How to Create a Customer Service Chatbots & Best Practices
Are you looking to create the perfect chatbot to handle all your customer queries? The best time is now. A study by TIDIO found that 62% of customers would rather talk to a bot than wait for a human to answer. The boost came after the introduction of ChatGPT by OpenAI. Some big names like Meta, Canva, and Shopify already use the ChatGPT engine in their chatbots.
As more and more customers use AI-based text generators in daily activities, their acceptance with customer service chat bots increases. The latest development in AI chatbots is the GPT-4 engine. Businesses can now use it to give customers accurate human-level responses and provide more factual replies than GPT-3.5.
In this article, we will point out why your business should adopt an AI chatbot through compelling statistics and reasons. By the end of the article, you will understand the basic steps to build a chatbot, the time required to create a chatbot, and the development cost. In addition, you will be familiar with the best practices that can make chatbots respond naturally and humanely.
Part 1: What's new about AI chatbot customer services?
Initially, chatbots relied on catching keywords or phrases from the customer's text. Then it looked through its library to find a predefined customer service response. The answers were always the same and repetitive, making it sound more like a robot.
On the other hand, AI chatbot customer service is much more sophisticated. It can provide contextually correct and coherent replies. AI's ability to detect interests, biases, intent, and sentiment to generate personalized answers has led to its widespread adoption.
Chatbots' capability has skyrocketed due to the neural network model ChatGPT by OpenAI. More businesses are integrating ChatGPT API with their chatbots to handle complex conversations for reliable and accurate responses.
Moreover, OpenAI's latest GPT-4 engine, with its 170 trillion parameters, is the largest language model (LLM) available. It is a multimodal design, which means it can accept text and images as customer input. The engine can relate them to answer customers' queries or provide suggestions.
Building a conventional rule-based chatbot used to require loads of working hours to develop. In contrast, Generative Pre-trained Transformer (GPT) is pre-trained on a large data set, allowing it to understand how language works.
The IT experts can add their data layer to train the model for their requirements. Later, with some fine-tuning and prompt engineering, their chatbot can create a humane response that fits the situation perfectly.
Part 2: Key Features of Customer Service Chat Bots
The exciting new capabilities of AI chatbots are off the charts. GPT-4 is opening new horizons for developers to bring creativity to how bots interact. It's benefiting businesses through saving resources and making customer service convenient for everyone.
However, there are a few drawbacks companies should be aware of. Here is the list of AI support chatbot pros and cons:
Scalability
Maintaining a presence across multiple channels, including websites, apps, and social media, is critical in today’s digital landscape. Without the assistance of a chatbot, managing conversations from all these platforms would require a lot of manpower.
In contrast, a single chatbot can simultaneously integrate and handle conversations across all platforms. As evidence of this trend, Facebook has already integrated 300,000 chatbots into their Messenger platform.
Multilingual
A chatbot powered by a GPT engine can converse in multiple languages. It can understand and reply in more than 20+ global languages, benefiting businesses with a worldwide presence serving a larger customer base.
Contextual
Chatbots can leverage previous conversations to provide contextual answers. The latest chatbot powered by the GPT-4 engine can now store up to 32,000 tokens, equivalent to roughly 50 pages of conversational text. This expanded memory enables the chatbot to deliver more personalized responses tailored to each customer’s preferences and nature.
24/7 Availability
Bots are accessible round the clock, and can significantly enhance customer satisfaction by providing prompt responses that resemble human interaction.
Save Resources
Chatbots can accurately answer 30% - 80% of repetitive queries while ensuring customer satisfaction. Customer support agents can then focus on handling complex tasks and addressing unique requests, saving valuable time and money spent on customer services.
Few Limitations
Limited Scenarios
AI is clever but can only deal with specific conversations. It's still prone to ‘hallucinations’ (makes up answers) that are not related.
Customer Frustration
A complex problem can lead an AI chatbot to take the wrong route, potentially resulting in a dead end in the conversation or getting stuck in an endless loop with no viable solution, which can cause frustration for customers.
Human Touch
GPT-4 has recently achieved a significant milestone by successfully passing the Bar Exam and demonstrating its ability to solve complicated legal problems. However, it still lacks the human touch. While bots can accurately respond to frequently asked or repetitive questions, there may arise situations where customers require the assistance of a human for more specific conditions.
Part 3: 8 Steps to Develop Your Customer Support Chatbot
There are many paths to developing a customer support chatbot. Businesses can choose the easy route with a general approach or a more personalized chatbot using a framework. In our comprehensive guide, we will cover both methods step by step. Without further ado, let’s dive in.
1Purpose and Goal
Before going into coding and complexity, determining the chatbot's purpose and goals is essential. They will define how the bot will steer the conversation if it is a customer support chatbot.
You can start with finding the answers to the following questions:
- Are you looking for a rule-based chatbot that prioritizes safety or an intelligent machine-learning AI bot?
- Will it be designed to direct users to the appropriate customer service expert?
- Is its primary function to handle FAQs until the customer specifically requests human intervention?
- Should it engage users in creative conversations and occasionally encourage them to make a purchase?
- Do you want your bot to be able to process both images and text?
Answering these questions will set the path for your chatbot. If you have a database of recent customer queries, it's best to gather them now.
2Selecting a Platform or Framework
Chatbot development platforms are tools businesses can use to create a bot. It is easier than ever to build your bot with ChatInsight. ChatInsight offers a platform for businesses to create their bot easily. It's easy to understand, require no coding, allowing for the management of Knowledge from different teams, optimizing the input data for custom training their bots. It helps businesses save time cost for answering repeating questions, enables company efficiency without increasing cost.
