Top 8 data science use cases in Support
Actually the unlocking of hidden benefits and true potential of the data is an essential task for business. Also, customers` satisfaction appears to be a pushing force for evolution of services and products. If you have loads of customer data available for analysis the work on the improvement of the customers` satisfaction rates becomes much easier. Customers` satisfaction relies on many factors. Support services are among these factors.
Digital customer services and support are called upon to meet the customers’ needs through digital channels. Digital technologies have changed the level of services the customers expect to get.
Let’s take a closer look at several data science use cases that proved to improve the level of customer satisfaction.
Customer data management is a way how the company organizes storage and analysis of the customer’s data. A correct application of the insights that may derive from customer data brings numerous benefits and improvements to the company metrics. The more you know about problems your customers face, the better support you can provide.
Customers like to feel unique and cared for. Having at hand all the information about your customer, of course within the framework of GDPR, you can provide better customer support services.
Personalization is exclusively concentrated on the improvement of customers` experience. Support is an essential part of the customer’s experience. Therefore, personalization considers the customers’ experience directly.
The companies need to have access to the database containing personal information, contacts, ticket history, etc. to provide high-quality support services. This data reveals the expectations of the customers without any additional inquiry. Also, customer reports and real-time analytics allow analyzing all the open support tickets simultaneously. This allows paying special attention to those who need it right now. Customer support experience easily integrates with other smart data solutions to provide an omnichannel customer experience.
Feeling special and getting relevant information is an essential thing for a customer. Recommendation engines proved their efficiency in delivering those points.
Recommendation engines work as filters to provide products or services that are the most relevant: these smart solutions use content-based and collaborative filtering or a hybrid model. The same techniques may be used to avoid customer frustration related to technical complications or other problematic issues. Recommendation engines help to create websites that are easy to navigate and comprehend for a particular customer.
The AI (Artificial Intelligence) powered recommendation solutions complete these tasks faster and more efficiently. Real-Time recommendations support customers in their choice of products or services and assist in routing while searching or facing difficulties.
Artificial Intelligence is a smart technique that is used to solve tasks of various complexity at the same time mimicking human characteristics. Popularity of AI-powered solutions and tools is growing every minute. They prove to be efficient, spend less time searching for solutions and work with multiple clients at the same time.
The AI-powered chatbots are on the rise in providing support services. These virtual assistants can initiate the conversation with a customer, help with routing, engage and interact. The chatbots trained with NLP/ML can easily answer questions, provide further instructions and collect critical customers` insights.
A key benefit of the customer support chatbots is their 24/7 availability. Thus, the customers can get a recommendation or solve some problematic matter any minute, while the companies get a perfect employee who needs no rest.
Thousand of requests related to various matters come to the companies` service desks every day. Providing high-quality assistance and responses in real-time mode is a challenging task.
