Artificial Intelligence and Machine Learning Tools for Customer Service Team

Artificial Intelligence and Machine Learning Tools for Customer Service Team

Any business leader will attest to the fact that customer service has become the top priority for businesses. It even occupies a great place in the retention of the existing customers rather than attracting new ones. Customer service is gaining momentum due to the proliferation of the internet and smart digital devices. With digital devices in customers’ hands, they are using smart solutions to carry out their daily activities. For businesses, these activities generate enough insights/data to understand their behavior and mindset. And using Artificial (AI) and Machine Learning (ML), businesses can take true advantage of these insights to deliver the right customer service.


AI-powered customer service solutions improve every aspect of a business. It helps in delivering excellent customer experience and maintaining loyalty, brand reputation, preventive assistance, and much more. And this development is going to increase in the future. As per Moguls, over 85% of customer support communications will be handled without engaging any customer service representatives by 2020.

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AI for Customer Service

As per a Zendesk Study, around 42% of B2C customers purchased a product/service after experiencing good customer service. The study also revealed that around 52% of them stayed away from a brand that could not deliver expected customer service. With the implementation of AI in customer service, and offering personalized service, businesses understand their customer better and mitigate the challenges of customers selecting the competing brand.

AI has two important factors in delivering improved customer service:

  • Machine Learning — A powerful computing system that analyzes a large amount of data to learn from it. The recognition of spam mails and the feature “recommended videos” in any online video streaming service are simple examples of Machine Learning.
  • Natural Language Processing — NLP deals with users interacting with the AI software to process and interpret spoken/written messages. Virtual assistants such as Alexa, Cortana, Siri are the perfect examples of NLP.

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AI Tools for Customer Service

Virtual Assistants For Automated Customer Service

According to Gartner, by 2022, two-thirds of all customer experience projects will make use of IT for various devices, applications, and services. Currently, around 37% of customer service leaders are using AI bots and Virtual Customer Assistants.

Virtual assistants comprise bots, chatbots, and digital assistants that interact directly with customers to provide information, process inquiries, and solve problems. These assistants range from simple scripted experience to advanced technology such as NLP and NLU (Natural Language Understanding). These assistants work collectively with human agents in tandem.

Sentiment Analysis

Sentiment analysis is a very helpful tool that helps marketers to drive positive outcomes using customer interactions. It detects polarity (positive/negative opinion) within the text, document, paragraph, sentence, etc. It helps in analyzing customers’ feedback, whether it may be via opinions in survey responses or social media conversations. It also allows brands to get to know their customers, and tailor products and services to meet their needs.

However, sentiment analysis comes from data that customers generate every day. This data, in fact, is 80% unstructured in the world. Sentiment analysis makes sense of all this data by automatically understanding, processing, and tagging it.


Businesses also can gain actionable insights from customer conversations and detect urgent issues before they spiral out of control. An example of this would be the text classification and organizing incoming support queries by topic and priority. Later, these queries can be sent to the right department and to make sure that most urgent cases are handled right away. Another example would be analyzing numerous reviews about the service/product to determine customer feedback about the pricing plans and customer service.

Automated Routing

The continuous evolution of Omnichannel contact centers has given rise to customer routing. IDG research states that 44% of companies have started implementing a digital-first approach to customer engagement from 2018. It is also reported that 73% of customers are annoyed by long hold times and 70% when transferred to a new agent.

Deployed with AI, automated routing can get to know customers’ intentions — whether they are trying to get information, asking for a refund, or want to update their delivery address. With AI, businesses get ample time in routing such queries to the right place/department as compared to human assistants.

Robotic Process Automation

RPA helps businesses to improve process efficiency and workforce productivity. By integrating RPA applications in customer service, enterprises can automate several repetitive tasks that are time-consuming and labor-intensive. Being widely adopted, its market size is expected to reach USD 3.97 billion by 2025, according to research.

Robotic Process Automation mimics how a human would complete a task within a specific workflow. ICICI Bank-one of India’s largest banks – has integrated RPA in over 200 functions across its pipeline, including in retail banking, foreign exchange, trade, and HR management. The bank has been able to cut customer response time by 60% and has decreased the error rate to zero.

In the modern world, AI has achieved a significant place in customers’ day-to-day lives. For businesses, it provides better customer analytics with in-depth insights into the customers’ needs. With time, businesses will increasingly rely on AI.

As per RobertHalf, 39% of IT leaders are currently using AI, 33% have said will use it in the three years, and 19% will use it within the next five years. By deploying AI, businesses can better understand their customers, take advantage of chatbots in solving minor queries quickly, understand customers’ feedback towards the brand using sentiment analysis, automate processes to cut waiting time in half, and overall, engage customers to deliver improved experiences.

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