In the search world, there are fancy terms for understanding searchers’ language to improve search results. Semantic search, natural language processing, computational linguistics, sentiment analysis, and so forth. These techniques should all be used to support one important principle: the customer’s language is always the right language.
Manufacturers and merchants have industry standards and internal product classification schemes, but customers search with a vocabulary of their own expressions and words that often do not match to existing product data. If left alone, these gaps between customer language and industry language mean that relevant products will not be shown in search. Which means they will not be purchased.
We’ve advised elsewhere to listen to what your customers are saying. It is crucial to find the gaps between customer language and industry language.
But after you listen, what then?
Product descriptions, titles, and metadata need to be mapped to the language of your customers, rather than forcing customers to guess at how products are described. This is important for general product searches and also for more specific searches that include attributes such as color, size, material, feature, seasonality, and so forth. A manufacturer or merchant may have a marketing reason to call something “cerulean” or “cloudless sky,” but customers will search for blue.
By ensuring that products are described with descriptions and attribute values that correspond to how people are searching for them, searchers will get relevant results. And in turn, customers will be assured that we are listening and engaging in a conversation with them.