Everything you need to know about Conversational interfaces —
By: Volodymyr Bilyk
Tags: Conversational Interfaces, Chatbots, Machine Learning, Chatbots For Business

The emergence of conversational interfaces and wide adoption of virtual assistants was long overdue. They make things a little bit simpler in the increasingly chaotic everyday life.

We are witnessing how conversational UI is slowly but surely becoming a commonplace of the customer services. And all that is made possible by significant breakthroughs of machine learning and natural language processing.

However, there is still not enough understanding of what the “Conversational Interface” concept means. Because of that — let’s sort things out.

What is Conversational UI?

The conversational user interface is a type of the user interface where the primary mode of input is verbal and informal (i.e., not strictly structured). It is important to note that it doesn’t have to be purely voice-controlled — it is simply operating in a less organized and more casual manner — i.e., conversation.

Conversational interfaces are the natural continuation of the good old command lines. The significant step up from them is that Conversational UI goes far beyond merely doing what it is told to do. It is a more comfortable tool that also generates numerous valuable insights if it tackles users.

Now let’s look at how Conversational interfaces work.

How Conversational interface works?

Conversational UI operates on 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 during the operation.

Overall, the operation requires:

Natural language processing to interpret the text input,

to interpret the text input, Image recognition for images and text-filled images,

for images and text-filled images, Natural language generation features to provides coherent responses.

features to provides coherent responses. Text-to-speech, speech-to-speech output features.

There are three ways user input can be presented:

by text input (requires Natural Language Processing/Generation);

(requires Natural Language Processing/Generation); by image input (less common; usually an additional option. Needs optical character and image recognition);

(less common; usually an additional option. Needs optical character and image recognition); by speech input (in addition to NLP — requires speech recognition and text-to-speech tools).

Natural Language Processing algorithms interpret the input. Here’s how it works step-by-step:

Define the type of query aka intent (i.e., do something or find something, etc.);

the type of query aka intent (i.e., do something or find something, etc.); Extract the query’s vital elements (i.e., understanding what action to do to which object);

the query’s vital elements (i.e., understanding what action to do to which object); Deliver the output represented either by performing requested actions or providing a response.

Conversational interface feeds on massive amounts of user data to provide the most efficient services. The following methods achieve the data:

User Account Data

Direct input

Past use data

Supplementary data used to interpret the context.

The purpose of this data is to:

operate the conversational platform,

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 working process by simply saying or typing commands.

What are Virtual Assistants?

The most common usage of the conversational interface is virtual assistants. These two terms mean two sides of one coin.

conversational UI is technology;

Virtual Assistants or chatbots are the packages — products that utilize the conversational interface to its full potential.

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.”

Types of Conversational Interfaces

It is important to note that “conversational interface” is an umbrella term that covers almost every type of conversation-based interaction services.

Some consider conversational UI being just another, much more flashy, 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 — 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 provide extracts from FAQ.

— 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 provide extracts from FAQ. Customer Support interface (AKA the most common type). Based on 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 if that is impossible — the system redirects to the human operator.

(AKA the most common type). Based on 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 if that is impossible — the system redirects to the human operator. User Engagement interface — this type is quickly becoming a commonplace amongst the companies. This bot is essentially helping the user navigate 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.

— this type is quickly becoming a commonplace amongst the companies. This bot is essentially helping the user navigate 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. Organizer interface — this is more of a PDA organizer type. This interface design aims to keep the user checked with his schedule, manage to-do lists, remind of different things, and perform basic action without jumping around the windows/applications. It is a reverse bot that integrates with other services.

— this is more of a PDA organizer type. This interface design aims to keep the user checked with his schedule, manage to-do lists, remind of different things, and perform basic action without jumping around the windows/applications. It is a reverse bot that integrates with other services. Multi-purpose Intelligent Virtual Assistant AKA AI Assistant — these are big tech Internet of Things solutions like Amazon Alexa, Apple Siri, Microsoft Cortana.

Now let’s look at some of the tools to build a conversational interface.

Conversational Interface Tools

Platforms

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.

— 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.

— this platform got many different feature templates, and you can construct your assistant out of building blocks. Sequel — this one is easy to use for informational services, such as providing excerpts and redirects.

— this one is easy to use for informational services, such as providing excerpts and redirects. Hubspot Free Chatbot Software (formerly Motion.ai) — one of the more diverse platforms. Highly compatible with social media platforms. You test the bot on the go. You can use it for full-on lead generation and user engagement.

Natural Language Processing

Dialogflow — this one is a streamlined no-nonsense tool where you can program the framework and hone it over time. With Dialogflow , you can quickly train multiple scenarios of reaction and diverse interpretations of intent and content. It is free and a starting point for many.

— this one is a streamlined no-nonsense tool where you can program the framework and hone it over time. With , you can quickly train multiple scenarios of reaction and diverse interpretations of intent and content. It is free and a 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.

— 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 & SAP Conversational UI — these platforms take a more narrative-based approach, making it useful if your goal is user engagement and lead generation. You can build multiple story points for every turn of events and automatically proceed with the initial stage of contacting a potential client.

& — these platforms take a more narrative-based approach, making it useful if your goal is user engagement and lead generation. You can build multiple story points for every turn of events and automatically proceed with the initial stage of contacting a potential client. IBM Watson Assistant — swiss army knife of NLP platforms. With Watson, you can build an entire neural network around the bot and gather much information than usual. It is perfect if you want to get handy customer insights without breaking a sweat.

