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AI is transforming how enterprises provide customer service in the digital age. With the rise of digital channels like social media, chatbots and AI-powered virtual assistants, customer expectations are changing. There is a growing need for enterprises to provide faster and more personalized support.
Artificial intelligence in customer service context can generate most of the conversations and provide support around the clock. At the same time it can analyze customer data and provide personalized experiences. For businesses it also opens up more efficient ways for analytics and insights by letting the AI analyze and learn from customer interactions or reviews.
The use of AI in customer service has experienced significant growth over the last years. Surveys show that over 80 percent of organizations want to increase their investment in AI and generative AI over the next months [1].
This guide outlines the key takeaways such as understanding the core benefits of AI in customer service. From the fundamentals that you can apply right away to advanced topics that require more setup you will learn about implementation best practices, discovering useful tools and examples.
I explain each use case with a getting started section and example prompts. Many of the prompts contain variables (indicated by curly braces {$VARIABLE}) that you need to fill with your specific data to receive good results. Most of the popular LLMs (ChatGPT, Gemini, Claude, etc.) support uploading files as context.
Outcomes of AI in customer service
Customers increasingly value their time and expect prompt responses. The use of AI allows much quicker response times. By using AI agents or chatbots you can provide instant support 24/7. AI allows global coverage across timezones. This could overall improve the lead conversion rate through immediate engagement.
With the help of AI you can optimize the allocation of your resources. This can lead to significant cost reductions. Estimates are that within three years, AI will cut costs by up to 30 percent, increase B2C revenues by 2 to 4 percent, improve customer satisfaction by 10 to 20 percent, and reduce churn by as much as 30 percent [2].
On the other hand you can benefit from scaling your customer support without the need to hire more staff. You can reach higher support capacity through automation, better handling volume spikes. At the same time your agents can provide better multi-language support.
AI enables your team to come up with more creative solutions more quickly which can lead to higher customer satisfaction and loyalty.
A big topic for customer service is the use of AI to provide personalized support. Personalization converts more leads to customers. You can achieve this by integrating customer history. AI then can give contextual recommendations and analyze the customer's intent. Service quality can improve additionally by keeping a consistent tone and language across all channels. AI systems can also retain and build upon customer service knowledge enabling them to learn and improve continuously over time.
Start by assessing your current customer service operations.
Identify high-impact automation opportunities. In this guide you will find examples of common use cases and workflows that are good candidates for AI automation.
Start with ChatGPT and basic AI tools. Common, low-risk queries are good to start with. Maintain human oversight for complex issues and to ensure quality. Create response templates for common queries and update them as needed.
Core implementation areas
Use case: Generate responses to common customer inquiries to reduce workload for your support team.
Example of a FAQ implementation on a company website
Getting started: Identify the most frequent questions, input them into ChatGPT and use the responses to feed your website’s chatbot or FAQ page. You can also ask ChatGPT about common questions in your niche. You can upload your policies or notes as context for the LLM. Let ChatGPT generate full answers.
Example prompt:
Create a comprehensive set of responses to address common customer questions about the {$INDUSTRY_OR_COMPANY}'s shipping policies, return policies, and product availability. The responses should be based on the provided {$POLICY_DETAILS} and should cover the most {$COMMON_CUSTOMER_QUESTIONS}.
For each question, provide a response that includes the following:- A brief introduction to the relevant policy or information- Key details about the policy or information, such as timeframes, fees, or restrictions- Any additional context or guidance that would be helpful for the customer
If a customer's question is not covered by the provided information, please respond with:I'm sorry, but I don't have the information needed to answer that question. Would you like me to connect you with a customer service representative who can assist you further?
Remember to always be polite and professional in your responses, and do not make any promises or statements that are not explicitly supported by the provided policy details.
Use cases:
Example of an AI chatbot for a logistics company
Getting started: Feed customer request or complaint into ChatGPT
Example prompt:
Respond to a customer inquiry regarding a damaged product they received, providing either a replacement or a refund based on the relevant policy.
