Category: Chat

As organizations look at customer satisfaction as a crucial element of competitive strategy, conversational artificial intelligence (AI) has assumed vital importance. Chatbots are the most visible adoption of AI. Research by Forrester on the impact of IBM Watson chatbots found that organizations saved $5.50 per contained conversation, resulting in an impressive 237% RoI. Chatbots are also finding favor with customers. 40% of millennials use chatbots daily to connect to all kinds of businesses. As businesses are using chatbots to serve various use cases, including marketing & sales to support, it has already become as essential a tool as email.

While the underlying implementation of conversational AI is complex, it is possible to start benefiting even if you don’t have excellent coding capabilities, a development team, and a large budget. This article will describe ways you can build and utilize the chatbots without coding.

Types of Chatbots: A Comparative

We can categorize the chatbots into two broad categories: rule-based and conversational chatbots.

Rule-Based Chatbots

The rule-based chatbots work based on predefined inputs and keywords. Developers code instructions beforehand that respond to specific input patterns and commands. The bot asks questions and provides options to customers for choice. Based on the selected option, the bot performs the next steps. Such chatbots can also identify the intent based on keywords from the sentences or answers you type. For example, an insurance chatbot may present types of insurance policies (e.g., life insurance, vehicle insurance, or health insurance).

Depending upon the answer you choose or type (e.g., I want to purchase a life insurance policy), it identifies the action and responds based on its program. The back end dictionary can be extensive so that rule-based chatbots can serve a wide range of use cases. However, they make the customer adapt to the commands and patterns. The customers need to either remember those commands, or the bot must provide appropriate but limited options. Rule-based chatbots are similar to how traditional IVR works.

Trending Bot Articles:

1. Case Study: Building Appointment Booking Chatbot

2. IBM Watson Assistant provides better intent classification than other commercial products according to published study

3. Testing Conversational AI

4. How intelligent and automated conversational systems are driving B2C revenue and growth.

If not implemented optimally, customers will find the experience of using a rule-based chatbot unsatisfactory. For short customer journeys, such an implementation might work. But if your process has many steps, customers might experience navigation fatigue. The time taken to complete the process hurts customer engagement.

Conversational AI Chatbots

The Conversational AI chatbots , on the other hand, can find customer intentions from a natural conversation. Customers interact with these bots naturally as they converse with other humans. The bot is intelligent enough to identify what the customer wants.

Such implementation is made possible by Machine Learning (ML) and AI algorithms that support Natural Language Processing (NLP). Depending upon the use case, certain implementations can also use Computer Vision (CV) to identify objects, including humans and documents.

You need to train the underlying algorithms extensively for AI chatbots to work effectively. The training refers to providing the algorithms enormous sets of historical data and the signals about how that data is interpreted. Based on such training, the algorithms can identify the patterns in conversations they are holding. The accuracy of such chatbots depends upon the volume and quality of the historical data. However, as more people use it, these machine learning (ML) algorithms improve their accuracy by automatically learning from new conversations.

Challenges of Developing Chatbots

While it is possible to create a chatbot from scratch with code, there are significant challenges. Building even rule-based chatbots from scratch is complicated. While you can integrate limited, simple questions & answers easily, any substantial use case has complexities. For example, any decent chatbot would require useful UI/UX elements and integrations with APIs and databases. Depending upon the geographies that your business operates in, you might also need to support multiple languages.

We recommend this article on Usability Heuristics to Design Better Chatbots to decode the UX fundamentals for designing chatbots.

The challenges increase with conversational chatbots. The first challenge is to train the underlying ML algorithms with massive datasets. Unless the bot responses are accurate, they can be counterproductive. If you are looking at serving customers meaningfully, you will need your chatbots to support complex business workflows. For example, if a hospitality business decides to implement a chatbot to serve their customers across hotel chains, they need to enable booking inquiries, reservation and cancellations, and invoicing processes. Adding voice support for such chatbots adds another layer of complexity given the accents and variations in spoken languages.

Deploying to multiple channels can be difficult, as every platform and channel has unique requirements. Building a bot for Facebook Messenger is quite different from building a bot for WhatsApp or Slack. Coding, maintaining, and upgrading each channel’s bots demands considerable effort and time.

There are two other ways of creating a chatbot.

