Category: Chat

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 👏 !

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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 👏 !

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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.

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Life can come at you in unpredictable ways, and having yourself safely insured is always a smart investment plan. The core of any insurance plan is to provide you with protection. Making small investments in insurance can provide you with financial security in advance. Now, with technological advancements and messaging platforms growing popular, the insurance sector has seen a significant surge in the way it has been running all along. Insurance is a data-driven sector and in the last many years, data corruption has been a persistent problem in this sector. Hence, AI-driven chatbots are a boon!

Making use of chatbots in the insurance sector, companies have been able to uplift their services, communication, efficiency, and customer support. So in this blog, let’s dig a little deep into how chatbots for insurance are proving to be advantageous.

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Trend- Insurance chatbots for agents?

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Insurance Chatbots are growing into the hottest trends in technology for the last 2 years. Chatbot algorithms that enable intelligent conversations with humans are now being used to assist both customers and agents in many areas of the insurance sector. These insurance chatbots enable new functionalities by assisting hundreds of queries all at the same time, and as more companies come up with newer technologies to make bots more intelligent, they’re bound to offer more applications in the insurance industry in the coming years!

Though insurers have been using voice assistants at call centers for a while now, the development of new technologies has been enabling far more complex interactions. chatbots for insurance are being used extensively within the industry to support agents, and customer applications such as onboarding new clients, processing claims and renewals. Even though chatbots offer many benefits, insurers must make sure they’re being supported with the right intelligent tool.‍

Challenges That Insurance Chatbots Can Solve

Today, the insurance industry faces countless challenges. With the world becoming more digital each day, consumer expectations change at a rapid pace. Consumers look for policies from online websites and compare prices themselves before contacting an agent. As information has been made easily available to the consumers, the insurance companies are using chatbots to overcome these commonly faced challenges to build better relationships with their policyholders.

1. Consumers don’t easily trust insurance companies

According to a recent survey, people trust insurance companies way less than they trust any other sector. People are very hesitant to invest their money in insurance. They’re always looking out for any hidden clauses in the policy fearing ending up with something they don’t want to buy, while the insurance companies are often labeled with a bad reputation if they’re open about their policy!

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Customers don’t trust insurance companies easily‍

To overcome this, insurance companies are building website chatbots that can make quick and reliable engagements while building trust among its users. Insurance Chatbots communicate with the consumers, provide them with details and quick solutions in simple language which builds a good rapport between the consumers and the brand.‍

2. Consumers don’t always understand insurance policies properly

People lack proper knowledge bases about insurance policies and their advantages, which makes it even more difficult for insurance companies to sell their products. This can be easily solved with online insurance chatbots. Insurance companies usually have a web of complex technical terms that are hard to understand from a layman’s perspective. Chatbots can easily communicate these terms to the customers in an interactive manner using simple language, aiding customers in having a clearer picture of the policy and many things as such.‍

3. Insurance claims and renewals are complicated processes

Insurance claims and renewals have always been a complicated process and time-consuming when done manually. That’s why insurance companies are building chatbots that allow customers to make claims or renewals directly through the chatbot and there is no need for a live agent to do so. This automation in claims and renewals has made the whole process really quick and easy, both for the agents as well as the policyholders.‍

4. Consumers have different communication preferences

Consumer-company interactions happen in three ways- online communication via texts, talking over the phone, or in-person meetings. As more and more people turn towards messaging as their chosen communication medium, insurance companies have to cater to this preference quickly. And what better online conversational tool than a chatbot? Insurance chatbots become the ideal customer support agents the buyers are looking for. They’re always available, you can just drop in a text and have a solution/reply immediately!

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The future of insurance in a digital world

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In this digital age, opportunities and threats are often different sides of the same coin. Customers are less loyal and more demanding; competitors are entering the industry with radical ideas unburdened by legacy systems or mindsets. Disruption is inevitable; insurers must decide whether they will be among the disruptors or one of their casualties.

Now, digital insurance companies are creating unique customer experiences through new combinations of information, business resources and digital technologies. As AI becomes more deeply integrated in the industry, carriers must position themselves to respond to the changing business landscape. Insurance executives must understand the factors that will contribute to this change and how AI will reshape claims, distribution, and underwriting and pricing.

