The Relation Between Data Analytics and Customer Service

Nowadays, most industries are purely dependent on customer satisfaction. In this day and age, only customer-centric service providers can manage to stay afloat in this highly competitive world. The best way to ensure that your customer service is able to perform with the utmost efficiency is through the incorporation and use of data analytics.

The relationship between data analytics and customer service is concurrent. The quality of customer service defines the service provider’s ability to successfully attain and retain a client for a long time. If accurate client-centric data cannot be collected, then it becomes very difficult for marketers to make profitable decisions that may lead to growth and improve client satisfaction.

Advanced-Data Analytics assists a company in gaining an approximate understanding of consumer expectations so that it can plan and prepare accordingly to meet them. It can also assist the company in identifying potential areas for improvement. But most importantly, it can help a business evaluate the key customer service metrics to create a connection with its clients that can last a long time.

65% of participants in a survey conducted by Econsultancy and Adobe have conveyed that, “improving data analysis capabilities to better understand customer experience requirements was the most important internal factor in delivering a great future customer experience." 

Various research papers and statistical analyses have successfully highlighted the significance of data analytics in improving customer service in every way possible. For instance, did you know that statistics suggest that an organization that is purely data-driven can acquire 23 times more clients than the average business? And their chances of retaining clients also improve by six times, which can translate into 19 times more profit. Hence, if your business is not incorporating data analytics to reach the full potential of customer service, then you have already lost your edge in the competition.

Now, before we dive deeper into what role data analytics has played in the improvement of customer service, let’s first get a clear concept of data analytics in customer service.

What is Data Analytics in Customer Service?

The concept of data analytics is deeply rooted in the domain of data science. Through the synchronisation and development of various processes and techniques, raw data analysis is streamlined so the user can make sense of the data. This data is then used for different purposes.

These data can be used to identify the events leading up to a situation, which is called descriptive or diagnostic analytics. Another great use of this data is to figure out a future outcome, which is called predictive analytics. Generally, the data that is fed to the sophisticated data analytic algorithm is known as "big data."

"Big Data" is large sets of complex and unstructured data that are read by data processing application software so that it can organise them in a structural manner to provide a concluding decision.

Therefore, the use of data analytics in customer service involves using a vast collection of customer data gathered from various online sources to create an all-encompassing customer profile to easily analyse and identify consumer behaviour and demands, to optimise the marketing strategy of products and services, and provide the optimum customer experience.

The Role of Data Analytics in the Improvement of Customer Service

Identifying Upcoming Trends

When customers share their past experiences, what they liked about certain products or services, or what caused them inconveniences through social media or product review surveys, these are the raw, unstructured data that can help the data analytics of an organization create a meaningful customer experience.

Using this information, a company can alter its offering to meet the anticipated needs of its target consumers. Data analytics can provide the perfect support any organisation needs to ensure most, if not all, of its customers’ expectations, are met or exceeded. It also helps businesses predict future trends and demands based on the sheer volume of inquiries and manage resources and products accordingly. Access to accurate, reliable, and evidence-based consumer insights is the key to boosting a company’s growth.

Companies can now get a clear insight into the client’s thoughts by executing a customer experience machine learning system that can read multitudes of exclusive data elements throughout customer lifecycles. The system can also implement the next-best-action recommendation, both automatically and with system assistance. And this system’s algorithm is ever-evolving with time and accumulated experience.

Customer Data Analysis

The data collected from customer insights can help in creating customised experiences that guarantee customer satisfaction. Data analysis can help brands form a mutual relationship with consumers by delivering personalized offers, products, services, and experiences.

Data analysis can also help marketers choose a digital channel most used by their client base and potential client demographic, a keyword to encourage or promote content consumption, and the right path to promote your product or service (ads or existing client testimonials). Data, basically, empowers marketers to come up with strategic plans to deliver positive experiences across all mediums.

Predictive analytics can create a world where marketers are well aware of the client's demands to create personalised collections through the use of data-driven technology to add a human touch. It can also create unparalleled opportunities by discovering various patterns that can direct companies towards properly rationing their resources.

In today’s market, a “one size fits all” mindset is no longer effective. Marketers need to be aware of how to promote their products and services after selecting which customer demographics they want to attract by using all this data and customer insights and making sense of it with the help of data analytics.

Optimise Pricing

One of the main concerns that affect customers’ decisions to spend or not in the current economic situation is the pricing of different products and services. However, lowering the price is not always the best solution, as you also need to make a profit to stay in the game. In this situation, offering competitive prices to the client base should be among the top priorities of a company. This can be a task as it is almost impossible to understand the consumers' views on pricing.

Data can play a significant role in this regard. If a company wants to make a profit in this economy and secure opportunities for expansion while also addressing consumer concerns, then being flexible and agile is the best possible route. With this intention, companies should gather real-time consumer data from psychographics and demographics to get an estimation of buyer intent, examine the current market prices and compare them with possible future trends, identify any and all scopes of improvement, innovation, and growth, and focus on creating a customer-centric R&D department.

Maintaining a proper balance between customer satisfaction and price is one of the key components for retaining a successful business. Using past sales records and market trends as data, the perceived value of different products and services can be evaluated with the proper use of data analytics.