Importance of Artificial Intelligence in Customer Service
Ask any business leader and they will stress why exceptional customer experience (CX) is the top priority for any business in any industry domain. While attracting new customers is important, customer retention occupies a higher place in any business space be it in online retail, software & technology, or travel & tourism.
At the same time, companies are embracing technology tools to provide a seamless service and experience to their customers. The growing use of artificial intelligence or AI-powered chatbots like Dialogflow and Chatflow is transforming customer service by providing 24/7 customer support and increase customer engagement.
The use of artificial intelligence in customer service is not only revolutionizing the customer support function but also improving customer loyalty and its brand reputation. Thanks to the availability of tools like AI-powered customer service bots, companies in the B2C industry segment are increasingly entering an age of automated customer service that is boosting the brand experience for customers. A Gartner report predicts that by next year (2020), around 85% of customer interactions with businesses will be automated without any human intervention.
What is automated customer service and what is the role of AI in improving customer experience? We shall discuss this with the industry use cases in the following sections.
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What is Automated Customer Service and how it improves Customer Satisfaction?
By definition, automated customer service is a business process aimed to eliminate (or reduce) the level of human involvement in providing customer service. How does any business achieve automation in their customer service? Besides the use of artificial intelligence in customer service, automated customer service can be implemented in multiple ways including:
- Customer self-service: Why is this so important? Because most customers like to solve a problem or answer queries on their own. Among the leading lessons learned from customer service statistics, 73% of customers want to solve product or service-related issues on their own, while 64% try to solve their issues before contacting customer service. An example of customer self-service is the Frequently Asked Questions (or FAQ) page on business websites.
- Chatbots: Among the major benefits of AI in customer service, small and large enterprises are scaling up their customer services (while keeping their costs down) through the deployment of intelligent chatbots. The 2019 Chatbot report predicts that the use of AI in banking customer service will automate 90% of customer interactions using chatbots by the year 2022.
What about customers? Do they like interacting with automated chatbots? 60% of online customers do not like to wait for more than 60 seconds for a response to their query. The use of chatbots is drastically reducing customer wait times and quicker resolution of their queries, thus improving customer loyalty. Chatbots with Natural Language Processing or NLP capabilities are resolving customer complaints through faster responses, thus elevating customer satisfaction.
- Customer Experience: Beyond the use of chatbots and self-service platforms, AI-driven automation is transforming the customer experience by customizing product offerings personalized to their individual needs. Online companies like Amazon and Netflix are using AI to analyze customer data and position the right products or services to them. For example, AI technology in the E-commerce sector is being used to improve product recommendations based on personal customer needs, buying behavior, and user profile.
Can AI help in obtaining and analyzing quality customer feedback? Let’s evaluate that in the following section with the help of some AI-powered customer service use cases.
Role of AI Technology in Customer Feedback
Quoted by Sir David Brailsford of the British Cycling Squad, “Clear feedback is the cornerstone of improvement.” This principle is also crucial for any customer-centric business. Customer feedback enables businesses to understand the pain points of their customers and provide the right solution. Additionally, customer feedback analysis enables businesses to:
- Develop their business strategy on the basis of positive and negative comments and sales trends.
- Identify the root of customer problems and evaluate possible business solutions.
- Identify customer needs and build a strong business connection with users.
Being qualitative in nature, most businesses were unable to do a text analysis of customer feedback and turn them into actionable insights. AI technology can now automate this process through the analysis of descriptive customer data, thus making customer review analysis more actionable. According to a Marketing Charts study in August 2018, 64% of the market researchers believe that AI will take over the task of “finding relevant insights in feedback data” in the next 10 years.
Obtaining Customer Feedback using AI
As a business, how do you obtain effective client feedback using AI-powered tools? Here are some use cases that use AI techniques:
- Sentiment analysis
Sentiment analysis in customer feedback is a proven technique of evaluating what customers think of your company and brand. AI-based text analytics tools can gauge and categorize the feedback as positive, negative, or neutral. NLP methods can be used to group all words (in a comment) together and extract the right insight.
Additionally, CX metrics like Net Promoter Score (NPS) or Customer Effort Score (CES) can be effective indicators of the overall customer sentiment and their perception of the business.
Here’s an example of analyzing customer sentiments using NLP:
- Text analysis
Text analysis in customer feedback is a form of qualitative analysis where you can assess your customer’s sentiments and feedback in a more detailed model. AI-powered text analytics tools collect and analyze all customer comments from online feedback forms and determine the sentiment (positive or negative) based on the use of some keywords.
For instance, here’s a collection of commonly used words in the banking and finance sector.
Text analysis can use a group of words to provide business insight. For example, if a customer comment uses a combination of the words (costs, expenses, and monthly), then it can be derived that most customers find the monthly costs of your service too expensive.
- Customer service analytics
Customer service (or CS) analytics is an effective mode of evaluating all CS-related activities and determine how to improve its quality and cut costs. An example of CS analytics using AI is chatbot analytics that is a rich source of customer conversations and can be used to measure metrics such as customer retention rate, user satisfaction, and goal completion rate. Other forms of CS analytics include advanced call analysis and customer review analysis that can improve customer satisfaction and operational efficiency.
- Categorization of customer feedback using machine learning
Among the leading implementation of machine learning for customer feedback, machine learning algorithms can be used to categorize customer feedback on the basis of common feedback points such as:
- Price and quality of the product
- Quality of customer service
- Delivery of products
- Online availability
Categorization is a crucial tool for businesses to determine client perception about your products and services, along with the common issue to be handled. Automatic categorization can be implemented using predefined tags (for example, “product” or “invoice”) thus making it more manageable to handle large volumes of customer feedback.
- Customer reviews
Machine learning tools in customer reviews can be used to analyze product reviews and categorizing them as either positive, negative, or neutral. Product review analysis using machine learning can be used to determine:
- What customers like or don’t like about your product or service.
- Make a detailed comparison of your product reviews with that of your competitors.
- Gain 24/7 real-time insights about your latest products.
- Understand the overall feedback and sentiments about your newly launched product in a quick time.
Conclusion
In any competitive business environment, listening to customers and solving their pain points is the only way for businesses to retain customers and build loyalty. The growth of artificial intelligence in customer service has enabled business to gather and analyze customer feedback for more meaningful and actionable insights.
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