Conversational Interfaces – The Future of UI

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.

What is Conversational UI?

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.

How Conversational Interface Works

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.

What are Virtual Assistants?

Virtual Assistants are also known as Chatbots and they are the products that use the conversational UI to communicate with the user.

Do you want to know more about chatbot benefits? or chatbot challenges?

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. 

Types of Conversational Interfaces

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.

Conversational Interface Tools


  • 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.
  • – 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

  • (Dialogflow) – 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 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.
  • & – 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.

Conversational Interface Use Cases

Basic Customer Support

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.

Conversational Navigation / Service Guidance

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

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:

  • Name
  • 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.

Content Suggestion

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.

Conversational Marketing

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
  • Follow-ups

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.

Conversational Interfaces Challenges

Defining Relevant Use Cases

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.

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, 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.

Natural Language Processing Configuration

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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Privacy concerns

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.

What’s next?

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.

Want to integrate a conversational UI into your project?

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5 Key Mobile Marketing Metrics

Not so long ago, app developers were all about deciding the success of apps based on analytics. The statistics were assessed to determine just how good an app was doing in terms of user appeal and feedback. At one time, developers measured the app’s overall success based on its ratings and the download count (or sales). These were key mobile marketing metrics.

However, these days there are better ways to assess the overall success of any mobile application. These marketing metrics are something every developer needs to know about if they are to decide the success of an application.

Every measurement shared here consists of a formula or a way of getting information that you can use to draw insights about the app and how well it is doing. You can figure out how much revenue an app is generating per user, the costs per loyal user, and the costs per install and compare it with mobile application development costs

The latter figures will reveal how successful you’re marketing is and if users are finding your app useful or enjoyable. You can also find out how people are engaging with the app, the parts of the app that are most enticing, and if your application requires changes or improvements.

Mobile marketing metrics to track in 2017

1. Know Your ARPU

ARPU - mobile marketing metric

ARPU is an acronym for Average Revenue per User. The ARPU is broken down into a formula consisting of the amount of money the app generates within the framework of a set period over the number of active app users within the same timeframe.

Bear in mind that ARPU will be different for every mobile app category as well as for every revenue model. However, there are some comparisons that can be made that can prove useful in determining app success. ARPU by revenue model can help you take another look at the revenue brought in through advertising; fee-based downloads in-app purchases, freemium apps, and subscriptions for ease of comparison.

The ARPU can help you in finding out the average revenue the app is generating per user. The latter figure is important because you can use it in other metrics. First, in CPLU (Cost per Loyal User), you can compare the ARPU to the CPLU and if the latter is less than the former, you’re using your marketing methods correctly.

Essentially, it reveals you are generating more income than it costs to acquire the attention of the consumer. Next, in the Retention metric, the ARPU is part of finding out the lifetime value of those consumers you have as loyal users. Therefore, if you have an app that makes $0.50 a month and you retain a consumer for about 5 months on average, the lifetime value is $2.50.

2. Know Your CPLU & CPI

CPI - mobile marketing metric for applications
CPLU - mobile marketing metric for applications

CPI is an acronym for Cost per Install. CPLU is also an acronym standing for Cost per Loyal User. The CPI is a formula that equals the advertising dollars you spend divided by the number of new installations you get within a given advertising campaign. The CPLU however, is the number of advertising dollars you spend divided by the amount of new and loyal application uses you get from the ad campaign.

These two figures can be used along with your ARPU to find out the return on investment you are getting from all of your advertising and marketing. For your marketing to prove successful, your CPLU needs to be less than your ARPU.

3. How Are Your Users Engaging?

Engagement is not something you can figure out by a formula, and it requires an assessment of how your users are making use of the mobile app in question. In understanding a user’s behaviors, you can get an inside view of what is most appealing to your users and what features may be unnecessary.

Engagement is an umbrella term covering a variety of user actions including an assessment of app screens viewed per session, the conversion rates, interactions, opt-ins, opt-outs, and session intervals and lengths. Let’s look at each of these analytics a bit more in-depth.

Customer loyalty measures how often your users return to the app and use it. Your customer’s engagement is vital to the success of the app, as engaged users are being called the bread and butter of an application’s overall success. If you have users that are remaining engaged with the app, it means they like what the app has to offer and are therefore more likely to spread the good word about the app to others – word of mouth advertising goes a long way in the mobile app industry.

What’s more, you can track the usage of loyal users over a longer time to see if trends in application development crop up or if the users repeat actions that may offer insight into how you can improve the application or enhance user engagement.

4. Know Your Love Ratio

Love Ratio - metric for mobile apps

The love ratio is a mobile marketing metric that’s been in use for a while and a broad assessment of statistics reveal that the answer is yes, just over 57.70% of the time. The measurement gives you a good idea just how much your users are enjoying the app and if the numbers are low it suggests you’ll need to make some changes to the app to make it more loveable.

Bear in mind, this figure can also vary if a person is not giving an honest answer to the survey and is just clicking on whatever answer will dismiss the survey question quickly. Despite potential biased answers and incomplete assessment values due to lack of user response, the Love Ratio formula still reveals vital information to the app developer, especially over the course of version histories and time.

