When you visit an e-commerce website, suddenly there is a "ding" from the bottom right corner of the screen, and a friendly chatbot appears and asks if you need help, which you are used to.
In recent years, these chatbots have become increasingly popular as customer support personnel because the benefits are obvious. They are available around the clock and ready to answer simple questions, giving human agents more time to work on more complex questions. They also increase consumer engagement and, if they provide a positive experience, improve your website's search engine optimization.
However, a recent Gartner survey by a technology information company showed that only 8% of customers have used chatbots in recent customer service interactions, and only a quarter of them said they would use chatbots again. From the data, it can be seen that there are differences in the resolution rate of chatbots in different problem areas. Returns/cancellations and orders/purchases have relatively high resolution rates of 48% and 52%, respectively, possibly because these issues often have clearer resolution steps. Product information that needs to be answered and handled flexibly, billing disputes, are the lowest resolution rate, only less than 20%.
Conclusions
The customer service chatbots you come across have mostly been rules-based, not AI-driven. However, this is changing, as some companies begin to integrate technologies like ChatGPT.
ChatGPT is a generative AI chatbot that utilizes advanced natural language processing (NLP) technology and machine learning algorithms to understand and generate human-like responses. Trained on large amounts of data, these chatbots can analyze and interpret the context and intent behind user queries to provide more nuanced, personalized responses. While they are more advanced than rule-based chatbots, they also require more time and financial investment to set up.
Since OpenAI released ChatGPT last November, this type of chatbot has attracted a lot of attention. Companies are investigating integrating generative AI into their chatbots to make them more advanced.
Here are some of the ways ChatGPT is used in the customer service field:
- Self-service and FAQs: ChatGPT can be used as part of a self-service platform to help users answer frequently asked questions and provide information about products or services. Users can get the help they need by having a conversation with ChatGPT without waiting for a response from a customer service agent.
- Intelligent customer service assistance: ChatGPT can be used with human customer service staff to provide intelligent assistance for customer service teams. When agents encounter busy or complex situations, they can rely on the advice and answers provided by ChatGPT to better serve users. This improves the efficiency and accuracy of the customer service team.
- Personalized user experience: ChatGPT can learn and analyze the user's preferences, preferences, and history to provide a personalized service experience. Through conversation records and user feedback, ChatGPT can gradually understand the needs of users and provide corresponding suggestions and recommendations according to their preferences.
- Sentiment analysis and emotional support: ChatGPT can understand the emotional state of users by analyzing their tone, emotion, and expression, and respond accordingly. This helps to provide more human support and assistance that meets the emotional needs of users.
But this evolution is not without risk. ChatGPT is a generative AI, which means that it theoretically generates new sentences for each prompt. This unpredictability is often not ideal for brands because it carries certain risks:
- Hallucinatory errors of generative AI
In this context, "hallucination" refers to the fact that generative AI chatbots may generate responses that seem reasonable but are actually wrong or misleading. This can be because the model has biased or inaccurate information in the training data, causing them to be biased or wrong in generating responses. This illusion can mislead users and negatively affect their decision-making.
For example, generative AI may make erroneous inferences or provide inaccurate explanations based on known information. This may adversely affect the user's purchasing decisions, health advice, or legal issues. Therefore, for this type of chatbot, it is crucial to ensure that the responses it generates are accurate, reliable, and unbiased.
- It is easy to learn bad arrogance and prejudice
AI bots can learn biases from the data they receive, especially its "teachers," which have everything. In 2016, tech giant Microsoft released a generative chatbot on its social media platform called Tay. However, Tay made provocative and offensive statements that took only a few hours as it learned from its interactions with users who deliberately provoked it. Due to its inappropriate and offensive behavior, Microsoft was forced to shut down Tay and issue an apology.
- Poor emotional communication and emotional processing
In fact, they can lead to an increase in customer dissatisfaction, not a decrease. The so-called "once difficult to water", the more advanced the chatbot, the easier it is to be contacted by the public, and the visitor users who have been exposed to advanced robots themselves have certain positive expectations for chatbots, if the newly contacted robots cannot reach the same level of chat, it will lead to user anger and frustration, and the robot itself can not handle the situation well. A 2021 study found that the personification of chatbots can negatively impact customer satisfaction, overall company evaluation, and subsequent purchase intentions if customers are already feeling angry.
To mitigate these risks, developers and researchers should rigorously train and test generative AI and use diverse datasets to eliminate bias and improve model accuracy. They should also implement oversight and feedback mechanisms, as well as processes for reviewing and correcting generated responses.
In addition, transparency and explainability are also key factors in solving these problems. Users should be able to understand how chatbots generate responses, as well as their limitations and uncertainties. Providing users with explanations for model decisions and revealing machine-generated responses can help users better understand and evaluate what chatbots can and can't do.
While there's still room for improvement, generative AI chatbots are the way to deliver their services to you. Let's look forward to this intelligent transformation of customer service and usher in a more personalized and intelligent service experience.
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