Year: 2021

submitted by /u/ingoray
[link] [comments]

submitted by /u/SympathyCommercial38
[link] [comments]

Anonymous messaging in Telegram

I was starting an hour long commute home when my phone came abuzz. A friend was organizing the annual Angel & Mortal game for our residential college community and was interested in leveraging some online platform for anonymous communication between players. The rules of Angel & Mortal are simple:

• Each player is assigned a Mortal to whom they act as an Angel.
• The Angel must give gifts to or play pranks on their Mortal without being discovered.

Typically, players would send messages to the organizers to be manually forwarded to their Mortal or Angel, asking for their likes/dislikes or informing them of new gifts. I immediately found this project to be simple yet impactful and was compelled to help. Unfortunately the game was starting soon, in a week, so I had to work fast.

Overview

This bot was developed in Python using the python-telegram-bot package. This allows you to run a simple server that will poll for messages sent to your bot on telegram. Before starting, you will need to set up a bot using the BotFather and generate a token (instructions here). The initial player data are loaded from CSV.

Original code used when I was running the bot (sorry if it’s messy!) can be found here: https://github.com/kstonekuan/angel-mortal-bot

Development

Player class

Before diving into the chatbot logic, it’s important to understand the theory of the game itself. Each player has an Angel and a Mortal. Furthermore, your Angel is another player in the game who has you as their Mortal and vice versa for your Mortal. From an OOP perspective, each player can be represented as an object that references other player objects. Along with some telegram attributes, this can be represented by a Player class.

Trending Bot Articles:

1. How Conversational AI can Automate Customer Service

2. Automated vs Live Chats: What will the Future of Customer Service Look Like?

3. Chatbots As Medical Assistants In COVID-19 Pandemic

4. Chatbot Vs. Intelligent Virtual Assistant — What’s the difference & Why Care?

While not crucial, you might recognise this as a graph node from graph theory. In particular, the game is designed as a directed graph. Each node has two outgoing edges (to the Angel and Mortal) and two incoming edges (from the Angel and Mortal).

Loading players

While it might be possible to use our system to generate Angel-Mortal mappings, organizers often have a more in-depth understanding of social dynamics within the group. I chose to load provided mappings from a CSV file, as opposed to using something more complex (but efficient at scale), as it was simpler for organizers to modify the data. After loading, players can be stored in a dictionary or hash map in memory, similar to an adjacency list

Forwarding messages

Note: I reference parts of the python-telegram-bot API without explaining them, please refer to their tutorials for this.

You might have noticed that our Player class includes a username and chat_id. The username is used to identify players and is provided by the organizers while chat_id is what is used to send messages. We cannot send messages directly to a username as it would be unclear how we want the bot to communicate such as in a private message or group chat. Each user needs to start a chat with the bot first in order to get the chat_id that is generated by Telegram. I also went one step further to reject users that do not appear as a key in the dictionary as they are not registered players in the game.

Conversation handlers can be used to create simple menus to guide users through your bot flow. For this bot the user must first choose either their Angel or Mortal before sending their messages across. They may cancel the conversation at any time to switch between the two. Thanks to the class created in previous steps, it is not difficult to find the reference to their Mortal or Angel and get their respective chat_id to forward messages.

The result should look something like this (with custom names for Angel and Mortal):

Deployment

So far you might have been testing your bot locally but it is probably not a good idea to keep to run this on your own machine for the entire duration of such events. I personally made use of Microsoft Azure free student credits to host but there are many options available.

Improvements

As this was built in a few days with little time for testing there are many improvements you may want to make.

1. Common feedback from players was that they could not send pictures or emojis through the bot. I had forgotten to account for this which might have to do with the Filters module.
2. For long term games that require modifications or continuity you may also consider building a proper backend database such as using SQL or NoSQL instead of a simple CSV.
3. Using the Player class and directed graph design, you can develop advanced algorithms to generate, validate or even modify the player graph for larger games.

I really enjoyed building this bot and helping out my community. This is my first article about programming so I hope you enjoyed reading and am always open to feedback about my writing!

Don’t forget to give us your 👏 !

---

Building a chatbot for Angel & Mortal was originally published in Chatbots Life on Medium, where people are continuing the conversation by highlighting and responding to this story.

Who would have thought a simple tech can integrate your business across platforms like Facebook Messenger, Google Assistant, Website, Telephone, Telegram, SMS, etc. Chatbots have made this possible. Chatbots have been a growing trend for the last few years. Many of us have interacted with a chatbot at some point. As chatbots are getting smarter day by day, it is becoming useful to various industries. Whether you want a chatbot to respond to “life advice” questions in light of the new year or want spiritual guidance for peace of mind or want to book an appointment with the doctor, the chatbot will always grab your attention.

