The emergence of conversational interfaces and the broad adoption of virtual assistants was long overdue. They make things a little bit simpler in our increasingly chaotic everyday lives.
At the moment, we are witnessing how conversational UI is slowly but surely becoming commonplace in customer service, which is made possible by a significant breakthrough in machine learning and natural language processing.
However, there is still not enough understanding of what the concept of “Conversational Interface” really means. Because of that, let’s sort things out.
A conversational user interface is a type of user experience where the input is not strictly structured (i.e., more informal and more like, well, a conversation.)
It can be verbal or voice-controlled (like Siri or Alexa) or written, and it is more casual as if you’re talking to another human instead of typing phrases, like “outsourcing project development adtech Ukraine.”
While the name is slightly misleading (interface versus experience), many platforms already have UI that you have to fit into (for example, Facebook Messenger) therefore it’s the experience that users get.
Conversational interfaces are a natural continuation of the good old command lines. The significant step up from them is that the conversational interface goes far beyond just doing what it is told to do. It is a more comfortable tool, which also generates numerous valuable insights as it works with users.
Conversational UI is built around the call and response approach. Natural language processing and machine learning algorithms are parts of conversational UI design. They shape their input-output features and improve their efficiency on the go.
Overall, the operation requires:
- Natural Language Processing (NLP) to interpret the text input
- Image Recognition for images and text-filled images
- Natural Language Generation features to provide coherent responses
- Text-to-speech and speech-to-speech output features
There are three ways users can “talk”:
- Using text input – typing the questions. This option requires Natural Language Processing and Generation.
- It is using image input – using pictures or written text to communicate the idea. This is a less common, usually a secondary, option. It needs optical character and image recognition.
- Using speech input – literally talking out loud. In addition to NLP, this would require speech recognition and text-to-speech tools.
Natural Language Processing algorithms interpret the message and do the following actions:
- Define the type of query aka intent (i.e., do something or find something, etc.);
- Extract the essential elements of the query (i.e., understanding what action to do to which object);
- Answer the question, which is represented either by performing requested activities or providing a response.
Conversational interface feeds on massive amounts of user data to provide the most efficient services. There are several sources of data:
- User Account Data
- Direct input
- Past use data
- Supplementary data used to interpret the context
The gathered information is used for:
- the conversational platform operations
- the natural language processing platform
- machine-learning algorithms
- speech recognition and generation platforms
As a result, the user gets relevant results or suggestions to their queries and streamlines his or her working process by saying or typing commands.
Virtual Assistants are also known as Chatbots and they are the products that use the conversational UI to communicate with the user.
The standard definition of “Virtual Assistant” (also known as “Virtual Intelligent Assistant” and “AI Assistant”) is a “verbally-operated program designed to perform certain actions routinely or upon request.” However, as we mentioned above, the requests can also be typed.
A “conversational interface” is an umbrella term that covers almost every kind of conversation-based interaction service.
Some consider conversational UI to be just a flashier word for “chatbot.” While there is a direct connection, chatbots represent only a particular type of conversational interface that involves conversational elements for enabling its operation but is not defined by it.
From the use case point of view, there are several distinct types of conversational interfaces:
- Q&A web chat interface is the most basic form. It doesn’t require Natural Language Processing or Machine Learning. It is an algorithm that delivers information upon request straight from its database. Often used to navigate content and give extracts from FAQ.
- Customer Support interface is the most common type. It’s based on a call and response, template-based system. Able to provide general information. Includes another layer of interaction that assesses the ability of the bot to satisfy the incoming request. In case that is impossible, the system redirects the user to the human operator.
- User Engagement interface is quickly becoming commonplace amongst the companies. This bot helps the user to navigate through the website, answer basic questions, and exchange information. Can be used for content or product suggestion. As an extension of its features, it can also provide initial lead generation activities. A good example of this approach can be Nuance’s recently unveiled project Pathfinder.
