Search Engines vs. Recommendation Engines: How to Be Proactive in E-Commerce Selling

As e-commerce has evolved, so has the importance of leveraging tools to both help customers find what they’re looking for as well as show them what they may like, as well. For e-commerce retailers who have hundreds or thousands of SKUs, product discoverability is a key component of their success. Making users scroll page after page is a risk. A solid search engine is table stakes, but what other discovery mechanisms are necessary? And what's the best way to go about implementing them?

So how do you leverage both search engines and recommendation engines, and how do you proactively drive sales to your e-commerce business using these tools? Search and recommendations serve different functions in creating personalized experiences for shoppers. Here's how retailers should prioritize them differently based on certain criteria, which can increase both conversions and brand loyalty.

What is the difference between search engines and recommendation engines?

Search engines allow users to find information broadly across the internet or on a specific website by entering keywords or phrases. It crawls things like product pages and SKU metadata, indexes the content, and then ranks the results based on relevance to the user's query. 

A recommendation engine, on the other hand, is a tool that makes personalized recommendations to users based on their behavior and preferences. It uses algorithms to analyze data on users’ interactions with a product or service and then suggests similar or related items.

In short, a search engine helps users find specific products, while a recommendation engine helps users discover new products related to their taste.

In the e-commerce landscape, both types of engines can make a huge impression on a business’ bottom line and foster customer loyalty. Effective search engines have to provide results quickly, and those results need to be highly accurate to a user’s query. Recommendation engines must be able to anticipate what a customer is interested in and keep them on the site longer, eventually leading to more conversions and a better overall customer experience.

How do search and recommendations help with buyer intent?

Having a search engine on your store’s site that provides accurate results is table stakes for any e-commerce business. At the very least, you must be able to quickly surface an item that a customer is actively looking for. A much better approach to conversions, however, includes taking buyer intent into consideration. By incorporating a recommendation engine so that you can proactively surface items that a user will like, you increase the chances that if they come for one item, they’ll leave your store having purchased it as well as something else they didn’t know they wanted before they arrived. It also encourages a customer to visit your site even when they aren’t specifically looking for anything at all, because they know they’ll receive recommendations based on their taste. 

Behavior-based recommendations are particularly relevant for retailers who sell products that are inherent to taste as opposed to functionality.

For example, when someone is buying a box of nails from a hardware store, they know exactly what they’re looking for. However, the piece of artwork they are hanging using those nails most likely had a lot more to do with the individual’s taste and preferences. For the latter, recommendation is key to helping create conversions.

A women's apparel store is an example of somewhere customers would be doing taste-based shopping.

Need-based or taste-based shopping?

Every e-commerce store is unique, based on the types of products it offers and how users interact with those products. Shoppers behave differently when they buy different items. 

Need-based shopping

In the example above, a shopper who’s buying a box of nails already knows the specifications required before entering a store. This is an example of need-based shopping, which is particularly relevant for certain stores & item categories including:

  • Hardware
  • Electronics accessories
  • Automotive parts

These companies should prioritize smart search, so that a user can quickly find the exact thing they need: e.g., “brake pads for 2021 Acura RDX."

Taste-based shopping

However, this isn’t how you shop for your next pair of jeans or overcoat. Shoppers who are faced with a multitude of options related to their personal taste - like color, design, texture, and style - don’t know exactly what they want until they find something they like. Taste-based shoppers enter a store hoping to discover something that matches their personal preferences. Taste-based shopping includes stores and item categories including but not limited to:

  • Apparel
  • Jewelry
  • Beauty Products
  • Arts & Entertainment

These companies should prioritize behavior-based recommendations instead of search. These recommendations understand what customers want and proactively surface those items, enabling customers to quickly find something that matches their taste.

Recommendation engines on e-commerce websites

A recommendation engine on an e-commerce or retail website works by analyzing data on users’ interactions with or behavior on the website, such as the products they view, the items they add to their cart, and the purchases they make. The recommendation engine uses this data to create a profile of each user’s preferences and interests, essentially making a “customer DNA.”

Once a user profile is created, the recommendation engine can use various algorithms such as collaborative filtering, content-based filtering, and hybrid methods to suggest similar or related products to the user.

Collaborative filtering algorithms analyze the patterns of products that users with similar browsing and buying behaviors tend to purchase, and suggests similar products to the current user. On the other hand, content-based filtering algorithm analyzes the attributes of products that the user has viewed or bought in the past and suggests products with similar attributes.

Some recommendation engines can also take into account other factors such as the user's location, browsing history to enhance recommendations; however, the use of third-party data to market to users has fallen out of favor due to privacy concerns. Incidentally, taking stock of a user’s behavior on-site tends to be a much better way of creating a personalized experience for customers than relying on their demographic information.

In summary, a recommendation engine on a retail website works by analyzing data on users’ interactions with the website, creating user profiles based on their preferences and interests, and then using various algorithms to suggest similar or related products to the user. A sophisticated and highly accurate recommendation engine effectively allows your site to act as a digital salesperson, suggesting items that a user will love without them having to search for items themselves. This greatly increases the likelihood of a higher average order value (AOV).

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Search engines on e-commerce websites

A search engine on an e-commerce or retail website works by indexing the products and product information available on the website. This indexing process involves analyzing the store’s content, including product titles, descriptions, and categories, as well as any metadata, such as product images and pricing. Once the products have been indexed, the search engine uses an algorithm to match a user's query with the most relevant products in the index.

When a user enters a query into the search bar on a retail website, the search engine uses the algorithm to find the best match for the query from the indexed products. The algorithm takes into account various ranking factors such as relevance, popularity, and user engagement, to return the most relevant products to the user.

Some search engines also allow users to refine their search results by applying various filters such as price, brand, and product features. This enables the users to sort the results based on different criteria such as relevance, popularity, price, etc. Depending on how sophisticated the search engine is, it may also be able to account for a user’s typos and still deliver what the person is after.

For this reason, the speed and accuracy of the search engine is of utmost importance. A long search time or irrelevant results are likely to irritate users, creating the danger of site abandonment.

An effective recommendation engine can increase average order value.

How does using a recommendation engine influence sales?

Here are some ways a recommendation engine can positively influence sales:

  • Increased customer engagement: By providing personalized product recommendations, a recommendation engine can keep customers engaged on a retail website and increase the likelihood of them making a purchase.
  • Increased AOV: By recommending complementary or related products, a recommendation engine can increase the number of items in a customer's cart, leading to an increase in the average order value.
  • Reduced bounce rate: A recommendation engine can help keep customers on a website by providing them with relevant product suggestions, which can reduce the bounce rate and increase the time spent on the website.
  • Increased customer loyalty: By providing personalized and relevant product recommendations, a recommendation engine can help create a positive customer experience, which can lead to increased customer loyalty and repeat purchases.
  • Bundling and upselling: A recommendation engine can help retailers cross-sell and upsell their products by suggesting complementary or higher-priced items to customers, which can increase sales
  • Inventory management: A recommendation engine can help retailers to optimize their inventory management by analyzing customer data and suggesting products that are likely to sell well in the future.
Author
John Calderon
Senior Manager, Marketing Communications

John has over a decade of marketing experience, with an emphasis on content creation across email, web, social, and white papers. He holds a Bachelor of Arts in English from UCLA.