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Answering your customers’ queries just got a whole lot easier thanks to a ‘did you mean’ feature, which allows users to find the information most relevant to them.

You’re counting down the days until you can put on your out-of-office and enjoy that long awaited holiday. Organising your schedule, you realise that your flight arrives early in the morning and you want to know the check-in time at your hotel. 

Contacting their Conversational AI chatbot, you ask what’s the earliest time you can check-in at that particular hotel.

“Our regular check in time is 2pm,” the assistant replies. 

You’re about to start exploring ways to kill time when a second message appears, offering three separate click-through options:

  • Would you be interested in an early check-in?
  • Would you like to learn about checking in online
  • How about a complimentary drink voucher at our hotel bar on arrival?

That’s convenient. And it’s only the start.

We’ve updated the Pattr NLU database with a ‘did you mean’ function and a super easy-to-use new interface, so that you can pump more knowledge into your customer conversations — and empower them with further search options if the first answer they’ve provided isn’t quite right, or you want to give options to help them find what they truly need (like the option of an early check-in or a complimentary drink).

In this scenario, the ‘did you mean’ function not only offers great customer service by providing the relevant information you need, but analyses what you’re asking and offers a range of extras to make your stay more comfortable.

This time, it’s personal

A study by research firm Forrester found that customers want to find information on their own, preferring knowledge bases over all other self-service channels. 

However, organisations often struggle to display the most relevant information to their customers who are searching for specific articles or information when using their chatbot. Traditional knowledge bases limit them to showing one closest matching article — and even then, there’s plenty of room for error.

In the event the first response served is not relevant, it is usually because their query is too vague, or there are closely related articles. This is where our ‘did you mean?’ function comes in — if the initial response isn’t right, Pattr can offer multiple articles to a customer, allowing them to locate the information they’re after quickly and easily. 

Returning to our hotel scenario, the options being offered are helping to solve any issues before they occur, and it’s saving everyone’s time dealing with what is a relatively straightforward, frequently asked question.

Confidence is key

Pattr’s new interface allows you to set the confidence threshold ratings for your customers’ searches, meaning that depending on your customer experience model and the nature of queries, you can offer a narrow or broad range of alternatives through the ‘did you mean?’ function.

If the first answer isn’t what the customer was looking for, Pattr scans the knowledge base to reshape the idea. It works with the expertise and preferences you have loaded into the system through the confidence ratings you have pre-assigned (and you can change through our new interface at any time).

Watch this space — we’re working on further enhancements to our NLU knowledge base, including updated analytics to support your decision making.

By expanding the functionality of our Knowledge Base App — and putting you in the driver’s seat with a brand new, easy to use interface — you can now manage responses to customers’ frequently asked questions. 

If you want to find out more about Pattr’s Knowledge Base App and how it can support your team, don’t hesitate to get in touch today with our Sales Team.

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Need to know lingo

What’s all this jargon?

Natural language understanding (NLU) is the branch of natural language processing (NLP) that handles unstructured conversations — the kind that humans make (complete with slang, spelling errors, and misunderstandings) — that computers don’t always understand. 

NLP processes text literally (it reads what was written), while NLU extracts context and intent (it reads what was meant).

A knowledge base (KB) is an organisation’s encyclopedia, containing information such as product guides and FAQs. In the context of Pattr Conversational AI, businesses can upload their ‘encyclopedias,’ creating a central place where their customers can find the information relevant to them via a conversation. 

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Pattr brings you closer to your customers.

A conversational AI SaaS platform to power, enable, enrich and understand conversations between you and your customers, in real-time and at scale.

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