3Design the Conversation Flow
To begin, you can create a straightforward conversation diagram. The diagram should include components such as a greeting, asking, informing, confirming, checking, error, apologizing, suggesting, and concluding. While the specific details may differ depending on the application, this diagram will provide valuable insights into how the bot will handle various situations.
4Collect and Prepare Training Data
AI engines are readily available and pre-trained on vast datasets, but customization is needed to meet your company's requirements. If you already have a customer service team in place, you can involve them in collecting or creating the database. The following correspondences can be utilized as training data:
- Questions asked by customers
- Emails to the customer support team
- Social media direct messages
- Customer feedback
- Customer service team feedback
5Train the Chatbot
Training a customer service chatbot on Chatinsight is both fast and convenient. Different chatbot creation platforms and frameworks adopt different approaches to training. You can custom the knowledge based to training the customer support bots as the business required. It can be trained to answer enterprise-specific questions that makes further breakthrough on large language models like ChatGPT.
6Integrate with Existing Systems
Once your model has been trained with ample data to initiate natural conversations, developers can seamlessly integrate it with other systems. Depending on the capabilities of the chatbot builder, it can be integrated with CRM systems as well as various social media platforms like Facebook Messenger, Instagram, Twitter, and more.
Before selecting a developer, ensure that they support all the social media platforms where your business maintains a presence.
7Test it Out
You can now assess your bot's performance by using different prompts. The chosen platform or framework for creating the chatbot may provide a testing interface for fine-tuning.
Ask your customer service representative to provide complex commands they typically handle on a daily basis. Observe the output, evaluate its accuracy, refine it to minimize errors, and retest it until you achieve satisfactory results.
8Deploy and Monitor
Finally, you can deploy your creation on the World Wide Web. Your customer support chatbot is now ready to address customer queries across various platforms, including social media platforms, official websites, and messaging apps. Continuously monitor its performance and refine your design. Achieving a flawless and natural conversation from your bot may require iterative actions.
Part 4: A Few Best Practices to Keep in Mind
Here are five best practices that business owners or chatbot developers should keep in mind while deploying their bots.
Understand your customer
Gaining a thorough understanding of your customers is crucial to ensure that your customer service chat bot is trained on a relevant dataset.
Here are some approaches that can help you achieve a deeper understanding of your customers:
- Consult with your customer support team: Collaborate with your customer support team to gather detailed customer insights. They can provide valuable information about customer attitudes, interests, and opinions. This input will refine the datasets and contribute to a more customer-centric knowledge base.
- Combine demographic, behavioral, and psychographic data: Merge the collected data from interactions with the conversational text. This will help you develop specialized bot responses tailored to the characteristics of our customers.
- Gather feedback from customers: Collect feedback from customers through questionnaires or emails and integrate the acquired data into your internal system. This feedback will provide valuable information for improving the performance of your customer service chat bot.
By incorporating these approaches, you can enhance the knowledge base of your customer service chat bot, enabling it to deliver more accurate and personalized responses.
Keep improving your chat bot
AI-based chatbots learn and improve their responses as they engage in conversations with customers. However, their replies are limited to the information provided in their initial dataset training.
To keep the chatbot up to date, it is necessary to fuel its knowledge base constantly. This can be achieved by incorporating collected information such as customer feedback, staying updated on the latest trends, and including new product information. By continuously training the chatbot dataset, an improved knowledge base is created, serving as a foundation for initiating conversations with customers.
If a new AI engine becomes available, your business can analyze its capabilities and adopt it as soon as possible. The newly integrated AI chatbot engine may behave differently when responding to prompts. Therefore, enhancing the conversational skills of your customer service chat bots is a must. You can do so by using prompt engineering and feedback data.
Keep customer service team in the loop
Your experienced customer service team will play a crucial role in training and deploying the chatbot. They can address the following inquiries:
- ● At which point should the agent intervene in the conversation?
- ● Which dataset is irrelevant to your customer's queries?
- ● Is the chatbot conversation meeting expectations?
- ● Are there any emerging trends that the chatbot needs to be trained on?
Any customer service representative can offer valuable insights into user behavior and responses, especially in complex situations. They can also help in prompt engineering by providing frequently asked questions to save development time and improve customer satisfaction.
Customers can always find a real person in the process
According to a study conducted by Simply, 80% of customers are willing to utilize chatbots as long as they have the option to reach a real person when needed. The acceptance rate varies based on demographics, with tech-savvy generations like GenZ and Millennials being more inclined towards embracing technological advancements, including the use of chatbots.
Userlike, a customer service platform, states that 60% of customers prefer waiting in a queue to connect with a human agent, but they also expect a first response within a 15-second timeframe. These statistics highlight the importance of having a chatbot as the initial point of contact. Chatbots can efficiently handle repetitive queries and seamlessly transfer to a human agent when faced with complex situations or encountering errors in artificial intelligence.
Monitor chatbot performance
Lastly, continuously monitor the performance of your chatbot. It will serve as the basis for improving customer satisfaction and building trust. Following are some of the aspects to keep under observation:
● User Interaction: Develop a method for calculating the response rate of your chatbot. A successful bot typically achieves an engagement rate between 30-40%. If the response rate is low, it indicates that your bot requires improvement.
● Human Interaction: Keep track of how many interactions end up being transferred to a customer service agent. This indicates instances where the bot was unable to resolve queries on its own.
● Track Traffic: To efficiently allocate human resources, observing the hourly traffic pattern is a must. This provides insights into the specific times of the day when customers interact most with the chatbot.
● Customer Retention: Observe the number of customers who use the chatbot multiple times to find resolutions to their problems. Ideally, this number should be as high as possible. Moreover, gathering feedback from these customers can lead to in-depth analysis and improvement.
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