AI for Customer Service | Top 10 Use Cases

Customer queries are endless for any company. With the rise of different problems or to gain familiarity with the offerings, every company strives to lower the response time and pace up the resolution process. The more efficient system in such a scenario is generative AI-based compared to traditional ones of humans. Generative AI is capable of generating novel data compared to conventional AI systems. It utilizes the Large Language Models (LLMs) and deep learning techniques to interpret the natural conversational responses. More advancements and research are currently in progress to easily understand the complex inquiries, with a fraction of it visible through the current chatbot-based customer queries.

Benefits of Using Generative AI in Customer Services

• Improved Response Time: Incorporation of generative AI reduces the waiting time for customers, provides swift responses and aims at solving the issue spontaneously and promptly. 

• Scalability and 24/7 Availability: Eliminating the tedious process of hiring multiple individuals to deal with customers, scalable operations using AI require minimum effort and cost, making it a sustainable, scalable option. The time limitation is also eliminated. It ensures support regardless of business hours and the presence of employees. 

• Enhanced Personalization: The customer experience is enhanced further with faster provision and all-time availability. The customers are heard and efficiently responded to compared to humans, as often witnessed by almost all of us. 

• Consistency in Responses: With AI-generated responses, the customers receive consistency and accuracy in the solutions. They can maintain a uniform tone and informational output. 

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Which Generative AI to Use for Customer Service?

The general customer service and Artificial Intelligence customer service for each company varies depending on their dealings. Factors like technical expertise, use cases, and budget are among the crucial determinants. The stepwise action should be to introduce yourself to different generative AI models and then choose the right one that suits the necessities. 

Different Generative AI Models

Language Models (e.g., GPT-3)

It is an older version that comes with pre-training to generate human-mimicking text. GPT-3 is a versatile AI with fine-tuning ability for multiple applications. In customer service, it is more suited for answering common queries, generating written content and providing information. The language models are easily available through APIs and support multiple languages. They, however, may require certain significant data and expertise for fine-tuning specific customer service tasks. 

Chatbots and virtual assistants

These are specifically designed for end-to-end conversational customer interaction. They can be powered either by rules or Natural Language Processing models. The chatbots and virtual assistants are utilized to handle routine inquiries, automate customer interactions and offer immediate responses. They are customizable according to the brand’s tone and style. These types of generative AI provide real-time customer support, can handle high volumes of queries, and are integrated with messaging platforms. They may pose difficulty in responding to complex queries. 

Customized Generative Models

The customization refers to training the AI models for company-specific customer service tasks. The customization may depend on existing frameworks or might begin from scratch. The application of AI customer care is during unique customer service requirements. Offering flexibility and control due to company-based training, these generative AI are tailored to handle specialized tasks. The creation is associated with technical expertise and requires an investment of time and money. 

Choice of the Right Model For Your Customer Service Needs 

The mentioned types exhibit different demands of companies. Hence, the choice of the right model is also subjective. Analyzing the following steps tends to help in making logical decisions:

• Understand the requirement, which includes the type of query to be handled and the specific task to be performed
• Know the available capital, resources and availability of technical expertise
• Glance over the available data and its suitability for different models 
• Scalability is a must to consider owing to growth expectations
• Check with generative models fit your testing and evaluation parameters

10 Ways to Use AI for Customer Service

Here are 10 ways to leverage AI for customer service, along with pro tips to excel in each approach:

Chatbots and Virtual Assistants

Chatbots and virtual assistants are AI-powered solutions that enable businesses to provide immediate and efficient customer support. They can handle routine inquiries, such as frequently asked questions, account inquiries, or basic troubleshooting. Using natural language processing (NLP) algorithms, chatbots can understand and respond to customer queries conversationally, making the interaction more human-like. This 24/7 availability ensures that customers receive assistance outside regular business hours, enhancing overall satisfaction.

• Pro Tip: Design conversational chatbots that provide quick and relevant responses. Ensure seamless handoffs to human agents when complex issues arise.

Also Read: Complete Guide to Build Your AI Chatbot with NLP in Python

Natural Language Processing (NLP)

NLP is a fundamental technology that underpins many AI-powered customer service applications. It allows machines to understand and interpret human language, enabling chatbots and virtual assistants to engage in meaningful customer conversations. NLP also aids in sentiment analysis, which helps companies gauge customer emotions and address issues promptly. Furthermore, NLP assists in language translation, allowing businesses to provide support in multiple languages, and broadening their customer base.

• Pro Tip: Implement sentiment analysis to gauge customer emotions and tailor responses accordingly. Prioritize clarity and empathy in automated replies.

