Ultimate Guide to
AI Customer Service

In 2024, waiting on hold for a support agent or wading through dense FAQs can lead to frustration and dissatisfaction among today's impatient Gen-Z customers. And 52% of this generation of customers want to use AI to navigate a website or product.

"Instead of focusing on the competition, focus on the customer." — Scott Cook, Former director of eBay

With changing customer behavior, this focus holds more importance for businesses.

AI customer service has made it possible to take this focus to new heights wherein you can use artificial intelligence to mimic human interactions to solve customer support tickets. This enables your support agents to work on urgent tickets requiring expertise and attention.

Thanks to the latest AI-powered customer service bots, you can make your customer service instantaneous, personalized, flexible, and always available.

This guide gives a detailed analysis of how the AI-powered customer service industry is shaping and improving customer experience through AI Integration.

What is AI Customer Service?

Customer service involves supporting the new or existing customer before, during, and after engaging with your product or service.

It involves a direct interaction between the customer service team and the customer — and you can imagine how much time and resources this will consume if you have too many support tickets to work on.

With the advent of chatbots and live chat support tools, it became possible to automate a few aspects of these interactions with conditional replies. Now, using Generative AI-powered customer service tools , you can replace repetitive conversations and support flows.

AI customer service uses artificial intelligence technologies like Natural Language Processing (NLP), Generative AI (GenAI), and Machine Learning (ML) to automate, enhance, and personalize interactions between businesses and their customers. This automation frees up human agents to focus on more complex and high-value interactions.

The AI customer service solutions range includes:

• AI support agents and AI chatbots to automate conversations
• Predictive analytics tools to forecast churn
• Sentiment analysis on customer interactions
• Speech recognition for enabling voice-based support
• Augmented Reality and Virtual Reality for immersive customer experiences
• Retrieving information from a knowledge base
• Miscellaneous tools like summarizing conversations

Example of AI (Artificial Intelligence) in Customer Service — Reducing 65% of repetitive queries by Messe Duesseldorf

Messe Duesseldorf GMBH is amongst the top ten trade fair organizers in the world. With a global reach and multiple trade fairs taking place simultaneously, they received repetitive queries about location, timings, routes, and other event details.

Using Kommuicate's AI-powered chatbots, they built a Facebook Messenger chatbot for their Facebook Page which automatically answered these queries. It also captured leads to promote future events

As a result, Messe Duesseldorf GMBH saved 1000 minutes every month in customer support and resolved 5000 conversations monthly.

Understanding the AI technologies used in customer service

Today, AI can improve customer service across the customer journey.

Here are five key technologies that have enabled AI customer service solutions to reach human-level interactivity with better efficiency:

1. Chatbots

Chatbots for customer service provide a convenient user interface to interact with the customer. These can be deployed on websites, mobile apps, and social media platforms to provide immediate responses.

2. Generative AI

Generative AI for customer service leverages large language models to understand customer queries and uses your product’s knowledge base, website content, and existing documents to generate human-like responses in text and speech. Generative AI offers capabilities such as real-time transcriptions, LLM Knowledge Assist, smart replies, and summarization.

3. Sentiment analysis

Also known as opinion mining, it involves analyzing text to determine the emotional tone behind it. This helps deliver more human responses to customer support queries and gauge customer satisfaction.

4. Predictive analytics

Predictive analytics uses statistical techniques and machine learning algorithms to analyze historical data to predict future events. It helps forecast customer needs, predict churn, proactively support based on user interactions or sentiments, and more.

5. Robotic process automation (RPA)

RPA involves using software robots or "bots" to automate repetitive, rule-based tasks. In customer service, RPA can handle tasks such as data entry, form processing, and transaction processing, which are time-consuming and prone to human error.

6 key benefits of AI in customer service

AI customer service solutions will soon become the norm - because they significantly improve customer service as follows:

1. Always be available

AI customer service enables 24/7 round-the-clock customer support on websites, social media, and other customer channels, thus helping you become globally available.

For example, California State University, San Bernardino (CSUSB) used Kommunicate’s AI chatbots to provide 24/7 information on various topics, such as financial aid, location, and courses. Despite the complexity of multiple query categories, they designed a customized chatbot that efficiently handled inquiries and smoothly transitioned from bot to human support when needed.