1. Use platform APIs and frameworks to develop the core portion of the chatbot but build surrounding parts through code.
2. Use tools that allow you to create chatbots without coding

Let’s evaluate both these approaches.

Creating Chatbots with APIs

Most of the known platforms (Google, Amazon, IBM, Microsoft) provide APIs and frameworks that allow you to leverage their services for creating chatbots. Apart from these, there are known APIs like Wit.ai and platform-specific APIs like Facebook Messenger APIs, Slack Bot APIs, and Telegram Bot APIs.

While these APIs provide the support for underlying ML & AI capabilities for a few use cases, you will still need to build the rest of the workflows yourself. Depending upon your requirements, you may need to develop the ML models and training the algorithms too. The platform-based APIs also restrict the bots to their respective channels (e.g., Facebook Messenger); hence you might have disparate codebases to maintain.

Create Chatbots Without Coding

Many capable frameworks exist that allow you to create rule-based or channel-specific chatbots without code. However, there are fewer choices of platforms that will enable you to make more capable, conversational chatbots that can serve various needs and use cases. Deploying a chatbot will be one of the critical initiatives, and hence you must choose your platform carefully.

The following are the characteristics of a good platform that allows you to build chatbots without coding.

• What kind of business processes can the platform support? Is there any out-of-the-box support for a variety of end-to-end workflows? For example, can you automate your entire hotel booking process, starting from inquiry to payment and confirmation? A good platform may make predefined journeys available by default. If not, how quickly and easily can you build that?
• How does the platform support languages? Does it have multi-lingual support already built-in? If not, how easy or difficult is it to add a new language? Since the conversational aspect is critical, supporting various languages becomes vital if you have a multilingual customer base.
• Does it support voice conversations? Voice-based conversational bots are gaining momentum given their benefits. It helps if the platform you choose supports integrating NLP capabilities to support voice conversations.
• How does the platform support building an excellent user experience and interface? For example, if you are making a chatbot for your eCommerce store, how well can the chatbot present your merchandise? The aesthetics and experience play a big part in customer purchase decisions and satisfaction. Your platform of choice must support building great interfaces quickly.
• What kind of channels (e.g., Facebook Messenger or WhatsApp) does the platform support? Can you utilize the chatbot built with the platform on web, iOS, and Android applications? How many other platforms can it support?
• How well does the bot support an omnichannel experience? Customers value a consistent experience, no matter the channel they use. A good bot framework should enable you to develop such consistent experiences. Building such omnichannel bots also means it can seamlessly use the information across channels without the customer having to repeat themselves.
• How easy is it to use third-party APIs with the chatbot built with the platform? If you want to support a complicated use case, you will likely need data from multiple sources. Some data will come from other applications you are using, while some other capabilities might require specialized APIs. Unless such integrations are easy to implement, you may have to invest extra efforts or find sub-optimal workarounds.
• How well can the platform support bot-to-human transfer by intelligently assessing the customer needs? Recent research by G2 Crowd shows that conversational chatbots have a 73 percent satisfaction rate among their users (virtualpbx.com), but there would be times when customers would want to speak to a human agent.

Additional Factors to Consider

The ease of building no-code chatbots doesn’t mean it is trivial to build such bots. To use such platforms, you need to have a good grasp of business needs and customer mindset. While these platforms make developing sophisticated chatbots easy, you must also know how to use the platform itself. Choosing the right platform becomes critical in this case, as otherwise, the constraints of the platform can create challenges to achieve your goals.

Along with these functional and technical aspects, you must also look at the possible RoI on your chatbot development and maintenance investments. While some platforms may prove cost-effective in the short term, scaling them as you progress might mean added investments, thus increasing your lifetime costs. A balance between immediate needs and future scalability is essential.

Haptik’s Conversation Studio is one of the leading platforms that can enable you to build a capable code-less chatbot quickly. The chatbots built using this platform can effectively perform the intended tasks. They also use the conversational data to make the underlying ML algorithms more accurate.

The Conversation Studio compliments Haptik’s Smart Skills , the optimized ready-to-deploy user journey recipes that can instantly enable you to automate many standard use cases and workflows. Additionally, you can also leverage the rich Integration Ecosystem with out-of-the-box integrations with leading ERP & CRM systems and helpdesk solutions. You can also deliver an omnichannel experience, leverage seamless AI to agent handoff, and gain valuable 360-degree customer insights to enhance the value even more.