Use the following information to construct your response:
- Customer Issue Details: {$CUSTOMER_ISSUE_DETAILS}- Company Refund Policy: {$COMPANY_REFUND_POLICY}- Customer Contact Information: {$CUSTOMER_CONTACT_INFO}
Ensure the response is polite, helpful, and addresses the customer's concerns fully, offering the suitable next steps for a refund or replacement based on the company's refund policies.
1. Begin with a warm acknowledgment of the customer's concern. - Empathize with their situation and appreciate their patience.
2. Summarize the customer's problem using the provided details. - Reference the damaged product and acknowledge their request for either a refund or replacement.
3. Clarify the next steps based on the given refund policy. - Clearly provide the customer with information on how to proceed for a refund or replacement.
4. Offer further assistance. - Encourage the customer to get in touch if they have any other questions or need additional help.
5. Tone and Professionalism - Maintain a courteous and understanding tone. - Provide clear and actionable information without over-promising.
Note:
Handling customer requests is an excellent use case for AI agents. With the help of the ChatGPT API we can build tools that can reply automatically to the user, decreasing response time. This could be deployed as a chatbot or voice agent.
AI agents could route customer queries to the appropriate team, automating follow-up emails after a service or handling product availability inquiries. AI agents could also guide customers through modifying their orders or subscriptions, respond to billing inquiries or suggest additional products or services during customer support interactions.
Use cases:
Example of personalized product recommendations in an e-commerce shop
Getting started: Feed customer data into ChatGPT (e.g. purchase history) to tailor the response accordingly.
Example prompt:
You are an experienced customer service agent for an online retail company that specializes in photography equipment. Your task is to write a customer service email reply that meets the following objectives:
1. Acknowledge the customer's loyalty and thank them for their continued business.
2. Demonstrate your understanding of the customer's latest query about product restocking.
3. Provide relevant product recommendations based on the customer's recent purchase of a camera, suggesting accessories such as lenses, tripods, and memory cards that would complement their existing purchase.
Use the provided information to construct your response:
- Customer Query Details: {$CUSTOMER_QUERY_DETAILS}- Recent Customer Purchase Details: {$RECENT_CUSTOMER_PURCHASE_DETAILS}
Ensure the response is polite, helpful, and fully addresses the customer's concerns, offering suitable product recommendations that align with their recent purchase.
Note: If you’re in eCommerce integrate this with the eCommerce platform to recommend products based on browsing and purchase behavior.
Use cases:
Getting started: Feed a product manual or setup guide into ChatGPT and provide detailed, simplified instructions for your customers. Offer step-by-step instructions for product setups or troubleshooting providing customers with potential fixes.
Example prompt:
You are an experienced customer service agent for a leading home security company. Your goal is to provide clear and comprehensive instructions for setting up the newest home security system, including how to connect it to Wi-Fi and troubleshoot common issues.
Begin by providing an overview of the home security system, including its key features and capabilities. Then, outline the step-by-step process for setting up the system, including any necessary hardware or software installations and the timeline for the full setup.
Next, address potential troubleshooting issues that customers may encounter, such as connectivity problems or error messages. Provide detailed solutions for each issue, referencing relevant sections of the system's documentation or user manual.
Throughout the instructions, maintain a friendly and helpful tone, and anticipate any follow-up questions the customer might have. If a customer asks a question that is not covered in the provided information, politely suggest that you will need to connect them with a human representative who can better assist with their specific query.
Remember to always keep the customer's needs and concerns at the forefront of your responses, and use clear, concise language to ensure a seamless setup experience.
Use the provided information to construct your response:
- System Details: {$SYSTEM_DETAILS}- Setup Timeline: {$SETUP_TIMELINE}- Troubleshooting Issues: {$TROUBLESHOOTING_ISSUES}- Troubleshooting Solutions: {$TROUBLESHOOTING_SOLUTIONS}
- Maintain friendly and empathetic responses at all times.- Keep instructions clear, using numbered or bullet-point formats wherever appropriate.- Adapt or shorten explanations if the customer appears to be more experienced with similar tasks.
