Customer service bots have become popular among small, medium, and large companies because they automate customer service, resolving simpler issues and freeing human agents to handle critical and/or high-value cases.

According to Gartner, in 2021, 15% of all customer service interactions will be completed using AI, with significant adoption of bots. The bot market, in turn, was valued at $17.1 billion in 2020, with a forecast to reach $102.2 billion by 2026, according to data from Mordor Intelligence.

With growing interest from businesses and society as a whole, some interesting claims are emerging, such as bots being able to learn on their own. But that’s not quite true.

 

Google’s robots

 

Google’s robots developed the ability to communicate with each other, and most impressively: in secret.

Researchers at Google Brain (the company’s artificial intelligence division) created three software robots, named Alice, Bob, and Eve, with well-defined missions.

Alice was supposed to send an encrypted message to Bob, who was tasked with decoding it while avoiding Eve’s espionage, whose goal was to intercept and read the message.

Using neural network techniques, Alice and Bob developed their own encryption method and communicated entirely confidentially, preventing Eve from decoding the information.

For the first time in history, two artificial intelligence entities managed to independently create a means to communicate secretly.

However, this does not mean these bots learned on their own; rather, they managed to evolve the programming they were given.

 

Customer service bots

 

A customer service bot is one that, when asked by a client, consumer, or user, can promptly respond with relevant information to clarify doubts or resolve the requester’s issues on the first contact, without the need for human intervention.

A customer service bot can consult (collect data), think (process data), and act (apply the rule). This is how the bot can closely mimic human behavior.

In other words, a bot can be considered intelligent if it collects, stores, and decides based on information generated from its interactions with users.

And, of course, the great advantage of a bot is that it can solve real problems, allowing users to perceive the value of the technology, thereby increasing their satisfaction and admiration for the company.

But ultimately, with such intelligence, can bots learn on their own or not?

 

Bots cannot learn on their own—yet

 

It’s an urban myth! For now, bots are not capable of learning on their own. In the future, who knows? With the advancement of Artificial Intelligence, we might have self-taught robots that read a training manual about a product and start serving consumers right afterward. Currently, that’s just a myth.

The truth is that behind a bot, there is an entire team analyzing and improving the bot’s interactions with users, the data, and the entire conversational flow.

The message we receive from a bot goes through a process of evolution, where every word is carefully chosen, from creating the knowledge base to defining its vocabulary, tone of voice, and more.

This detailed analysis is usually (and should be) conducted by a multidisciplinary team made up of professionals from various areas of the business: management, administration, sales, legal, marketing, and others.

After all, bot developers and programmers are experts in the technical aspects but generally lack broad knowledge of all business-related matters and customer service.

Therefore, a single phrase or response may go through analysis and approval by several professionals from different departments before being integrated into the bot.

 

The evolution of a bot

 

The first step in evolving your bot is to have a very well-defined purpose, so it operates strictly within its context, according to the objectives set by the company before its development.

Every conversation your chatbot or voicebot has with a client generates data that, if well-utilized, can be crucial for its evolution. Therefore, it’s also essential for your business to have tools to collect this information.

This data collection can determine your bot’s success level and also identify areas for improvement that need adjustment for increasingly accurate and efficient service.

Finally, after collecting, it’s time to analyze the data. Intent rate, interaction, activation, retention, conversion, fallback, session time, response time, and more.

There are numerous metrics that must be constantly evaluated to determine whether your bot is useful, helping your clients with what they want or need.

It’s a process of continuous improvement for your bot that, with this step-by-step approach, can become the main asset of your business’s customer service.

The ultimate goal is to achieve a high rate of successful interactions without requiring the involvement of a human agent.

That’s it! We hope we’ve clarified that no, bots still cannot learn on their own, but they can evolve if well-managed.

Thank you for your attention. See you next time!

 

Also read: ASR: Everything You Need to Know About Voicebot Speech Recognition

Speech is the most common means of human communication, defined as the expression of thoughts and feelings through the articulation of sounds, in other words, through voice.

Obviously, through speech, we can communicate much faster. While a person can type an average of 40 words per minute, they can speak 160 words in the same timeframe.

However, it’s not just about numbers. While we speak, certain factors can make a big difference in the listener’s understanding: context, tone of voice, slang, etc.. Not to mention the audience and the language being spoken.

