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The Next Generation - Machine Learning and Customer Service


In this age of hyper-connected businesses and customers, the connection between customers and businesses is more important than ever. Businesses should ideally be looking to consolidate, expand and nurture this relationship. One of the best ways to do this is through great customer service.


Why Customer Service Is Important

Great customer service offers companies a competitive edge in the market as it helps maintain a good relationship between the company and its customers. Being connected to customers and helping them assess their problems reduces churn and helps acquire new customers through great word of mouth, ensuring increased customer satisfaction.

This also helps cut marketing costs, as it is your customer that becomes your biggest fan, in essence, by giving feedback to others in their circle. This is cost effective in more ways than one, since it is always better, economically speaking, to retain an existing customer than to spend time getting new ones. This ends up being mutually beneficial, as it helps keep brand loyalty from existing customers, which in turn encourages greater lifetime loyalty, ending up creating CLV (Customer Lifetime Value).

While customers stay on, they expect greater interaction with the company, to help clear any doubts or queries that may arise due to confusing marketing or problems during service. Good customer service builds trust as mistakes are easily forgiven.

Customer service can help create better processes and ease of business by engaging the customer in an open-loop, feedback oriented, iterative process that can help create the foundation for continuous improvement at the company.

Better company culture, in turn, promotes better work as well as acts as a bonus to the product, adding overall value. People will happily pay more for a product with great customer service. This way, quality customer service provides direct value addition to your product and company.


How does Machine Learning fit in with customer service?

To compete effectively in the modern world, moving with the times is important. A fast-moving world affords faster opportunities to grow, evolve and leap forward. Applying Machine Learning in customer service is one such way of applying a modern, technological tool to a traditional process, transforming the way it is used completely. Machine learning in customer service helps apply data gleaned from customer insights through customer service in ways that can optimize the user experience. This is different from traditional data analytics software, since AI continuously learns and improves from the data it analyzes. This allows brands to increase sales opportunities, provide relevant solutions and improve the customer experience. You can help provide the right product to the right customer based on previous data. Access to data provides superior personalization, providing increasingly personalized input to consumers. This saves time as customers don’t have to repeat the same processes again and again. For calls, machine learning can predict which agent suits which customer, and can assign based on issue, type of customer, query etc. NLP (Natural Language Processing) tools understand, interpret and manipulate human language in a way that solves issues quicker and more efficiently. The insights gained from combining Machine learning with NLP helps to correlate actions that solve customer needs before they arise, not just for that particular interaction, but the next as well.

By processing visual, auditory and emotional signals, one can fully understand a customer’s needs and emotional turns. This can help reduce churn by paying special attention to those that are angry or disinterested. Machine learning also helps improve customer analytics by predicting behavioral trends and patterns. With predictive analytics, a company can detect precisely when consumers need assistance, helping identify problems and predict sales assistance before a point of contact.

AI predictive tools can engage in data mining, statistics and modeling to produce real-time, accurate insights in a very short span of time to give precise, actionable solutions to customers. This is often referred to as predictive engagement. It is also easier to identify fraud, through modern security practices, and less compromised customer data, due to having more data in database management. By applying analytics to different variations of customer feedback from call, customer review etc. one can use data collective processes to help with data tagging and adding labeled data tags to unlabeled data processes, keeping things more organized and efficient than ever before, in a time-efficient manner. Self-serving tools such as virtual assistants, chatbots and other AI-enhanced tools give customers a chance to do self-service and be more empowered in their decision making. These tools can help with things such as invoice management, order tracking and account management - realms of service that often do not require direct human intervention. Interaction in this space is much faster, leaving much needed time, energy and space for things that require soft skills to employees. With AI-powered tools such as chatbots getting increasingly multi-modal and being able to jump from one platform/channel to another, it is easier than ever to adapt, organize and attack the right solutions with a forward-thinking mindset. All things considered, in the next few years, machine learning in customer service is just one of the many branches in which AI will prove to be a competitive advantage. For emerging businesses, there is no question that an investment in this step will pay back multifold.


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