- The organizer interface is more of a PDA organizer type. This type of interface is designed to keep the user in check with his schedule, manage to-do lists, remind them of different things, and perform simple actions without jumping around the windows/applications. It is a reverse bot that integrates with other services.
- Multi-purpose Intelligent Virtual Assistant (aka AI Assistant) are big tech Internet of Things solutions like Amazon Alexa, Apple Siri, Microsoft Cortana.
Now let’s look at some of the tools that are used to build your conversational interface.
- Chatfuel – a platform for simple Q&A / customer support chatbots with the website and social media integration. Easy to use due to the visual interface.
- Botsify – this platform got many different feature templates, and you can construct your assistant out of building blocks.
- MobileMonkey – a platform that helps to create a Facebook Messenger chatbot, which is quite convenient for businesses that have a Facebook page (which is, by now, pretty much a requirement for trust.)
- Sequel – this one is easy to use for informational services, such as providing excerpts and redirects.
- Motion.ai – one of the more diverse platforms. Highly compatible with social media platforms. You test the bot on the go. Can be used for full-on lead generation and user engagement.
Natural Language Processing
- Api.ai (Dialogflow) – this one is a streamlined, no-nonsense tool where you can program the framework and hone it over time. With Api.ai you can quickly train multiple scenarios of reaction and diverse interpretations of intent and content. It is free and is a great starting point for many.
- Microsoft Language Understanding Intelligent Service (LUIS) – this is a go-to tool to program conversational intelligence tailor-made for your cause. You can use the existing templates from Bing and Cortana.
- Wit.ai & Recast.ai – these platforms take a more narrative-based approach which works if your goal is user engagement and lead generation. You can build multiple story points for every turn of events and automatically proceed the initial stage of making contact with a potential client.
- IBM Watson Assistant – the Swiss army knife of NLP platforms. With Watson, you can build an entire neural network around the bot and gather much more information than usual. It works well if you want to get handy customer insights without breaking a sweat.
To provide simple customer support, the UI takes the requested information straight from the source material or reinterprets it by natural language processing features to fit the context of the conversation.
In more sophisticated cases, a customer support assistant can also handle notifications, invoices, reports, and follow-up information.
The system can also redirect to the human operator in case of queries beyond the bot’s reach.
Streamlining the user journey is a vital element for improving customer experience. A natural language user interface is one of the ways it can be achieved.
Here are the types of assistance:
- Guiding through the checkout process (for example, for money transactions);
- Filling web forms, subscriptions, sign-ups, etc.
- Offering operating options (download, sign-up, etc.);
- Suggesting content
The primary purpose of an assistant is to gather correct data and use it for the benefit of the customer experience.
An excellent example of this is a CRM assistant. Depending on the configuration, it can:
- answer questions,
- suggest options (depending on the context of the situation)
- perform actions (sort contacts, make a report, etc.).
Lead Generation is the next step from simple customer support. Instead of operating upon request, it engages with the user – the conversational interface is used to extract as much valuable information as possible via more convenient conversational user experiences.
The reason why it works is simple – a conversation is an excellent way to engage the user and turn him into a customer.
The nature of the questions may vary, but the goal is usually to get contact information and business details about the user, such as:
- Job title
- Company name
- Contact information (email or phone)
This information then goes straight to the customer relationship management platform and is used to nurture the leads and turn them into legitimate business opportunities.
Conversational UI is applied mainly to enhance productivity. It’s no wonder – there are just many routine things to keep track of.
Such an assistant is a command line that can understand simple, more natural-sounding questions, and be connected to the applications on the computer or mobile device.
Productivity conversational interface is designed to streamline the working process, make it less messy, and avoid the dubious points of routine where possible.
For instance, productivity assistants can handle basic task management duties such as:
- Task management – creation, assigning, and status updates
- Retrieving reports, facilitating communication
- Time management – keeping the schedule intact, booking rooms, making appointments, setting reminders
- Research – delivering search results in a processed form, most commonly as a summary or digest
Also, such an interface can be used to provide metrics regarding performance based on the task management framework.