Use case: Generate and maintain support articles that customers can access for self-service.
Getting started: Feed the information into ChatGPT to automatically generate and update knowledge base articles based on user needs and common inquiries.
Example prompt:
You are an expert customer service agent for the {$PORTAL_NAME} customer portal. Your goal is to provide clear and comprehensive instructions for how the customer can reset their password, tailored for the company's website.
Review the provided password reset steps {$PASSWORD_RESET_STEPS} and use them to create detailed support content. Ensure all steps are explained thoroughly, so even users with minimal technical knowledge can easily follow along.
Begin with a brief introduction, explaining the importance of being able to reset passwords securely and effectively. Outline the steps necessary for resetting a password, including any verification processes or details that users need to have ready beforehand.
Add troubleshooting tips for users who may encounter issues during the reset process, such as not receiving a confirmation email, verifying identity, or dealing with expired reset links. Offer solutions clearly, referencing appropriate contact details or help resources when stepping beyond the portal capabilities.
Throughout the instructions, adopt a friendly and empathetic tone, anticipating customer concerns, and avoid using jargon wherever possible. If additional help is needed, provide guidance on how a customer can get in touch with a support representative for further assistance.
Use case: Use ChatGPT to respond to customers in different languages without needing to hire multilingual staff.
Getting started: Input the target language or let it ChatGPT automatically detect and respond in the customer’s preferred language.
Example prompt:
Translate the provided customer service message {$CUSTOMER_SERVICE_MESSAGE} to the specified target language, ensuring the original message's tone, formality, and cultural nuances are retained.
1. Analyze Original Message: Examine the message in English to fully understand its intended meaning, tone, and context.
2. Consider Target Language: Take into account the cultural and linguistic norms of the target language, as well as the level of formality requested.
3. Identify Nuances: Recognize cultural or linguistic elements in the original message that need to be preserved or adapted for the target language.
4. Translate the Message: Accurately translate the customer service message. Maintain the meaning, tone, and level of formality as is appropriate in the target language.
5. Review Translation: - Confirm the translation maintains the original message's intent. - Ensure the tone and formality level matches the original. - Verify the translation reads naturally and fluently in the target language. - Check for the inclusion of cultural or linguistic factors that may be important to the translation’s accuracy.- Ensure that the translation accurately reflects a balance of tone, formality, and readability without overly simplifying or complicating the language.- Verify that cultural differences are addressed, such as avoiding informal language when inappropriately used in formal customer service contexts.- Always prioritize maintaining empathy and approachability within the message.
Use cases:
Example of a customer review with automated reply and sentiment analysis
Getting started: Copy and paste the reviews. Send a survey after a customer interaction, using the feedback to improve your service.
Example prompt:
You will be responding to a customer who has left a negative review about slow delivery times for their recent order from our company. Your goal is to craft a polite and empathetic response that addresses the customer's concerns, provides relevant information, and offers a discount on their next purchase.
Review the customer's feedback details and the typical delivery time for our orders. Craft a response that includes the following:- Acknowledge the customer's frustration with the slow delivery- Provide the typical delivery time for our orders to set proper expectations- Offer a discount on the customer's next purchase to compensate for the negative experience- Express our commitment to improving delivery times and customer satisfaction
- Typical Delivery Time: {$TYPICAL_DELIVERY_TIME}- Discount Offer: {$DISCOUNT_OFFER}- Customer Review: {$CUSTOMER_REVIEW}
Example prompt:
Create a short, targeted customer satisfaction survey that focuses on the key aspects of the customer's recent support experience with the company. The survey should consist of 5 questions and be designed to be easily completed by the customer through their preferred delivery method.
The survey questions should cover the following areas:1. Ease of communication with the support team2. Responsiveness of the support team3. Time taken to resolve the customer's issue4. Overall satisfaction with the support experience5. Likelihood of the customer recommending the company's support services to others
The survey should be designed to be concise, with clear and straightforward questions that can be easily answered by the customer. The survey delivery method should be flexible, allowing customers to complete the survey through their preferred channel, such as email, SMS, or a web form.