Chatbots are bound to play a more significant role in the future to come. But let’s explore how they change the customer experience while assisting your agents and looking after the smooth running of your organization.

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The rise of the automated insurance agent

he Insurance sector has invested an average of $124million on AI and related processes, and this value is projected to rise exponentially as more investment on diverse applications is on the immediate horizon. The automation of several processes like broking, low-level claims processing, standardized underwriting is already implemented, and more automation is expected to follow.

Automation for insurance has also helped to mechanize the repetitive tasks that once needed a dedicated workforce.

Simplification

With Chatbots to help lead customers to the exact information, they are seeking, or provide solutions instantly, customers no longer need to navigate the website or any other interface, saving precious time. They can simply ask or type what they need, and multi-step actions are compressed into a single command followed by the chatbot. For example, instead of searching for information on all the different kinds of health insurance that alight with a customer’s needs and then make the effort of emailing it to oneself, the customer can ask the chatbot to find, compile and email the information on his behalf.

Streamlined process

One of the biggest challenges for insurers is the expectation of a faster-than-ever-before timeline for claims management and approval. Customers too dread the tedious process of filling out endless paperwork, only to have their claims rejected due to incompletion or technicalities. Chatbots can be enabled to cut down on unnecessary paperwork and steps within the claims filing and approval process. As machine learning and AI technology evolve, chatbots too will be able to assist customers faster and more accurately, approving valid claims within minutes.

Multi-tasking

Because customers are required to put in minimal effort — they only need to ask or type their queries — they are able to leverage chatbots to do multiple things at the same time. For example, a customer can ask a Chatbot to walk them through the differences and benefits of term-life and whole-life insurances, while, say, drafting emails for another task.

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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?‍

How Insurance Chatbots Help Brands

1. Drive higher sales

Chatbots optimize the response to user queries and reduces time to action, thereby reducing drop-offs. The click and point nature of mobile apps and the web reduce down to users asking pinpoint questions of their needs thereby increasing the efficiency of information retrieval and quicker decisions.‍

2. Generates leads

Chatbots are well-known for lead generation and turning up the marketing scales. They engage visitors on your website and keep them hooked with various methods like asking them relevant questions, recommending policies, and providing details. Once the visitor shows interest, the chatbot can assign an agent to them for further decision-making. They recognize hot leads and push them down the sales pipeline through proper customer engagement.‍

3. Product selection

‍Bots can also suggest policies and products based on a query-based path because it’s important to capture customer needs.‍

4. Premium payments

Customers can be sent alerts on renewals; policy lapses; notifications on due dates, and a lot more status information in an insurance sale or support cycle.‍

5. Personalized customer service

Based on AI and Machine learning, chatbots are capable of reading and memorizing user data from all their conversations and hence, personalization comes into play! Insurance Chatbots interact with the customers and collect user data like their preferences, what kind of insurance they are looking for, and so on. An insurance chatbot can work as an artificial insurance agent that recommends appropriate insurance to clients based on their requirements and lifestyle habits.‍

6. Help in finding the right policy

Chatbots assist clients to choose the right insurance policy. They collect large amounts of data and are able to provide all the educational support required for the consumer to understand each product. With all the collected data, chatbots are capable of tailoring products according to each consumer’s needs. After helping them decide on a product, they can further assist you in going ahead and purchasing it hassle-free.‍

7. Manage claims and renewals

Healthcare or employee-initiated policyholders are always eager with their claims. With the help of chatbots, they can check their coverages, how to file for a claim as well as track status. They also assist in renewal processes similarly.‍

8. Services round-the-clock

The insurance sector has to be available 24/7 because critical moments can strike at any hour of the day! Hence, has been a great assistance tool for not just the prospects, but also the doctors round the clock!

Consumers can interact with chatbots at any given time, make claims, renewals, or even find details on the policy at any given time. Their easy-to-reach and 24-hour availability have made chatbots the best tool for automation in the insurance sector.‍

According to the Global Trends Study 2017, insurance invests an average of $124 million per company in this alluring technology.
– VentureBeat report on Insurance Chatbots.‍

Key features of an insurance chatbot

Incorporating a chatbot into a company’s environment is not as easy as it seems to be. A chatbot should have several fundamental features that could allow it to function successfully.