One successful restaurant chain businessman from New York has made a startling statement in its annual report. “We believe the development of artificial intelligence and machine learning, combined with other disruptive technologies like a chatbot, will reshape the food industry landscape in the coming years.”

The statement was very crisp and many other players were echoing that statement. So what is it in chatbot which makes it such desirable tech? And is chatbot really the future for small-scale Restaurant/Food outlets? Let us discuss that.

Automation is not an alien for the food industry. In fact, the Food industry has been leveraging automation for many years in terms of scaling production through robots or setting up vending machines or restaurant reservations.

Websites and smartphone applications have gained the bulk of the share of online food ordering systems over the last decade. The not so enjoyable part of ordering food using a website food ordering portal or using the mobile app is, customers have to follow the language of the food ordering portal/mobile portal. Another most annoying part of ordering food through mobile apps is, customers have to carry different apps of respective restaurants/outlets to order food, whereas chatbot lets customers order food in their conventional language just as you order food from a waiter in a restaurant. Also, customers are not required to install so many apps. Chatbot being capable of integrating on different chat channels like Facebook Messenger, Website, Google Assistant, Telegram, SMS, Telephone will allow customers to order food from their preferred chat channels.

We’re seeing chatbots being able to provide a whole variety of assistance. Not only can customers order yummy foods like pizzas, sushi, noodle soup, tacos, salads, and hamburgers all within two minutes but customers can make the reservation or get answers to FAQs. Let’s take the example of Pk Foods.

You can try the website demo here: PK Food Chatbot

PK Foods is a Dialogflow based Food ordering chatbot for restaurants and food outlets. PK Foods helps customers to browse the menu and allows them to place an order of their favorite food. Customers can add or remove items while placing an order. PK foods chatbot can be integrated with Facebook Messenger, Website, Google Assistant, Telegram which help business owners to be present across the platforms. In the coming days, we are also planning to integrate this chatbot with more chat channels like SMS, Telephone, Alexa.

With PK Foods Chatbot customers can choose two delivery options: 1. Home Delivery 2. Store Pickup. Considering this pandemic situation, both these two options become very useful and imperative.

After the customer places an order, PK Foods chatbot sends the order summary to both customer and the Kitchen department via email. In the coming days, we are also planning to integrate a few payment gateways where customers can pay via chatbot itself.

Trending Bot Articles:

1. How Conversational AI can Automate Customer Service

2. Automated vs Live Chats: What will the Future of Customer Service Look Like?

3. Chatbots As Medical Assistants In COVID-19 Pandemic

4. Chatbot Vs. Intelligent Virtual Assistant — What’s the difference & Why Care?

What makes chatbot so desirable

Chatbot is nothing but an AI-based computer program that simulates human conversations. Chatbots are also known as virtual assistants which have the capability of understanding conventional human language. Chatbot interprets and processes the user requests and gives prompt relevant answers.

As each day passes, machines are becoming smarter and customers are increasingly getting more reliant on them. And it is quite spectacular to see more and more people are embracing voice technology like google assistants, Siri and Alexa nowadays. This forces many business owners to make their business available on such platforms. And when you think from a consumer’s perspective, don’t you think it would be great if a customer can order dinner while heading back home in a car rather than going into a restaurant and asking for an order and waiting till your food gets packed? This seems a small thing in a way but it greatly impacts the customer’s experience. There are many more benefits of having a food ordering chatbot like saving human resources, eliminating human error, streamlining the process, and so on.

Chatbots are also increasingly becoming a great marketing tool. Just like Subway, which has introduced its food ordering chatbot, where customers can order sub using Facebook messenger. Subway’s chatbot also allows customers to choose the ingredients. Those tempting images used in chatbots definitely can provoke anyone’s taste buds. Subway can run the Facebook ad either for special festival discounts or for new product launches and users can order the food from the Facebook messenger itself.

Chatbot has the unique capability of engaging the customer, which is a great help not only to the marketing team but it is beneficial to so many aspects of the business, be it in delivering service to customers in a polite manner during a bottleneck situation or gathering feedback regarding food quality, etc. A large number of local players in the food industry are also integrating the chatbot in their existing food ordering system and they are getting benefits in terms of streamlining the activities and reducing the cost. The operating cost of a food ordering chatbot is very minimal. For certain platforms like Facebook messenger and website operating cost is as minimal as zero.

Seeing the trends, Chatbots are going to play a key role in automating the number of repetitive tasks in the coming years. The chatbot offers so many benefits to the company, it is a time when companies should leverage technology.

Don’t forget to give us your 👏 !

---

Is Chatbot really the future for small-scale Restaurant/Food outlets? was originally published in Chatbots Life on Medium, where people are continuing the conversation by highlighting and responding to this story.

When customers reach out to the company for service, they expect instant responses to their problems. However, a customer care provider can only cater to a certain number of cases at a time.

How do you scale support? Have you thought about the customers?