Advantages of No-Code Chatbots

No-code chatbots offer multiple advantages to businesses. Let’s see a few of the critical benefits.

Increased Cost Savings

Chatbots offer substantial cost savings. A Juniper study predicts that by 2022, conversational chatbots will deliver $8 billion in cost savings. As the business environment gets fiercer, the need to quickly finetune customer experience to match their expectations also increases. The no-code chatbots offer agility to businesses to roll-out solutions. Apart from getting a competitive advantage, such quicker and frequent releases allow enterprises to test assumptions and finetune the customer journeys optimally.

Using the no-code platforms, you can develop chatbots faster than when you code them from scratch. Since most of the building blocks would be already available on the platform, you can focus on core business features that enhance the chatbots’ effectiveness. Your development team doesn’t need to focus on basic features that don’t offer any competitive advantage but are essential for the bots’ smooth functioning.

Better Strategic Alignment

There are other advantages too. One of the most significant benefits is a closer alignment between business requirements and chatbot outputs. Often, the strategic needs get lost in communication between business and technology teams. As platforms that offer chatbots development without coding allow cross-functional participation, it is easier to ensure that the end product is closely aligned to customer expectations and business needs.

In many cases, business users with deep domain expertise can build and refine bots without the friction of having to explain their ideas to others. Such flexibility results in better outcomes while still enabling a mature technical foundation.

Increased Security

Another critical aspect of chatbot deployment is security. The security threats are getting more severe and sophisticated with enhancements in technology. Poorly coded chatbots can make businesses vulnerable to all kinds of attacks. Your use case may involve using chatbots for enabling financial transactions or exchanging sensitive customer and business information. In such cases, the threat severity increases. To ensure protection for chatbots, code developed in isolation can leave businesses more vulnerable. Platforms that offer chatbots without coding offer better safety and security to the bots.

To Sum Up

In the age where customer engagement and satisfaction are difficult to achieve, chatbots have become an essential tool and not just nice-to-have for businesses. However, getting the chatbot equation right is tricky. Chatbots are inherently complex to build. Utilizing appropriate ML and AI algorithms, training those algorithms with enough training data, and achieving the required accuracy demands extensive time, effort, and skills. Any small mistake can have catastrophic consequences.

With evolving technology, No-Code chatbot building platforms can provide accurate implementation without the challenges of building a bot from scratch. Forrester estimates that 57% of companies are already using such enterprise no-code chatbot platforms, and the trend is going to continue upwards. These platforms take care of the hardest parts of chatbot implementation while enabling you to reap the benefits.

Want to develop an Intelligent Virtual Assistant solution for your brand?

GET IN TOUCH

Don’t forget to give us your 👏 !

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No Code Chatbots: Leverage Technology To Boost Customer Satisfaction was originally published in Chatbots Life on Medium, where people are continuing the conversation by highlighting and responding to this story.

Building Conversational Bot with Custom workflows using Google DialogFlow Contexts

With emergences of smart speakers , there is boom going on building conversational bots. So as Google and Amazon are providing free build and deploy platform make it even easier for build your own custom apps. One of most important part voice application to have robust workflow with repetitive decision making steps . Below is sample workflow :-

As you notice there is multiple level for confirmation in flow and user can respond with “Yes” or “No” . In this case, NLP model should be aware of current state of your journey otherwise it could result in wrong path in flow. One of approach that comes handy in DialogFlow is contexts in these types of scenarios as DialogFlow NLP engine looks for current contexts in conversion along with user utterances to figure out intent . This gives So building application This is section is first part of building simple conversation app using Google DialogFlow.

Login to actions on google using your google account :- https://console.actions.google.com/

1. Create a new project

2. Type project and then select custom

This will create action and then go to DialogFlow link

3. Create following new intents :-

4. Below is final conversional setup:-

In case you notice that there are input contexts setup for couple of intents , with this setup DialogFlow engine looks for context in input payload along with utterance to figure out intent . This helps navigation to desired step even multiple intents have similar training data setup.

