impact of chatbots on moderns business operations

impact of chatbots on moderns business operations

1. The Impact of Chatbots on Modern Business Operations June 20, 2024

2. Research Status 2 Personal Insights 3 Table Of Contents Background Introduction 1

3. Background Introduction 1

4. Background Introduction Artificial intelligence and other underlying technologies, such as natural language processing and machine learning, have made significant strides in recent years, which has led to the rise in popularity of chatbots Increased Internet interactivity and the proliferation of more advanced mobile devices have directly attracted a greater number of customers who are looking for improved and more individualized customer service that is provided by chatbots Chatbots are more intelligent and responsive than traditional customer care solutions since they guarantee that clients receive the instant services they require

5. Evolution of Chatbot Customer Service and support as a communication channel that connects consumers and businesses. Companies are increasingly adopting social media platforms like Facebook and LiveStream to provide customer service. Chatbots encourage knowledge sharing by empowering users to develop and collaborate on material. Using technology to facilitate product learning can enhance the experience. Chatbots are being used to provide personalized customer care, especially in e-commerce . Artificial intelligence enables enhanced human-computer interaction. Chatbots help vendors and customers interact and create connections more effortlessly.

6. Potential Impact on the Business Field Provide fast, 24/7 customer service Offer more personalized experiences (consumers who interact with chatbots expect their data will be used to personalize future interactions) Deliver multilingual support Ensure more consistent support Deliver omnichannel support (phone, email, social media, etc) Improve service with every interaction Collect customer feedback Boost customer engagement Lower employee churn Reduce business costs

7. Research Status 2

8. Chatbots Objectives

9. Proposed Model Customer Service 2.0 In a typical scenario, the service agent, whether human or artificial intelligence based, communicates with the customer via a chat window in the form of text messages and will focus on the agent's task of giving product information Variables having an impact on customer satisfaction in the specified situation Perceived Information Quality Perceived Waiting Time Pleasure Arousal The proposed model shown in Fig. in the next page

10. Proposed Model Customer Service 2.0 * Human Front Line Employee

11. Result and Discussion Findings indicate that Chatbots equipped with emotional intelligence algorithms led to a 20% increase in customer satisfaction scores compared to standard Chatbots. Customers reported feeling more understood and valued in interactions where the AI displayed emotional responsiveness. The integration of emotional intelligence in AI-driven interactions is pivotal in creating empathetic and human-like experiences. Businesses should consider investing in AI models capable of recognizing and responding to a diverse range of emotions to enhance customer relationships.

12. Result and Discussion (2) The implementation of cross-channel AI integration resulted in a 15% reduction in response times and a 25% improvement in issue resolution across different customer touchpoints. Businesses should prioritize integrating AI solutions that can harmonize customer interactions across diverse channels to maintain a unified and positive brand image. Customers responded positively to personalized interactions that extended beyond product recommendations, including tailored communication styles and content. Advanced personalization is a key driver for enhancing customer loyalty and engagement.

13. Building Chatbots Natural Language Processing (NLP) machine learning technology that gives computers the ability to interpret, manipulate, and comprehend human language Large Language Model (LLM) machine learning models that can comprehend and generate human language text Machine Learning (ML) the capability of a machine to imitate intelligent human behavior Artificial Intelligence (AI) the science of making machines that can think like humans

14. How Chatbots Built

15. Dataset •In this project, we used a custom dataset written in English that focuses on complaints from Customer to the Business Service •In addition, its dataset was preprocessed and tokenized to be compatible with the GPT-2 model, ensuring the quality and coherence of the generated content. In total, there are 8000+ words in the dataset.

16. Dataset In this project, we used a custom dataset written in English that focuses on complaints from Customer to the Business Service In addition, its dataset was preprocessed and tokenized to be compatible with the GPT-2 model, ensuring the quality and coherence of the generated content. In total, there are 8000+ words in the dataset.

17. Dataset

18. Tokenizing the Dataset •The first step in training the GPT-2 model is tokenizing the dataset. •The tokenizer takes raw text and converts it into tokens, which are numerical representations that the model can process. •This involves breaking down the text into words, subwords, or characters. •Let's say we have the sentence : “Hello Worlds” •The sentence is broken down into individual words ["Hello", ",", "world", "!"]

19. Training the Model Once the dataset is tokenized, we proceed with training the model. During this phase, the model learns to predict the next token in a sequence based on the previous tokens. This is achieved through iterative training, where the model adjusts its weights to minimize prediction errors. In to get best result we set the parameters to 100 epoch, 8 batch and 2e-5 # Learning rate. Epochs Determines how many times the model will see the entire dataset during training, batch Determines how many samples are processed in one iteration, learning rate determines the step size for updating the model's weights

20. Text Generation (Inference) In the inference phase, the user provides an input prompt that serves as the starting point for text generation. This prompt is tokenized into token IDs, just like the training data. The tokenized prompt is fed into the model to initiate the text generation process. This step ensures that the model understands the context provided by the user's prompt.

21. Top-K Sampling After the model predicts the probability distribution for the next token, Top-K Sampling is applied. This technique selects the top K tokens with the highest probabilities from the distribution, limiting the selection to the most likely candidates. In this project we limit top_k=60, it means that only the 60 tokens with the highest probabilities will be considered for the next token generation.

22. Text Generation Using the tokens selected through Top-K Sampling and adjusted by temperature, the model generates the next token in the sequence. This process is repeated iteratively to produce the complete text.

23. Google Text To Speech After the text is generated, we use Google Text-to-Speech (TTS) to convert the generated text into speech. This integration provides an auditory output of the generated content, enhancing the user interaction.

24. Chatbot Demonstration

25. Personal Insight 3

26. Chatbot Pros high volume problem user solving continuous worldwide service coverage customization reduce cost

27. Chatbot Cons Inaccurate processing of complex queries Maintenance and updates Limitations due to data Misinterpretation of human language subtleties

28. Develop after now Future trends 1.Knowledge base expansion 2.Improvement of natural language capabilities 3.Personalized service Main challenges 1.Data limitations 2.Counter-attack 3.Regulatory compliance Opportunities 1.Enterprise digital transformation 2.Industry vertical applications 3.New technology integration

29. References: Chatbots in customer service: Their relevance and impact on service quality Chiara Valentina Misischia, Flora Poecze, Christine Strauss Procedia Computer Science, Volume 201, 2022, Pages 421-428, https://doi.org/10.1016/j.procs.2022.03.055. Customer Service 2.0: The Influence of Chatbots and AI Solutions" Sarathsimha Bhattaru, Mahendar Goli, T. Swetha, R. Soujanya, Alok Jain MATEC Web Conf. 392 01041 (2024) DOI: 10.1051/matecconf/202439201041

30. Thank you! The Impact of Chatbots on Modern Business Operations

31. Michael Gagliano Technology makes what was once impossible possible.