We often use search engines by typing search terms or keywords straight into the box out of habit. This usually returns thousands or even millions of results that do not match what we are actually looking for. What many people do not know is that they can avoid this by using Boolean search. Boolean is a type of search that lets users apply syntax such as quotation marks and operators (or modifiers) such as AND, NOT and OR to produce more relevant results. However, not everyone has this knowledge, and what they want is simply to find a product. Assuming that customers must also know Boolean to get relevant results is a risky strategy. Large consumer sites such as Amazon, Walmart, Dell and Wayfair have done away with the need for that knowledge.
When shoppers know what they want and cannot find it, they get frustrated. Searching the catalog using keyword matching alone does not help customers who misspell words, do not use the right terms or do not know the product number. The search box can be configured to help customers find what they are looking for by offering relevant options even as they type.
The default internal search engine of any e-commerce store or corporate website is very limited. When users try to search for products without the exact name, with spelling mistakes, using synonyms or with a few specifications, the results will not be relevant or the product will not be found at all. Instead, they will get hundreds of answers and results that slow the sale down considerably and give the user a negative experience, and the only way to get the expected results is to use Boolean search operators to broaden or narrow the search results, which is not an option. Shoppers have little patience and expect immediate answers and experiences. If after one or two searches they still have not found the product they want, they will abandon the purchase, and there is nothing more painful than losing a customer interested in a product not because of quality or price, but because it could not be found on the shopping platform. The obvious solution in these cases is to implement an Advanced Search Engine and stop offering a generic sales experience.
With the IFindIT intelligent search engine, you can offer customers exactly what they are looking for on your platform. Intelligent search is a broad term for search systems powered by machine learning (ML), natural language processing (NLP) and artificial intelligence (AI). It encompasses semantic vector search, smart search and cognitive search. By combining these technologies, intelligent search can infer what a user is looking for by taking into account their goals, history and the subject of their search. In other words, intelligent search streamlines a customer’s ability to find what they are looking for.
Intelligent search relies on a combination of the technologies above to build the most accurate picture of what users are looking for. Natural language processing allows intelligent search to understand search terms, even when they are not exact-match queries.
AI and machine learning work together to determine the context in which a user searches for something through a prompt, taking into account their history and how it informs their future goals. With it, brands can create highly personalized digital shopping experiences, replicate the in-store experience or enable service agents to explore their knowledge base and find the materials they need to deliver high-quality customer service on the fly.
Intelligent search is highly personal and therefore depends on the user. However, any intelligent search strategy has a few characteristics to consider. It must be conversational. A search session is rarely a single query or a single set of answers. It is an ongoing conversation about understanding the bigger picture. Users often have a specific goal in mind and may not have the right search term. Intelligent search fills these gaps by understanding how people usually speak, responds appropriately and delivers accurate results.
Search must be natural. Similar to the previous point, people do not think in terms of “search queries.” They simply want to type the words that convey what they are looking for and, hopefully, find it in the results. The days of empty keywords are gone. Technologies such as NLP make it easier for systems to understand what a user is trying to convey when they communicate in the language they are familiar with, and ML makes it easier for systems to improve that communication.
Intelligent search needs personalization. Everyone has a unique set of goals when they set out on a mission. With virtually unlimited answers to a single question, personalization makes intelligent search a meaningful experience that makes users feel understood. Personalization is possible when the system has a complete picture of the user: their search history, search frequency and so on. These small pieces of data form an accurate picture of the intent behind what they may be looking for. It should be actionable. To run a natural, personalized intelligent search system, brands must listen to their users, understand their true intentions and be able to address those true intentions.
Finally, when it comes to leveraging the advantages of intelligent search, the top reasons shoppers stay loyal to a brand come down to excellent product recommendations that understand who they are and what they like. The key to a personalized experience is to use first-party data to draw conclusions during a shopper’s visit, understand their goal at that exact moment and offer the best solution, whether by guiding them to relevant products, how-to guides or customer support. Brands must be able to deliver this level of personalization from a search bar powered by IFindIT.