Trending Bot Articles:

1. Case Study: Building Appointment Booking Chatbot

2. IBM Watson Assistant provides better intent classification than other commercial products according to published study

3. Testing Conversational AI

4. How intelligent and automated conversational systems are driving B2C revenue and growth.

In our case, we are setting up output context from fulfillment. This helps to make navigation dynamic and easily customizable for any future changes .

Few points to remember for contexts while setting outputs context :-

• Context name should be same including cases while setting it from backend
• In case you want to move forward , clearly previous contexts

Sample to set context Node.js fulfillment code:-

conv.contexts.set(‘hungry_followup’, 5, {});

Once everything is setup then conversational will work as shown below :-

Notice that even though user responded with yes still NLP followed workflow and navigated to next steps

Conclusion

This shows contexts are very essence for DialogFlow NLP engine and provide needed navigation controls on user journey in flow.

I will post code and projects in coming articles.

Connect with me on LinkedIn

Don’t forget to give us your 👏 !

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Building Conversational Application having Custom workflow using Google DialogFlow Contexts was originally published in Chatbots Life on Medium, where people are continuing the conversation by highlighting and responding to this story.

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It’s hard for SMEs to compete with big tech companies with limited resources nowadays. Whenever you build an app, you need to craft the web version, an Android app, and an iOS equivalent. You probably need a UX designer and a few developers with different skillsets to work on the different platforms. It’s slow and expensive.

And worst of all, your app is doomed from the beginning. People now already have hundreds of apps installed on their mobile phones. And human brains can only remember the last 7 apps they used. Your app will probably be hidden in some folders and be forgotten forever if not uninstalled.

That’s why we created a chatbot to replace the traditional app. And it solves a few problems all at once.

One app rules them all

A chatbot is basically an app that you can interact with the commonly used chat interface. It does not exist in itself, instead, you can use it on your website or in a chat app like Whatsapp, Facebook Messanger, Teams, Slack, Line, etc. So you don’t need to build different apps for different devices.

Once you build your bot for Whatsapp, users can use it wherever they can use Whatsapp. You don’t need to worry about OS or browser compatibility. And the best part, users don’t need to install anything. Just add your phone number to their WhatsApp, they can start using your bot. (For other applications like MS Teams, they may need to search for your bot in the Teams market place and “install” it.)

And just like fashion, UI design changes all the time, but what doesn’t change is the way people chat. When you type something, you expect some message response, it’s simple as that, and it won’t change dramatically in the coming years.

Trending Bot Articles:

1. Case Study: Building Appointment Booking Chatbot

2. IBM Watson Assistant provides better intent classification than other commercial products according to published study

3. Testing Conversational AI

4. How intelligent and automated conversational systems are driving B2C revenue and growth.

Better user experience

Form-based user inputs haven’t changed for many years. For people old enough like me, we use to fill in a lot of forms on paper. Nowadays all things are online, but it’s still form-based. Nothing much has changed about that. Even when employees tried to apply for leave, you present them a form, ask them which “Leave Type” they want to apply, and whats are the “Start Time” and “End Time”

What users really want to do is simply “apply ABC leave on XYZ date”. A form is unnecessarily complicated. A simple text message can get the job done in a simple and natural way.

Another problem with the traditional UI design is navigation. You have a lot of features in your app, but you can’t put all of them on your home screen. So we need to create secondary menus to hide them. No matter how well you design your secondary menus, some features will be buried deeply and very difficult to find.

With a chatbot, you don’t need to remember where the buttons are or which menu it’s hidden. Every feature is just one message away. (Of course, your users need to know the feature exists in the first place).

Connect with your users

Emails are dead. My personal email is full of marketing materials and useless information that cleaning up mailbox is becoming a daily chore. Yes, I already set up countless filters to block 99% of them.

And good luck with your in-app push notifications, most of the time people will just clear the unimportant notification as soon as they can and if you are too aggressive, they will uninstall the app altogether.

Residing inside their favorite chat app has the advantage to be really seen by the users. It’s like in the old days, when you really like a girl, you will go and ask “Can I have your phone number”, so when you send her a message, she will probably really read it. Nobody will ask about an email address, but yet that’s what most companies are doing.

This messaging nature also makes a lot of company workflows easier. When there’s something pending your approval, you don’t need to open a separate web page, just send your response in the chatbot. And the counterpart will receive a message in real-time as well.

