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8 Ways Machine Learning Provide Efficient Customer Service

Machine learning (ML) is a key to unlocking customer interactions apart from artificial intelligence (AI) or natural language processing (NLP).

Why? Because it can enhance business processes and deliver fine-tuned conversations to keep the customer interactions lively.

It automates the process of conversations and makes suggestions based on the customer response. One of the major use cases of machine learning is to understand the intent of the customer. It helps in predicting the flow of information and routing queries to various departments as per the requirements.

Over the years, we have seen a multitude of effective machine-learning applications in marketing.

Let’s learn the eight ways in which machine learning can be applied to achieve effective customer service for mobile marketing.

1. Categorize and Tag

Business units work well with categorization and tagging. Machine learning systems can categorize and tag customers based on their interactions. It organizes them based on urgency and dedicates time or associates to help them solve issues or concerns.

Such divisions unclutter a lot of confusion and directly lead the customer to the right departments. It helps in lead nurturing, lead generation, upselling, sales, improving customer experience, and much more.

2. Superior Personalization

Chatbots are a common application of machine learning in the business world. ML-equipped chatbots can understand the intent of conversations and predict the flow and purpose. The system can respond based on this and address the core issues the customer is facing. It can directly give suggestions or contact the concerned department executive for further progress.

3. Quick Assistance

Customers dislike waiting long for a response. ML tools are available 24/7 through the internet or by phone. With the help of AI and NLP, ML solutions can interact directly with customers and address their issues in time. It can either solve the issue or direct the customer to the service provider.

4. Understand Needs

Machine learning helps the brand understand its customers. It can work in parallel with the tool or website customers interact with and learn about the customer. For example behavior, choice, preference, selection criterion, influence factors, etc. It supports the customer support teams to answer FAQs, support requests, and provide fast solutions.

5. Predictive Analysis

The more the ML system interacts with the customers, the more it learns about them. It has its own algorithms that can learn and understand what the customers want. Based on the data collected, the system can predict and make suggestions to the customer. For example, ‘similar product’ suggestions in e-commerce websites.

It considers a customer’s historical data, behavioral patterns, selection criteria, and decision-making factors to gain insights. This insight can help the machine learning platform to provide better customer service.

6. Correct Match

Any machine learning platform wired to learn about customer’s historical data can offer suggestions on the go, whether it’s in e-commerce platforms for mobile commerce or in mobile CRM systems. It can analyze their patterns and provide them with the right service they are looking for, optimizing user experience across various mobile channels. For example, product suggestions in e-commerce sites or regional weather reports.

Even if it’s a new customer, the ML-enabled solutions can understand customers, and provide the correct service they are looking for.

7. Fraud Identification

Fraudulent activities are a major concern for any business, especially in the current digital world where more transactions occur. Machine learning can provide an extra layer of protection along with AI or NLP.

ML can monitor the online payment systems with best security practices and compliances, gaining information from the transactions. By monitoring financial transactions, ML solutions can avoid many fraudulent activities, minimizing risks.

8. Quantify Satisfaction

Quantifying customer satisfaction with programming was a challenge in the past, but not now. Integrated programs or algorithms with ML, AI, or NLP can quantify satisfaction to an extent.

The various ML-enabled solutions can read the mood, choices, influence factors, demands, reactions, etc of each customer to understand if they are happy or not. This kind of analysis is reflected in the customer queries and actions. ML systems can provide or change suggestions or take actions that suit the customer’s mood, providing better customer service.

To Conclude

Whether it is AI, ML, or NLP, technology is here to stay. In business, customer satisfaction is important. The correct utilization of the right technology at the right time is important to gain more customer satisfaction. It helps the brands grow and provide better customer service.

Samita Nayak
Samita Nayak
Samita Nayak is a content writer working at Anteriad. She writes about business, technology, HR, marketing, cryptocurrency, and sales. When not writing, she can usually be found reading a book, watching movies, or spending far too much time with her Golden Retriever.

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