Human speech, which for us is natural and a skill we learn from childhood, is quite complex for machines, even those equipped with Artificial Intelligence.

From the 1950s to now, speech recognition systems have evolved significantly, especially with the advent of technologies like Machine Learning and Deep Learning, for example.

Technologies like Alexa, Google Home, Siri, Cortana, among others, emerged to make our interactions with machines easier, changing the way we shop, for instance.

And in this scenario, ASR appears.

 

What is ASR?

 

ASR (Automatic Speech Recognition) is a technology that allows speech recognition software to analyze sounds and transcribe them into text.

An Automatic Speech Recognition (ASR) system simulates a human listener, listening, understanding, and responding to what is spoken, converting sound into text. In other words, transforming speech into words.

When capturing sound, ASR translates the vibrations emitted by the voice, transforming them into text that can be understood by various software and hardware, thus simulating a human conversation.

This technology is increasing the speed and efficiency of customer service for companies and enhancing the experience of customers who are increasingly demanding and impatient.

ASR is a feature that speeds up the work of human agents, freeing them up for more strategic activities. It is essential in call centers when integrated with automated service systems like IVRs and voicebots.

 

How does it work?

 

Basically, ASR consists of voice recognition software and a hardware component, which in this case is the microphone.

First, we speak on the phone, or through a smartphone or virtual assistant like Alexa, for example. Then, the microphone on these devices captures our voice and creates a digital file.

This file stores our words, where noise is removed, and volume is equalized. Next, these sound waves are divided into phonemes. Finally, ASR technology analyzes and deduces words to form texts. All this, of course, happens in milliseconds.

Once the text is obtained, another component similar to chatbots, called NLP (Natural Language Processing), comes into play to infer the semantics or meaning of the text, typically understood as the intent and entity/entities pair.

 

What are its components?

 

Now that you know what it is and how it works, let’s explore the components an ASR system is generally composed of:

Digital representation: A method to extract the input (speech).

Speech extraction: This component identifies speech and transforms it into acoustic parameters.

Database: Acts as a voice library with annotations and transcriptions, essential for covering varied speech patterns.

Acoustic models: Identifies the speech waveform and divides it into small fragments, predicting the most likely phonemes in the speech.

Phonetic models: Identifies sounds and converts them into words, associating them with their phonetic representations.

Linguistic models: Here, the identified words are turned into sentences with the most likely sequence.

Algorithms: Also known as decoders, this component combines predictions from acoustic and linguistic models, generating the most probable transcriptions for each speech.

In summary, these are the components of ASR.

But it’s important to remember that along with all this, there are numerous peculiarities in human speech like accents, slang, as well as the speaker’s age, gender, and even mood, which make Automatic Speech Recognition (ASR) a more complex equation.

Nonetheless, when properly implemented under the supervision of a multidisciplinary team dedicated to the technology’s evolution, this feature can optimize customer service and relationships.

In today’s world, where most interactions are digital, this is an innovative system that can add significant value to companies.

 

What are the benefits for companies?

 

Now that you know what ASR is, how it works, and its components, let’s look at some benefits for your business:

• Reduced need for human intervention
• Lower staffing costs
• Optimized human service
• Freeing agents for strategic activities
• Automation of service processes
• Increased self-service efficiency
• Speech analysis (Speech Analytics)
• Enhanced customer experience
• Increased customer satisfaction
• Vocal imprint authentication, avoiding the need to memorize passwords
• Sentiment analysis (satisfaction vs. frustration)

 

And so on! These are just some of the main advantages. To leverage all these and more, you need to implement ASR in your business as soon as possible.

We hope you enjoyed the content. See you next time!

 

Also read: Should the bot’s persona always be female?

According to Gartner, in 2021, 15% of all customer service interactions will be completed using Artificial Intelligence—with significant adoption of text or voice customer service bots (chatbots or voicebots).

The consultancy had also projected that, in 2020, Brazilians would talk more with customer service robots than with their spouses and families.

In other words, bots are a trend and will be increasingly used by companies for customer service, being present in all stages of the consumer journey.

If we will talk more and more with bots, let’s address the issue of the bot’s gender. Should your business’s bot always have a female persona? Or a male one? Or both?

Here, you will find tips on how to create the ideal customer service bot, without excluding gender, race, or sexual orientation.

 

But first, let’s go to the concept!

