In the world of e-commerce, the search bar is often viewed merely as a tool for users to find products. However, this feature has historically fallen short of expectations, providing subpar results and causing frustration. Retailers are now undergoing a significant transformation of their search functionalities by harnessing the power of artificial intelligence (AI) to create more user-friendly and efficient shopping experiences.
The typical customer experience when using a traditional search bar is riddled with obstacles. For instance, a search for “black straight leg jeans” on a popular retail site might yield unexpected results, such as blue jeans or unrelated styles. Such inaccuracies can lead to a high bounce rate as customers abandon their search in frustration, ultimately hurting sales. Malte Ubl, a former Google executive and current CTO at Vercel, states, “The search world was really optimised for ultra-large corpora. That’s what those algorithms work at.” This observation highlights the flaws inherent in existing search technologies, which struggle to address the nuances of retail environments.
To combat these challenges, brands like Revolve and Vestiaire Collective are refining their internal search algorithms to meet evolving consumer expectations. Alongside these efforts, promising tech providers such as Constructor, Lily AI, Vantage Discovery, and Nosto are stepping in to offer advanced solutions that enhance e-commerce search capabilities. This trend has proven attractive, with PitchBook reporting that search-as-a-service start-ups have garnered approximately $1.7 billion in venture capital funding since 2019.
Despite the growing focus on upgrading e-commerce search capabilities, there remains a disconnect between retailer perceptions and customer experiences. A Deloitte Digital survey revealed that while 79% of brands rated their search and discovery features as good or excellent, only 63% of consumers agreed with that assessment. Eric Bellomo, an analyst at PitchBook, emphasizes that bridging this gap necessitates financial investment to improve search functionalities, making it more reliable and efficient.
The impact of search effectiveness on sales cannot be understated. For example, while only 10% of users resorted to the search function on specific apparel sites studied by the Baymard Institute, those engaged in text search were crucial to sales at Vestiaire Collective, where a staggering 40% of orders stem from text searches. Such statistics underscore the vital role that an intuitive search feature plays in driving e-commerce success.
AI technologies are at the forefront of transforming how consumers locate products online. Retailers like J.Crew are collaborating with firms like Lily AI to optimize their product data. This partnership aims to ensure that the language used in product descriptions aligns with what customers search for. For instance, a retailer might identify a shade as “oxblood,” while customers might simply search for “red.” Advanced AI systems enhance product discoverability by leveraging machine learning and computer vision, leading to an increase in clicks, impressions, and conversions.
The breakthroughs in generative AI significantly augment this shift. Large language models, such as those that power ChatGPT, excel at predicting user intent and understanding contextual queries. Nigel Daley, COO at Vantage Discovery, encapsulates this transformation, stating, “This is the perfect use case for AI to clean up and understand your product catalogue.” By improving the matching process between user queries and product offerings, businesses can deliver a more personalized shopping experience.
Personalization is a key focus for modern e-commerce platforms. Vantage Discovery enables visual search capabilities alongside personalized results based on users’ browsing habits and past purchases. This boosts user satisfaction and increases the likelihood of conversion. For example, if a customer frequently buys activewear, searching for “blue shirt” might yield results more aligned with their shopping patterns rather than generic options.
Open-source models offer an alternative approach for brands looking to enhance their search features independently. However, integrating these technologies can be resource-intensive; many retailers depend on standardized e-commerce solutions with basic search functionalities. Seamless integration requires technical expertise, which can be a challenge for smaller businesses. Fortunately, partnerships with tech providers like Vercel can facilitate this process.
In the competitive landscape of e-commerce, Vestiaire Collective has proactively unveiled a new search engine specifically designed to translate keyword searches into image pattern recognition. This system allows users to visualize matching results and has led to significantly improved accuracy and relevance in item recommendations. Highlighting the importance of continuous enhancement, Revolve recently transitioned to an internally developed AI search algorithm after years of relying on third-party technology. The results showed improvements in revenue per search while reducing operational costs, demonstrating the potential return on investment from investing in search technology.
As the e-commerce sector continues to adapt to changing consumer expectations, the evolution of the search bar will be instrumental in enhancing the overall shopping journey. If done successfully, companies can create a search experience that no longer feels like a last resort but rather becomes the preferred method for customers to find exactly what they are looking for efficiently.
The future of e-commerce is set to be brighter, with AI-driven search capabilities promising to transform how consumers shop online. As brands increasingly recognize the significance of this essential tool, customers can anticipate far more satisfying online shopping experiences.