There are so many service horror sagas that customers have experienced like long wait lines where the agent is unavailable, one where the agent is unable to deliver answers to customer’s questions, agents ghosting on a customer on call, and inconsistent answers provided by service agents.

If only there could be one place where the customers can go to, where there is an instant answer to their queries, quicker issue resolution and minimum agent transfers?

Botspice- AI-Powered Customer support chatbots

Customer care bot by Botspice, an AI-powered chat interface that answers customer FAQs and dynamically resolves customer concerns via smart interactive conversations. Breakthroughs in technology have completely changed the way businesses communicate with their customers. Organizations are under tremendous pressure to drive customer interactions effectively and respond to their query in minimal time. Gartner reports that virtual assistant usage in the workplace will climb to 25 percent by the end of 2021. To meet business goals and generate more revenues, companies must implement a system that engages customers more effectively.

According to the definition from Chatbots Magazine,

“A chatbot is a service, powered by rules and sometimes artificial intelligence, that you interact with via a chat interface.”

Chatbots are also often compared with apps, as Gartner highlights it in its definition.

Why AI-powered bots? Do businesses need it?

Merely engaging customers through chatbots powered by rules is not enough, engaging them through smart interactive conversations, using machine learning & NLP and continually getting better at answering those questions in the future is an essential attribute of a chatbot. Like all successful automation efforts, customer service chatbots can reduce costs, but the improvements they make in customer experience are far more impactful. Bots are available 24 hours a day, 7 days a week, and often, customers’ questions are answered more quickly than human agents.

Do businesses need Workbots?

Well, the main goal of a customer service chatbot is to help businesses optimize resources and reduce agent turnover. How can a business achieve this goal? By providing exceptional customer service across a customer’s journey and also add value in the post-sales journey. Workbot is your best bet when it comes to creating engaging and interactive conversations. Workbot increases user engagement by using interactive workflows and making the conversation more human-centric.

Trending Bot Articles:

1. How Conversational AI can Automate Customer Service

2. Automated vs Live Chats: What will the Future of Customer Service Look Like?

3. Chatbots As Medical Assistants In COVID-19 Pandemic

4. Chatbot Vs. Intelligent Virtual Assistant — What’s the difference & Why Care?

What are the benefits and outcomes of implementing the Workbot for customer service?

Technology shift expects businesses to take ownership of the customer engagement process. Optimizing the customer experience and implementing real-time and consistent interactions is the way forward. Workbot does just that by creating a positive and memorable experience at various customer journey touchpoints.

By automating customer service using AI-powered bots, companies can

• Minimize agent turnover and increase customer service productivity.

A successful customer service chatbot will minimize the number of agents needed at a given period and provide consistent service with increased productivity. Often customers hesitate to call the customer service agents simply because they have to repeat themselves over and over again. By using friendly and approachable AI-powered chatbots, businesses can ensure that customers can have their queries understood and addressed via a simple text request.

57% of customers would instead contact companies via digital media than use voice-based customer support, as per Ameyo’s research.

Forrester says, nearly 1/3 of customers send a mobile/SMS message to the company requesting assistance.

• Gather customer data through conversation funnels.

Perhaps the biggest reason why so many companies collect consumer data is that it helps them to get a much better understanding of the way their consumers behave online, define their overall demographics, and identify how they can improve the overall customer experience. You may have heard the phrase “Content is King” first coined by Bill Gates following his essay of the same name in 1996.

Susan Bidel from Forrester says, ‘In the Age of the Customer, Data Is King’

It is therefore crucial to be able to access information about customer’s choices, demographics through conversational funnels. Precise user data and metrics allow organizations to optimize marketing plans. Knowing what does and doesn’t work allows organizations to be far more agile, responsive and targeted in their marketing and customer outreach efforts. AI chatbots can analyse millions of customer data touchpoints in real-time and help organizations access customer information at any point in time.

• Analyze trends in customer inquiries and behaviors via a visual analytics dashboard.

Organizations can analyse the statistical data and record market behaviour over time to generate valuable insights for strategizing and forecasting future business plans. Through analytics, dashboard organizations will be able to drill down and measure how effective the marketing strategies were in converting a potential lead to sales. Analytics dashboard can also predict what isn’t working in a customer journey. For example, if a customer drops off mid-way from a chatbot conversation, the organization can determine what exactly is not creating engagement which, in turn, can create new growth opportunities.

• Customize interaction and strengthen brand loyalty.

AI-powered chatbots communicate via a two-way conversational channel. It allows users to ask what they want without limiting themselves to a pre-determined set of questions. Workbots are available 24/7 and can help keep a business stay connected to its customers and provide consistently high support quality even when the support agent might not be available. Implementing Workbots can help build the customer’s trust, create loyalty and increase CLV customer lifetime value.

• Drive revenue by automating transactions and providing up-sell opportunities.