Personalized Recommendations

AI-driven recommendation engines analyze customer behavior and preferences to suggest products or services tailored to each individual. To provide personalized recommendations, these systems consider past purchases, browsing history, and demographic data. This increases sales and enhances the customer experience by simplifying the decision-making process and making customers feel understood and valued.

• Pro Tip: Utilize machine learning algorithms to analyze customer data and suggest personalized products or services. Continuously refine recommendations to enhance user satisfaction.

Predictive Analytics

Predictive analytics uses AI to forecast future customer behavior based on historical data. Companies can use this technology to anticipate customer needs, identify potential churn risks, and tailor their marketing and support efforts accordingly. For instance, predictive analytics can help businesses send targeted offers to customers who are likely to make a purchase or intervene proactively with customers showing signs of dissatisfaction.

• Pro Tip: Predict customer needs and issues before they arise. Proactively address potential problems, reducing customer effort and improving loyalty.

Voice Assistants

Voice assistants, like Amazon’s Alexa and Google Assistant, have become prevalent in customer service. These AI-driven devices enable customers to interact with businesses through voice commands, making tasks like ordering products, checking account balances, or scheduling appointments more convenient. Voice assistants are particularly useful for hands-free and multitasking scenarios, enhancing accessibility for many customers.

• Pro Tip: Ensure voice recognition accuracy and offer voice-based self-service options. Provide clear instructions for customers to navigate through voice-activated systems.

Automated Email Responses

AI-powered email response systems can automatically categorize and respond to customer emails, reducing response times and increasing efficiency. These systems use NLP to understand the content of emails and generate relevant responses. They can also route emails to the appropriate department or agent based on the nature of the inquiry. Automated email responses ensure customer inquiries are addressed promptly and accurately, improving overall satisfaction.

• Pro Tip: Craft personalized and empathetic email templates for common customer queries. Include options for further assistance if needed.

Social Media Monitoring

Many customers turn to social media to voice their opinions and seek assistance. AI tools can monitor social media platforms for mentions, comments, and messages related to a brand. This real-time monitoring allows businesses to engage with customers promptly, addressing concerns and resolving issues before they escalate. Social media sentiment analysis also provides insights into customer perceptions and trends, enabling businesses to adapt their strategies accordingly.

• Pro Tip: Monitor social media channels in real-time to identify and address customer concerns promptly. Use sentiment analysis to gauge public sentiment.

Customer Feedback Analysis

Gathering customer feedback is crucial for improving products and services. AI-driven sentiment analysis tools can process vast amounts of feedback data from sources like surveys, reviews, and social media comments. These tools categorize feedback as positive, negative, or neutral and identify common themes and trends. Businesses can then make data-driven decisions to enhance their offerings, address pain points, and improve customer satisfaction.

• Pro Tip: Analyze customer feedback using AI-powered tools to identify trends and areas for improvement. Act on insights to enhance the customer experience.

Self-Service Portals

Self-service portals powered by AI empower customers to find solutions to their problems independently. These portals often include knowledge bases, FAQs, and troubleshooting guides. AI algorithms help customers search for relevant information more efficiently by understanding their queries and providing relevant content. By offering self-service options, companies can reduce support ticket volumes, lower costs, and give customers the autonomy to resolve issues on their own terms.

• Pro Tip: Create intuitive self-service portals with AI-powered search functionality. Offer step-by-step guides and tutorials to assist customers in finding solutions independently.

Omnichannel Support – Pro Tip

AI enables businesses to provide seamless support across various communication channels like email, chat, voice, and social media. This ensures customers can reach out using their preferred method and receive consistent, personalized service. AI-powered systems can also maintain a unified customer profile, allowing agents to access relevant information quickly, regardless of the channel through which the customer initiated contact. This fosters a cohesive and efficient customer service experience.

• Pro Tip: Integrate AI across multiple customer service channels for a seamless experience. Ensure consistency in responses and customer data across all touchpoints.

How Generative AI Can Lead To Higher Customer Satisfaction

While serving numerous benefits on human replacement, AI can largely benefit the organization by customer satisfaction through the following modes: 

Personalized Customer Interactions

Generative AI’s capability to personalize conversations is the biggest flex, leading to human replacement. Being more efficient in the task of AI customer care, it utilizes data analysis, segmentation and predictive analytics to understand the customer needs and subsequently provide personalized feedback. The data analysis encompasses purchase history, demographic information and browsing behavior to generate tailored responses and recommendations. For instance, a common example of search result alignment with their interest is seen in recommendations of products generally previously searched for. 

