Imagine it’s pre-Amazon times, and you’ve wandered into a large bookstore with stacks and stacks of books. This could be a dream come true — or an overwhelming nightmare. Even if you know what you’re looking for, it will be incredibly time-consuming to search through each individual shelf. You need the next cliff-hanging installment of your favorite book series, stat.
A friendly clerk approaches you and enters your book list into the system using keywords, quickly pulling out matching titles. And after she guides you to the right section in the bookstore, you happen across other interesting, similar titles. So you grab one of those, too.
In a brick-and-mortar nutshell, that’s product search and discovery. In ecommerce, differences between the two concepts can make or break a customer’s shopping experience. It boils down to more than just terminology, and acting on the nuances of each can make a major impact on business KPIs, like conversion rates and customer retention.
Let’s take a deeper look at product search vs. product discovery in ecommerce, including how to provide the best customer experience while saving your merchandising team’s precious time.
Also known as the “using the search bar,” product search involves typing keywords, phrases, or product names into a site’s search function, allowing shoppers to pinpoint specific items within your site’s product catalog. It makes the shopping experience more streamlined and user-friendly.
Search is just one part of the entire product discovery experience. And since almost half of users go directly to the search bar when landing on a site, it’s a major factor in driving (or preventing) conversions.
Since it’s such a big deal for shoppers, a lot goes into getting it right.
The end goal of product search is to help users find products they want more quickly and efficiently. This is possible thanks to various AI models and algorithms that are built to improve precision and recall, or the ability to return a wide breadth of attractive product results, and understand user queries better.
One such branch of AI is known as natural language search, or “conversational search.” This model lets users perform their search in everyday language, which is a more humanized approach. So, for queries such as “show me a yellow summer dress for less than $40,” the NLP search engine would return a list of yellow summer dresses under $40.
It’s also language-agnostic, as it has the ability to identify new relationships between words. This allows global ecommerce companies to power search in different languages other than English.
Because of these reasons (and more), NLP is just one of many technologies that are paving the way for the future of product search and discovery.
Several features of product search also make the experience more enjoyable for the end user. One such feature is called faceted search, which allows shoppers to filter results to further refine their search. Each facet represents a specific product attribute, like price range or color. By selecting options within different facets, users can narrow down their search results to find an item that suits their needs and preferences without getting bogged down in dozens of irrelevant results.
When searching for perfume gift sets, it may be useful to provide a facet for "fragrance family."
At the end of the day, product search is all about making the search experience convenient to increase conversions. Another search feature that can do just that is autocomplete.
Also known as predictive text, autocomplete provides users product suggestions in real time as they type. When paired with tried-and-true UX optimizations, a robust autocomplete solution can help inch ecommerce businesses closer to hitting their KPIs. And when paired with Generative AI (GenAI), the search experience can become even more robust as autocomplete flawlessly suggests items that align with the shopper’s needs, tastes, and intent.
When integrated into the search bar, GenAI seamlessly acts as a style assistant, as seen for the query “show outfits for an outdoor wedding in 90 degree weather.”
This search feature exposes customers to new products and promotes bundling, which increases average order value (AOV) and revenue. And it leads to a more guided experience, which is characteristic of product discovery.
Product discovery is a more exploratory, passive process than search. It’s like wandering into a grocery store without a list. Many shoppers would know what they like and want, but some may not have any specific recipes nor ingredients in mind.
Because of this, product discovery can be particularly effective for impulse buys and increasing AOV. After all, sometimes all it takes is just being in a fully-stocked baked goods aisle to end up with one too many cookies.
Product discovery is all about guiding customers to products they might not have been explicitly searching for, but are likely to purchase. Many ecommerce strategies help foster a seamless product discovery experience, such as curated recommendations, featured products, or categories.
Recommendation pods are personalized to users on a 1:1 basis, thanks to first-party behavioral data and advanced machine learning that starts personalizing as soon as a user clicks or searches on your website. The algorithms continuously learn from user interactions, which improves the accuracy and relevance of recommendations over time to support your established KPIs.
Returning complementary items can drive conversions and larger basket sizes.
Depending on the recommendations pod strategy implemented, recommendation pods can enhance the product discovery experience, expose customers to a greater assortment of products in your catalog, and more.
Interactive experiences, like product recommendation quizzes, can further personalize product recommendations and the entire product discovery experience. Their implementation has benefits for customers and ecommerce businesses alike:
This international fashion & apparel retailer uses Quizzes to help shoppers find the perfect pair of leggings for their lifestyle.
Last but not least, and just as faceted search makes Search tick, faceted navigation is key to enhancing the product discovery journey as well.
Faceted navigation applies to the filterable items in Browse, or category pages. These facet groups can better guide customers through a vast array of products, helping them find what they need quickly and efficiently while also discovering new items.
In the “Toys Best Sellers” category page on Kmart Australia’s website, shoppers can filter down results by product type, category, price, size, and more facets.
Product search and product discovery are essentially invisible tour guides for users during their shopping journey. While product search helps users find specific items quickly, product discovery introduces them to new products and encourages exploration. When these two elements work in tandem, they create a seamless and effective shopping experience that benefits both customers and businesses.
Integrating product search and product discovery is essential to:
Investing in AI-powered solutions is a strategic step toward building an efficient, synergistic approach for ranking products site-wide. Having a capable merchandising team is also hugely important. After all, it’s up to merchandisers to identify where to position products in a way that’s attractive and good for business goals. So, don’t hold back in empowering your team with the proper tools that make their jobs less manual.
With AI-native Merchant Controls, merchandisers have the necessary tools at their fingertips to employ a variety of strategies to make the shopping experience more enjoyable while supporting business goals.
When paired with Merchant Intelligence, teams can be even more strategic about product rankings, basing their actions on hard data to make more informed business decisions.
See how Constructor’s holistic suite of product search and discovery tools can empower merchandisers and product managers to consistently create personalized shopping experiences that don't just improve the product discovery experience, but drive critical KPIs.