal search metrics to optimize your store for maximum customer satisfaction and sales. We will also look at the various types of search metrics and strategies for improving them.

Let’s dive in.

The importance of internal search metrics

Internal search metrics are critical for understanding how customers use the search function on your website. Look for such an e-commerce search feature like Reports, Stats, and Analytics.

They provide valuable insights into customer behavior, which can be used to optimize the search experience and improve conversion rates.

By tracking search metrics, you can better understand what customers are looking for and how they are using the search bar. You can also identify areas where the search could be improved and make changes accordingly.

External and internal sources of word-of-mouth influence customer decisions and underscore the value of optimizing internal search mechanisms [2].

Understanding ecommerce search metrics

When it comes to ecommerce search metrics, several key metrics should be tracked. These include search terms, search volume, search intent, click-through rate, and conversion rate.

  • Search terms are the words or phrases customers use when searching for products or services on your website.
  • Search volume is the number of searches performed on your website.
  • Search intent is the purpose behind the search, such as whether the customer is looking to make a purchase or simply to research a product.
  • The click-through rate is the percentage of users who click on a search result.
  • The conversion rate is the percentage of users who purchase after clicking on a search result.

Types of ecommerce search metrics

Several types of search metrics can be tracked to understand how customers use the search bar on your website.

These include the following:

  • Average search time: The time it takes for customers to find the product or service they’re looking for.
  • A number of searches: The total number of searches performed on your website.
  • Query types: Whether customers search for products or services or use filters to narrow down their search results.
  • Popular search terms: The most popular words or phrases used by customers when searching for products or services.
  • Search conversion rate: The percentage of customers who purchase searching for a product or service.

Different dimensions of online shopping experiences influence customer loyalty, emphasizing the role of effective search interfaces [3].

Analyzing and improving internal search metrics

Once you’ve collected and analyzed your search metrics, you can use the insights to improve the search experience.

Identify areas where customers experience difficulties finding what they’re looking for. It can help you adjust your product offerings and make sure customers can find what they need quickly and easily.

Search and recommendation tools reduce search costs, increasing consumer surplus and enabling online retailers to promote and sell more products profitably [4].

How to improve internal search metrics?

There are several strategies that you can use to improve your internal search metrics. These include optimizing your search algorithm, improving the search interface, and making it easier for customers to find what they want.

You can also use machine learning and Natural Language Processing (NLP) technologies to improve search results. Machine learning algorithms can learn from customer searches and adjust the search results accordingly. NLP understands the natural user language, so it gets into the core of even the most complex search queries.

Although there are many ongoing challenges and opportunities in ecommerce search and recommendations, continuous optimization of search algorithms is the key to success [5].

Finally, you can use A/B testing to compare different search experiences and identify which works best for your customers. It can help you make improvements to the search experience.

The influence of rankings on how consumers search and buy online emphasizes the importance of using analytics to improve search results [6].

Leveraging the power of search metrics to refine the ecommerce search experience stands as a pivotal strategy for enhancing customer satisfaction and driving sales.

Make sure to pick an e-commerce search that supports internal search metrics (such as LupaSearch).

The utilization of search-integrated analytic tools like Reports, Stats, and Analytics in LupaSearch can help you track key search metrics, such as search terms, search volume, and click-through rate. They can also help you analyze customer searches and identify areas where search could be improved.

Request a free product demo at LupaSearch, see it in action with your current product feed, and witness the user satisfaction growth.

References

  1. 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. https://www.researchgate.net/publication/329732482_Google_search_effect_on_experience_product_sales_and_users'_motivation_to_search_Empirical_evidence_from_the_hotel_industry.

  2. Gu, B., Park, J., & Konana, P. (2012). Research Note - The Impact of External Word-of-Mouth Sources on Retailer Sales of High-Involvement Products. Inf. Syst. Res. https://pubsonline.informs.org/doi/10.1287/isre.1100.0343.

  3. Yin, W., & Xu, B. (2021). Effect of online shopping experience on customer loyalty in apparel business-to-consumer ecommerce. Textile Research Journal. https://journals.sagepub.com/doi/10.1177/00405175211016559.

  4. Hinz, O., & Eckert, J. (2010). The Impact of Search and Recommendation Systems on Sales in Electronic Commerce. Business & Information Systems Engineering. https://link.springer.com/article/10.1007/s12599-010-0092-x.

  5. 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. https://dl.acm.org/doi/10.1145/3451964.3451966.

  6. Ursu, R. M. (2018). The Power of Rankings: Quantifying the Effect of Rankings on Online Consumer Search and Purchase Decisions. Mark. Sci. https://pubsonline.informs.org/doi/10.1287/mksc.2017.1072.