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We have some good and bad news for you.

The good news is that correctly implemented ecommerce site search can double (or, in some cases, shoot 6X) your store’s conversion rates.

The bad news is that nearly 84% of companies do not actively measure their ecommerce search results and lose their growth potential.

If you do not analyze the effectiveness of on-site search, take this blog post as an opportunity to scale your business to the next level.

Measure the quality of your ecommerce search results, improve them continuously, and drastically boost your business’s success.

Why does measuring the search quality matter?

Customers come to an online store to find the product they need quickly and easily. If they cannot find it, they will soon abandon your website.

That is why it is essential to measure the quality of your ecommerce search results regularly. You might be losing a purchase per every website visitor.

Analysis of search results can help you identify weak search areas where you lose revenue. By understanding how well your search engine is performing, you can improve its effectiveness and increase your sales.

Here’s how.

It all boils down to search relevance.

A well-implemented and relevant site search boosts conversion rates [1].

Therefore, the key metric for measuring the effectiveness of your search engine is relevance.

Relevance indicates how well the search results match the query entered by the user.

If a customer types in “short-sleeve green summer dress,” they expect to see short-sleeved green light dresses in their search results. Not red or yellow ones, not long-sleeved or made from wool.

Relevance is critical because it directly impacts the customer experience. If a customer sees irrelevant search results, they’re less likely to continue browsing and more likely to leave the site, resulting in lost sales.

The study on web pages’ load time further emphasizes the importance of search relevance, showing its direct link to conversion rates and customer satisfaction [2].

Therefore, your task as a shop administrator is to ensure the site search returns only the most relevant results. And if it fails, adjust the algorithm to do that.

There are several ways to measure search relevance: click-through rate, conversion rate, and zero-result pages. Go to the Reports, Stats, and Analytics section in your e-commerce search and analyze the numbers.

Click-through rate (CTR)

Click-through rate (CTR) measures how often customers click on search results. Precisely, it tracks the ratio of user engagement events (search result item clicks or adds to cart) and the total number of search queries.

The higher the CTR, the more relevant the search results. It means the site search returned the products the users were looking for.

CTR is a very useful metric to track the overall performance of your search settings, as well as to see how various configuration changes affect this metric over time.

Conversion rate (CR)

Conversion rate measures how often those clicks result in a sale. If your website sales are low or they decrease, you can take it as an indication that users cannot find the products they are searching for.

Statistics suggest that searchers alone can accumulate as much as 40% of total business revenue. So, if you have a default site search that fails to meet user needs, you might be missing a great opportunity.

So, take the CR metric as an overall indication of how well your ecommerce store is performing. Track it at least once a week, identify the reasons, and fix the areas that might be causing lost revenue.

McDowell, Wilson, and Kile (2016) found that certain website design features, which can influence CTR and CR, are significant predictors of online conversion rates, underscoring their importance as performance metrics [3].

Zero-result pages

Last, always check the number of zero-result pages. These are pages that appear when a customer searches for a specific product or term, but the search doesn’t return any results.

By monitoring the number of zero-result pages, you can identify gaps in your product offerings and content that may be causing these pages to appear.

For example, if customers frequently search for a particular product that you do not offer, you may want to consider adding it to your inventory.

The significance of managing zero-result pages is highlighted in another study, which demonstrates how search listings and customer reviews impact conversion rates, suggesting areas for search optimization [4].

Additionally, you can use zero-result pages to identify popular search terms that are not returning results and optimize your product descriptions or add synonyms to improve search relevance.

Investing in an AI-driven, advanced ecommerce search can be a game-changer for your business.

The effectiveness of AI-driven search solutions is evidenced by Diamantaras et al. (2021), who utilized LSTM recurrent neural networks to predict e-commerce users’ shopping intent with high accuracy, showcasing the potential of AI to enhance search relevance and conversion rates over time [5].

AI-powered ecommerce search solutions like LupaSearch use machine learning algorithms to understand customer behavior and improve relevance over time.

LupaSearch offers an intuitive, user-friendly dashboard so that even non-tech-savvy shop administrators can analyze the effectiveness of search results.

The best thing is that you do not need extensive IT knowledge. The LupaSearch team supports you and helps you continuously improve search relevance, simultaneously growing your business revenue.

Contact us, and let’s have a productive conversation.

References

  1. Fatta, D., Patton, D., & Viglia, G. (2018). The determinants of conversion rates in SME e-commerce websites. Journal of Retailing and Consumer Services, 41, 161-168. doi: 10.1016/J.JRETCONSER.2017.12.008. https://linkinghub.elsevier.com/retrieve/pii/S0969698917306525

  2. Stadnik, W., & Nowak, Z. (2017). The Impact of Web Pages’ Load Time on the Conversion Rate of an E-Commerce Platform. , 336-345. doi: 10.1007/978-3-319-67220-5_31. https://link.springer.com/chapter/10.1007/978-3-319-67220-5_31

  3. McDowell, W., Wilson, R., & Kile, C. (2016). An examination of retail website design and conversion rate. Journal of Business Research, 69, 4837-4842. doi: 10.1016/J.JBUSRES.2016.04.040. https://www.sciencedirect.com/science/article/abs/pii/S014829631630203X?via%3Dihub.

  4. Cezar, A., & Öğüt, H. (2016). Analyzing conversion rates in online hotel booking: The role of customer reviews, recommendations and rank order in search listings. International Journal of Contemporary Hospitality Management, 28, 286-304. doi: 10.1108/IJCHM-05-2014-0249. https://www.emerald.com/insight/content/doi/10.1108/IJCHM-05-2014-0249/full/html.

  5. Diamantaras, K., Salampasis, M., Katsalis, A., & Christantonis, K. (2021). Predicting Shopping Intent of e-Commerce Users using LSTM Recurrent Neural Networks. , 252-259. doi: 10.5220/0010554102520259. https://www.scitepress.org/Link.aspx?doi=10.5220/0010554102520259.