Workbots can be programmed to track your customer’s behaviours and choices based on their purchase decisions and the questions they ask. This can help organizations to generate up-sell opportunities and entice customers, thus driving revenue increase.

• Reduce operational cost

E-commerce statistics show that businesses spend around $1.3 trillion on customer requests every year.

With Workbots customer service costs can be reduced to a significant amount. It can be exceptionally expensive to keep up and scale support with ever increasing customer service demands. This is especially so when one thinks of the manpower involved. Recruiting, training and retraining agents can be expensive especially as the organization grows. Artificial Intelligence-powered chatbots will be a smart solution for development and integration. Once deployed, it can help a business scale up service and sales interactions across multiple channels and enrich customer journey touchpoints.

All these are just statistical benefits but the one benefit that organizations look for is increasing revenue-per-case while reducing cost-per-case That is what differentiates a mediocre chatbot from an AI-powered Workbot.

Botspice delivers a vertical Artificial Intelligence that owns the entire workflow to solve targeted customer needs. It develops a complete product from end-to-end, starting with understanding the business case to optimizing product performance.

Every brand needs an expert interactive agent!

How does this work? How can I implement Workbots?

How does a Workbot work? Workbot uses Natural Language Processing to analyse the user’s request to understand the intent and extract relevant information. It then responds to the user with an appropriate answer which could be either:

1. A pre-defined text message.
2. Relevant information from a knowledge base.

Botspice simplifies the design, development and deployment of bots across multiple channels like messaging apps, digital assistants, collaboration tools and enterprise applications. It allows organizations to customize Workbots with rich media and a dynamic conversational flow. AI-based knowledge “Brains” enable Workbots to respond to queries 24/7 with consistency. Once ready, your AI chatbot is ready to be published.

Workbots improve over time. The number of intents they can recognize will expand as your company identifies which questions are of high volume handled by the chatbot.

In conclusion

A successful virtual assistant will reduce cost and scale support especially if you’ve set it up with the right intention and deployed them to the right channels to optimize success. AI-powered chatbots are crucial to a well-designed customer service strategy. Workbots can help either strengthen or substitute the need for two-way human intervention thus cutting total operational costs and improving customer satisfaction. When implemented correctly, Workbots can enhance the user experience, improve engagement and provide customers with actionable solutions. How will you implement chatbots into your customer service strategy from now on?

Don’t forget to give us your 👏 !

---

AI-powered chatbots to scale customer service support. was originally published in Chatbots Life on Medium, where people are continuing the conversation by highlighting and responding to this story.

submitted by /u/Botmywork [link] [comments]

submitted by /u/Mfor_Mary
[link] [comments]

Chatbots, a smart asynchronous means of communication, powered by AI has seen a high acceptance and penetration during the last few months. A number of industries started to believe they can be a great help for their business and can support when they are focussed on fixing a number of other issues.

A number of such chatbots are now deployed for the healthcare industry where medical professionals use them to support the cause of social distancing and help users with relevant answers.

Chatbots in healthcare are extremely critical for the advancement of efficient and accurate patient care. They are also expected to support the world’s need of creating affordable and sustainable health care for every individual on the planet.

Why are chatbots in healthcare growing so fast?

Continuous availability and ongoing support

Covid has been an eye-opener for the world why creating an ecosystem for public health is paramount. During hours of crisis, even paramedics and other allied servicemen become crucial to handle crises.

This also makes the world vulnerable to patients who are looking for urgent medical attention and guidance. Unfortunately, doctors have limited time, and having a set of requirements makes it impossible for them to cater to everyone.

Chatbots can play a wonderful role in supporting these patients and guide them with authentic and reliable information.

Scheduling Appointments and Other FAQs

A number of patients complain of very busy helpdesks. This often results in dissatisfaction among the visitors and their loved ones. Since a number of patients connect with health care institutions primarily to take appointments or find answers to generic issues, this can be extremely well handled by chatbots. Today’s chatbots can help in booking appointments and also respond to regular questions using the existing knowledge base.
They can also take up relevant information after booking the appointment which can help doctors understand the patient well and track all of their visits, follow-ups.

Building Trust Among Patients

Every medical institution needs to look progressive and the one who is looking to keep up with advancements in the world of technology. Chatbots have become a great way to build this trust as it caters with authentic information to users and at the right time.

For example, if someone is looking to meet a doctor then the chatbot by knowing its symptoms can easily recommend if she needs urgent attention or some basic medicines before she comes up for the checkup.

The reasons listed above have been the biggest drivers yesterday, today, and tomorrow for the growth of chatbots in the healthcare domain. However, one must also look at the reasons and risks involved which can stop this growth.