I’ve built a chatbot to replace/supplement our Employee Self Service web app which our users use to manage our leave and payslips records online. The use case is fairly simple and it fits into the bots perfectly. It can handle common scenarios like

• Apply ABC Leave on XYZ date
• What’s my leave balance now?
• Show my leave history
• Who is on leave on XYZ date/Is ABC on leave on XYZ date?
• Send my payslip in XYZ month
• Approve/Decline employee’s leave

I started this bot as a part-time pet project, and it grows into a fully-featured bot in around 2 months time. The development time is significantly shorter than building mobile apps, yet all the main features are achieved. In fact, some features are actually better than traditional apps, for example, workflow actions are easily integrated into the message, and you can send a voice message if you are too lazy to type.

Of course, there’s no one-size-fits-all solution, but it could be a good alternative in future app development.

Don’t forget to give us your 👏 !

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Why Chatbots could be the next big thing for SMEs was originally published in Chatbots Life on Medium, where people are continuing the conversation by highlighting and responding to this story.

How to build Alexa Skills without
coding knowledge (2021)

When talking about Alexa Skills building, the common things that come to someone’s mind is AI, programming language, complicated coding, and so on.

You may also think that projecting and executing the creation process is very complicated too.

For most of the process, all of the difficulties mentioned above remain true. We state “for most of the process” since nowadays, it has become easy to build your own Alexa Skill, where all is needed is the idea of what you want it to be for.

Now, it is much easier to create them since the No Code trend has influenced how Amazon manages the Skill creation process.

How Amazon Alexa and Skill building started

It started in November 2014, when Amazon launched its series of Amazon Echo devices, which were Alexa-enabled smart speakers.

They weren’t Alexa themselves, but they were the best channel of receiving the voice request, sending it to Amazon Servers where the hard processing work is made, and obtaining the answer or action requested.

It started with nearly 100 abilities Alexa was capable of doing, even though it now has more than 120.000 Skills available.

This all was made possible in mid-2015 when Amazon released a dedicated platform that would allow every interested developer to create Alexa Skills: the Alexa Skills Kit.

How Amazon made it possible for everyone to build Alexa Skills

Since the developers weren’t the only ones with the desire to build Alexa Skills, use and publish them, Amazon came with a bright solution in 2018.

That solution was Alexa Skill Blueprints. A way for non-developers to build simple Alexa Skills and use them. They even prepared an entire series of tutorials on how to use them.

In their effort to gather more developers and work for creating more Alexa Skills, Amazon made it easier through the Amazon Web Services (AWS) console and Alexa Developer Portal.

This would take the Skill-building process to arise, especially with Amazon supporting the developers and those interested in improving the voice technology. It was the Alexa Fund: dedicated to those with the intention of working in new voice technologies.
There are also the Alexa Developer Rewards and Alexa developer promotions to encourage the developers committed to adding and improving the Skills available in the Alexa Skill Store. Great support would be AWS promotional credits, which would reduce the cost of AWS resources used by the developers while building Alexa Skills.

Trending Bot Articles:

1. Case Study: Building Appointment Booking Chatbot

2. IBM Watson Assistant provides better intent classification than other commercial products according to published study

3. Testing Conversational AI

4. How intelligent and automated conversational systems are driving B2C revenue and growth.

How to build your Alexa Skill without coding or technical knowledge

With everyone, be it an individual or a business, looking to create an Alexa Skill, new opportunities are arising. Every developer or developing platform is trying to simplify every step of the Skill Building process.

The number of people aiming to create an Alexa Skill is increasing. Not only to become a professional Alexa Skill developer but also for personal use. Be it as a hobby or for their businesses.

While for individual users the process of Skill building is simple, for the business ones is more complicated.

This is where a third party Developing Platform comes in to help.

With a simple interface, you can implement all the parts needed to complete the Skill and make it ready for publishing.

Not only that but also modify the content of the Skill if needed and also, test it, publish it and check how your Skill performs when made available in the Alexa Skill Store.

All of this is available and free to access on Ipervox.

Why is Ipervox the right choice to build your Alexa Skill?

In case you’re a complete beginner and need more information about what are Alexa Skills and how to create one, you can start by using the Ipervox platform.

Viewing the tutorials made available from our team or checking our FAQ site will make everything understandable.

