Which On-Site Search Changes to Watch Most Closely
E-commerce site search has been evolving dramatically recently, moving away from a legacy keyword-driven approach, that often yielded inaccurate and confusing results, towards a leaner, more intuitive approach that puts user experience at its heart.
The adoption of several worthy to mention technologies played a fundamental role in this evolution.
NLP and Self-Learning Technologies
Natural language processing (NLP) has already started to make a big impact in search, and its take-up is likely to accelerate in the coming years. NLP takes search away from the simplistic “is this keyword present in the title or description” approach, and starts asking “what is the customer really asking for?”. By taking a semantic angle to evaluating search results, the results produced are far more accurate and relevant to the customer’s actual search intent. Factor in the self-learning capabilities of many NLP-based engines, and suddenly site search starts to look very different.
In the past, keyword-based search algorithms have often led to something of a ‘chicken and egg’ situation, with website visitors trying to ‘guess’ what terms they need to enter in the search box, in order to get the results they want from their search.
Clearly, this is not an ideal approach, and, historically, it has led to high levels of frustration for information seekers.
Natural language processing (NLP) is a component of artificial intelligence (AI) that enables computer programs and functions to understand human speech as it is spoken. In commerce-oriented websites and apps, NLP supports meaning-based search, allowing shoppers to search for items in their own language while still producing relevant results, even if the search terms do not directly match keywords in product records.
All of this is possible because of NLP-based search, switches the focus from keywords to the actual meaning. Taking a semantic approach means that search results have a ‘connection’ to the search terms, rather than having to actually contain those search terms.
Therefore NLP search can deliver accurate results and a successful user experience, where traditional text-based searches would typically fail.
NLP and Linguistic Nuances, Synonyms, Misspellings
NLP in site search must be able to recognize similar (though not identical) search terms, based on individual searchers unique lexicon preferences and context. NLP must be able to identify these items as being one and the same thing, producing the same relevant results regardless of the exact terminology being used.
Most sites can’t afford to lose conversions to human errors. NLP can help ensure that a misspelled search for “red jacket” will deliver the same relevant results as a correctly typed search.
NLP search can also outperform traditional search by learning about common miss-spellings, mixed-up brand names and other potential issues that could be entered into the search box. With self-learning abilities, NLP search never stands still, and continually becomes more accurate and better able to ‘understand’ customer intent.
Conclusion
Applying NLP to search can be incredibly powerful because it switches the focus from keywords to actual meaning, allowing humans to be humans while a machine does the work of accurate, intent-based interpretation. When applied to text-based site search, the NLP capabilities described above can play a key role in creating the kind of frictionless, seamless interactions that drive conversions, in a way that antiquated text-based searches simply can’t hope to.
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