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How Big Data Can Drive Smart Customer Service

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Organizations are using big data to implement smarter customer service, leading to happier customers and increased business.

Big data is now an integral part of all business domains, and the customer service industry is no exception. This is one area where the impacts are direct, measurable and clearly visible. Data has been used in the industry for a while now, but the full potential is only measurable in the current scenario of big data and predictive analytics.

Here we will try to explore the ways that big data can help drive smart customer service.

Customer Service Pain Points

It is a well-known fact that a business can prosper only when it understands the needs of the consumer. However, many businesses crash and burn because they fail to understand what the consumer wants or cannot give them the desired service. Furthermore, sometimes businesses are unable to effectively communicate with their customers and their services cannot properly reach all their customers.

This is when the customer can turn to competitors because of unsatisfactory performance. If the present IT infrastructure cannot help a company to quickly diagnose problems in its billing and online shopping system, it is a major drawback. Thus, these are the pain points that have to be prevented at all costs, which can be done with the help of big data analytics and digital transformation.

So, in simple terms, the main pain point in customer service is not being able to connect with the customers in a quick and efficient manner.

What Areas Need Improvement?

There are a lot of things that must be done to improve customer experience. This can be achieved by a complete digital transformation of a company. A business will almost certainly prosper through the adoption of big data through digital transformation. (For more on improving customer experience, see Implementing a Smarter Customer Engagement Strategy Using Big Data.)


Before a digital transformation and the adoption of big data, a company isn’t fully connected to its clients. It cannot completely understand the thought process of its clients, so its products often fail to make much of a mark on their customers. Also, many small-scale companies aren’t digitally sophisticated enough to compete in the modern market.

That’s why digital transformation is necessary to make such companies digitally self-reliant and modern enough to understand the needs of the customer. In this way, they’ll be able to provide smart customer service and will be able to beat their competitors.

How Big Data and Analytics Can Help

Big data and analytics are known to have a huge impact on the field of customer service. The businesses which can properly use big data can gain huge profits and easily race ahead of their competitors.

The companies which make investments in the field of big data and improve their IT infrastructure will be able to collect, analyze and integrate this data coming from a variety of digital devices used by their customers. This data can be used to gain useful insights and create models which are extremely precise.

These models can then be used to run simulations of real-world sales experiments. For example, if they connect this model with retailers, they will be able to set market prices easily.

Using big data and analytics, the businesses will also be able to get more information about their customers’ likes and dislikes. In this way, they’ll be able to get insights about their customers, and improve their customer service model. The data obtained can additionally be used for finding quick solutions to business problems.

Success in Adopting Big Data

The advent of big data in the information age has helped many organizations. Every hour, terabytes of data are being created by various electronic devices used by consumers. This rate of data generation is growing every hour, as newer devices are being produced and used.

This data shouldn’t go to waste, as it shows the behavior and patterns of the consumers. This data can be properly utilized by businesses to get insights on the preferences and problems of their target audience and introduce solutions. If the digital communication and e-commerce system can be improved through this, then the customer service system will improve noticeably. This can further smooth out the overall customer experience and increase customer satisfaction.

Small companies can adopt big data in order to quickly race ahead. Big data and analytics plays a very important part in this era, so the companies will have to face the reality that those which adopt big data will continue in the modern market, while those that do not, will perish.

The Way Forward

In the future, big data will play an even bigger role in customer service. Nowadays, companies are adopting big data and are going through digital transformations. Seeing the present rate of data growth, it is expected that the rate of growth in the future will be magnanimous. Thus, the companies will have to go through a complete transformation to make use of this data. (To learn more about customer relations, see Top 6 Trends in Customer Relationship Management.)

As the amount of data will be huge and the data will be diverse, newer technologies will be needed to analyze it properly. In the future, nearly every business will have a website that allows them to interact with consumers and understand their problems and preferences. Big data will help in the diagnosis of problems in the existing IT structure of the company. Thus, big data will become even more essential in the near future.

Practical Implementation

One of the best implementations of digital transformation with the help of big data is the adiVerse Virtual Footwear Wall. This allows real footwear to be shown virtually on a shelf. This is possible through a very innovative screen and big data analytics. The computer connected to the screen analyses all the data related to the current fashion trends and the patterns of shoe shopping. This allows the customers to easily get their desired piece of footwear online without any hassles.

Another great implementation is that of smart vending machines (SVMs). For understanding the use of big data in this, let us think of a maintenance worker, who will have to restock or repair a normal vending machine. Very often, the maintenance worker will find himself in a difficult position, as he does not actually know which component or product has to be replaced.

However, this is not the case with SVMs. These vending machines work according to a data platform analyzing data in real time. Thus, the machine is able to become much more interactive. It can interact with the customers and the servicemen, and can analyze user data to provide tailored offers and regular alerts, so the customer or maintenance worker’s experience will be greatly improved.


A business with good customer service can easily win someone over. Customer service is very important for a business because it helps the business to understand more about the consumer’s likes and dislikes, so that it can launch the right product at the right time. Good customer service can also help the business to find out about any problems which the customer is having in the communication or e-commerce system. Thus, improving the customer service quality of the company should be the first priority.

Nowadays, a lot of data is being generated by all the devices used by customers. This data, if properly collected and analyzed, can reveal a lot about the preferences of the consumer. Data transformation can also help in improving the interaction with customers. Thus, big data analytics and digital transformation are the key to improving customer service.


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Kaushik Pal
Technology writer
Kaushik Pal
Technology writer

Kaushik is a technical architect and software consultant with over 23 years of experience in software analysis, development, architecture, design, testing and training. He has an interest in new technologies and areas of innovation. He focuses on web architecture, web technologies, Java/J2EE, open source software, WebRTC, big data and semantic technologies. He has demonstrated expertise in requirements analysis, architectural design and implementation, technical use cases and software development. His experience has covered various industries such as insurance, banking, airlines, shipping, document management and product development, etc. He has worked on a wide range of technologies ranging from large scale (IBM…