This development platform has a simple and user-friendly interface, which allows every user to effortlessly build the Skill they want and how they want it to be.

With a well guided interface and a structure oriented in detailed Skill Building, Ipervox allows you to manage and easily control every step.

It helps reach the desired product at the end, giving shape to the initial idea that inspired you to create the Alexa Skill.

Above we mentioned Alexa as a tool for businesses. Ipervox has created several tools and guides to assist every business and entrepreneur in building their own Alexa Skill.

This will help you reach your audience and customers with the most straightforward tool there is: the Voice.

Voice technologies are emerging as the best and promising tool of the future. Not only to reach but also to engage with all of your clients.

Embracing it now means you will have a safe spot in the future. A future where Voice Apps become a common thing for businesses to use.

Create your Alexa Skill using the Ipervox online platform with the set of instruments made available from us. It will help you improve the interaction with your audience, gain their attention and their hearts.

If you want to create your Alexa Skill right now, all you need to do is click “Start for Free”, and you can start building a new channel. A channel that will connect you with your new audience.

Don’t forget to give us your 👏 !

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How to build Alexa Skills without
coding knowledge was originally published in Chatbots Life on Medium, where people are continuing the conversation by highlighting and responding to this story.

Getting started with the Relay SDK

Hey folks! Let’s get started with a quick example showing how you can use the Relay node.js SDK to create a simple number game. For this example, we’ll use Heroku to host a Websocket that will maintain a connection with the Relay Server.

First, create a new app using Heroku. We’ll be naming ours ‘relay-wf’:

Next, let’s setup our environment with git and Heroku CLI (instructions for windows/linux):

$ brew tap heroku/brew && brew install heroku
$ heroku login

Next, let’s initialize our environment:

$ cd relay-wf
$ git init
$ npm init
$ npm install relay-js

Next, we’ll setup our example interaction & deploy it to heroku. Here we are creating a basic app that let’s users guess two numbers and returns the one who guest closest

Trending Bot Articles:

1. Case Study: Building Appointment Booking Chatbot

2. IBM Watson Assistant provides better intent classification than other commercial products according to published study

3. Testing Conversational AI

4. How intelligent and automated conversational systems are driving B2C revenue and growth.

# workflow.js

import relay from 'relay-js'

const app = relay()

app.workflow(`numbers`, workflow => {
  relay.on(`start`, async () => {
    const user = await relay.getDeviceName()
    const random = Math.floor(Math.random() * 10) + 1
    await relay.say(`Player One, pick a number between 1 and 10`)
    const numberOne = await relay.listen(["$DIGIT_SEQUENCE"])
    await relay.say(`Player Two, pick a number between 1 and 10`)
    const numberTwo = await relay.listen(["$DIGIT_SEQUENCE"])
    if (Math.abs(numberOne - random) < Math.abs(numberTwo - random)) { 
      await relay.say(`Player One wins! ${numberOne} was closest to ${random}!`)
    } else { 
      await relay.say(`Player Two wins! ${numberTwo} was closest to ${random}!`)
    }
    await relay.terminate()
  })
})

Next, we’ll add our workflow configuration to Relay servers. First, fire up Dash by going to api-dash.relaygo.com (for production, this is dash.relaygo.com) and navigate to the Workflows section and select the Create button for ‘Custom Workflow’ :

From there, you can enter your workflow configuration. This includes the name of your workflow (here we’ve named ours ‘numbers’), the devices you’d like it on (we’ll push to just one device here, ‘Ibraheem’) and URI hosting the workflow (relay-wf.herokuapp.com). We’re also using the spoken phrase ‘pick a number’ to initiate our workflow from the device.

Save your workflow and then let’s deploy our workflow node.js app to heroku:

$ git commit -am 'Initial deploy'
$ export HEROKU_APP=relay-wf
$ git push master heroku
$ heroku logs --tail

And that’s it! If everything worked, you should now be able to speak ‘pick a number’ into the Relay assistant and trigger your number game!

Ready to start developing with Relay? Click here to signup for our Relay SDK beta.

Don’t forget to give us your 👏 !

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Getting started with the Relay SDK — node.js edition was originally published in Chatbots Life on Medium, where people are continuing the conversation by highlighting and responding to this story.