Searchandising is the art and science of blending search and merchandising to drive ecommerce conversions. It is one of the most effective ecommerce sales-boosting techniques.

How can searchandising help you show the right products to the right customers in an engaging and sales-driving way? How can you design it to bring the most profit?

Keep on reading to find out.

Why Should You Exploit Searchandising?

Nearly 30% of ecommerce shoppers go directly to the search box. They expect to find relevant and accurate results that match their intent and preferences. If they don’t find it, they usually switch to a competitor’s store.

Searchandising helps to meet those expectations and maximize the chance of a conversion. By optimizing the ranking and display of products, you can highlight your bestsellers, new arrivals, or high-margin items, so the shoppers notice them first.

Studies have shown that well-implemented recommendation systems, which are a key part of searchandising, can significantly boost sales and increase customer satisfaction by providing relevant and personalized suggestions [1, 2].

How to Design Searchandising to Boost Conversion Rates?

1. Use Ecommerce Search that Supports Searchandising

First things first, you need to have a flexible search engine that can handle complex queries, synonyms, typos, filters, facets, and more.

Default search solutions often do not support advanced features like searchandising, which are crucial for a converting site search experience.

Implementing a robust search engine like LupaSearch that supports these functionalities is critical to maximizing the effectiveness of your searchandising strategy [3].

2. Develop Search Rules Based on Business Goals

Develop search rules based on your business goals, KPIs, seasonality, inventory levels, etc. Adjust the ranking and display of products according to these rules.

For example, you can boost certain products to the top of the results based on popularity, profitability, availability, or relevance.

3. Use Product Recommender

Use product recommender to highlight and boost specific products or categories you want to promote. For example, promoting products with high ratings, reviews, discounts, or special offers can significantly impact sales.

Balancing accuracy and profitability in product recommendations can positively affect purchasing behavior without affecting customer trust [4].

4. Use Real-time Personalization to Customize Product Ranking

Customize the ranking of products based on customer preferences or behavior. Personalized recommendations have been found to increase sales by offering relevant suggestions that match the customer’s past behavior and preferences [5].

5. Implement an Autocomplete Feature

Implementing autocomplete features can make it easier for customers to find the right products while they type a search query.

This not only speeds up the search process but also improves the accuracy of search results, which is crucial for enhancing user experience [6].

6. Refine Search with Faceted Search and Filters

Apply filters and faceted search to help customers narrow their search results based on specific criteria such as price, color, or size.

Effective use of these tools has been shown to improve search relevance and reduce the time taken to find desired products [7].

7. Use Recommendation Algorithms

Use smart recommendation algorithms based on user interactions to promote related or complementary products to customers while they are searching or browsing.

User-focused recommendation systems have been shown to significantly boost sales and enhance customer satisfaction [8].

8. Showcase Promotions and Discounts on Search Results Pages

Encourage customers to make purchases by showcasing promotions and discounts directly on the search results pages.

9. Conduct A/B Testing

Conduct A/B testing to evaluate different searchandising strategies and determine which are most effective in boosting conversion rates and sales.

A/B testing helps in refining the search experience and ensures that the implemented strategies align with user preferences and behaviors [9].

10. Continuously Improve Search Relevance

Look beyond the obvious and continuously improve search relevance. Consider testing how well the search processes long-tail keywords, understands complex queries, and delivers query-matching results.

Frequent search data analysis and continuous improvement are key to maintaining an effective searchandising strategy as customer preferences and market conditions evolve [10].

A Key to Searchandising Success - Continuous Improvement

As said, searchandising is both an art and a data science. While you can always apply UI-friendly changes, don’t forget to get back to hard data.

Customer behavior, preferences, and expectations can change over time, as well as market trends, competitors’ strategies, and product availability.

To keep your searchandising effective, regularly collect and analyze user behavior data to apply well-reasoned changes.

LupaSearch - Your Go-To Partner for Exploiting Searchandising

Partner up with LupaSearch and exploit the benefits of searchandising. This AI-driven ecommerce search is compatible with all the major platforms and is easy to use.

LupaSearch comes with all the advanced and most-wanted site search features needed for your business growth: Natural Language Processing, search customization, personalization, and data reports, to name a few.

Partner with LupaSearch to create a better shopping experience for your customers and drive more revenue to your store.

Book a free product demo, and take a step towards your e-commerce business growth.

References

  1. Belluf, T., Xavier, L., & Giglio, R. (2012). Case study on the business value impact of personalized recommendations on a large online retailer. Proceedings of the sixth ACM conference on Recommender systems. https://doi.org/10.1145/2365952.2366014.

  2. Pathak, B., Garfinkel, R., Gopal, R., Venkatesan, R., & Yin, F. (2010). Empirical Analysis of the Impact of Recommender Systems on Sales. Journal of Management Information Systems, 27, 159 - 188. https://doi.org/10.2753/MIS0742-122227020.

  3. Vandic, D., Frasincar, F., & Kaymak, U. (2013). Facet selection algorithms for web product search. Proceedings of the 22nd ACM international conference on Information & Knowledge Management. https://doi.org/10.1145/2505515.2505664.

  4. Panniello, U., Hill, S., & Gorgoglione, M. (2016). The impact of profit incentives on the relevance of online recommendations. Electron. Commer. Res. Appl., 20, 87-104. https://doi.org/10.1016/j.elerap.2016.10.003.

  5. Yan, Q., Zhang, L., Li, Y., Wu, S., Sun, T., Wang, L., & Chen, H. (2016). Effects of product portfolios and recommendation timing in the efficiency of personalized recommendation. Journal of Consumer Behaviour, 15, 516-526. https://doi.org/10.1002/CB.1588.

  6. 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.

  7. Vandic, D., Frasincar, F., & Kaymak, U. (2013). Facet selection algorithms for web product search. Proceedings of the 22nd ACM international conference on Information & Knowledge Management. https://doi.org/10.1145/2505515.2505664.

  8. Jannach, D., & Hegelich, K. (2009). A case study on the effectiveness of recommendations in the mobile internet. , 205-208. https://doi.org/10.1145/1639714.1639749.

  9. Belluf, T., Xavier, L., & Giglio, R. (2012). Case study on the business value impact of personalized recommendations on a large online retailer. Proceedings of the sixth ACM conference on Recommender systems. https://doi.org/10.1145/2365952.2366014.

  10. Hinz, O., & Eckert, J. (2010). The Impact of Search and Recommendation Systems on Sales in Electronic Commerce. Business & Information Systems Engineering, 2, 67-77. https://doi.org/10.1007/s12599-010-0092-x.