Analytics

4 Ways Search Merchandising Boosts Online Retail Conversion Rates

e-commerce search merchandising increases conversion rates for online retailers

Search engines are powerful tools for guiding shoppers to relevant products on online retail sites. However, to maximize revenue and deliver the best possible shopping experience, relying on search engines alone isn’t usually the best approach.

Instead, retailers should also take advantage of search merchandising. By providing additional control over search results, search merchandising helps retailers implement business objectives that search engines often fail to address.

To prove the point, let’s examine four key benefits of search merchandising and how e-commerce retailers can leverage them effectively.

What Is Search Merchandising?

Search merchandising, also known as “searchandising,” is the art and science of presenting search results in ways that account for relevancy, shopper intent, and business objectives.

Typically, search merchandising works by taking the results generated by a search engine and then modifying them in some way that adds value. Examples of the practice include:

  • Personalizing search results by highlighting products that a shopper is most likely to find relevant based on their search and purchasing history
  • Highlighting or promoting products and brands that have a higher profit margin
  • Dynamically presenting facets and filters based on shopper context

In short, instead of simply relying on the results generated by a search engine to determine how products are presented, search merchandising leverages analytics, shopper behavior, and other contextual signals, along with marketing and sales goals, to enhance the effectiveness of search results.

4 Key Benefits of Search Merchandising

Now that we’ve covered the basics of search merchandising, let’s talk about the benefits the practice offers online retailers and their shoppers.

1. Improved Search Relevance

One key advantage of search merchandising is that it increases the chances that shoppers will find search results relevant, which is good for them because it improves their experience and is good for businesses because it increases conversions.

Most online retailers recognize that product data and shopper preferences change over time and need to evaluate and account for those changes. A well-tuned baseline search algorithm is the cornerstone of an effective search merchandising strategy. However, search merchandising is most effective when the search engine’s baseline results are calibrated to deliver accurate, relevant products for the broad, common queries (also known as head queries) that shoppers frequently use.

Regularly evaluating and fine-tuning your search engine to adapt to changes in your product catalog, queries, and shopper behaviors will ensure the best results. Metrics such as revenue, margin, sales rank, customer ratings, and reviews are extremely valuable for improving search performance and relevancy. Using these metrics as boost factors singularly or in concert can help ensure that high-performing and highly rated products are readily visible. When popular and profitable items are visible to shoppers, the likelihood of a purchase increases.

2. Personalized Shopper Experience

Search merchandising is also a great way to deliver personalized shopper experiences. Rather than displaying the same results for a given search query to every site visitor, retailers can use search merchandising to tailor results for specific ones based on context like clickstream engagement, purchasing history, and geographic region using techniques like the following:

  • AI and machine learning: Recent advancements in AI and machine learning are delivering new and exciting ways to accurately analyze shopper behavior, search history, and related interactions to determine user intent. These technologies enable the dynamic ranking of search results, ensuring that the most relevant products consistently appear at the top.
  • Shopper behavior data: Clickstream data can provide a wealth of insight into the paths shoppers take on your site, which in turn provides information on their preferences and interests. Additional data such as purchase history, cart additions, and demographic information help create a deeper view of each shopper’s preferences, which merchants can use to tailor and rank search results.

Presenting search results informed by shopper behavior ensures that users see the most relevant products. When individual data is limited, aggregating behavior data from all shoppers is a great way to go. Aggregating behavior data and presenting search results based on shopper behavior help showcase popular and highly rated products, appealing to a broader audience and enhancing the overall shopping experience.

3. Strategic Product Promotion

Search engines typically have no way of knowing which products a company wants to promote at a given point in time. However, search merchandising empowers online retailers to strategically influence and control product placement within search results. By giving merchandisers control, search merchandising can boost or bury specific products or brands, ensuring they have maximum visibility while less relevant ones are de-emphasized.

One powerful aspect of this approach is the ability to pin products to the most valuable positions within search results. This ensures that top-selling or promotional items receive maximum exposure.

Additionally, merchandisers can provide content for dedicated merchandising zones, where select products, brands, and categories are prominently featured, immediately catching the shopper’s eye. Another advantage is the capability to curate collections of products tailored to specific themes, seasons, or campaigns.

This level of precision allows for highly personalized shopping experiences that present shoppers with products that align with their needs and preferences. By leveraging human touch control, merchandisers can create search experiences that meet business goals, enhance customer satisfaction, and boost conversions.

4. Enhanced Shopper Experience

Effective search merchandising enhances the shopping experience by delivering seamless, intuitive experiences that reduce friction while maintaining engagement, which ultimately boosts conversion rates. Search merchandising helps in this area in several ways:

  • Improved navigation: Using techniques like machine learning, sites can improve the navigation experience by generating facets and filters dynamically based on search queries, for example, leading them to their desired products quickly and intuitively. Visual navigation elements, which can be driven by machine learning or merchandiser curation, are particularly useful when search terms may be too broad to yield optimal results.
  • Visual highlights: Strategically placed banners can draw attention to important brands, promotions, or featured products. They highlight key products, promotions, or calls to action, enhancing the overall shopping experience by providing visual interest and breaking up extensive product listings.
  • Advanced query handling: Features like typo tolerance, synonyms, and query rewrites help ensure that minor errors or variations in search queries don’t hinder the shopper’s journey. Focusing on autocomplete suggestions, which can guide shoppers to popular searches and relevant products quickly, is another area worthy of investment. Each of these features is a strong candidate for using AI and machine learning, which can also provide the added value of understanding the context of search queries.

Search Merchandising for Online Retail Success

Search engines are a powerful tool for helping shoppers find what they want. But they’re even more powerful when paired with search merchandising. By giving retailers finer-grained controls over search results, search merchandising can help boost conversions and revenue while enabling a smoother and more rewarding experience for shoppers.

Keith Mericle

Keith Mericle is the CEO of Innovent Solutions, the provider of FindTuner, an e-commerce search merchandising and machine-learning solution for Solr. .

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