The Boston Consulting Group (BCG) points out that applying generative AI in customer service can create the fastest and largest value for businesses. The most concrete way to implement this is by establishing generative AI chatbots. Unlike traditional chatbots, generative AI chatbots can engage in natural, seamless conversations based on the company’s knowledge base while learning and evolving from customer feedback. So, how can an effective generative AI chatbot be built? The key lies in fully understanding the “Customer Journey.”
Customer Journey Map
First, we can fully understand the customer journey by creating a customer journey map. Analyze the entire market structure with a wide-angle view to evaluate markets worth studying, then delve into the journeys of specific customers with a microscopic view. We can gradually collect and analyze key information through quantitative and qualitative research methods, aiming to understand the customer’s profile and every touchpoint in the customer experience. Ultimately, this will build a comprehensive 360-degree customer journey map.
By clarifying and defining existing sales and customer service touchpoints and their pain points, and using these insights to train your AI model accordingly, you ensure that customers receive the most efficient and personalized experience.
Steps to Build an AI Chatbot
After obtaining a customer journey map and insights, how do you build an AI chatbot? If your business has information security concerns, you can develop a proprietary AI chatbot. You can either develop it internally or outsource the task to a development company. If there are no security concerns, you can use chatbot development platforms available on the market. Many AI chatbot services offer drag-and-drop interfaces and modules, making it easy to get started.
Next, based on the journey map and insights, create a proprietary knowledge base for your business. This will enable the AI chatbot to quickly respond to customer inquiries.
However, this is not the end of the journey. Continuously improving the knowledge base is crucial. By using a Human-in-the-Loop (HITL) (Note 1) collaboration process to identify and address any knowledge gaps, we continuously enhance the chatbot’s capabilities. Our ultimate goal is to develop a powerful chatbot that not only addresses customer service issues but also actively helps close sales.
Ultimate Application of AI Chatbots
Imagine a chatbot that not only responds to inquiries but also optimizes the content each customer sees and intelligently monitors the entire marketing, sales, and customer service funnel. This will make customers feel genuinely helped and cared for, thus increasing loyalty and repurchase rates. This is the ultimate goal of generative AI in customer service: future chatbots will become assets driving business success, not just support tools.
Applications of Customer Journey Map
The customer journey map is not only the foundation of the AI knowledge base but also can be applied in the following areas:
- Customer-Centric Business Transformation: Use the customer journey map and personas to facilitate customer-centric discussions across departments, plan cross-departmental initiatives, and enhance the overall customer experience.
- Customer-Centric Digital Marketing Strategy: Formulate a customer-centric digital marketing strategy that spans the journey, expanding the market, increasing conversion rates, and maximizing customer lifetime value (CLV).
Additional Great Value
In the process of creating the journey map, by thinking through the entire process from the customer’s perspective and observing and discussing carefully, we can gain the following great value:
- Discovering New Business Opportunities: By fully empathizing with the customer and grasping the big picture and details, we can uncover opportunities hidden in customer dissatisfaction and discomfort, which is also the discovery of the Moment of Truth.
- Breaking Down Organizational Barriers: Through objective data and discussion, we can resolve differences of opinion between departments. This approach helps achieve a consensus on customer understanding and promotes more efficient team collaboration.
- Seamless Integration of Marketing and Sales Funnels: By visualizing the journey, marketing and sales can better understand each other’s tasks and impacts and promote smoother communication.
- Identifying Potential Customers: Thoroughly researching customers can identify the most potential target customers, concentrating resources to attract more target customers.
- Comprehensive Understanding of Stakeholders: The dependency relationships of customer stakeholders become clear, increasing control over customers and making decision-making more flexible.
By deeply understanding the customer journey, we can effectively build and continuously optimize AI chatbots, ultimately driving business growth. Proper use of the customer journey can further enhance corporate competitiveness and create immense value.
Note 1：Human-in-the-loop (HITL) machine learning is a collaborative approach that integrates human input and expertise into the lifecycle of machine learning (ML) and artificial intelligence systems. - Google Cloud
Reference:
- What’s Possible? Generative AI and Customer Experience, Boston Consulting Group, 2023/07/11
- Three Ways GenAI Will Transform Customer Experience, Boston Consulting Group, 2024/02/21
- Putting AI to work for Customer Service, IBM Technology, 2023/11/30
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Peiling Wu ｜ Founder & Director
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