Trending Bot Articles:

1. How Conversational AI can Automate Customer Service

2. Automated vs Live Chats: What will the Future of Customer Service Look Like?

3. Chatbots As Medical Assistants In COVID-19 Pandemic

4. Chatbot Vs. Intelligent Virtual Assistant — What’s the difference & Why Care?

Challenges We Must Not Forget

Low Usage Among Senior Citizens

Some of the most vulnerable classes for healthcare don’t find it easy to use and confusing. One of the bigger challenges in front of healthcare providers is to get onboard this section of society. They have to look beyond just chat and can look to integrate the option of text as well. This can allow users to get answers and create a feeling similar to being treated by a doctor.

IT Vulnerability and Hacking Probabilities

One of the biggest challenges in front of chatbot creators is minimizing the vulnerability risks involved in creating sensitive and regulated information. A number of information shared to chatbots need to be highly secured and can’t be leaked. A number of precautions need to be taken to minimize cyber attacks and also any kind of information leaks

Well Tested Both From Performance and Information

Information that is shared with these chatbots is a matter of life and death sometimes. It is extremely vital that this information is well researched and also tested extensively to avoid any kind of mishaps. The information should be coming from a well researched and reliable source to avoid any last kind of issues

While we have understood the importance, need and risks involved while building a chatbot, it is extremely important for us to understand the process behind it as well. This will help us understand the technicalities far better and also explain the same to our visitors time and again.

Chatbots across all the industries work in a similar manner so understanding one can help you understand the rest too. Here the working mechanism has been considered for all three kinds of chatbots i.e. Rule-based, intellectually independent, and machine-based. You can read more about them from our last blog on “How chatbot works”.

From the above diagram you are able to understand how the data is processed and using the NLP layer the user’s inputs are validated and processed for the relevant answer.

Bonus: How to Build Coronavirus Chatbot

However, the following are the key highlights of the process

1. Some of the chatbots divide certain words into smaller tokens so that they can easily understand the process and also define its applications.

2. Chatbox is used for named entity fields like user name, gender, age, etc to understand better details about the user and then recommend the relevant answer

3. One of the key highlights of using NLP is understanding the phrases of our users and deciphering the exact meaning of the same. With dependency parsing, chatbots are able to identify different parts of speech such as nouns, verbs etc and understand the meaning of complex sentences as well.

Also, using sentiment analysis they can understand the real meaning of the words which are often called “homonyms”.

While we have understood the need and process of implementing chatbots it is crucial for us to understand the process of building it as well

How to build your own healthcare chatbot

A number of tools today can help you create your first chatbot in minutes. Let us understand the process of the same.

However, to use these tools you are no longer required to understand the world of coding but are primarily driven by drag and drop processes

Here I am using the NLP based codeless bot builder Kompose. You sign up here for 30 days free trial and follow further steps to build your healthcare chatbot.

1. Login to your Kommunicate dashboard and click on Bot integration
2. In the bot ‘Bot Integration‘ section >> Select Kompose >> Setup Bot
3. You can create the following right away.

Welcome Message

A simple message on how your bot will behave once you are user initiates a conversation with your bot.

Answers

While you are discovering how to welcome your user it is also critical to see what are the probable answers for the bots to share. This is vital as understanding the user is crucial.

Small Talk

What are the various FAQs which the bots need to know?

You can feed all them here and users can get quick answers to that.

Fallback

This is important as leaving everything on the bot is not recommended. Creating a backup plan where a human can forward when the bot is not able to understand is crucial.

You can also check out the step by step process on How to make a chatbot

Once you are able to follow the above-listed steps you are almost halfway through the process of creating a basic chatbot for your business. It is vital that you are testing it extensively across multiple languages and devices to ensure that the experience is a good one for your user.

Some Useful Templates For Healthcare Chatbots

Now that you have a good understanding on how to create a chatbot your healthcare institute, here are some of the best practices for you to get started.

Booking a doctors appointment

Cato: Welcome to <Name of the entity>. We are a super specialty clinic with more than <number of doctors> under one roof. How can I help you today?
User: I need to book an appointment
Cato: Ok! Please let me know what kind of treatment you are looking for?
User: I need to consult a cardiologist
Cato: Ok. What is the best date and time for you to drop by?
User: 5th October anytime
Cato: Here are the available options
5th Oct, 10: 00 AM
7th Oct, 11:00 AM
9th Oct, 3.30 PM

Healthcare Services

Cato: Hi, Welcome to <Name of healthcare institute>, a super specialty clinic.
How can we help you today?
User: I am looking for a medical recommendation
Cato: Ok, I understand. To get started: Please do share your Name, Age, sex
User: I am Katen. I am 35 years old and a male.
Cato: Thanks a lot for sharing. Do let us know what are the challenges you are facing
User: I am having a mild fever for the last few days and also a pain in the throat
Cato: Ok, I see. Let me help you. I have a few more questions to ask: Is that ok?
User: Yes

So what are the use-cases of Chatbots in the healthcare industry?