Many companies have deployed chatbots for insurance, but not all of them are up to standard. They have limited capabilities and can be tedious to use. In the end, customers still end up speaking to a live agent.

The most proficient virtual assistants provide advice and go beyond the functions of an FAQ chatbot. To do so, they must know what customers want, fully comprehend the services the business provides, and be able to learn from real data to interact with customers and engage as a human would.

Comprehensive speech recognition

This software feature will reduce word error rates and improve machine translations. Improvements in speech and language processing technologies make chatbots more capable, expanding their potential applications across the enterprise.‍

Perform operations

This feature is vital in the insurance field as we expect an agent to give us the requested information and perform some actions (send quotes, open accounts, sign claims, etc.).‍

Connectivity

There should be a range of communication methods with customers, including web, mobile apps, messaging platforms, etc.‍

Security

Customers provide sensitive data to insurance companies and expect the company to follow regulations regarding privacy. Protected data is an important part that builds stable customers’ loyalty to the insurer‍

Reliability

A vendor should design a chatbot thoroughly so it operates flawlessly and there are no errors that can push away an insurance company customer.

Insurance Chatbot Case Studies

Here’s how 3 of the largest insurers in the Middle East region create fantastic experiences and drive sales through the use of chatbots!

1. Tokio Marine Insurance Company

Operating in the UAE market since 1976, Tokio Marine & Nichido Fire Insurance Co., Ltd., is one of Japan’s oldest and largest non-life Insurance companies. With customer trust as the foundation for all of its activities, TMNF provides the safety and security necessary to provide comfort to its customers. The UAE brand aspired to shorten its sales cycle through proactive support.

Their chatbot, Tokio, helps customers answer their insurance requirements over Web, WhatsApp, and Messenger 24/7. The bot helps customers get quotes, track claims, and renew policies with zero human intervention.

Currently, Tokio handles 70% of TMNF’s inbound queries. To engage global and local audiences, TMNF will extend Tokio’s capabilities by providing support in both Arabic and English.

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2. Qatar Insurance Company

Founded in 1964, Qatar Insurance Company is the market leader in Qatar and a dominant insurer in the GCC and MENA region. As the volume of incoming queries started overwhelming their agents, they were looking for solutions that could automate query resolution 24/7 without human intervention. They were also interested in engaging customers in their preferred channels to boost lead generation and shorten the sales cycle.

With their intelligent chatbot, Around, they were able to help customers buy policies in under a minute. The bot was deployed over the web and mobile, functioning across Qatar, Oman, UAE, and Kuwait.

Around provides customers with highly personalized recommendations and also allows customers to renew policies and make claims without assistance from insurance agents. As a result, the number of daily users increased to over 500, and now there have been over 500,000 interactions to date.

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3. Oman Insurance Company

Oman Insurance Company is a composite insurance company headquartered in Dubai, UAE that engages in insurance solutions for individuals and businesses in UAE, Oman, and Turkey. Since the insurance landscape was becoming digital, Oman Insurance Company wanted to dabble in solutions that could drive sales without involving agents. An idea that emerged was creating new channels of lead generation to reduce customer effort.

They decided to opt for an AI-powered website chatbot to help empower customers to renew their policies with minimum effort. The bot also facilitated processes like buying insurance plans, making claims, etc. After seeing great success with the website chatbot, they extended the bot to WhatsApp

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To sum it up

Chatbots in the insurance sector are able to assist people faster and make the agents’ tasks much easier. They contribute to an overall increase in the efficiency of an organization and also builds better customer relationships. The future of chatbots in insurance looks quite promising. With the growing sense of independence and self-service among consumers these days, the old methods of insurance assistance will be long gone before chatbot replaces them. Engati chatbots are getting intelligent enough to offer a varied level of experience by computing patterns of information and implying them to make interactions more meaningful, relevant, and real-time.

It’s time to automate your business processes and drive past your competition!

