Conversational AI Customer Service Using Machine Learning and Chatbots

Conversational AI is the use of natural language processing, machine learning, and other technologies to create intelligent agents that can interact with customers through text or voice.  

Conversational AI can provide various benefits for customer service, such as improving customer satisfaction, reducing costs, increasing efficiency, and generating insights.  

Conversational AI can be applied to different domains and scenarios, such as chatbots, voice assistants, virtual assistants, and social robots.  

Conversational AI can also be integrated with other systems and platforms, such as CRM, ERP, e-commerce, and social media.

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Transcript

Today’s podcast is on "Conversational AI Customer Service Using Machine Learning and Chatbots". 

Conversational AI is the use of natural language processing, machine learning, and other technologies to create intelligent agents that can interact with customers through text or voice.  Conversational AI can provide various benefits for customer service, such as improving customer satisfaction, reducing costs, increasing efficiency, and generating insights.  Conversational AI can be applied to different domains and scenarios, such as chatbots, voice assistants, virtual assistants, and social robots.  Conversational AI can also be integrated with other systems and platforms, such as CRM, ERP, e-commerce, and social media.

Welcome to "The Digital Revolution" podcast, Jim Kunkle here and I’m your Host.  This podcast series explores the latest trends and insights in digital transformation.  Also you’ll get discussions on how businesses can leverage digital technologies to drive growth, improve customer experience, and stay ahead of the competition. Our guests will include industry experts, thought leaders, and business executives who have successfully navigated the digital landscape.  Join me as I dive into topics such as artificial intelligence, big data, cloud computing, cybersecurity, and more. Stay tuned for upcoming episodes, where I’ll share practical tips and strategies for your digital transformation journey.

Customer service and the customer experience is evolving with the assistance of artificial intelligence.  Let’s get into this topic.

Conversational AI is a form of artificial intelligence that enables people to engage in a dialogue with their computers using text or voice.  Conversational AI uses natural language processing and machine learning to understand what humans are saying or typing and to generate appropriate responses.  Conversational AI can be applied to different domains and scenarios, such as chatbots, voice assistants, virtual assistants, and social robots.  Conversational AI can also be integrated with other systems and platforms, such as CRM, ERP, e-commerce, and social media. 

Let me expand on some of the scenarios that conversational AI can be applied to.

• Virtual assistants:  These are software applications that can perform tasks or services for users based on voice or text commands.  Some popular virtual assistants are Siri, Alexa, Google Assistant, and Cortana.
• Chatbots:  These are computer programs that can simulate a conversation with a human user through text or voice.  Chatbots can be used for various purposes, such as customer service, entertainment, education, and marketing.  Some well-known chatbots are Ada, Replika, Mitsuku, and Woebot.
• Voice assistants:  These are devices that use voice recognition and natural language processing to provide information, entertainment, or assistance to users.  Some examples of voice assistants are Amazon Echo, Google Home, Apple HomePod, and Facebook Portal.
• Virtual agents:  These are conversational AI systems that can act as representatives or guides for users in specific domains or scenarios. Virtual agents can provide personalized and contextualized responses and actions based on user preferences and goals.  Some examples of virtual agents are Meena, Watson Assistant, and Rasa.

A question that I encounter is on chatbots and how are they created, or more properly, how are they generated?  For the remaining time of this episode, I’m going to focus on chatbots, as they are the most recognized conversational AI by the general public.

There are different ways to create a chatbot, depending on your level of technical skills and the purpose of your chatbot.  Here are some common methods:

• Using a chatbot platform:  This is the easiest and fastest way to create a chatbot without coding. You can use a chatbot platform like Tidio, Chatfuel, or Appy Pie to design your chatbot conversation, customize your chatbot appearance, and integrate your chatbot with various channels and platforms.  Chatbot platforms usually offer templates, drag-and-drop interfaces, and pre-built skills to help you create your chatbot in minutes.
• Using a code-based framework:  This is a more advanced and flexible way to create a chatbot with coding.  You can use a code-based framework like Rasa, Microsoft Bot Framework, or Dialogflow to build your chatbot from scratch using a programming language like Python, C#, or Java.  Code-based frameworks provide you with tools to customize your chatbot logic, database, analytics, and AI capabilities.  You can also use code-based frameworks to create more complex and dynamic chatbots that can handle natural language and user intents.
• Using a chatbot builder:  This is a hybrid way to create a chatbot with minimal coding. You can use a chatbot builder like Amazon Lex, IBM Watson Assistant, or Verloop to create your chatbot using a graphical user interface and some coding.  Chatbot builders allow you to design your chatbot conversation, train your chatbot with natural language processing, and deploy your chatbot to various channels and platforms.  Chatbot builders also offer features like voice recognition, sentiment analysis, and machine learning to enhance your chatbot performance.