Lead Generation:

Never miss out any of the visitors who are looking to know more about your services or trying to book a service

Specific Chatbots:

This pandemic has already taught us how every healthcare professional is critical for the world. With disease-specific chatbots, one can use the same for reaching out to multiple users at once with authentic information.

Customer Support:

Answer all the relevant questions or queries of the visitors in a flash. Have an extensive knowledge base for them to answer it quickly. You can train your bots too with time.

Feedbacks and Survey:

Take regular feedback about their experience and discover what all you can improve with time.

The use cases mentioned above are of generic nature and they can be expanded as per user behavior. Let us also look at some of the chatbots and its utility as per the current market requirements

1. Florence: A chatbot that reminds users to take pills, track body weight, track mood, and also menstrual periods. It aims to behave like a nurse for an individual.
2. Safedrugbot: A chatbot which aims to offer assistance to medical professionals with an extensive knowledge base about drug dosage and more.
3. CancerChatbot: As the name suggests it is a chatbot used to guide patients and their loved ones on everything related to cancer. Whenever a user shares a query, the chatbot finds the best possible answer from a reliable database and shares.
4. Sensely: A smart chatbot that is used to track symptoms from both text and speech. It’s the ability to understand the symptoms and recommend a diagnosis that has been a great attraction point for users and doctors.
5. Woebot: A mental health chatbot which aims to understand the need and symptoms of users around mental health. The chatbot studies the mood, personality and recommends the best way forward.

Wrapping up

One thing we should remember after reading the evolution, growth and use cases about chatbot in healthcare domain. It is just the beginning of a revolution and it has just started.

The very purpose of introducing chatbots is to make life easy for users. In today’s world of extremely busy and hectic life traveling often becomes tedious. Also, finding an authentic and reliable resource is also becoming a challenge with so much noise around

These virtual assistants can be a great way to address these growing challenges as they can keep users engaged, help them when needed and also guide them to the best possible solution. The world can easily rely on them for creating an ecosystem of affordable and authentic healthcare.

In the future conversational AI will become the best way to help patients navigate through major challenges. The time ahead is surely exciting for chatbots in healthcare domain.

This article was originally published here.

Don’t forget to give us your 👏 !

---

Step by step guide to build healthcare chatbot without any coding. was originally published in Chatbots Life on Medium, where people are continuing the conversation by highlighting and responding to this story.

Dialogflow is a Natural Language Processing (NLP) platform by Google that provides users with the service to build, and design conversational interfaces; that can be integrated into applications, websites, and chatbots with ease. Dialogflow can analyze multiple inputs of media like audio, text, and video. It also has the capability of replying in these multiple formats.

Benefits of using Dialogflow

• Developer friendly: With a built-in, inline code editor; Dialogflow simplifies the coding process for developers. Agents can connect to their applications and code with ease through the cloud and on-premise facilities.
• Powered by Google’s machine learning: App developers are given the platform with which they can train their agents to gain a better understanding of conversing with people. The app extracts relevant information and provides it to AI for better understanding.
• Pre-built templates: Dialogflow comes with more than 30 pre-built templates that act as a foundation for developers.
• Can express itself through natural conversation: Chatbots created with the aid of Dialogflow can have the ability to speak to people using natural language. This means it can understand abbreviations, and converse with people in an informal tone ensuring that it doesn’t sound too robotic.

Trending Bot Articles:

1. How Conversational AI can Automate Customer Service

2. Automated vs Live Chats: What will the Future of Customer Service Look Like?

3. Chatbots As Medical Assistants In COVID-19 Pandemic

4. Chatbot Vs. Intelligent Virtual Assistant — What’s the difference & Why Care?

Apps that connect with Dialogflow

• Facebook: Build a conversational bot on messenger.
• Wix: Build a conversational bot on your website.
• WhatsApp: Build a shopping bot or FAQ bot on WhatsApp.
• Intercom: Enhance live chat service on your platform.

These are just a few applications you can pair Dialogflow with, explore our Dialogflow page to know more about automating Dialogflow and get in touch with us in case you don’t find an app you’d like to pair Dialogflow with!

Don’t forget to give us your 👏 !

---

App Highlights: Dialogflow was originally published in Chatbots Life on Medium, where people are continuing the conversation by highlighting and responding to this story.

Nowadays, almost every site uses Chatbots, whether it’s a social network, a website, or an e-commerce platform. Chatbots help expand your business and manage your CRM (Customer Retention Management) interaction like a professional.

The futuristic benefits and timely investment into chatbots have leveraged many companies to realize their full potential. A chatbot proves to be an excellent addition to enhance your marketing plans and benefit your organization if applied correctly.

However, successfully deploying a chatbot doesn’t warrant goal achievement. You need thorough testing before applying it to your marketing strategy. If you’re a beginner to this topic, you might wonder about the techniques available for testing a chatbot?