When generating the survey questions, consider the specific details provided in {$CUSTOMER_EXPERIENCE_DETAILS}. Tailor the questions to address any relevant aspects of the customer's recent support experience.
The survey should be designed to be completed within {$SURVEY_LENGTH} minutes. This will help ensure a high completion rate among customers.
Finally, the survey should be delivered to the customer through {$SURVEY_DELIVERY_METHOD}, ensuring that the customer can access and complete the survey conveniently.
Use case: Generate customer success stories by describing the customer and their success.
Example of a customer success story crafted by AI
Getting started: Feed the customer info, problems, solutions and other metrics into ChatGPT.
Example prompt:
You are a customer success expert. Craft a customer success story of one of our clients, showcasing their experience, the challenge they faced, and the successful solution implemented.
1. Identify the Customer: - Begin by describing the customer's background, including relevant attributes such as industry, size, and key demographics.
2. Define the Challenge: - Describe the specific problem or challenge faced by the customer before finding a solution.
3. Detail the Customer's Success: - Clearly outline the solution provided, the changes experienced, and the measurable success achieved by the customer.
4. Summarize the Outcome: - Provide a conclusion summarizing the impact of the solution on the customer, focusing on business efficiency, productivity, satisfaction, or other key results.
Provide the final customer success story in a narrative and engaging style, aiming for a logical, coherent flow. Each story should include:
- Customer Introduction: {$Customer_Name}, {$Industry}, and {$Company_Size} to set the context.- Challenge Encountered: The initial problem the customer faced using {$Problem_Description} and {$Impact_Of_Challenge}.- Solution Implementation: The solution provided and components—{$Solution_Description}.- Success & Results: Include both quantitative and qualitative success metrics—{$Quantitative_Results}, {$Qualitative_Impact}, {$Specific_Benefits}, and {$Customer_Testimonial}.
ChatGPT and other LLMs can be integrated into existing IT infrastructure or custom developed tools through APIs. Popular is the so called Retrieval Augmented Generation (RAG) approach. RAG combines large language models with your organization's specific knowledge base to provide more accurate and contextual responses.
Another revolutionary approach is called function calling. It allows LLMs to execute specific functions based on the request. That means the LLM analyzes the customer request. It determines which system actions are needed. It calls the appropriate functions with the required parameters. It then executes the actions and returns the results incorporated in the response.
The principle of function calling
Example workflow:
Modern help desk platforms increasingly incorporate AI capabilities to enhance customer support operations. These solutions combine traditional ticketing systems with AI-powered features like automated responses, smart routing, and predictive analytics.
Here's a comparison of leading AI-enabled help desk platforms:
Platform | Key AI Features | Best For | Starting Price | Integration Capabilities |
|---|---|---|---|---|
Zendesk |
| Enterprise-level organizations needing comprehensive support solutions | $55/agent/month | Extensive marketplace with 1000+ integrations |
Freshdesk |
| SMBs looking for scalable solutions | $15/agent/month | Good integration options, API access |
ServiceNow |
| Large enterprises with complex IT needs | Custom pricing | Enterprise-grade integrations |
Intercom |
| SaaS companies and digital-first businesses | $85/seat/month | Strong focus on modern SaaS integrations |
HubSpot Service Hub |
| Companies using HubSpot ecosystem | $90/seat/month | Seamless HubSpot integration |
The market offers a range of chatbot development tools, from enterprise-grade platforms to no-code solutions. Enterprise platforms like Dialogflow and Amazon Lex provide deep customization and advanced NLP capabilities, while no-code tools enable quick deployment for simpler use cases. When selecting a platform, consider factors like integration capabilities, language support, and development complexity.