Now that your chatbot has been generated, here’s how Natural Language Processing can train your chatbot.

Training a chatbot with natural language processing (NLP) involves teaching the chatbot to understand and generate natural language. There are different methods and techniques to do this, depending on the type and complexity of your chatbot.  Here are some common steps to train a chatbot with NLP:

• Define the goal and scope of your chatbot.  What is the purpose of your chatbot?  What kind of questions or tasks can it handle?  What are the expected outcomes and responses?
• Collect and preprocess the data.  You need a large and diverse dataset of natural language conversations that are relevant to your chatbot domain.  You can use existing datasets, web scraping, surveys, or manual data collection.  You also need to clean and format the data, such as removing noise, spelling errors, punctuation, etc.
• Choose the NLP techniques and tools.  You need to decide what NLP techniques and tools you want to use for your chatbot.  Some common NLP techniques are tokenization, stemming, lemmatization, part-of-speech tagging, named entity recognition, sentiment analysis, etc.  Some common NLP tools are NLTK, spaCy, Gensim, TensorFlow, PyTorch, etc.
• Build and train the chatbot model.  You need to design and implement the chatbot model using the chosen NLP techniques and tools.  You can use rule-based, retrieval-based, or generative models, or a combination of them.  You also need to train the chatbot model using the preprocessed data, and evaluate its performance using metrics such as accuracy, precision, recall, F1-score, etc.
• Test and deploy the chatbot.  You need to test the chatbot with real users and collect feedback.  You can use online platforms, ones that I mentioned earlier such as Chatfuel, Tidio, or Appy Pie, to create and deploy your chatbot.  You can also integrate your chatbot with other systems and platforms, such as CRM, ERP, e-commerce, and social media.

Evaluating your chatbot's performance is an important step to ensure that your chatbot is meeting your goals and satisfying your customers.  There are different chatbot metrics that you can use to measure various aspects of your chatbot's performance, such as:

• Automation rate:  This metric shows the percentage of conversations that are handled by the chatbot without any human intervention.  A high automation rate indicates that your chatbot can handle most of the queries and tasks that your customers have, reducing the workload for your human agents and saving costs.
• Unrecognized questions:  This metric shows the percentage of questions that the chatbot fails to understand or answer.  A low percentage of unrecognized questions means that your chatbot has a good natural language understanding and can provide relevant and accurate responses to your customers.
• Customer satisfaction:  This metric shows how happy your customers are with the chatbot's service.  You can measure customer satisfaction by using ratings, feedback, surveys, or sentiment analysis.  A high customer satisfaction score means that your chatbot is providing a positive and engaging experience for your customers, increasing their loyalty and retention.
• Conversation length:  This metric shows the average number of messages or time that the chatbot exchanges with a customer.  The optimal conversation length depends on the purpose and complexity of your chatbot.  For simple and transactional chatbots, a short conversation length may indicate efficiency and convenience.  For conversational and relational chatbots, a longer conversation length may indicate rapport and trust.

These are some of the common chatbot metrics that you can use to evaluate your chatbot's performance.  However, depending on your chatbot's goal and domain, you may also want to use other metrics that are more specific and relevant to your business. 

Some common mistakes in chatbot design are:

• Pretending to be human: Chatbots should not try to deceive users into thinking they are talking to a real person.  Users may feel betrayed or annoyed if they discover the truth.  Chatbots should be transparent about their identity and capabilities, and use a friendly and consistent tone.
• Pointless banter:  Chatbots should not engage in irrelevant or excessive small talk that does not add value to the conversation. Users may lose interest or patience if the chatbot does not address their needs or goals.  Chatbots should focus on the user's intent and provide clear and concise responses.
• Messages that may appear rude:  Chatbots should not use language or expressions that may offend or insult users.  Users may react negatively or abandon the conversation if the chatbot is impolite or insensitive.  Chatbots should use polite and respectful language, and avoid sarcasm, humor, or slang that may be misunderstood or inappropriate.
• Chatbot conversation scripts based only on NLP:  Chatbots should not rely solely on natural language processing (NLP) to understand and generate natural language.  NLP is not perfect and may fail to handle complex or ambiguous user inputs.  Chatbots should also use other techniques, such as predefined options, buttons, menus, or images, to guide the user and reduce errors.
• Typos and irritating grammar mistakes:  Chatbots should not have spelling or grammar errors in their messages.  Users may lose trust or confidence in the chatbot if it makes mistakes that affect its credibility or professionalism.  Chatbots should use proper spelling and grammar, and proofread their messages before sending them.
• Messages that are too long:  Chatbots should not send messages that are too long or verbose.  Users may find it hard or tedious to read and comprehend long messages, especially on mobile devices. Chatbots should use short and simple sentences, and break up long messages into smaller chunks or bullet points.