This guide will help in solving your concerns regarding tips and techniques related to chatbot testing. Let’s take a closer look below:

Regulation of testing frameworks

Generally, almost every testing procedure is devoid of standardization. It becomes challenging to measure the amount of communication covered by test cases, particularly before launching a bot. The objective of the testing procedure should be to include the most anticipated use cases.

The testing framework developed broadly follows three categories listed below.

• Expected scenarios
• Possible scenarios
• Almost impossible scenarios.

These testing use cases can be charted to sigma distances. When testing for the third category (nearly impossible use cases) is completed (known as 3-sigma distance), the chatbot’s performance can be said to be evaluated at a 99% confidence interval. Any testing beyond this level would incur high costs because there are endless possibilities in which humans use language.

Domain of testing:

Chatbot testing offers seven kinds of domain for testing:-

• Conversational flow
• Natural language processing model
• Intelligence onboarding
• Personality
• Understanding
• Answering
• Security
• Speed
• Navigation
• Error management
• Intelligence
• Response time

Agile and regular Testing

Chatbots are good instances of software technology developed using the Agile approach. It offers the best possible viable products that can be obtained after every loop. It captures new phrases with the help of error handling functions.

To prevent bugs from creeping into the bot, testing needs to be done at each iteration. The initial phase includes manual testing, which ensures the execution of the business workflow. The end phases include automatic testing that reduces wastage of time and helps programmers to launch better versions to market.

Trending Bot Articles:

1. How Conversational AI can Automate Customer Service

2. Automated vs Live Chats: What will the Future of Customer Service Look Like?

3. Chatbots As Medical Assistants In COVID-19 Pandemic

4. Chatbot Vs. Intelligent Virtual Assistant — What’s the difference & Why Care?

Developer Testing

This kind of testing is simple and direct, which the developers are familiar with. It includes executing a validation and verification test and defining the chatbot’s answers and user questions beforehand. These tests will check whether the bot gives precise answers to an imaginary question or cannot do so.

Chatbot Testing Frameworks

Developers and testers have to follow analytical thinking to overcome elements of uncertainty in their test objects to understand how a chatbot will operate. The following list shows some of the techniques testers can utilize when dealing with a chatbot:

• Advanced automation framework: it is essential to test for the point-to-point flow of conversation, understanding natural language, and scope for self-improvement.
• Domain-specific testing: evaluating selected products and services mainly for the business- and consumer end benefits limits the range of testing. Therefore domain testing helps test all possible use cases.
• Real-time monitoring and KPI (Key Performance Indicators): KPI for measuring chatbot performance are different and include parameters like rate of goal accomplishment, AI and machine learning rate, self-service rate, and fallback rate.
• Advanced security mechanism: The security mechanism must incorporate user authentication, end-to-end encryption, two-factor authentication, compliance validations, authentication timeout, channel authentication, intent authorization, and self-destructing messages.

Testing the latest technologies and practically implementing them can be exciting and challenging at the same time. Especially viewing the approaches and tools that have worked in your favor failing in chatbot testing can cause frustration among the best developers. Therefore, considering these up-to-date strategies will assist the developers in testing a chatbot in a better way.

Botanalytics

Botanalytics is a dynamic AI-enabled conversational analytics tool that assists you in capturing engagement across your user lifecycle. With the cutting-edge AI-based solution, you can enhance the capability to interact through sentiment analysis and A/B testing, etc.

Chatbottest

Chatbottest is a free guide with 120 questions that help you assess your chatbots’ user experience. It evaluates the bot based on the seven domains of testing mentioned above.

Dimon

Dimon is a tool for testing tests your chatbot’s flow of conversation and user experience. It even has added functionality of integrating with social media platforms such as Facebook, Slack, Messenger, Telegram, and WeChat.

Techniques for Testing Chatbots

There are different techniques available to test chatbots which depends upon the type of tool being used. A straightforward way is to try the entire training data in your model and correctly predict your model. Moreover, the testing techniques are broadly divided into two main types:

Industry Standard Cross-Validation Techniques

Models based on MI (Machine Learning) are tested using a statistical approach called cross-validation. In this testing, the model’s capability to predict new data dissimilar from the one used for training is evaluated. This kind of testing in interactive AI systems implies testing the bot using queries from the scope examples used for training it.

Standard practices include Leave-one-out cross-validation (LOOCV) and K-fold. The K-fold method divides the data into k groups, in which one part is used for testing the model and the remaining part (k-1) for training. It is iterated k times with every split taking turns at being used for testing.

The LOOCV approach is an extensive method in which the model is tested over possible combinations that the original sample could be divided into testing and training sets. It is bears less computational cost and is appropriate for training small data sets. Experts recommend using cross-validation techniques before switching to blind testing.

