Top 8 Data Science Use Cases in Support

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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. 

Managing customer data

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. 

Personalized marketing

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. 

Recommendation engines

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.  

Customer support chatbots

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.

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Ticket response

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. Therefore, advanced ticket management systems actively apply smart data solutions and AI-powered technologies. This allows processing the tickets in the queues faster and more efficiently.

Machine learning algorithms go through a huge amount of tickets, find relations and patterns between the matters described in the tickets and the database of possible solutions and answers. This process helps to organize the tickets flows into separate streams in which the inquiries are grouped by similar topics. After that, these flows are automatically routed to teams, responsible for dealing with such types of issues.

Predictive analytics is often applied here and also for prediction of future possible relevant matters for tickets. Data science optimizes these processes helping the companies to improve customer satisfaction rates and avoid previous mistakes. 

Real-time customization

Customer support services should be easily customizable and flexible so that they could work efficiently for a particular type of business.  Customization is called upon to adapt your website to the needs and preferences of the customers. In such a way they facilitate customers' choice and improve satisfaction.

Real-time customization takes into account customers' preferences, actions and navigation history, search history, previous experience and interactions to provide a unique experience for each customer. Customization helps to retain customers, reduce the time spent on decision making and develop communications. Even the efficiency of used CTA grows due to their appropriate placing for particular customers. 

The same works for support. The more customizable is the support system, the more personalized experience the customers get.

Biometric authentication

Customers authentication may be beneficial both for business and a customer in many ways. The advantages are numerous starting with providing better-tailored recommendations and ending with providing individual support solutions. Due to advanced techniques, the authentication process is now easy and fast.

Smart support systems provide several options of biometric authentication which present no difficulty or complication for a customer eager to get support:

1. Active voice authentication

Application of the passphrase that is easily recognized with the help of the natural language processing algorithms.

2. Passive voice authentication

Submission of the unique voice print during the initial registration that may be later compared to the active speech with the help of the natural language processing algorithms without interruption or other inconveniences.

3. Selfie authentication

The use of facial recognition algorithms for instant authentication.

4. Behavioral authentication

Real-time scoring and monitoring of the users' interaction, movements, and patterns in the activity for continuous authentication. 

Sentiment analysis

Understanding the customers' intentions and mood is crucial in provision of customer support. Sentiment analysis helps in this challenging task.

Sentiment analysis as a branch of branch analytics is concentrated on the evaluation of the emotional states expressed via conversations. Sentiment analysis is usually performed with the help of natural language processing algorithms. It enables identifying the tone of a customer`s expression.

In the area of customer support the sentiment analysis is applied to the customers' interaction with a customer service agent, taking into account keywords, phrases and general expression of mood. This allows us to provide high-quality support and to rank the queries according to the urgency or complexity.

Answering in the same tone, or paying particular attention to those messages that express urgency or irritation create the feeling of personal touch.

Conclusion 

Crucial components of the perfect customer support service are personal touch, anticipation of customers' needs, empathy, compassion, and attention. Of course, there are far more features that may be added to this list. However, we addressed those which are easily available with the application of data science in the sphere of the support services.

All the use cases mentioned in the article have proved their efficiency. The evolution of smart algorithms, tools, and AI-powered bots make support easier for the companies and more useful for the users.

Irrespectively from the business type or size, application of advanced technologies for customer support is always a good choice.