Register with Engati today and build your very own Insurance Chatbot for free!

Don’t forget to give us your 👏 !

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Insurance Chatbot | The success story of 3 largest companies was originally published in Chatbots Life on Medium, where people are continuing the conversation by highlighting and responding to this story.

Most Americans are unsure of how consciously companies behave while using and protecting personal info. Nearly 81% of them report being insecure about potential risks of data collection, and 66% claim to feel the same about government data collection.

It`s really difficult to weigh the level of potential risks and understand the anticipated harm that irresponsible behavior with personal data can cause. How can this affect our further actions, what restrictions and changes will it bring?

Let`s analyze the main points of what happens when disclosing personal data and how to properly protect yourself using the example of the recent situation in South Korea.

Korean company ScatterLab launched a “scientific and data-driven” app, which was supposed to predict the degree of attachment in relationships.

It relies on KakaoTalk, the most popular messenger app in South Korea that is used by about 90 percent of the population.

So, the analysis of your romantic feelings (or their absence) costs just around $4.50 per conversation processing.
“Science of Love” worked like this: it studied the conversation and based on certain factors (such as average response time, how often the companion writes to you first, the fact of some trigger phrases, and emotions usage) gave a conclusion on whether there is a romantic connection between the interlocutors of the dialogue.

You can say “Come on! Does anyone know that better than your inner sensation? How an app can be aware of what is going on in a person`s head or heart while they are texting you?” Well, there is some common sense here.
But the fact is that by June 2020 Science of Love had received about 2.5 million downloads in South Korea and 5 million in Japan, and was preparing to expand its business to the United States.

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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?

So, let`s reveal why does it become so popular among Korean guys and girls?
“Because I felt like the app understood me, I felt safe and sympathized. It felt good because it felt like having a love doctor by my side,” one of the users tells in the review.

In December 2020, the company introduced an A.I. chatbot Lee-Luda.

As a well-trained AI consultant, the bot was positioned, taught on more than 10 billion conversation logs from the app. “20-year-old female” Lee-Luda is ready to set a true friendship with everybody.
As the company`s CEO mentioned, CEO the purpose of Lee-Luda was to become “an A.I. chatbot that people prefer as a conversation partner over a person.”

Just after a couple of weeks of the bot launch users could not help but pay attention to the harsh treatment and statements from the bot towards certain social groups and minorities (LGBTQ+, people with disabilities, feminists, etc.).

The developer company, ScatterLab, explained this phenomenon by the fact that the bot took information from the basic dataset for training, not from personal user discussions.
Thus, it is clear that the company did not properly filter out the set of phrases and profanity before starting the bot training.

The developers just “failed to remove some personal information depending on the context,” (Well, it is what it is).

Lee-Luda could not have learned how to include such personal information in its responses unless they existed in the training dataset.
But there some “good news” as well: it is possible to recover the training dataset from the AI chatbot. So, if personal information existed in the training dataset, it can be extracted by querying the chatbot.

Still going not so bad, huh?
To make things worse, ScatterLab had uploaded a training set of 1,700 sentences, which was a part of the larger dataset is collected, on Github.
It exposed names of more than 20 people, along with the locations they have been to, their relationship status, and some of their medical information.

ScatterLab issued statements of clarification of the incident intended to soothe the public’s concerns, but they ended up infuriating people even more. The company statements indicated that “Lee-Luda is a childlike A.I. that just started conversing with people,” that it “has a lot to learn,” and “will learn what is a better answer and a more appropriate answer through trial and error.” However, is it ethical to violate individuals’ privacy and safety for a chatbot’s “trial and error” learning process? No.

Despite the fact that this situation has become a high-profile event in Korea, it has not received attention on a global scale (and we think quite unfairly).
It’s not about the negligence and dishonesty of the creators, this incident reflects the general trend in the development of the AI industry. Users of software based on technology have little control over the collection and use of personal data.
Situations like this should make you think about more careful and conscientious data management.

The pace of technology development is significantly ahead of the adoption of regulatory standards for their use. It is hard to foresee where the technology will lead us in a couple of years.

