Can Data Analytics Impact Customer Support: Here’s an Evaluation.

According to a Edelman Digital finding, over 50% of customers expect a response from customer support within 1 hour of posting a query or complaint (even during weekends). On a similar note, Freshdesk reports that 71% of customers express their frustration with business enterprises if they do not receive a personalized experience from the company. Additionally, Gartner predicts that 25% of all customer support operations will be use chatbot or virtual assistants by 2020 (up from just 2% in 2017).

While customer data has always been used to improve customer service in the past, advanced data analytics is now being adopted by both small and large corporations to collect and analyse more information from online customers leading to higher personalization and improved customer experience (or CX).

As a result of these trends, global companies are investing in real-time customer analytics for improving CX and customer loyalty. Over 60% of business leaders believe in the growing potential of data analytics across various customer touch points. This percentage is estimated to grow to 79% in the next few years. 

By industry estimates, each online user is expected to generate 1.7MB worth of data each second by the year 2020. Companies across industry domains that can leverage on real-time customer analytics can build on data-driven business strategies and improve their operational processes for more returns.

Through this article, we shall discuss how effective data analytics is improving customer support systems across business enterprises.

Types of Customer Analytics

For improving their customer service, companies must consider the following three primary types of customer analytics and use them optimally to improve their customer experience:

  • Descriptive analytics

Identified as the most common type of data analytics that companies use, descriptive analytics comprise of all the data insights gathered from business trends or raw customer data. This includes data centred around product sales, supply-demand ratio, and conversions. 

Descriptive analytics is useful for determining future demand, product-wise budgeting, and operational efficiency. In short, it is all about “what has happened?”

  • Predictive analytics

Among the most effective tools for customer service managers, predictive analytics analyses past business trends along with a prediction of what’s going to happen next. Predictive analytics is useful in determining customer behaviour, demand forecasts, and in enhancing customer experience. In short, it is all about “what is likely to happen?”

  • Prescriptive analytics

Developed as a forward-looking approach to predictive analytics, prescriptive analytics (powered by artificial intelligence or AI systems) provides businesses with the optimized recommendations on how to act on data-driven trends and their possible outcomes. Prescriptive analytics is effective in optimizing customer experience. In short, it is all about “what should be done?”

How Customer Analytics is Transforming Customer Service

How is data or customer analytics transforming customer service? Here are some of the leading pointers:

  • Providing a personalized experience

Be it through a smartphone app or website, today’s online customers have come to expect a personalized experience from business. A 2017 Forbes report highlights that 85% of digital marketers have reported marketing success (in terms of higher engagement, conversions, and business revenue) using personalization. In the digital marketplace, over 50% of the customers have switched from companies due to a poor customer experience.

Data or business insights are transforming the way in which customers interact with business brands. According to McKinsey & Company, personalization can boost company sales by 10% and returns on marketing investments by 8x. Additionally, 63% of travel companies are using data analytics to personalize their website content.

A prime example of personalization is from customer messaging company, Intercom, that enables real-time personalized custom bot messages for individual customers.

  • Anticipating customer needs and expectations

Predictive analytics can enable businesses anticipate customer needs and expectations thus improving customer service and optimizing targeted campaigns, among other benefits. Using predictive analytics, business enterprises can leverage the potential of artificial intelligence and machine learning to interpret customer-centric data and find solutions.

For instance, in a call centre business, predictive analytics can improve crucial KPIs such as the average wait time (for calling customers), call completion time, and measuring customer satisfaction.

AI-powered predictive analytics is transforming the way brands interact with their customers. An instance of this trend is the adoption of AI-powered algorithms by American carrier company, Sprint, that can identify customers (at high risk of churn) and has drastically reduced the churn rate by predicting and proactively offering what customers want from the business. Similarly, popular motorcycle company, Harley Davidson has successfully used predictive analytics tools to identify potential customers that are most likely to make a high value purchase.

  • Boosting product launches

While introducing new products is critical for any successful businesses, it’s also important for product companies to have a great marketing strategy and plan for the product launch. Data-driven marketing messages that can inspire curiosity and anticipation in the market are crucial for increasing brand awareness and sales. For example, the market launch of the Spectacles sunglasses by Snapchat through the use of vending machines in areas with high foot traffic.

Even for online product launches, the online customer’s purchase path can be charted through the proper landing page and checkout page. An example of this is the online launch of the “Peanut Butter Fudge” ice-cream by Ben & Jerry’s. 

Similarly, the online sales and marketing platform, Drift deployed “conversational marketing” to boost their own growth and disrupt the industry. 

Analysing customer touch points

According to industry stats, 73% of customers use multiple channels or touch points before making a purchase. Typically, customer touch points include business websites, call centres, social media platforms, and in-store client interactions. Business brands can use data analytics to integrate all these touch points and provide a unified and consistent experience to their customers.

Data analytics tools that can derive insights from multiple customer touch points can be effective in building brand perception and improving customer satisfaction. An effective example of using customer touch points is by Apple through the use of in-store product demos and online product descriptions.

Text analytics tools can be useful in capturing customer data from support calls and chats, which in turn, can be used by any customer analytics tool. 

  • Resolving customer issues

Whether it is through chatbots or intelligent call routing, customer analytics can help in identifying and resolving customer issues and complaints, thus leading to higher levels of customer satisfaction.

As a classic example, customer service chatbots are equipped to handle most of customer queries or transfer their calls to human agents, when required. Additionally, automated bots can listen and analyse agent calls and suggest answers to specific customer queries 

Through social listening, business can also leverage on natural language processing (or NLP) to respond appropriately to customers or assign them to the right service agent. Other means of elevating customer satisfaction is through the use of voice biometrics for authenticating customers. 


In summary, effective customer analytics is a major differentiating factor in the quality of customer service for business firms. As a result of data analytics tools, businesses can significantly leverage on the value offered by Big data and improve their customer experiences. This can, in turn, drive more customer loyalty and a competitive edge in today’s global market.

We have tried to highlight some of the key areas where data analytics is transforming customer service and enhancing their interaction with the business. We would like to know what you think about the role of data analytics in the domain of customer service. Leave behind your comments below. Are you looking to advance your knowledge in the field of data science or big data analytics? Then do check out Manipal Pro Learn’s Advanced courses here!


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