AI's Stealthy Influence on E-Commerce Search and Product Exploration
In the era of dominant search engines like Google and the emergence of intelligent assistants such as ChatGPT, we've come to expect high-quality search results with minimal effort. We've grown accustomed to having information readily available at our disposal, but this convenience veils the intricate processes that help us find what we're seeking.
Typically, search is overlooked or undervalued in the realm of e-commerce. However, optimizing this aspect can significantly impact your business. By successfully matching customers with relevant products, conversions increase, while trust and loyalty towards your brand flourish as customers understand their relevance to their specific needs. Artificial Intelligence (AI) is driving a revolution in this field, with major players like Amazon and Shopify implementing AI-powered search to personalize results and categorize products beyond basic keywords.
It's important to view search as a two-way street, connecting your products to customers. AI greatly enhances our comprehension of both customer preferences and product offerings, enabling a more effective search process that bridges the shortest possible gap between the two entities.
Stepping Beyond Keywords
Historically, search has relied on keywords, presenting results that closely match the search terms chosen by the user. However, this method has several limitations. Firstly, it necessitates precise classification of products and expectations from customers about their exact search parameters. While keyword search remains essential, it is notorious for a failure rate of up to 17% in initial searches and an excessive percentage of irrelevant results.
For instance, in the context of an online fashion brand or food delivery service, keyword search can be useful but it fails to account for the diversity of customer preferences and requirements. Brands must consider broader categories and frequently used phrases like 'healthy food,' 'quick delivery,' or 'relaxed fit,' ensuring their search is adaptable to various customer needs.
At Atom.com, our focus lies in brandable domains. Owing to their distinct naming styles, discovering these categories is less straightforward than keyword-driven searches. The primary appeal of our domains may lie in their metaphors or emotive connotations, often distanced from direct keyword relevance. Across e-commerce, brands may encounter product categories with unclear keyword relevance, necessitating an intelligent search algorithm that can decipher underlying consumer intentions to present complementary products.
AI Transforming Search and Discovery
In September 2024, we integrated AI into our search platform, significantly improving results by continuously refining it. By training our AI models to mimic buyer behavior, we were able to identify and eliminate irrelevant results, thereby ensuring that outcome aligns with buyer expectations. As a result, our search engine has seen remarkable improvements - 17.4% better user engagement and a 14.6% increase in conversions. The following are the ways AI revolutionizes search and discovery:
Deep Classification
AI enables more comprehensive and nuanced keyword classification than was previously possible by human intervention. Our buyers often begin by searching for a broad market sector, such as the fashion or beauty industry, to build and establish their brands within these categories. With countless domain names in these markets, robustly recommending the most suitable names is practically impossible, even with an extensive lexicon of root keywords.
We employed AI to build a detailed set of interrelated, relevant keywords, including themes, emotions, and unique naming styles. This implies that when a buyer conducts a search using keywords like 'sustainable clothing brand' or 'natural skincare brand,' our search engine prioritizes discovering relevant names such as 'PurityCompass,' which aligns with the brand's intended goals without being rooted in direct keywords. Instead of delivering names primarily built around the user's search terms, our AI-driven search process returns an intuitive and contextualized solution based on buyer requirements.
Deep classification enables better discoverability, even with less specific search queries. By providing customers with diverse yet relevant options, you gain insights into their search behavior, permitting more precise personalization and accurate segmentation.
Synergy With the Buyer Journey
Deep classification is only one facet of enabling better discoverability. The other component is user behavior and searches, which should be synergized with the classification data. By understanding how buyers browse your marketplace, you can create meaningful relationships based on their real-world search terms. In most industries, it is likely that a customer will return to use your search function more than once before making a purchase, or even after the initial conversion.
The data collected from these interactions can be used to create detailed, dynamic user profiles, revealing insights into their search preferences, preferences, and search behavior. For example, if a user searches for a specific keyword and shortlists five different names, our AI algorithms begin building dynamic associations based on search intent and domain attributes. With a monthly visitor count of 3 million unique users, we gather and utilize these insights to enhance the search experience across the board.
Deep classification is critical, enabling your search algorithm to provide highly relevant responses even when users search with imprecise keywords. Furthermore, by offering users diverse yet relevant products, you learn more about their behavior, enabling individualized personalization and accurate segmentation. AI's advanced machine learning capabilities allow for dynamic user profiles to adjust in real-time, providing ever-improving search results that align with a user's preferences and requirements at each stage of their buying journey.
A successful search and find process necessitates a two-fold strategy. Initially, a more intricate and sophisticated categorization of your products. Secondly, grasping your consumers' search intentions and preferences.
When both of these aspects are mastered, search transforms from a mere gamble for your customers into a precise and efficient instrument for connecting the appropriate items with the right buyers. Search should be capable of catering to customers at various phases of their buying journey, providing suitable and appealing results for those with varying levels of familiarity and information about your stock. It can serve as the final step in locating and purchasing a product or as a mid-process assessment of your offerings, making it an indispensable part of your e-commerce operations!
In the realm of online businesses, optimizing AI-powered search is crucial for matching customer preferences with relevant products. This can lead to increased conversions and customer loyalty, as customers perceive their needs being met effectively. For instance, an online fashion brand might need an intelligent search algorithm that can account for diverse customer preferences beyond simple keywords, such as 'healthy food' for a food delivery service or 'relaxed fit' for clothing.
In the quest for unique domain names, Atom.com uses AI to decipher underlying consumer intentions and present complementary options. This approach can be particularly beneficial when dealing with less specific or unclear keyword relevance, making the search process more intuitive and contextualized for buyers.