So, the global question is “Are AI and tech companies able to independently control the ethical component of the used and developed innovations?”.
Is it worth going back to the concept of “corporate social responsibility”? And where is this golden mean (Innovation VS Humanity)?

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A true friend who betrays all your secrets: Korean AI сhatbot turned a data protection failure was originally published in Chatbots Life on Medium, where people are continuing the conversation by highlighting and responding to this story.

It is said that the language keeps changing every 100 miles you travel, irrespective of your demographics or direction. But, when it comes to expanding your business globally, you need to be capable of communicating in multiple languages no matter what your geographical location is. Businesses are now able to see the benefits of being multilingual while communicating with global customers as they expand their territories for revenue growth.

Since multilingual capabilities are not possessed by every human agent communicating with customers, business strategists are now targeting to deploy AI-powered intelligent chatbots into their support system. These multilingual chatbots are capable of supporting vernacular languages that help businesses to expand their global customer base to the regions where English is not a primary language.

According to a survey by Oracle, when asked which emerging technologies they are already using, 80% of respondents said they have already used or are planning to use AI-powered intelligent chatbots by 2021.

The survey showed that business leaders are preferring to dote on AI automation technologies, which includes chatbots, for sales, marketing, and customer service. 42% of the participants believe that AI-powered automation technologies in these areas will augment the CX curve for their businesses. Also, 48% said that they are already using chatbots for their business functions, with 40% planning to implement some form of cognitive AI-powered technology by 2021.

Although complete automation of the customer service workforce is not feasible, automating customer management and sales positions using AI chatbots in the US would result in considerable savings. According to Public Tableau, 29% of customer service positions in the US could be automated through chatbots and other cognitive AI-powered IVAs that estimates upto $23 billion in savings from annual salaries.

While AI-chatbots will become omnipresent in the next few years, multilingual capabilities will further help businesses to drive growth.

What is a Multilingual Chatbot?

A Multilingual Chatbot is an AI-powered intelligent virtual assistant (IVA) that allows businesses to communicate with users speaking various local and regional languages other than English. Conventional chatbot technology has a limitation of conducting a conversation only in one specific language. Multilingual Chatbots are interactive software platforms that reside in apps, live chat, email, or SMS, and can communicate with customers in the languages they are most comfortable with.

For example, if your business is in the US and you are planning to open a market in Asia, your website might have a chatbot that communicates in English, but not everyone in Asia speaks English. While it is helpful for US customers, the chatbot’s inability to communicate in other languages will hinder companies’ growth in Asia. All this results in the multiplication of efforts, time, and costs.

Multilingual chatbots are capable of communicating in multiple languages — not just translating the dialects. Gone are the days where multilingual meant “Translate and Understand”.

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Building multilingual chatbots requires more than just processing text or dialogue in English through a language translator. To effectively communicate in multiple languages, a chatbot must be aware of the end-users’ culture and able to understand regional nuances. This can be done using AI NLU models.

Natural language understanding (NLU) is a branch of artificial intelligence that uses these intelligent chatbots to understand input made in the form of text or speech, and respond in the same manner and context.

In NLU, machine learning models improve over time as they learn to recognize syntax, context, language patterns, unique definitions, sentiment, and intent. Business applications often rely on NLU to understand what people are saying in both spoken and written language. It helps them provide a personalized response to their customers that improves the end-user experience.

With consistent development in Artificial Intelligence technology and Natural Language Understanding (NLU) models, building a multilingual chatbot is no longer a big deal. However, staying on top of the emerging AI technologies is the real need of the hour for business leaders who see it as a topmost priority.

Why is it needed?

While translator services are rapidly improving their capabilities to detect and translate, Natural Language Understanding (NLU) services are much ahead of the curve in language processing. The growing demand for language and locale understanding capabilities in chatbots is driving the continuous evolution of these services.

Business studies suggest that most customers, in spite of being multilingual, prefer to interact with your brand in their native language. In order to localize a brand, one must ensure that customers can interact in a language they are most comfortable with. Multilingual chatbots are thereby the most efficient and cost-effective answer to meet this demand. Here are a few reasons why a multilingual chatbot becomes a priority for global businesses:

Benefits of a Multilingual Chatbot

Multilingual chatbots help businesses stay ahead of the curve in terms of customer service experience. Contact centres leveraging multilingual chatbots help and troubleshoot problems for customers in a language that they are most comfortable with. A multilingual chatbot can seamlessly switch between languages, hence enhancing and personalizing the customer service experience.

