The problem is that many retailers still rely on legacy search solutions to power their eCommerce experience. These older platforms and eCommerce search engines rely on text-based matching techniques and require manual rules to learn what products customers want. The result? Retailers can't keep up with the ever-changing customer preferences when maintaining these legacy platforms and miss out on both in-the-moment and future sales. A massive 82% of consumers say they avoid websites where they've experienced search difficulties.
With this in mind, retailers must adapt and deliver hyper-personalized product search and recommendation functionality. This means implementing a next-gen, AI-powered search experience that can understand natural, conversational queries and understands the customer's intent and purchase habits. These next-generation search engines go above and beyond by adding additional calculations such as relevance, product availability, buyability, and personalization to deliver results matching consumer preferences.
How Legacy Search Engines Work
As a result, legacy search engines often treat queries blindly, matching products to requests word-for-word without considering the customer's intent or context. For instance, someone searching for a date night dress might return results with a palm tree print (also known as a date palm tree) when they're actually looking for the quintessential little black dress. Though not wrong in the literal sense, those search results fail to consider customer intent and standard customer search behavior.
Because of this, legacy search engines often miss important context and nuances in a customer's query, leading to irrelevant search results and frustrating customer experiences. As such, they often fall short of providing the seamless and personalized search experience that customers now expect.
In addition, these search engines require hundreds, if not thousands, of manually maintained rules to deliver only somewhat-personalized results and recommendations. This method is so time-consuming that most brands can only optimize a fraction of their search queries. And even then, manual rules are inflexible and do not always match user intent. For example, two customers may enter the same search, but expect different products. Or they may be looking for the same products, and enter vastly different searches.
Newer versions of these legacy applications will often add a layer of artificial intelligence over top of these legacy engines to improve search relevancy and personalization. However, the base search engine is still this legacy technology which limits performance and, ultimately, results.
How Next-Generation Search Engines Work
Not only can they process and "think" faster due to the AI, but they can also consider context, understanding typical customer behavior, individual customer habits and customer intent, helping to deliver the right product to the right customer at the right time. These search engines can provide more accurate and relevant results by analyzing the context of a search query.
This is where our partner, Google, has gone above and beyond with their next-gen search engine. Using insights gleaned from their flagship properties such as Google.com, Google Shopping, and YouTube, Google has created the only next-generation retail search engine currently on the market. Google’s Retail Search Engine goes beyond keyword matching and simple relevancy, adding additional calculations to account for buyability of products and personalization – maximizing profit and customer experience at the same time. Considering these factors helps brands deliver search results matching the user's tastes and preferences. Google then arranges these results to maximize revenue, providing a personalized shopping experience designed to convert customer intent into sales.
Additionally, next-gen features like auto-complete and spelling correction help to deliver the most relevant and profitable items even when the customer uses misspelled query terms. This helps to reduce the friction of the search experience and ensure that customers find the products they are looking for quickly and easily.
Next-Gen Search Considers Retailer's Needs, Too
Next-generation search engines also use algorithms that are capable of considering various parameters like colors, brands, prices, size, and seasonality. This provides an even more personalized shopping experience for customers, which is proven to increase both overall conversion rate, as well as per-visit revenue. By delivering search results that match the user's tastes and ranking them to maximize revenue, these search engines help retailers stay ahead of the competition and drive sales.
Overall, next-generation search engines offer numerous advantages over legacy search engines, especially a superior, personalized online shopping experience for customers that improves back-end efficiency and drives sales.
Embracing The Rise of Next-Generation Search Engines
AI-based solutions like GroupBy’s Product Discovery Platform powered by Google Discovery AI take advantage of the vast amounts of data consumers create today and help retailers leverage it to its full potential. Designed for B2B and B2C eCommerce, our platform understands true user intent and delivers relevant and buyable search results for even the broadest of queries.
It leverages Google's decades of advancements in query understanding and semantic search capabilities to minimize shopping friction while maximizing revenue. As a result, it can continuously adapt and self-optimize based on changing trends, product availability and seasonality.
Next-gen search solutions not only benefit retailers and consumers, fostering extraordinary digital customer experiences and significant top-line revenue gains, but are the future of retail and a key to both sustainability and longevity for both B2B and B2C brands worldwide.
