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This article has been updated 15/08/2023

B2B commerce, or business-to-business commerce, is a growing and vital sector within the ecommerce landscape, particularly in today's interconnected global economy. Unlike traditional retail that focuses on selling to individual consumers, B2B commerce is centred around businesses selling products, services, or information to other businesses. This often involves more complex transactions, including wholesale commerce, supply chain management, and enterprise-level negotiations.

With tools like ɫɫ�о���, B2B merchants can tap into advanced search functionalities, including personalized and relevant search results, to enhance their on-site merchandising strategies. By understanding corporate buying behavior and utilizing machine learning to predict business needs, ɫɫ�о��� provides an essential solution to facilitate smoother B2B transactions, increase conversion rates, and build lasting relationships between businesses.

Studying the behavior of online shoppers is a fundamental aspect to succeeding in today’s uber-competitive ecommerce environment. When it comes to on-site search preferences, data has shown that most web visitors do not initially gravitate toward the category and faceted navigation tools to conduct a search. While these features are extremely important, EConsultancy recently reported that 3/10 online visitors will go immediately to the search box. In addition, according to Findwise, for shoppers who actually do convert and make purchases, over 90% of them also start with the search tool. This is especially true on mobile, as screen space is at a premium.

The role of the merchandiser is extremely important to every ecommerce business. If the marketer’s job is to call a shopper’s attention to products, the merchandiser decides how they will be presented to them. ɫɫ�о��� has advanced tools to help merchandises fully optimize the on-site search box for both greater conversions and more control of search results.

So what do you get when you combine traditional ecommerce merchandising strategies with a powerful, on-site search and insight engine?  Searchandising! When it comes to Searchandising, i.e. using ɫɫ�о��� to help effectively present your products to shoppers, let’s take a look at some key features.

Relevancy – The Foundation Of Searchandising

Merchandising is a multifaceted approach to product presentation, combining both art and science to strategically display goods in a way that resonates with shoppers and encourages purchasing. In the world of ecommerce, especially within the B2B commerce sphere, merchandising goes beyond the visual arrangement of products; it involves the intelligent utilization of data, customer insights, and advanced tools like ɫɫ�о��� to optimize the on-site search experience.

From applying the Boost & Bury feature to control product visibility to implementing keyword replacement and synonym functionality for more relevant results, the modern merchandiser can tailor the shopping experience to meet individual business needs. Through the seamless integration of traditional merchandising principles with innovative search technology, ɫɫ�о��� empowers merchandisers to create a more engaging, efficient, and effective online shopping environment, leading to greater conversions and brand loyalty.

Ecommerce shoppers want to find exactly what they are looking for and fast. Being able to provide accurate search results for site visitors is critical to successful merchandising campaigns. Shoppers who cannot locate what they want to see will abandon their search efforts and try their luck on a competing website. ɫɫ�о��� was developed to provide extremely personalized and relevant search results for online buyers. Getting the right mix of products in front of a shopper will lead to much higher conversions and build loyalty to a brand as well.

Boost & Bury Rules

If the effective display of products is the key objective of a Searchandiser, the Boost & Bury feature found in ɫɫ�о��� can be a great help in accomplishing that goal. This tool provides merchandisers the ability to influence the results that shoppers receive after utilizing the search box. By prioritizing or deemphasizing the importance of certain products being displayed, merchandisers have direct control over what shoppers see. In addition, products can highlighted in search results based on time of year, topic, theme, upcoming holiday schedule or for any other reason. Conversely, items that are out of stock or discontinued can be hidden. The Boost & Bury feature is a great way to build product displays directly through the search box.

Synonyms & Key Word Replacement

Data has shown that people sometimes use different words to describe the same thing. With the synonym feature found in ɫɫ�о���, today’s Searchandiser can ensure that no conversion opportunities are missed due to unusual or alternative words being used in the search box. For example, let’s say an online shopper types in “red shoes”, but all related products on the website are described more specifically like maroon, crimson or scarlet. In this situation, ɫɫ�о��� will show search results for maroon, crimson and scarlet shoes. Regardless, if the shopper simply types in “red shoes”, the most relevant results are still displayed.

Keyword Replacement is also another important tool for the Searchandiser. If an online clothing retailer carries the popular brand “X”, but not the competing “Y” line of products, ɫɫ�о��� can still show relevant search results. Should a shopper search for the unavailable “Y” line of goods, ɫɫ�о��� will automatically substitute a display of “X” brand products instead. Keyword replacement is a great way to remain in the running for a conversion, even though certain search inquiries are not a direct match.

Machine Learning & Relevant Recommendations

Machine learning is a critical element in the ever-evolving area of Searchandising. Springing from the work done in artificial intelligence, machine learning is giving ecommerce retailers the ability to accurately foresee what shoppers want to buy. On-site search tools can now analyze an enormous amount of data, as it relates to online shopping behavior and patterns. From this information, predictions can be made as to what products should be displayed for site visitors.

Machine learning has played a big role in refining personalization and relevancy in search results. The add-on ɫɫ�о��� feature Relevant Recommendations can help searchandisers increase conversion rates by offering alternative products that already appeal to shoppers. The chances of a purchase substantially increases if a consumer already has an interest in something. Displaying products in search results through the Relevant Recommendations tool is a great way to engage in the classic sales strategy of suggestive selling. Happy Searchandising!