Users now expect their experiences to be more engaging, personalized, and effortless. There is huge potential here to give your users what they want: a more intelligent, more engaging experience that understands their intent. If you wanted to add something more complex, like a comprehensive interactive user guide, that option is also available – albeit more complicated to implement.īy leveraging AI and machine learning, these two services function together to provide your users with a smart chatbot that learns and becomes better at responding with every interaction. In its simplest form, using these together allows you to create a chatbot that can answer FAQs directly, without users having to search around for the information. When a user asks a question or makes an inquiry, LUIS will parse what is being asked in the chat and can answer it based on the content generated by the QnA maker. The combination of the two allows you to provide a smart chatbot within your app.
You can publish the FAQs as an endpoint via an API that is provided. It extracts all the data from a variety of sources – editorial content, comments, documents, FAQ URLs – and pairs potential questions and answers. The QnA Maker API crawls your content and creates FAQs. Like other Cognitive Services, LUIS learns and improves as it receives more data, meaning it becomes smarter with more interactions. It allows you to develop your own language models and also comes with a number of pre-built language models that can be implemented directly. The Language Understanding Intelligent Service (LUIS) works to help your mobile app or bot understand the intent behind user actions and commands in order to provide the right response. What is the Language Understanding Intelligent Service? This is where Azure’s Language Understanding Intelligent Service and QnA Maker come into play. Today, rule-based chatbots simply don’t offer the experience users expect. As Octane AI CEO Matt Schlicht points out, chatbots that function based on AI and machine learning are far more sophisticated than those based on rules, able to understand language and get smarter based on the conversations they have. However, there is a right way to do things. Human-computer interaction via chatbots is a promising solution. With human operators, this is highly-labor intensive, expensive, and often impractical. Your customers want to use messaging to interact with your business, and they want communication lines open 24/7. It’s no surprise then that Ubisend’s 2016 Mobile Messaging Report found that 51% customers think businesses should be available 24/7. They want to be able to do things when they want. Users have access anytime, from anywhere. Third, the nature of mobile apps invites immediacy. In fact, a survey by BI Intelligence found that almost 60% of users between the ages of 18 and 55 have used chatbots. Second, the adoption rate for chatbots is already high. Offering a channel for them to interact with your brand in a way they prefer is only logical. And many prefer to interact with your business this way – 45% of consumers claim they would rather use messaging than email to contact a business, and almost 50% preferring messaging over phone ( Ubisend’s 2016 Mobile Messaging Report ). Your customers are already familiar and engaged with this medium. Messaging apps like WhatsApp are exhibiting some of the highest growth of any app category. Image by Sabrina Chowdhury via Flickr Chatbots and Human-Machine Interactions In Mobile Appsīefore I dive into Cognitive Services, I want to give context on why chatbots and human-machine interaction are important for building smarter, more engaging mobile apps.įirst, usage of messaging apps has surpassed usage of social media apps. Specifically, I will be discussing the Language Understanding Intelligent Service and the QnA Maker API, and how they can be used together to enhance your mobile app. This time, I will be focusing on Microsoft Azure’s Cognitive Services Language and Knowledge APIs in relation to chatbots and automated customer service in mobile apps. Due to advances in artificial intelligence, big data, and machine learning, this is becoming more easily possible. In other words, your customers want apps that are smart. Users want effortless interaction experiences that are catered to them and apps that understand intent with less input and fewer taps. I highlighted 3 APIs out of the many that are offered: Custom Decision Service, Content Moderator, and the Speaker Recognition API.Ī driving point behind my last article was that users now expect a higher standard of experience from mobile apps. In my last post, I introduced Microsoft Azure’s Cognitive Services suite and its potential to make mobile apps smarter, more engaging, and more contextually relevant for users.
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