Editorial archive of Creangel. The references, figures and conditions correspond to the original publication.
Many times we use search engines to directly enter search words or keywords in the box according to our usual inertia. This often gives us billions or millions of results that do not match the results we seek. What many do not know is that they can avoid this by using the boolean search. Boolean is a type of search that allows users to use syntax as quotes and operators (or modifiers) such as quotes, AND, NOT and OR to produce more relevant results. However, not all people have this knowledge and what they want is to find a product. It is a risky strategy to assume that customers must also know Boolean to achieve a relevant search. Large consumer sites such as Amazon, Walmart, Dell and Wayfair have extinguished that knowledge.
When buyers know what they want and cannot find it, they feel frustrated. Searching in the catalog using only the match of keywords does not help customers who misspelling words, do not use the correct language or do not know the product number. The search box can be configured to help customers find what they are looking for by providing relevant options even while writing.
The default internal search engine of any ecommerce or corporate website is very limited. When trying to search for products without the exact name, with spelling faults, by synonyms or with some specifications the result will not be relevant, or will not be found, what will be found, will be hundreds of answers and results, that will make the sale much slower, giving the user a negative experience and the only way to achieve the expected results is to use boolean search operators to expand or reduce your search results which is not an option. Buyers do not have much patience and seek immediate response and experiences. If after doing one or two searches and they have not yet found the product they want to desist from the purchase and there is nothing more painful than losing a customer interested in a product not by quality, nor by price, but because it is not found on the shopping platforms. The imminent solution for these cases is the implementation of an Advanced Finder by failing to offer generic sales experience.
With IFINDIT Smart Finder you can offer customers just what they are looking for on their platform. Intelligent search is a broad term that refers to machine learning (ML)-driven search systems, natural language processing (NLP) and artificial intelligence (AI). It covers the search for semantic vectors, intelligent search and cognitive search. By combining these technologies, intelligent search can intuit what a user is looking for by taking into account their objectives, history and the theme of their search. In other words, smart search speeds up a customer's ability to find what they're looking for.
The Smart Search is based on a combination of the above technologies to create the most accurate image of what users are looking for. Processing the natural language allows the intelligent search to understand the search terms, even if they are not exact click queries.
AI and machine learning combine to determine the context in which a user looks for something through a notice, taking into account their history and how they report their future goals. These brands can create highly customized digital shopping experiences, replicate the experience in the store, or train service agents to explore their knowledge base and find the materials they need to deliver high-quality customer service on the go.
The intelligent search is very personal and therefore depends on the user. However, any smart search strategy has some features to consider: It should be conversational. A search session is not usually a single query or set of answers. It is a constant conversation about understanding the overall picture. Users often have a specific objective and may not have an adequate search term. It also fills these gaps by understanding how people often speak and respond properly and delivers accurate results.
The search must be natural. Similar to the previous point, people do not think in terms of “search queries”. They want to be able to simply enter the words that will convey what they are looking for and hopefully find it in the results. The days of empty words were left behind. Technologies such as NLP make it easy for systems to understand what a user is trying to transmit when communicating in the language they are familiar with, and ML makes it easier for systems to improve that communication.
Smart search needs customization. Everyone has a unique set of goals when they go on a mission. With virtually unlimited answers to a single question, personalization makes intelligent search a meaningful experience that makes users feel understood. Customization is possible if the system has a complete user image: your search history, search frequency, etc. These small data form a precise picture of the intent they might be looking for. It should be acceptable. To run a natural and personalized intelligent search system, brands must listen to their users, understand their true intentions and be able to address those true intentions.
In conclusion, taking advantage of smart search sales, the three main reasons why buyers are loyal to a brand are excellent product recommendations that understand who they are and what they like. The key to a personalized experience is to use first-hand data to draw conclusions during a buyer's visit to understand their goal at that exact time and offer the best solution, whether guiding them to relevant products, practical guides or customer service. Brands should be able to provide this level of customization from the search bar implemented with IFINDIT.