Learn more about this case study — CSUSB Partners With Kommunicate to Support their Students, Staff, and Faculty 24*7

2. Improved first response time

Today’s generation of users is quite impatient, and AI customer service will help you adjust with instant responses as soon as a support request is placed. It also helps in prioritizing customer support tickets, where the urgent ones or one with an angry customer gets immediate response and assignment of human resources if required.

For example, Saltside Technologies, a website-building company, faced delays in responding to customers who reached out to their customer support team through multiple channels, including email, Google Messenger, and social media.

Learn more about this case study — Saltside Technologies saved more than 3000 man-hours every month using Kommunicate

3. Hyper personalization

AI can analyze customer data and use memory to consider previous purchases, service history, browsing behavior, and current sentiments to deliver personalized experiences. AI brings a contextual understanding to give more relevant and accurate responses to support tickets.

For example, Amiga, a parenting and child behavior app, designed a chatbot to handle parents’ queries before they opted for professional therapy services. Using Kommunicate, they launched ‘Ask Amiga,’ a conversational AI chatbot that addresses repeated queries with pre-defined intents and personalized human-like responses. It manages around 800 conversations per month, saving 400 minutes daily.

Learn more about this case study — How Amiga and Kommunicate Came Together To Solve Parenting Woes

4. AI customer service makes cost reduction and scalability possible

AI customer service solutions can handle repetitive and routine tasks, allowing human agents to focus on more complex and value-added interactions. Thus, you don’t need to invest in a huge support team, onboard third-party support agents, or call centers anymore.

It also makes it possible to handle the sudden surge in customer inquiries during a promotional campaign or gradual growth over time.

Thus, without disrupting the support team’s performance, you can scale your business while controlling costs.

For example, Conte.IT, an Italian insurance company, used Kommunicate to create a self-service system for their customers. They automated repeated queries and designed step-by-step Do-It-Yourself guides that helped them navigate their website. This reduced dependency on live support agents, enabling the chatbot to handle 90% of queries and saving 4,300 hours of customer time.

Learn more about this case study — Conte.IT Boosted Customer Satisfaction by Automating 90% of their Conversations

5. Analyze large volumes of customer data

AI algorithms help you analyze customer information, support conversations, sentiments, documentation, browsing data, and more to:

• Recognize user behavior patterns for better customer profiling and segmentation
• Provide insights into customer satisfaction levels, concerns, and emotional triggers
• Predict churn, preferences, and the need for proactive customer support

All this involves large volumes of data, and AI customer service tools make it a breeze to generate insights from them.

6. Turn support agents into revenue generators

AI customer service agents can help you with personalized targeting to upsell or cross-sell products/services during support interactions. Additionally, AI automates routine tasks, freeing up agents to focus on high-value interactions and proactive engagement. Thus, you can monetize access to your human support agents as a part of a premium offering. Predictive analytics also help identify potential sales opportunities. It will alert agents to reach out with tailored offers.

This approach not only enhances customer satisfaction but also drives revenue growth, making customer support a strategic asset.

How to improve customer experience through AI Integration? — with 3 example AI customer service use cases

Here are five workflows that use an AI customer service approach that you can implement to get started:

1. Assign support tickets with intelligent workflows

AI customer service makes it possible to handle multiple and specialized support tickets. AI will understand the situation at hand for every conversation with the customer and make decisions for the assignment of tickets (if required).

Here’s a scenario — for a support query, first, the AI assistant greets the customer and asks for details about the issue, such as error messages, steps taken, and any other relevant information. It will try to resolve the query as much as possible using the knowledge base. If it cannot, the customer is too angry, or wants to speak with the support team, it identifies and segments the customer queries and automatically assigns them to relevant support agents experienced in dealing with such tickets.

2. Enable DIY customer service

There are three key ways you can use AI for setting up DIY customer support mechanisms:

For starters, AI can help you develop content for a comprehensive knowledge base that includes articles, FAQs, and step-by-step guides covering common issues and questions.

Second, you can set up intelligent search functionalities that use NLP to understand customer queries and provide relevant results.

Third, you can set up AI chatbot agents that can go through your knowledge base to generate relevant next steps for customers to follow and troubleshoot their issues.

