Read Time: 12 minutes
Table of Contents
Intro
Imagine having a brilliant assistant who can tell you anything you want to know and solve problems simultaneously. Eventually, this assistant starts picking up patterns to tailor a response whenever you ask a question. But what if this assistant wasn’t an actual person but a powerful AI system? It sounds almost too good to be true, right?
Generative AI is an artificial intelligence system used in customer service to offer personalized experiences and quick response times that make customers feel understood and valued.
This article will explore the uses and benefits of generative AI in customer service and how you can leverage this technology.
Key takeaways
- Understanding generative AI: Generative AI provides faster and more personalized customer service by analyzing customer patterns and behaviors.
- Implementing generative AI for customer support: Businesses can utilize generative AI with chatbots, automated responses, real-time agent support, and personalized care to create personalized customer experiences.
- Core benefits: Enjoy fast response times, cost savings through automation, consistent, high-quality interactions, and the ability to scale for improved customer satisfaction and loyalty.
- Future outlook of AI: The future of AI will use data-driven insights and support omnichannel platforms.
What is generative AI in customer service?
Generative AI vs. traditional AI
If you’re new to AI, you may wonder what the difference is between generative and traditional AI.
- Generative AI analyzes patterns and examples inserted by users to create personalized responses. Ex: ChatGPT
- Traditional AI uses a set of predefined rules and data to make decisions or solve problems. Ex: An Email Spam Filter
Five ways to implement generative AI in customer service
Generative AI can transform customer service, empowering companies to deliver faster, more personalized support. Here are five ways to implement generative AI in your business.
1. Utilize AI-powered chatbots for 24/7 customer support
If you’re a frequent internet user, you’ve probably used or encountered a chatbot at some point. Chatbots are valuable for communicating with customers in real time when human agents are off the clock. However, traditional chatbots may struggle to understand the nuances of customers’ questions, replying with predictable responses. With generative AI, chatbots use machine learning to understand customers’ questions and patterns and provide a more human-like interaction.
2. Automate response generation for frequently asked questions
Most of the time, customers ask similar questions about products and business operations. Creating pre-written responses to frequently asked questions (FAQs) is a great way to communicate quickly with customers and free up agents to tackle other priorities.
3. Encourage customer self-service with AI-powered content
Generative AI can create helpful FAQs, guides, and support articles for customers to read on your website anytime. By analyzing patterns in customer interactions, AI can identify common queries and use existing product materials to create how-to guides and other helpful resources. Be sure, though, to rewrite any AI-generated content in your own words if you plan on publishing it to your website to avoid potential SEO penalties.
4. Support live agents with AI-generated suggestions
Sometimes, on customer calls, there can be a question your agents don’t have an answer to. Generative AI prepares your agents for these moments with recommended responses and solutions during client interactions.
5. Personalize customer interactions at scale
Generative AI tailors customer interactions at scale by surveying vast amounts of customer data, making each interaction feel personalized, even when serving multiple customers at once.
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Benefits of generative AI in customer service
Generative AI offers multiple advantages to support your current customer service processes, including:
- Fast response times: Customers can anticipate a response within a few seconds to avoid long wait times.
- Consistent and high-quality customer interactions: While call service agents are away, customers can receive accurate and quick responses, whether they’re reaching out through a chatbot, email, or social media.
- Cost savings through automation: Generative AI allows you to automate regular tasks to save time and money elsewhere.
- Personalization at scale for better customer engagement: By understanding customers’ history and behavior, generative AI can craft personalized responses and promote current specials, promotions, or reward programs to boost sales.
- Proactive customer support and training: Generative AI can provide training guides and train new agents to practice escalated customer service scenarios.
Best practices for using generative AI in customer service
Like any new technology, you should establish clear guidelines and best practices to avoid potential risks. Here are a few best practices for using generative AI for customer support.
Address privacy and data security concerns
Train AI on relevant customer data and scenarios
Program your AI models with the latest information about your products and services to avoid miscommunication. You can also use customer data to create tailored responses and train call agents on scenarios they could encounter with a particular customer.
Monitor AI interactions regularly for quality assurance
While AI technology is a great benefit, it still comes with a few flaws. Checking regularly for quality assurance allows you to review inconsistencies and fact-check responses to avoid possible misinformation.
Use AI responsibly to enhance, not replace, human support
One of the simplest ways to avoid replacing human support roles is to figure out which tasks AI can support and which one’s agents can support. AI is most helpful with routine tasks and answering straightforward questions, whereas human agents can use customer insights to connect with customers on a more personal level.
Provide your customers with an option to speak with a human agent
Customers love to have options, and if they prefer speaking to a live agent, they should be able to. If a live agent isn’t available, give them an estimated time frame of when someone will be available or have them complete a form to gather their contact information if they disconnect.
Top strategies for using generative AI in customer service
Now that you understand generative AI and how to implement this technology responsibly, let’s talk strategy.
Determine your goals and objective
Before rolling out AI in your organization, determine your goals and objectives and how AI can support them. Once you’ve identified your goals and objectives, select a set of metrics you want to measure. For example, if your goal is to reduce wait times, you can use AI to track customer response time and adjust your daily operations accordingly.
Collect and analyze customer service data
Collecting and analyzing customer service data is a great way to identify customer pain points, inquiries, and patterns. Start by determining what type of customer information you want to collect and create a system to gather this information. For example, if you need email addresses to qualify leads, build a website form where customers can fill out their contact information.
Use the right AI tool
Choosing the right AI tool is vital in supporting your goals and objectives. Take time to test and evaluate existing AI tools to see which systems are easy to scale and integrate with your existing customer management software. If you find no tool is useful, consider building your own API for a more customizable solution.
Upload and categorize datasets
After you’ve selected the appropriate AI tool, start uploading customer data files in the data upload section of your AI model. Once the upload is complete, categorize your data in context, such as common complaints, product information, and billing processes. Also, be sure to remove personal identifiers and anything else from the data to protect customers’ privacy by using techniques like data masking or generalization.
Program and train AI models
Program and train your AI models to prepare for specific customer service scenarios. Include categorized datasets to provide accurate responses, such as answers to common questions, product information, and self-serve options.
Inspect and refine
Before rolling out AI in your customer service processes, it’s important to conduct a pilot test. To do this, select a few users from your target to collect feedback from in a controlled environment. After you have a sample of users, make a plan for obtaining your desired information and how you want AI to collect it. From there, call agents can fully deploy AI to start interacting with customers and provide feedback to make any refinements as needed.
Implement and seek feedback
Once you’ve fully integrated your customer service processes with generative AI, continue actively seeking customer feedback to understand the issues they face. Frequent feedback allows you to adjust your AI model and ensure you meet customers’ needs and demands.
The future of generative AI in customer service
Generative AI can transform the future of customer service by improving efficiency, personalization, and customer experience. Here are a few of our predictions and insights into the future of AI-assisted customer service.
Hyper-personalized customer interactions through AI
The future of hyper-personalization through AI may evolve into evaluating customer profiles and behavior to provide more individualized responses instead of generic ones.
AI-driven insights for continuous improvement
With AI-driven insights, including call transcriptions, customer emails, and more, you can improve your customer service by analyzing every interaction for patterns to retrain your AI model and provide more training for your agents.