Here are the key benefits of using multilingual chatbots:

1. Save Costs — Considering the difficulty in recruiting resources of varied language proficiency, businesses end up saving a lot of money by deploying multilingual chatbots. Companies still spend a huge amount of money on hiring multilingual staff to connect with the local customer base, which is not an efficient approach. Hiring and training resources incur more expenses to the business. Therefore, implementing a cost-efficient solution becomes the primary priority for decision-makers. Multilingual Chatbots make it possible for them.
2. Improve CSAT: Offering multilingual support doesn’t just drive CSAT scores, it can even impact churn. According to a recent research, 29% of businesses say they’ve lost customers because they don’t offer multilingual support. Additionally, 70% of end-users say they feel more loyal to businesses that provide support in their native language. A multilingual AI chatbot interacts with your customers of different local languages without any error. Multilingual chatbots are trained to answer repetitive queries in any language without skipping any important information. It’s going to stay 24/7 available and provide customers with the best possible experience.
3. Convert Leads Globally: With 57% of global companies already adopting Conversational AI technology, the percentage is way lower when it comes to adopting multilingual AI chatbots. So, there is a strong possibility that your competitors have still not adopted a multilingual chatbot. If so, deploying an AI-powered multilingual AI chatbot can easily be your deal-breaker. If you’re planning to go global and you have already deployed a multilingual AI chatbot that can communicate in 40+ languages, half of the lead-generation battle is already won.
4. Develop Local Customer Base: An AI-powered intelligent chatbot that converses in the local language can take customer communication to a next level. According to an article by Inside Intercom, 35% of end users would even be willing to switch products to one that offers support in their native language. As a result, most of the businesses are adopting localization strategies to expand their customer base and drive engagements in the local language. Localization means customizing your product/service offerings according to the demands and expectations of the local customers. For businesses willing to invest and adopt a comprehensive multilingual AI chatbot localization strategy, the payoff turns into a success story.

NLP: The Technology Behind Multilingual Chatbots

It is evident that for Conversational AI technology to be truly successful, it should be able to interact with users in the language they are most comfortable with. Most multilingual chatbots are either programmed with a sophisticated NLP engine that helps them identify language, or more commonly, a bot designer platform that redesigns and trains the chatbot to interact in that specific language. Natural Language Processing allows a machine to understand what we say in our natural language. That is, the way we naturally speak, with slang, abbreviations, mispronunciations and so on.

Key Capabilities that NLP provides:

• NLP allows chatbots to understand voice input as well as text.
• With NLP technology, the chatbot doesn’t need the exact correct syntax to understand customer’s expectations.
• On the basis of its programming mechanism, it can auto-detect languages, context, sentiment, and intent.
• Chatbots can either process their response through its NLP engine, or by analyzing customer’s browser preferences.

Final Thoughts

Chatbots have gained attention worldwide, and with the help of evolving AI and NLP technology, they are expected to become even more empowered in the coming days. With a number of businesses trying to use AI-powered chatbots in different innovative ways, building multilingual chatbots helps businesses connect with a wider and more diverse audience, in fact globally.

For an AI chatbot to be well-received and widely used, it is important that it can convincingly converse in a language that is most convenient to the customer. And since the business landscape has become largely global, multilingual communication has become a topmost priority. This is because an AI-powered multilingual chatbot solution can give wings to the customer experience transformation initiative and improve overall ROI for your business.

I hope this article helped explain how AI-powered multilingual chatbots stand out in the crowd. Designed by Haptik, these chatbots have been assisting brands to have more efficient and meaningful conversations in the language customers prefer. And it would be fair to say that the next generation of Conversational AI is at your doorstep and it’s time to embrace these impeccable AI-powered chatbot solutions.

Reach out to enterprise@haptik.ai to learn how Haptik can help your business!

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Multilingual Support: Scale Customer Experiences Globally 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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