What is AI in customer service?
It brings together cutting-edge technologies such as machine learning, natural language processing (NLP), and data analytics to optimise and improve the customer service experience. They enable businesses to offer quicker, more tailored and anticipatory support, all while becoming more efficient and lowering costs.
AI-driven processes allow businesses to incorporate intelligent chatbots, remove redundant tasks, study customer sentiment, and even anticipate customer requirements. These tools can do more than answer simple questions; they can welcome customers, present knowledge base articles, walk them through processes, dispatch field technicians for more involved requests and smoothly hand over more complex requests to human agents.
As per Sprinklr, AI is used in customer service in the following ways:
- Chatbots and voice bots
- Text analysis
- Speech and voice analytics
- Sentiment analysis
- Agent assistance
- Quality management
- Workforce management
Benefits of AI in customer service
Here are 6 advantages of using AI in customer service:
1. Higher productivity
Role of AI Tools in Conversation MonitoringAI tools assist service teams in speeding up task execution. AI can work as an intelligent assistant within the agent’s workflow:
Real-time recommendations.
Automate repetitive tasks such as ticket categorization and routing.
Lessen agent time by 14%.
2. Improved efficiency
Switching systems, looking for information and responding manually are all manual processes that are time-consuming and prone to error. AI does these tasks more efficiently by:
Aiding knowledge bases with smart suggestions.
Providing real-time conversational insights and customer data
3. Personalized interactions
The personalized experiences delivered by AI are possible due to the usage of data about a customer. For example:
If a chatbot opens a conversation, it can automatically fill in the customer’s name, location, type of account and preferred language.
If a field tech is required, AI sends granular details to the customer to prepare for a personalized service greeting.
4. Optimized operations
AI can identify patterns and trends in customer interactions by analyzing:
The 4 main reasons that lead to escalations
The time necessary to address problems:
Areas for optimization within the customer journey.
With generative AI, businesses can generate knowledge-based articles from historical cases, which not only increases agent efficiency but also provides self-service.
5. Reduced burnout and improved morale
AI reduces repetitive tasks away from agents so they can spend more time on actual problem-solving, and interacting with clients. This realignment decreases workloads, staves off burnout, and boosts team morale.
6. Proactive service
Ways AI helps businesses do that include:
Extracting insights from contracts, warranties, purchase history and a whole lot more.
Sending reminders to customers about subscription renewals, maintenance, upgrades, etc.
Ways businesses can use AI in Customer Service
1. AI-powered chatbots for instant support
AI chatbots utilize NLP and machine learning to deliver real-time help. AI chatbots interact similar to humans and can respond to complex queries, unlike scripted bots.
Example:
A chatbot for a telecom company takes customers through the steps to upgrade their plans, offering personal options without the need for a human intermediary.
Pro Tip:
Connect chatbots with your CRM to retrieve customer data for personalized responses. Training AI models on regular basis to improve your performance.
2. Predictive customer service using AI analytics
For example, AI tools for customer service analyze data to foresee customer needs or problems before they happen and proactively offer assistance.
Example:
Retailer (known as the product purchasing company): Spotting a trend of installation-related queries, a retailer sends out guides to customers who have purchased the product.
Pro Tip:
Use AI tools for data collection and continuous retraining of predictive models to remain up-to-date.
3. AI-enhanced self-service portals
These portals allow customers to resolve problems on their own. Intelligent search bars and interactive guides offer fast problem-solving.
Example:
A portal for a software company recommends resources based on users’ previous inquiries and product usage.
Pro Tip:
Create user-friendly portals and enhance them based on user interactions.
4. Automated ticketing systems
AI-powered ticketing systems prioritize and route customer queries based on context and urgency.
Example:
A financial services firm leverages AI to distribute fraud tickets among departments for quicker resolution.
Pro Tip:
Use existing data up to October 2023 and train your AI models on past tickets to get better accuracy in categorizing and prioritizing calls.
5. AI-driven sentiment analysis
Sentiment analysis is used to understand customer emotions from feedback, reviews, and interactions, facilitating timely responses.
Example:
Read more of the previous statement, along with several others, will show data points that indicate negative sentiment about wait times. This helps a restaurant chain respond proactively.
Pro Tip:
All of this is possible by training AI on multiple datasets to understand the nuances of language and integrating sentiment analysis with CRM systems.
6. Real-time language translation
AI-powered applications offer real-time translation capabilities and break language barriers to enhance global customer support.
Example:
In real time, an e-commerce platform employs AI to translate queries and replies among agents and customers.
Pro Tip:
Select either an anthropologist-style translation system or an updated one that incorporates cultural nuances.
7. AI-enabled personalization
AI serves customer data to provide personalized recommendations and solutions.
Example:
A streaming platform tailors content recommendations and troubleshooting to viewing habits.
Pro Tip:
State clearly how data will be used and give customers the power to manage their data preferences.
8. AI-assisted quality control and training
AI has been enhanced with a valuation of customer interactions for quality assurance and to identify training needs.
Example:
A telecommunications company analyzes recorded calls to customize training programs for agents.
Pro Tip:
The guidance of AI is great to start with, but make sure to rely on your own human brain to input as other situations may require a finer level of nuance.
9. Handling high-volume interactions
The Best Part — AI handling peak traffic
Example:
An airline’s artificial intelligence system fielded holiday-season inquiries, cutting wait times and forwarding complicated issues to agents.
Pro Tip:
Train the AI with the latest information and employ smooth handoffs to agents.
10. AI-driven customer journey mapping
AI tracks the customers through touch points to find out the pain point and where improvements are needed.
Example:
Examples include an online merchant streamlining its checkout process from AI insights about customers abandoning their carts.
Pro Tip:
Read customer data across all touchpoints, and re-fine-tune AI algorithms whenever customer behaviors change.
The Future of AI in Customer Service
AI Changes Customer Service with Faster, Smarter and Personalized Interactions As technology continues to evolve, its applications will only expand, translating into even greater efficiency, customer satisfaction, and loyalty. It is high time businesses finally embraced this new technology, not as a luxury, but as a business necessity — a ticket to keep pace with the ultimate, customer-driven industry.
GIPHY App Key not set. Please check settings