5. Monitoring Retention Rate

Retention rate as a key marketing metrci for applications

There’s a formula for determining your retention rate, which is a metric marking the number of users you have returning to your mobile app on a weekly basis. Aggregate retention is the number of actives you have during a month divided by the install amounts during the same period. To determine what the retention rate for a specified time is, you need to note the number of app users you’ve retained by the end of a specified time and divide it by the installs during that period.

Broad statistics suggest that as many as 40% of mobile UX apps continue to use the app following the first 30 after the installation, but this number dramatically declines to a little as 4% within the year following the date of install.

The retention rate will allow you to define, not just how many downloads you have, but how many users you have. Even better, you can figure out active users over the course of time. Even if you have 100,000 downloads, 50% of that may have retained users over the course of time, and only by knowing your retention will you know how many downloaders actually remained active users.

When you know the monthly active users, you can use the figure to multiply it against the monthly average revenue you’re making per user to determine your overall revenue.

In essence, there are plenty of ways to measure what is happening with your mobile app. There are formulas you can use to find out user behavior, actions, and if they like using the application you‘ve created. A developer can then use the insights gained to improve the application and subsequently increase revenue.

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4 Smartest Mobile Analytics Tools for Mobile Apps

While some mobile app developers count the number of app downloads as a method for measuring the likeability and desire for an application, the actual download count is not necessarily indicative of the app’s overall success. While high numbers painting a picture in hit charts might make it seem that a mobile app is doing well, there are other metrics to consider when it comes time to weigh the caliber of a mobile application.

When attempting to determine the desirability of a mobile app, the developer needs to take in a number of metrics into consideration in order to get a true picture of an app’s success rate. First, the developer will want to know what people are downloading the application: This will give the developer insight into what audience the app appeals to and if the mobile app is reaching its target audience.

The developer needs to examine how many people download the app, if the user opens it, and how each user is interacting with it. In fact, understanding how long a user makes use of a mobile app and the average time the user spends interacting with the app are also important figures to know. The latter information can give insight into what features in the app are appealing or not and thereby give the developer some hints about the necessary changes that can make the app even more successful.

READ ALSO: Price of app development

Other things a developer needs to consider is app feedback and comments. Knowing what people are saying about an app and if they will recommend it, helps the developer know of any potential changes are necessary as well as what features users like the most. The mobile app developer also needs to consider in what form users will be able to access the app. Will the app be free with advertisements supporting it? Will it be a lite version that leads to the purchase of the full version? Of course, offering in-app purchases is another option the developer has for monetizing a mobile application.

Mobile analytics will help ensure the success of your app. A good developer with any level of experience knows that the initial launch of an app is not the end of the story. In fact, applications are under a process of evolution and the developer continues to upgrade and perfects the app over the course of time.

With the latter notion in mind, it is easier to understand how the number of downloads associated with an app has no reflection on the app’s overall success since the number does not reflect the quality of the application or the version downloaded. Additionally, when the download numbers suddenly drop, turning to reviews and feedback may not be quite enough information for the developer to identify the issues with the app. It is because of situations like this that mobile analytics is of tremendous import.

Best analytics tools for mobile apps

Best Mobile Analytics Tools


Appsee is a user-friendly analytics program that will reveal the total users of an app, how many launches an app gets, and it will also reveal the average session length and if the app crashes. It also records some of the app user’s usage so you can look at the interaction and determine what is wrong with the app, if applicable.

Appsee lets you identify why a user might stop using the application, what features are most desirable, and when the user stops interacting with the application.

Price: Free.


A complete toolkit to measure the caliber of an app. Flurry is like the Facebook of mobile analytic programs, and in the industry, the program is the standard when it comes to mobile analytics program development. The program works with Windows, Blackberry, Android, and iOS devices. The program requires you use three lines of code to start getting analytical information on an app.

Flurry is a program that allows the user to customize the program in order to measure the most meaningful metrics for the developer. Users of Flurry can find out about demographics and other important statistics so the developer can increase the success of an app.

Price: Free.

Google Analytics

Yes, now Google Analytics includes an app that can measure the statistics associated with a mobile application. With the app, the developer can witness user action, demographics, in-app purchase information, and more. If the developer makes a decision on how to monetize an app, there is Google Mobile available for iOS and Android platforms as well.

With mobile analytics through Google, users can find out about relevant users, traffic sources, real-time reports, flow visualizations, event tracking, exception and crash reports, and custom reports, all through a single program. The types of reports you can get out of Google Mobile Analytics include app profiles, and iOS and Android software development kits (SDKs).

Price: Free.


Mixpanel is a mobile analytics resource supplying details on how a user discovers a mobile application, the location of the user, and details on how the user interacts with the app and for how long. Mixpanel relies on user surveys and A/B tests to generate analytic results. The user of Mixpanel gets access to custom reports revealing information on user retention and engagement.

With Mixpanel, users can access whether or not advertising campaigns are working or if the user needs to change up the way they are marketing an application. The app can even tell the developer if users are sharing the mobile app under analysis. Price: Varies, depending on the plan you choose.

Using one, a few, or even all of the above mobile analytics tools can help a developer begin to identify the real success of a mobile application. Rather than rely on the superficiality of a number of downloads count, the above programs give the developer insight into user behavior, interaction, and appealing features, but it will also help the developer find out about problems and issues with the application that require repair. The analytical programs available to developers are vital to the ongoing evolution of a mobile application and to the long-term success of the app as well.