Conversational Interface Use Cases

Basic Customer Support

In the case of customer support operation, the conversational interface works to deliver information as requested. It can be taken straight from the source material or reinterpreted by natural language processing features to fit the context of the conversation.

In more sophisticated cases, customer support assistants can also handle notifications, invoices, reports, and follow-up information.

In addition to that, the system can redirect the human operator to queries beyond the bot’s reach.

Conversational Navigation / Service Guidance

Streamlining of the user journey is an essential element for improving the customer experience. The natural language user interface is one of the ways it to achieve.

Here are the types of assistance that you can use to do that:

guiding through the checkout process (for example, for money transactions);

filling web forms, subscriptions, sign-ups, etc.

Suggesting 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 CRM assistants. Depending on the configuration, they can:

answer questions,

suggest options (depending on the context of the situation)

perform specific actions (sort contacts, make a report, etc.).

Lead Generation

Lead Generation is the next step from simple customer support. Instead of operating upon request engages with the user — the conversational interface works to extract as much valuable information as possible via convenient conversational user experiences.

The reason it works is simple — conversation is an excellent way to engage the user and turn him into the customer.

The nature of the questions may vary, but the core is usually contact information and necessary business details about the user:

name,

position,

the name of the company,

Contact information (email or telephone et al.).

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.

Productivity

Productivity is one of the main fields where Conversational UI currently in use. No wonder — there is just too much of routine to keep up with.

Such an assistant is a command-line connected to all applications of the computer that can understand less strict commands.

Productivity conversational interface’s primary goal is to streamline the working process, make it less messy, and altogether avoid the dubious points of routine.

For instance, productivity assistants can handle basic task management duties such:

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 provide metrics regarding performance-based on the task management framework.

Content Suggestion

There is way too much content to get through these days. That’s a stone-cold fact. To get through the debris to the most valuable content — users need some extra tools that can sort the content and deliver only the important stuff.

The content recommendation is one of the central use cases for 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 aloud the headlines) or in a more formal packaging wit highlighting or summarizing content. For example, The New York Times offers bots that display articles in a conversational format.

Conversational Marketing

It is not a big secret that 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 for this kind of interface may vary. Generally, it is:

Basic information exchange

Content delivery / Content suggestion

News digests

Follow-ups

The most significant benefit of this kind of conversational UI is maintaining the presence throughout multiple platforms and facilitating customer engagement through a less formal approach.

Conversational Interfaces Challenges

Defining Relevant Use Cases

The implementation of a conversational interface revolves around one thing — the purpose of its use.

As it was mentioned before in the Use Case section, the use cases may vary from basic Q&A to the hands-on organizer to 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.

Different types of interfaces require various features and can’t be tweaked to do something else by a flick of the wrist.

How to do it, right? The insight doesn’t come out of nowhere. The definition of the use cases comes from the users themselves.

The key to success is to study:

what kind of actions can be beneficial for the users?

How can certain features contribute to the increase in the engagement?

What level of conversational UI accessibility is appropriate for the target audience?

Here’s what you need to do:

event monitoring — what kind of actions users are usually doing on your platform. What are the weak points? Where the dropoff occurs? What type of content is preferred? These questions will help you to round up the fields you can cover with a conversational interface.

— what kind of actions users are usually doing on your platform. What are the weak points? Where the dropoff occurs? What type of content is preferred? These questions will help you to round up the fields you can cover with a conversational interface. A/B testing routines will help to figure out the most fitting presentation.

Machine Learning Model Training

The other big stumbling block for conversational interfaces is machine learning model training. While ML is not required for every type of conversational UI — in case if your goal to provide personalized experience and lead generation — it is essential to set the right pattern.

The challenge is twofold.

At first, you need to teach the bot to interpret the input text and deliver relevant responses.

Then you need to hone the algorithms that will help the bot to adapt to a particular user profile to increase personalization and relevance of output.

Important to note 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 is all hunky-dory.

To configure well-oiled conversational UI — you need a combination of descriptive and predictive machine learning algorithms. The models depend on the use case.

Natural Language Processing Configuration

NLP is the front and center of conversational interfaces. And it is bad news if your system can’t deliver the goods because something is missing deep inside so that the user gets that sacrilegious “Sorry, I don’t understand that.”.

To avoid such occurrences, you need to set the coherent system of processing input and delivering output.

In practice, this means the following sequence:

Cleanup of the input information. This process includes:

of the input information. This process includes: Punctuation removal

Stopwords removal

Word tokenization

Words stemming, lemmatizing, vectorizing to interpret the message

the message “Decision-making component,” an integration with outside services to commit requested actions;

Output generation for responses.

That is how the most basic conversational UI framework functions in a nutshell.

Privacy concerns

With the growing concerns over the safety of user data — maintaining privacy and security of personal data becomes one of the significant challenges with conversational interfaces on the business side of things.

Conversational interfaces use data from user’s devices, emails, contacts, search history, and other data to provide adequate services.

However, given that all these operations often occur 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 does your conversational interface assistant operate?

What kind of user data is involved?

The other essential details to specify are:

How is user data handled?

Whether it is pseudonymized?

Whether information is disposed of after it served its cause and in what timeframe?

What’s next?

The emergence of Conversational interfaces was long-awaited. After decades of being a thing from science fiction, it is just another part of everyday life.

As the article shows — this technology can be beneficial in numerous operations and can provide a significant business advantage in good hands.

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