 

What is a customer service bot?

 

Customer service bots are conversational robots, also known as chatbots (for text) and voicebots (for voice), capable of interacting with humans through platforms such as WhatsApp, Facebook Messenger, Telegram, phone, and others.

Equipped with Artificial Intelligence, these customer service bots can also be integrated into your website’s chat to answer customer questions and resolve simple issues, thereby freeing up your human agents’ time.

Bots can be understood as computer programs, software, or applications developed to perform specific and repetitive tasks in an automated manner. These bots are capable of simulating human behavior but with much faster task execution, such as accessing data, remembering rules, calculating averages, etc.

 

What gender should you choose for your bot?

 

You need to optimize your customer service and have decided that a bot can be a good alternative. But when creating your robot, you’re faced with the question: should it have a male, female, or neutral personality?

Perhaps the best option is to create a neutral bot. While most bots are female, your bot can break stereotypes by taking a different approach.

Historically, the female gender has always been associated with roles such as secretary, assistant, etc. However, women have now taken their rightful place—wherever they want to be…

Fortunately, today we have women in leadership roles in politics and major companies. Therefore, it has never really made sense to associate a female voice with a virtual assistant, such as a customer service bot.

Happily, many companies have already realized this, creating male or neutral bots, but most are still female.

This is because bots are developed by humans, ordinary people who often have deep-seated biases, even if unconsciously.

Let’s look at examples from major companies. Apple has Siri, Amazon has Alexa, and Microsoft has Cortana. All are female virtual assistants in their default settings.

However, your company has the opportunity to change this, creating a male or neutral bot.

Some experts suggest the idea of three different personas, one for each type of audience: (1) a “persuasive” bot for sales, (2) an “assertive” bot for support, and (3) an “exciting” bot for investors.

Here, it’s also worth basing your choice on data. Would your male clients prefer to be served by men or women? And your female clients, what gender would they prefer?

It’s important to understand these preferences to define your bot’s gender.

 

How to create your bot’s personality?

 

A bot imitates a human being, so it must have a personality. Conduct research and interviews with your clients before defining the ideal gender and voice, and also pay attention to the tone of voice your bot uses during interactions.

Before starting to develop your voice robot, it is essential to know and understand your customers—their desires and needs—and also to know which platforms they prefer.

Do they access the website or app more often? Do they prefer to be served via WhatsApp or phone? Do they interact more on Facebook or Instagram? Answering these questions is crucial to creating your customer service bot.

 

Remember: talking to your robot should be objective and pleasant at the same time, generating empathy and trust.

 

Be careful with accents and slang

 

If your company operates only in the South of the country, for example, it might be worth hiring a scriptwriter from that region. The same applies to other regions.

If your business operates across the entire country, it’s best to choose a voice, in the case of a voicebot, that is easily understood by everyone.

Be cautious with slang, as it can be a positive or negative differentiator depending on how sensibly it is used.

That’s it! Perhaps a neutral gender is the best choice for your customer service bot. But if you decide on a male or female gender, remember to avoid prejudiced phrases or expressions.

Keep in mind that your customer service bot is seen by the public as the face of your company, and despite being a robot, it says a lot about your business.

 

We hope you enjoyed it. See you next time!

 

Also read: Chatbots: On Which Platforms Can They Operate?

The Concept of Chatbots

A chatbot is a customer service bot capable of conducting human-like interactions and conversations through text messages, in an automated way, and can be integrated with human chat platforms, CRM, and legacy systems.

Optimizing customer service with chatbots is not a passing trend. Alexa, Siri, and Cortana are examples of Artificial Intelligence that make users’ lives easier.

Chatbots handle simpler interactions and escalate only the more complex or high-value cases to human agents. This reduces costs, shortens queues, and optimizes the time of agents and, most importantly, customers.

 

The Platforms Where Chatbots Can Operate

 

Check out the main platforms where chatbots can operate and automate customer service for your business.

Website

Nowadays, a company without a website almost doesn’t exist. That’s because when a consumer wants to purchase a product or service, they search online.

As we mentioned at the beginning of this article, e-commerce is growing every day, and with a chatbot, companies can engage, answer questions, solve problems, and even sell online, all in an automated way.

 

Application

Today, many companies start as apps, while others develop them over time. Offering your own app is an attractive feature for your customers, and when this tool provides quick and efficient service, your business can retain and build customer loyalty.