There is way too much content to get through these days. To get to the most valuable content, users need some extra tools that can sort the content and deliver only the relevant stuff.
The content recommendation is one of the main use cases for of conversational interface. Via machine learning, the bot can adapt content selection according to the user’s preference and/or expressed behavior.
The results can be presented in a conversational manner (such as reading out loud the headlines) or in a more formal packaging with highlighted or summarized content. For example, The New York Times offers bots that display articles in a conversational format.
Chatbots can be a weapon of mass engagement in the hands of the right marketing team. Just as email marketing makes a case for the brand presentation, chatbots can do the same on multiple platforms.
Such an approach is not limited to your website – it is also relevant for social networks. The features of this kind of interface may vary. Generally, they are:
- Basic information exchange
- Content delivery / content suggestion
- News digests
The biggest benefit from this kind of conversational UI is maintaining a presence throughout multiple platforms and facilitating customer engagement through a less formal approach.
The implementation of a conversational interface revolves around one thing – the purpose of its use.
As mentioned before in the Types section, the use cases may vary from basic a Q&A to a hands-on organizer to a powerful lead generation and marketing tool.
It is essential to understand what you want to do with the conversational interface before embarking on its development. Also, you need to think about the budget you have for such a tool – creating a customized assistant is not the cheapest of endeavors (although there are exceptions).
Different types of interfaces require different features and can’t be tweaked to do something else with the flick of the wrist.
The key to success is to decide:
- What kind of actions can be beneficial for users?
- How can certain features contribute to the increase in engagement?
- What level of conversational UI accessibility is appropriate for the target audience?
Here are some things that can help decide what’s best for you:
- Event monitoring. Study the analytics of your website: what kind of actions are users usually performing on your platform? What are the weak points? Where does the drop off occur? What type of content is preferred? These questions will help you to round up the fields you can cover with the conversational interface.
- A/B testing routines will help to figure out the most fitting presentation.
The other big stumbling block for conversational interfaces is machine learning model training. While ML is not required for every type of conversational UI, if your goal is to provide personalized experience and lead generation it is important to set the right pattern.
The challenge is twofold:
- You need to teach the bot to interpret the input text and deliver relevant responses.
- You need to hone the algorithms that will help the bot adapt to a particular user profile to increase personalization and relevance of output.
It should be noted that this challenge is more of a question of time than effort. It takes some time to optimize the systems, but once you have passed that stage – it’s all good.
To configure a well-oiled conversational UI, you need a combination of descriptive and predictive machine learning algorithms. The models depend on the use case.
NLP is at the front and center of conversational interfaces. When this is missing in the system, your users might end up getting the frustrating “Sorry, I don’t understand that” and leave.
To avoid such occurrences, you need to set a coherent system of processing input and delivering output.
In this example of the most basic conversational UI framework, here is the sequence:
- Cleanup of the input information. This includes:
- Punctuation removal
- Stopwords removal
- Word tokenization
- Words stemming, lemmatizing, vectorizing to interpret the message
- “Decision-making component,” an integration with outside services to commit requested actions;
- Output generation for responses.
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With the growing concerns over the safety of user data, maintaining the privacy and security of personal data becomes one of the major challenges of conversational interfaces on the business side of things.
Conversational interfaces use data from user’s devices, emails, contacts, search history, and other sources to provide adequate services.
However, given the fact that all these operations are often performed through third-party applications – the question of privacy is left hanging. There is always a danger that conversational UI is doing some extra work that is not required and there is no way to control it.
How to solve this issue? The only viable solution is to add an explanation of:
- How your conversational interface assistant operates
- The kind of user data that is gathered
Other important details to specify are:
- How is user data handled?
- Is it pseudonymized?
- Is the data disposed of after it served its cause and in what timeframe?
To understand the underlying legal challenges regarding personal information, check out the EU’s General Data Protection Regulation.
The emergence of Conversational interfaces has been long-awaited. Now, after decades of being something from science fiction, it has become just another part of everyday life.
This technology can be very effective in numerous operations and can provide a significant business advantage when used well.
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