Platform | Type | Key Features | Best For | Pricing Model |
|---|---|---|---|---|
Dialogflow | Enterprise |
| Large enterprises needing sophisticated chatbots | Usage-based |
Amazon Lex | Enterprise |
| AWS users needing voice/text bots | Pay-per-request |
IBM Watson Assistant | Enterprise |
| Enterprise with complex use cases | Tiered pricing |
MobileMonkey | No-code |
| SMBs needing quick deployment | From $19/month |
ManyChat | No-code |
| Social media marketing teams | Freemium |
For organizations seeking more control and customization, several robust open-source frameworks are available:
Framework | Key Features | Best For | Technical Requirements |
|---|---|---|---|
Rasa |
| Organizations needing complete control and customization | Python, Machine Learning expertise |
Botpress |
| Teams wanting balance between control and ease of use | JavaScript/Node.js |
Jarvis |
| Voice-first applications | Python, API integration skills |
Voice AI is becoming increasingly important for customer service:
These platforms offer features like:
Modern customer service requires seamless integration across multiple channels:
Popular Channels:
When measuring the ROI of AI implementation in customer service, you can consider these key metrics:
⚡ Efficiency Metrics:
😊 Customer Experience Metrics:
👥 Agent Performance Metrics:
Before going all-in with AI, you can start with routing 10-20% of the traffic to AI-powered solutions. Set clear targets for each KPI you want to monitor. Then track the results.
AI systems may struggle with understanding customer intent if the context that is provided is not detailed enough. Multiple back-and-forth exchanges might be needed for the LLM to fully understand the customer's request.
There is the risk of responses feeling robotic or impersonal. It's currently challenging for LLMs to build genuine relationships with customers.
Connecting AI tools to existing IT infrastructure can be challenging. Security and compliance also need to be considered.
Some customers might have a resistance to AI-powered customer service. The key is to balance automation with human intervention.
Think about clear escalation paths from AI to human agents. Start with using AI for initial interactions and routine queries. Maintain human oversight for complex or sensitive issues. So in the end AI is used to augment human agents rather than replacing them.
It can be beneficial to fine-tune models based on actual customer interactions and company data. Collect feedback from your team and customers. Then update and improve your knowledge base and response templates continuously. As mentioned before, a regular analysis of AI performance metrics is a good idea. Human agents also need to be trained to effectively use AI tools.
The customer service landscape is rapidly evolving with several groundbreaking technologies on the horizon:
AI systems that can understand and respond to various media types includign images, videos and screenshots. The AI is voice-enabled and can detect customer tone, emotions and intents. It can combine all these elements (text, voice and visual content) to create a more natural communication.
AI can detect customer emotion through voice or text in real-time and adapt the response accordingly. It can also instantly change its personality to match the customer.
Content for personalized responses can be created dynamically and the AI can translate conversations in real-time.
The next few years are likely to bring significant changes to customer service:
The AI systems will get better in remembering past interactions across all channels. They will predict support needs and craft personalized service journeys.
The AI will be self-learning and improving without human intervention.
The interaction between AI and human agents will be seamless. The role of blockchain will be more important for secure data exchange.
No, AI is not likely to completely replace human agents. Instead it will augment them and improve their efficiency:
Current Reality: AI handles routine and repetitive tasks. Humans manage complex issues requiring empathy. A hybrid approach typically yields the best results.
Future: Humans focus on high-value interactions and supervise and train AI systems. This way they can build deeper relationships with customers.
Implementation timeline varies based on the scope of the project:
Quick Wins (< 1 month):
Medium-Term (1-3 months):
Long-Term (> 3 months):
AI is revolutionizing customer service in several crucial ways. The 24/7 availability and instant response times of AI systems are leading to higher efficiency and customer satisfaction, at the same time reducing costs. The customer experience can be improved by personalizing interactions and providing a consistent experience across channels. Even for more complex issues, AI can help reduce wait times and solve problems faster. Additionally, businesses can make better decisions by using data-driven insights.
To successfully implement AI into your customer service, start small with simple use cases like FAQ automation and test with a subset of your customers. Gather feedback and measure the results. Afterwards, you can gradually expand the use of AI and integrate it more deeply into your business processes. To optimize your systems continously set and monitor clear goals and KPIs and adjust your strategy as needed.
And finally, stay updated with the latest AI advancements. Our free newsletter is a great way to do that.
Learn more in our article about all 215 ways to use AI.
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