All these significantly help reduce support tickets. Also, it gives you a chance to monetize human customer support which can be a part of the ‘paid’ tiers of your product or service.

3. Automate follow-up and feedback

Gathering insights via customer feedback is easier with AI support agents. It can run a complete feedback loop where it schedules automated follow-ups after customer service, categorizes feedback, and analyzes it to provide actionable insights. It also personalizes maintaining the context of interaction with the customer thus increasing the chances of getting feedback.

The issue gets resolved by a support agent with the help of AI suggestions. Post resolution, the customer receives a personalized email or message from the AI system thanking them for their patience and asking them to rate their experience and provide feedback. Then, it will analyze the survey responses to determine sentiment and extract key points mentioned by the customer.

If the AI customer service tool continues to aggregate similar feedback from other customers, and identifies a trend indicating that billing statements need improvement. This insight is passed to the billing department, which updates the billing statement format based on customer suggestions.

How to integrate AI into customer service – five-step process

Here are three key steps you can adopt to get started with implementing AI-powered customer service workflows for your organization:

1. Identify repetitive and manual processes to automate

Analyze your current customer service processes to identify pain points, repetitive tasks, and areas that could benefit from AI automation. Then, set clear objectives for AI integration — it could be reducing response times, improving customer satisfaction, or handling a higher volume of inquiries efficiently.

2. Understand customer behavior

Although AI customer service automation is great for every business, how much you automate it depends on customer behavior. Map out the customer journey to understand where AI can have the most impact. Then using existing customer feedback and data to pinpoint common issues and preferences that AI can address.

3. Choose the right AI customer service tool

Now that you have a problem statement ready with objectives defined — explore various options available for AI customer service tools.

We have shared a quick checklist for this in the next section.

4. Integrate and conduct pilots

Integrate your AI customer service tool to the relevant workflows and existing tools. Then begin with a pilot program focusing on a specific area. This allows you to test the effectiveness of AI in a controlled environment. For example, you can adopt AI customer service agents for handling basic inquiries and integrate them with CRM.

Then, sync data and use historical customer service data, FAQs, and interaction logs to train the AI systems.

For the KPIs defined in Step 1 — use them to see any changes. Then based on the results, implement continuous improvement techniques to iterate on responses and improve KPIs.

5. Scale and improve

Once the pilot program is successful, gradually scale customer service with AI integrations for more workflows. This could include more complex inquiries, proactive customer engagement, and personalized recommendations.

10 questionnaire checklist for choosing an AI for customer service tool

Since the launch of OpenAI’s ChatGPT, hundreds of AI customer service tools have been available to choose from. From IBM to startups – everyone has customer service features now and there is a tool for all budgets and company sizes.

Here’s a quick questionnaire to help you navigate this growing landscape of using AI for customer service:

• Features: Does the AI customer service tool have the necessary features and the required tools to measure success? Check if they provide key customer feedback features like CSAT rating and can seamlessly handle chatbot-to-human handoff.
• Data handling: How does the AI customer service tool synchronize data across different platforms and databases? Ensure they help you be honest and transparent with customers about how their data gets handled.
• Integrations: Can the AI tool integrate seamlessly with your existing customer service platforms (e.g., CRM, helpdesk, live chat systems)?
• AL/ML capabilities: Does the AI customer service tool have strong NLP capabilities to understand and accurately process customer inquiries? Can it handle multiple languages? Does it include Generative AI capabilities?
• User experience: Is the AI customer service tool user-friendly for both customers and customer service agents?
• Scalability: Can the AI customer service tool scale to handle peak volumes of customer interactions without compromising on performance?
• Data security: Does the AI tool adhere to industry-standard security protocols to protect customer data?
• Vendor due diligence: What is the vendor’s reputation in the market? Has the vendor successfully implemented similar solutions in businesses like yours?
• Vendor support: What kind of support does the vendor offer (e.g., 24/7 support, dedicated account managers)? How responsive is the vendor’s support team?
• Pricing: What is the pricing model (e.g., subscription-based, pay-as-you-go, one-time fee, token usage-based pricing, etc)? Check for hidden costs or additional fees for features, customization, or support.