One of the major trends in e-commerce in recent months has been product search. The many techniques that have been developed to improve the relevance of searches now lead us to look at this area as combining two challenges: responding in a relevant way to the customer's request, while at the same time helping them discover products that they were not necessarily looking for, but that are likely to appeal to them and meet the retailer's objectives.
Product discovery goes far beyond simple product search; it is a comprehensive process that improves the user experience on an e-commerce platform while maximizing sales opportunities.
Several components must be combined to engage the customer in product discovery :
Intelligent search :
On e-commerce platforms, intelligent search engines not only match keywords to products, but also understand the context of the search. Thanks to AI, they are also able to leverage the intent behind searches and purchase intent. For example, when someone searches for a bicycle, it may be better to present a BMX rather than a city bike, depending on the intent the engine has detected.
Product recommendation :
Product discovery also includes product suggestions based on browsing history, past purchases, and explicit or implicit user preferences. The goal is to display products that the user might buy in addition to what he or she is already looking for.
This recommendation can include cross-selling and upselling mechanisms to suggest complementary products or products from a higher range than the product they are initially looking for.
For example, a user whose interest in running has been identified could receive suggestions for sportswear or training equipment when visiting a footwear product page to encourage them to complete their purchase.
Assisted navigation :
Product discovery improves navigation by offering facets and filters that dynamically adapt to the user's context. This reduces search time and facilitates access to relevant products.
Product categorization can also contribute to product discovery by making navigation more intuitive and aligned with the way users think. For example, a site could group products by "use" or "lifestyle" rather than by technical categories, which may not be the way consumers like to browse the catalog.
Ranking management :
Products are discovered through search results, whether they are found through the search engine or through navigation. The art of product discovery is therefore to optimize the display of products on list pages by defining which products should appear first.
Depending on the site's commercial strategy, the choice may be to highlight certain brands, overstocked products, or those with the best margins. All this, of course, without forgetting the possibility of hyper-personalizing the results by showing each customer, from the very first results, the products in which he or she is most interested.
These numerous parameters make manual rule-based management difficult. For some sites with a large product catalog, the number of combinations is such that manual management is impossible. This is where AI can be used to intelligently manage product discovery, optimizing results and saving time for e-merchants.
This set of practices and technologies can therefore improve the way products are presented to customers, transforming a passive search into an active and engaging discovery. This is an essential development for e-commerce sites that want to remain competitive and maximize customer satisfaction. But today's technology platforms don't necessarily include all the features needed to take advantage of product discovery. In fact, the true value of product discovery is only realized when combined with personalization techniques throughout the customer journey.
Choosing a specific solution for each of the product discovery capabilities to address the shortcomings of e-commerce platforms runs the risk of complicating the IS, multiplying the number of vendors, and failing to deliver a consistent, personalized customer experience.
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