Blind Tests — Testing Data Sets with Statements

Blind testing includes testing data with utterances or questions that users might enquire about along with the equivalent exact answer. These queries are executed through the model via a batch test. During this process, every query is marked as to whether the prediction made by the model was correct or not.

Irrespective of the methods used, it is critical to detect the action steps relying on the result. Data visualization techniques help better understand how similarly or dissimilarly the model understands the data by displaying them as close or far away.

A confusion matrix is also quite beneficial in representing objectives predicted by the model so that the NLP trainer can detect patterns and retrain the objectives as per requirements.

Every project doesn’t need to evaluate through both kinds of tests. The selection relies on the developer’s knowledge and potential to conduct the tests.

Create a Good Test Set In Case of Non-Availability of Current Data

Both the testing of the interactive AI and its successful implementation mainly depend upon the selected data set. Before preparing a blind test set for AI, keep the following rules in mind.

• Scenario-based — reflect on as many scenarios possible that users could encounter while communicating with a conversational AI. It will help in grouping intent-based questions that map to unique answers.
• Well explained descriptions — having a detailed description of a problem is always considered better since the bot needs to offer a solution to the user. You need to incorporate the following things -user type, the difficulty faced, and how the user will express the query.
• Align interpretations- it is better to arrange the questions asked to the bot by the users in a systematic order.
• Well-defined answers — ensure that the queries used in the training set carry their corresponding solutions well-phrased and carry value.
• Questions based on ground reality — always opt for the best data source regarding testing that includes genuine questions asked by real users.

Few Common Errors to avoid

Often, the training test data set does not meet expectations when training the bot is concerned. It occurs due to common errors like –

• Irrelevant test questions for solving the scenario — improper preparation could lead to participants of the test ending up enquiring peculiar and arbitrary questions to test conversational AI or for fun.
• Similar expressions carry different intents — which could create conflict and confusion.
• The explanations of scenarios being very general
• The questions are lengthy and lacking clarity — often, while preparing the data set for training, questions could become verbose and include unrequired content.

Coverage Ratio — Critical Analytics Parameter

Continuous monitoring of the analytics tool is essential in a software deployment project. It becomes even more critical when iterative reviewing and testing of the chatbots’ performance is concerned.

Tune your analytics to track the Coverage Ratio. It will help you know what questions users ask and how many of those questions are featured in the AI-based assistant trained for answering correctly.

• For coverage greater and equal to at least 70 to 80%, the questions selected by you for training the chatbot are good and closely represent how actual users might ask.
• For coverage values lower than the above limit- implies that few queries made by you come under the training set for the chatbot but are not precisely what the actual user is asking.
• For such a scenario, the best option is to delete a few of these inappropriate queries and include many relevant questions that the users need assistance with.
• Having fewer examples per intent and similar expressions grouped into different purposes is the most common cause behind wrong predictions.
• It is essential to gather good examples to train the bot according to the predefined test sets. As per the rule, you should target 10–20 examples per intent.

Integrating Email and SMS With Chatbots

Email

With advanced technical frameworks available these days, your chatbots can be aligned with email marketing easily. These two technologies function together well, as can be seen through the following use-cases, highlighting three situations where chatbots can sync with email marketing.

• Chatbot for Email subscription list
• Conversational chatbots for email marketing
• Chatbots for online purchases

These are some of the most straightforward use cases that can be directed to sign up for a mailing list. When the lead gets successfully converted, the bot can ask for some basic details along with the subscriber’s email address.

So basically, the entire workflow comprises of the following-

• Email — data and essential documents that customers require for making a decision
• Chatbot — Answers to queries and delivers information instantly when leads are about to be converted
• Chatbots are even used for managing online purchases.

SMS Chatbot-Marketing Through Text

Just like email marketing chatbots, SMS chatbots are also being utilized for marketing and promoting brands. SMS marketing services offer a consistent channel for marketing since you re-engage with the user repeatedly.

It uses permission-based text messaging to spread marketing messages, such as new product launches, analyses, or feedback.

Conclusion

It eventually comes down to testing -which forms the foundation for including desired features of the conversational AI. However, these features can be enhanced with constant effort and deploying of apt technologies.

Chatbot testing forms the most critical characteristic of the whole chatbot lifecycle. The techniques mentioned above and tools will guide you in extensively checking your bot before launching it on any platform.

It would be better to ensure that your bot is interactive enough, execute a domain-specific test, and carefully examine the results. It should tell you how good your bots are at handling unexpected queries.

You can either go for manual checking through the developer’s help or use the described tools to evaluate them. Last but not least, to make the chatbot more interactive, always encourage small talk, look for matching intent, and define a fallback along with excellent navigation.

Don’t forget to give us your 👏 !

---

A Guide To Chatbot Testing Framework & Techniques 2021 was originally published in Chatbots Life on Medium, where people are continuing the conversation by highlighting and responding to this story.