Making your chatbot more engaging is a key factor to increase user satisfaction and retention.  Here are some tips to make your chatbot more engaging:

• Use a friendly and consistent tone:  Your chatbot should have a personality that matches your brand and audience.  You can use a friendly and consistent tone to make your chatbot more human-like and relatable.  You can also use emojis, GIFs, and humor to add some fun and emotion to your chatbot.
• Provide value and relevance:  Your chatbot should provide value and relevance to your users by answering their questions, solving their problems, or fulfilling their needs.  You can use natural language processing and machine learning to understand user intents and provide personalized and contextual responses.  You can also use data and analytics to optimize your chatbot performance and improve user satisfaction.
• Use visuals and media:  Using visuals and media can help make your chatbot experience more engaging.  Instead of just using text, consider incorporating images, videos, and GIFs into your chatbot. Visuals can help break up the conversation and provide context. They can also help make your chatbot more memorable and shareable.
• Personalize the experience:  Personalizing the experience can make your chatbot more engaging by creating a connection with your users.  You can use user data, such as name, location, preferences, or purchase history, to tailor your chatbot responses and actions. You can also use feedback and surveys to learn more about your users and improve your chatbot accordingly.
• Include interactive elements:  Including interactive elements can make your chatbot more engaging by giving your users more options and control.  Some examples include buttons, quick replies, carousels, menus, and images.  These interactive components allow users to choose from predefined options or scroll through a list of choices.  They can also help reduce errors and confusion by guiding the user through the conversation.
• Add gamification and incentives:  Adding gamification and incentives can make your chatbot more engaging by motivating your users to interact more with your chatbot.  You can use gamification techniques, such as points, badges, levels, or leaderboards, to reward your users for completing tasks or achieving goals.  You can also use incentives, such as discounts, coupons, or freebies, to entice your users to take action or make a purchase.

Before we end this episode, I’d like to answer a question that came into the Digital Revolution.  This question is from Jason R.  Jason asked: “What’s the BIG topic on artificial intelligence during 2024?”

Jason, according to WeForum.org there are five BIG topics related to artificial intelligence during 2024, here they are.

Artificial intelligence is a fast-growing and dynamic field that has many topics of interest and debate.  Some of the big topics on artificial intelligence during 2024 are:

1. Quantum AI: This topic explores how quantum computing can revolutionize data processing and AI capabilities.  Quantum AI can potentially solve complex problems that are beyond the reach of classical computers, such as optimization, cryptography, machine learning, and natural language processing.
2. AI Legislation:  This topic examines how laws and regulations can shape the future of AI governance.  AI legislation can address issues such as privacy, security, accountability, liability, and ethics of AI systems.  AI legislation can also balance the need for innovation and protection of human rights and values.
3. Ethical AI: This topic investigates how AI systems can be designed and deployed in a transparent and fair manner.  Ethical AI can ensure that AI systems respect human dignity, diversity, and autonomy, and do not cause harm or discrimination.  Ethical AI can also involve stakeholder participation, human oversight, and explainability of AI decisions.
4. Augmented Working:  This topic explores how AI can enhance human productivity and creativity in the workplace.  Augmented working can involve human-AI collaboration, where AI systems can assist, complement, or augment human workers in various tasks and domains.  Augmented working can also foster lifelong learning, reskilling, and upskilling of workers.
5. Next Generation of Generative AI:  This topic showcases how AI can push the boundaries of creativity and innovation.  Next generation of generative AI can create realistic and diverse content, such as text, images, videos, music, and code, based on user inputs or preferences.  Next generation of generative AI can also enable new forms of expression, communication, and entertainment.

Thank You Jason for your interesting question.  If you have any questions that I can address on upcoming episodes, please email the Digital Revolution at Jim@JimKunkle.com

Thank you for listening to "The Digital Revolution" podcast. We hope you enjoyed our discussion on “Conversational AI Customer Service Using Machine Learning and Chatbots” and you gained valuable insights. If you found this podcast informative, please share it with your friends and colleagues, leave a rating and review, or follow us on social media. Your feedback is important to us and helps us improve our content. Stay tuned for our upcoming episodes and bonus content, where we will continue to explore the latest trends and insights in digital transformation. Thanks again for tuning in!

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The Digital Revolution with Jim Kunkle - 2024