How Machine Learning In CRM And Chat Can Revolutionise Customer Support
Sheshgiri built his organization as a part of the third Google Launchpad Accelerator Program, and in five years, he has built an ecosystem of 500+ enterprises customers worldwide.
In a bid to keep up with the constantly changing market trends and customer preferences, enterprises across multiple industries are employing state-of-the-art technologies such as artificial intelligence (AI) and machine learning to understand their customers and their requirements in a better way. Customer-centric companies across the globe are increasingly leveraging machine learning in customer service to provide a greater level of convenience to end-customers and efficiency for their support teams.
Support-focused tools such as customer relationship management (CRM) solutions and enterprise-grade chat are now enabled with machine learning. These platforms are gaining widespread popularity, all thanks to their ease-of-use feature and successful applications across various industries. According to Gartner, within the next couple of years, 72 percent of customer interactions will involve an emerging technology such as machine learning.
So, what exactly is machine learning and how it is applied in customer service?
Machine learning (ML) is a process where a computing system uses multiple algorithms (rules, instructions, & exceptions) to learn and process a certain set of data. It broadly involves acquiring information and understanding the rules for how it should be used. Computer systems use machine learning to progressively improve their performance on a specific task with the help of many statistical models.
More often than not, machine learning-enabled applications are applied in areas that involve a lot of data processing. In customer support, ML is widely used for two use cases.
To help support agents: ML discovers and provides valuable insights that are helpful in optimizing customer support experiences. These insights could make support agents more knowledgeable.
To help end customers: One of the most popular machine learning applications is self-service. Using self-serve portals, customers can find answers to common questions and even perform simple, actionable tasks by themselves. Through tools such as these, machine learning can resolve simple tickets and respond to repeated queries by itself, reducing agent dependency to a great extent.
Machine learning in intelligent chatbots
Enterprises across several sectors such as healthcare, e-commerce, BFSI, consumer durables are deploying intelligent chatbots into their customer support workflow. These chat solutions use machine learning to engage customers in meaningful conversations and offer instant, contextual resolutions to their issues. Additionally, predictive messaging and natural language processing (NLP) employed by these chatbots enable them to help customers in making purchasing decisions, completing payments and get relevant information to their product queries.
AI chatbots can also respond with relevant offers, solutions, and suitable products, alongside sending out proactive alert messages, suggesting personalized offers & discounts and reaching out to leads even before they initiate a conversation
AI chatbots make use of an NLP engine to comprehend incoming customer queries and mirror human language. These chatbots understand the intent of the customer request or a query to respond with relevant solutions. Siri and Alexa are some of the best examples of evolved NLP.
Capabilities of an AI chatbot that can help in delivering delightful customer experiences
Capabilities of an AI chatbot range across a broad spectrum. Understanding the customer issues is one of the major parts of an effective solution. With its ability to recognize as well as predict customer issues, AI chatbots can be of a great help in this regard. It also helps in processing and learning from the acquired information, defining customer behavior pattern, and determining & predicting their needs and preferences. Going further, AI chatbots can also respond with relevant offers, solutions, and suitable products, alongside sending out proactive alert messages, suggesting personalized offers & discounts and reaching out to leads even before they initiate a conversation. This significantly minimizes customer abandonment rate and complaints.
Leveraging AI to scale important customer support metrics
When analyzed and harnessed well, the right reports and data can help enterprises revolutionize the way in which they engage with customers and maximize revenue. With the combined power of CRM analytics and machine learning, you can make the customer journey more enlivened and personalized.
Using machine learning-enabled helpdesk ticketing, self-service portals and chat solutions can help enterprises deliver seamless, personalized customer experiences round the clock. This, in turn, can greatly impact customer service interaction, engagement level, CSAT ratings, customer retention, and conversion metrics.
Applications leveraging machine learning are being increasingly used by customer-centric firms on a large scale to revolutionize customer service. AI in customer support easily translates to high-quality customer experience, personalized support, speed & efficiency as well as cost-saving.