The fear of privacy breaches is often witnessed among customers. However, the developments have led to businesses taking steps and informing customers about best practices. The regulations from the government have also been generated, leading to businesses providing complete information about the method of data usage, storage and further actions. The businesses balance personalization and privacy by adhering to the regulatory guidelines and maintaining data anonymization.  

Handling Complex Customer Inquiries

The different techniques being utilized to enable the handling of complex and technical inquiries. The knowledge base integration provides data for reference, and semantic understanding guides the AI to the context of the question. Further, AI utilizes the troubleshooting process to better understand the problem in a step-wise manner. The multilingual support helps understand local languages and provides detailed instructions for the user’s convenience. 

The usage of AI is common for technical support. You can witness the same when performing software troubleshooting, setting up and configuring the hardware, looking for debugging assistance and suggesting code optimizations. More example is seen in its ability to summarise product manuals and documentation to answer the query on specific information about the technical product. 

However, AI-human collaboration for technical and complex queries is way more beneficial than any of them working alone. With uninterpretable or novel problems non-existent in a database, humans are more preferred option. AI is still incapable of empathy, which is often required in cases of customer loss. Moreover, industries like healthcare and law involve ethical and legal nuances where AI reliability is completely unthinkable. Besides, AI is also dependent on humans to learn from complex situations. 

Multilingual Support and Global Reach

Businesses with the aim of expanding or already expanding to undeveloped local areas or higher developed areas have to face non-English speakers. To provide full support and to attract each customer, multi-lingual support is crucial. AI can be leveraged to perform real-time translation of queries and instantly provide desired responses. The consistency in those languages, when coupled with the right tone and style, provides a familiar environment for customers’ rebuilding trust. Generative AI’s scalable capability further eases the task while adhering to budgets.  

Expanding the customer base through multilingual support by merging AI and customer care gives a competitive advantage and provides the scope for global reach, as stated previously. The accessibility is also seen among different categories of customers belonging to different age groups, sectors and employments. The language is also associated with cultural values, requiring the right interpretation of the words. The interpretation will mostly depend on context rather than literal translation. Moreover, the AI’s capability to learn and adapt adds to the accuracy of responses. 

Case Studies and Success Stories

The real-world examples of the usage of generative AI for customer care with the depiction of measurable improvements in efficiency and customer satisfaction are: 

Google’s Chatbot for Google Fi

Google FI is a mobile network operator that uses chatbots to serve its customers. Resolving their issues and helping with billing inquiries and plan changes. The response time is lower, and the incorporation of chatbots has increased the efficiency of human employees due to the lack of need to focus on such automatable tasks. The customer feedback has also been positive on the Google Fi chatbot, appreciating it for quick and accurate responses. 

H&M’s Virtual Assistant

H&M is a fashion retailer brand that utilizes virtual assistants as AI customer care to help customers find clothing items and answer style-related questions. The virtual assistant handles multiple customers simultaneously and provides instant responses and recommendations on fashion-based topics. The shopping seasons have seen maximum benefits where they no longer need to wait for human assistants to get free. Humans, too, get exhausted owing to high inquiries during certain seasons, leading to lower productivity. The personalized fashion advice has been appreciated by customers, which has led to improved satisfaction and increased sales. 

Airbnb’s Multilingual Chatbot

Airbnb, a global brand for homestays, provides services for different local regions. It has replaced the need for translators with a multilingual chatbot that assists guests and hosts in booking inquiries and support requests. The easy and accurate interpretability in multiple languages has offered the marketplace of a complete world to Airbnb effortlessly. It has also increased the customer base and built the trust of customers. 

Netflix’s Generative AI

Netflix’s generative AI acts as the recommender that works on Machine Learning and Data Analysis to suggest new movies and TV shows. The data used here is previous watches, preferences and behavior on the platform. The Netflix recommendation algorithm helps to quickly discover the content of their taste. Customer satisfaction is visible in longer subscription retention and content consumption. It keeps the users engaged while delivering the content according to their needs and moods. 

Conclusion

Generative AI has revolutionized customer interactions, fostering loyalty through 24/7 support, swift issue resolution, and improved recommendations. While chatbots and virtual assistants enhance efficiency and personalization, a balanced approach, combining human expertise with AI, is essential. To successfully incorporate AI in customer service, businesses must define use cases, consider budgetary constraints, address regulatory concerns, and establish robust monitoring and evaluation mechanisms. This harmonious blend of human and AI ensures a promising future for Artificial Intelligence in customer service.

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