AI is helping brands move faster — streamlining work, improving experiences, and opening up possibilities that were out of reach just a few years ago.

And it still starts with the website. For many businesses, it’s where customers go to explore, ask questions, get support, and make decisions. More and more, they expect to do that in their own words — not by learning the language of the site.

Natural language processing (NLP) helps make that possible. It enables websites to understand language more like people use it in real life, improving experiences across search, chat, forms, support, personalization, and content.

This post will cover:

• What is natural language processing?
• Why NLP matters for your website
• Which parts of a website use NLP
• How to implement NLP on your site

What is natural language processing?

Natural language processing is the part of AI that helps computers understand, interpret, and generate human language — the way people actually speak and type, not the rigid phrasing older systems depended on.

In practice, NLP looks at how a request is put together — the words, phrases, grammar, and meaning — to understand what someone is really asking for. That’s what allows a search experience to recognize “blow dryer” and “hair dryer” as the same request, or understand that “cheapest flights to Chicago this weekend” includes a product, a destination, and a time frame in a single sentence.

Why NLP matters for your website.

People expect a brand’s website to work more like the AI assistants and search experiences they use every day: ask a real question in natural language, and get a response that reflects real understanding.

That expectation extends beyond search. It influences whether chat resolves an issue, whether support content appears at the right moment, and whether recommendations feel helpful or generic.

When a website falls short of that expectation, the impact adds up quickly: support journeys get abandoned, chat sessions end without resolution, and customers leave when a form, FAQ, or help experience can’t understand a simple question. In many cases, that also means support teams take on issues that better self-service could have resolved.

Baymard Institute's latest ecommerce Search UX benchmark found that 56% of sites fail to adequately support users’ search needs, and that 20% of sites have issues with "Product Type" searches.

Constructor's analysis of ecommerce searches found that site-search users convert at roughly 2.5 times the rate of non-searchers, and drive 57% of ecommerce site revenue despite representing only 25% of traffic.

There’s also a longer-term advantage: the language customers use across search, chat, support, and feedback is valuable data. It shows what people are trying to do, what questions they have, what features they’re looking for, and where they may be getting stuck.

NLP helps businesses understand language at scale across the customer journey. Those insights can improve more than search relevance — they can inform content strategy, product decisions, and improve user experience over time.

Which parts of a website use NLP.

Site search is usually the most visible example, but it’s not the only one. NLP can improve many parts of a customer-facing website, from discovery to support to ongoing optimization.

• Site search: Helping people find what they need using natural language
• Chatbots and virtual assistants: Making support and pre-sales interactions feel more useful and responsive
• FAQ and knowledge base matching: Connecting customers to the right answers even when they phrase questions in different ways
• Content tagging and categorization: Structuring content so it can be found, connected, and recommended more effectively
• Sentiment and feedback analysis: Turning reviews, transcripts, and feedback into insight about customer needs and friction points

How to implement NLP on your site.

Once a business decides to invest in NLP, the next question is how to implement it.

Building in-house often means adding NLP capabilities to a website's existing search infrastructure. It offers more control over how results are tuned and how the experience behaves. But it also requires ongoing investment in development, testing, and maintenance to keep pace as language, customer behavior, and content evolve.

Buying a platform means using a vendor solution that provides search and relevance capabilities, often with personalization features, out of the box. It can accelerate time to value and reduce the engineering effort needed to run the core system. The tradeoff is usually less control over the underlying platform, even if teams still retain meaningful control over tuning, rules, and merchandising.

Whichever path a business chooses, there are a few practical questions worth asking:

• How well does it handle typos, synonyms, and natural variations in phrasing out of the box?
• Does it support the languages and markets the business operates in?
• How are search and content insights shared with marketing and merchandising teams, not just engineering?
• How well does it integrate with the existing CMS, commerce platform, and content model?

Get started with NLP on your website.

If you want website experiences to better understand customer intent, the foundation matters as much as the interface.

Adobe Experience Manager Sites helps teams create structured, reusable content and deliver it across channels and experiences at scale — supporting better search, more connected content experiences, and a stronger foundation for AI-powered interactions.

Learn more about Adobe Experience Manager Sites.

https://business.adobe.com/fragments/resources/cards/thank-you-collections/experience-manager-sites