 

Facebook Messenger

Many customers turn to social networks to communicate with companies, and Facebook is one of the most accessed platforms. Chat for Facebook Messenger provides menus to streamline customer service, making it easier for consumers to access information and optimizing the work of human agents.

 

WhatsApp

WhatsApp is the most popular app among Brazilians, according to a study conducted by Mobile Time in partnership with Opinion Box. Throughout the day, it is the most-used app by Brazilians, and companies are taking advantage of this.

With a chatbot for WhatsApp, companies can provide customer service 24/7, improving customer relationships as well as sales.

 

Telegram, Slack, and Teams

Chatbots for Telegram, Slack, and Teams can also be used as a professional tool for internal notifications and improving team communication, whether on a regional, national, or global level.

As you’ve seen throughout the text, chatbots can be integrated with virtually any chat platform, automating your customer service and reducing the workload of your human agents.

 

We hope you enjoyed the content. See you soon!

 

Also read: Creating Bots Is Easy – Scaling Them Is a Different Story.

Customer service bots are conversational robots, also known as chatbots (for text) and voicebots (for voice), capable of interacting with humans through platforms like WhatsApp, Facebook Messenger, Telegram, phone, and others.

Equipped with Artificial Intelligence, these customer service bots can also be integrated into your website’s chat to answer client questions and resolve simple issues, freeing up your human agents to focus on higher-value tasks.

According to Gartner, in 2021, 15% of all customer service interactions will be completed using AI—with significant adoption of bots. In other words, bots are a trend and will be increasingly used by companies for customer service, being present at every stage of the customer journey.

Bots are tasked with solving simple problems. With a text or voice bot, your business can speed up customer service, free up agents’ time, reduce staffing costs, engage, retain, and build customer loyalty, identify failures, and even generate data on customer interactions.

Moreover, with a bot, your company can provide customer support 24 hours a day, 7 days a week, year-round. Therefore, a bot can be a strategic tool for your company’s customer service.

However, after creating a bot, it is necessary to ensure its constant evolution so that it becomes a robot capable of solving customer problems more efficiently and with greater satisfaction.

 

In this article, you’ll learn the importance of creating a bot for your business and how to scale it with a few simple but valuable tips. Enjoy your reading!

 

How to Scale Your Bot?

After creation, managing the bot is essential for its learning and evolution. Check out some valuable tips for scaling it below.

 

Perform a Financial Analysis

It is crucial to know how many interactions the bot handles per month on average, as well as the time and money invested in the bot compared to a human agent. In addition to measuring financial returns, this analysis can encourage other departments in the company to invest in the solution.

 

Define Approval Stages

Establishing and implementing approval stages ensures that specialists from different areas of the company approve or reject each process before it is implemented, based on their expertise. For example, the business owner should be involved in these approvals.

 

Have a Clear Focus for Your Bot

Your bot must have a well-defined purpose to operate strictly within its context, aligned with the objectives set by the company before development.

If your bot is too broad, it may stray from the specific topics that are usually the reason for its existence.

If your bot is simply intended to build customer relationships, you can still make it generalist while collecting, storing, and processing the data it generates.

 

Rely on a Multidisciplinary Team

In addition to developers, it is crucial to involve professionals who are experts in the subjects your bot addresses with customers and those who understand the technical aspects of the service process.

 

Collect All Possible Data

Every conversation your chatbot or voicebot has with a customer generates data that, if well-utilized, can be crucial for its evolution. Therefore, your business must have tools to collect, store, and analyze this information.

This data collection and analysis can identify the success level of your customer service bot and pinpoint areas for improvement that need to be adjusted for increasingly accurate and agile service.

You can also ask customers what they think about your bot, gaining insights directly from the source—the user—so you can improve their experience.

 

Continuously Evaluate Performance

Precision, interaction, rejection, retention, resolution, conversion, satisfaction, fallback, session time, response time, and more. Various metrics must be constantly evaluated to determine whether your bot is productive, helping your customers with what they need or want.

 

Read more: What Are the Main Performance Indicators (KPIs) for a Bot?

 

It was a pleasure to have your company. See you next time!

Every customer service bot, whether a chatbot or voicebot, should have performance indicators, or KPIs (Key Performance Indicators), to measure its effectiveness.

These metrics are essential to determine whether your customer service robot is delivering on its promises, identify potential issues compromising its quality, and highlight areas for improvement.

According to the study “Navigating the Post-Pandemic Customer Experience,” one in four consumers has already used chatbots for customer service in banks, stores, or e-commerce.

Of these, 69% reported an excellent or very good experience. This shows that, indeed, customer service bots are here to stay.

 

What is a bot?

 

A bot is a shortened form of the English word robot, which means robot in Portuguese.

Bots can be understood as programs, software, or computer applications designed to perform specific and repetitive tasks online in an automated way.

Bots operate independently, meaning they do not require human supervision once configured.

These bots use technologies like Artificial Intelligence (AI) and Computational Linguistics to simulate human behavior but execute tasks much faster.

Bots can be integrated into platforms like WhatsApp, Facebook Messenger, Twitter, Telegram, Teams, and others.

 

Why measure your bot’s performance?

 

Your bot’s KPIs should be defined even before development begins. These metrics are fundamental to the bot’s continuous improvement in delivering high-quality customer service.

A customer service bot is a technology in evolution, so analyzing its performance must be a continuous task. Delving into its performance indicators is also essential to debunk patterns or “certainties” that are often based on assumptions.

Additionally, every company can have its unique KPIs. Some are common across most organizations, but nothing prevents you from creating custom metrics tailored to your business model.

 

10 Performance Indicators to Evaluate

 

Discover the top performance indicators to evaluate your customer service bot and enhance its effectiveness!

 

1. Precision

 

Precision measures the bot’s ability to understand a question expressed differently but with the same meaning. For example: “I want my invoice,” “I didn’t receive my invoice,” or “I lost my invoice, it was misplaced.” This indicator assesses the level of Artificial Intelligence integrated into the customer service robot.

 

2. Objectivity

 

This metric examines how many messages, on average, your customers exchange with your bot to get what they need. It helps identify interaction steps that can be removed or added. If your bot is a conversational robot, the calculation differs.

 

3. Acceptance

 

Perhaps your website receives many daily visits, but not all visitors engage with your customer service bot. This may indicate that your chatbot is not attracting users’ attention. You can compare your website traffic to the number of conversations initiated with your bot.

 

4. Reusage

 

When a user returns to use your bot, it means they had a good experience and remembered your service. Keeping reusage rates high is positive. However, if a customer repeatedly interacts with your bot excessively, it may indicate unresolved queries or issues. Keep an eye on this.

 

5. Retention

 

As mentioned earlier, bots are evolving technologies. In the beginning, errors and difficulties are normal. Retention measures all instances where your bot successfully completes an interaction, following an efficient conversational flow.

 

6. NoMatch

 

This metric gauges the bot’s difficulty in understanding customer questions or messages. A high NoMatch rate means the bot’s knowledge base needs optimization by adding questions asked by users but left unanswered. When this occurs, the bot may simply escalate the interaction to a human agent.

 

7. Session Time

 

Average session time is a critical metric but must be analyzed carefully. A short session time may indicate the customer abandoned the interaction, especially if it lasts only a few seconds, which could point to rejection. On the other hand, a long session time may indicate a smooth interaction or that the user persisted without resolving their issue.

 

8. Responsiveness

 

Customers are increasingly demanding and impatient, seeking efficient service. This metric is crucial to know if your bot is quick to respond. To measure it, you can send a message to your chatbot yourself and assess how long it takes to reply.

 

9. Conversion

 

In institutional campaigns, the ultimate goal is conversion—sales. Evaluating this metric determines the success of your customer service bot. To improve this rate, create mental triggers and CTAs (calls to action) that encourage customers to take a specific action.

 

10. Satisfaction

 

NPS (Net Promoter Score) is commonly used to measure customer satisfaction. For your bot, it reveals the user’s experience. The question you can ask is: “On a scale of 0 to 10, how likely are you to recommend us to a friend or family member?” Scores of 9-10 are promoters, 7-8 are neutral, and below 7 are detractors.

 

By defining and assessing these 10 performance indicators, you can ensure your customer service bot is constantly evolving—a robot that learns and is well-received by your customers, enhancing your company’s image.

 

We hope you enjoyed it. See you soon!

 

Also read: Do Your Customers Like Your Voicebot? Learn How to Optimize It