Customer Lifetime Value (CLV) is a critical metric that represents the total revenue a company makes from any given customer during the entire lifecycle. Companies spend a lot of time and money to improve CLV because the longer customers stay with you, the more you generate profit and higher returns. Now the question is: how do you protect and enhance CLV? You do it by improving the customer experience utilizing data analytics. 

 Data is the fuel that powers a seamless, personalized, omnichannel experience customers want. It helps you understand who your customers are, what they want, how they prefer to engage and enables you to build more intelligent solutions to acquire, retain, and serve their accounts. 

According to a study by Harvard Business Review Analytic Services, because of the use of customer analytics, 58% of enterprises see a significant improvement in customer retention and loyalty. Owing this spike in customer retention, 70% of enterprises have raised their investment in real-time customer analytics over the past year.  

Unlike traditional contact centers that used static call scripts and one-size-fits-all strategies, today’s contact centers have the technology and tools, such as AI and machine learning, to leverage all available data to drive better customer interaction. They can even understand customer preferences, next best action, propensity to churn, customer satisfaction, sentiment, likelihood to buy, retention rates, etc. 

In other words, contact centers can instantly gain real insight into what is working and what is not, in a way that lets them take the immediate actions to improve. Data can also provide better insights to improve call handle times, contact resolution rates, customer relationship management, workforce optimization, and much more. Some of the common data analytics techniques that every contact center can derive real-time insights from include:  

  • Speech Analytics to monitor calls in real-time.
  • Text analytics to monitor all text-based customer interactions via SMS, email, social media etc. 
  • Selfcare analytics to track customer interactions via automated services, namely chatbots or web forms. 
  • Desktop analytics to track agent activity and productivity. 
  • Cross-channel analytics to measure the successful and seamless transition from one channel to another. 
  • Satisfaction analytics to measure the satisfaction rate of the customers on all interactions. 

How does the lack of data analytics affect the contact centers and their performance? 

  • Missed opportunities - Lack of real-time, cross-channel data makes it difficult to engage with customers at critical purchase decision points. 
  • Mistargeted messages - Without a complete cross-channel view of the customer, marketers resort to counterproductive, non-personalized communications. 
  • Manual efforts - Many marketing campaigns involve manual efforts across multiple teams, resulting in poor productivity and results. 

In this post, we will look at the top 6 ways contact centers can use data to improve their day to day operations, upselling/cross-selling and customer experience. 

Data-driven, intelligent call routing 

Call routing is a system that identifies the caller and the reason for the call to assign to the right agent. Traditional call routing systems choose the most capable agents based on their skill, track record, and training. They also take into account caller priority, caller’s previous inquiries, caller value, etc. However, these are not enough for intelligent call routing when it comes to unresolved, complex technical issues. Here is where data comes in. Data points like caller data, caller input, and historical customer data, linked with CRMs and corporate databases, can enable organizations to route the calls to the best possible resources without excessive transfers, complicated menus, or requiring the caller to repeat information. 

Personalization and customer profiling 

When it comes to customer experience in contact centers, there is no one-size-fits-all concept. Each customer would prefer a personalized experience, and the brands need to ensure the customers get what they ask for. According to Epsilon’s new research, 80% of customers are more likely to buy a product/service from a brand that provides personalized experiences. With data gathered from multiple channels, it is essential to leverage that data to build a robust customer profile, which plays a crucial role in processing a predictive analysis. 

Offering personalized customer experience is about treating people as people and empowering them through self-service with a personal touch. It leads to customer loyalty, confident agents, higher sales, and more competitive advantage. Many companies already have buyer personas, and looking at customer data is a great way to dive into customer demographics and develop accurate profiles because more personalized information leads to a more personalized experience. 

Customer Service First Response Time (FRT) 

An average response time to respond to customer service requests is approximately 12 hours and 10 minutes. That’s too long! A report by Forrester states that 71% of customers think that the best customer service motive that brands should adapt is to value their time. So, how can you reduce response times? 

It is a critical call for brands to move towards data-driven systems trained to integrate all available channels such as voice, text, email, Twitter, etc. without any increase in staffing while processing it in a matter of time. A tremendous amount of frustration to the customer can be eliminated if the interactions made by the systems can be interpreted accurately, and the response is quick and convenient. Although with any technology-driven scenario, customers cannot derive the same level of satisfaction that they receive from human interactions. When the agents have the analyzed data interpretations from different channels all in one display, the efficiency of the response increases, as does customer satisfaction. As much as time is a critical factor, brands cannot afford to compromise on the quality of the service they provide i.e., and it’s not just about the right time response but more of the right response at the right time. 

Driving data to enhance First Call Resolution (FCR) 

First call resolution (FCR) is one of the most critically acclaimed metrics in contact centers. The FCR is the resolving ratio of the customer’s calls by the first call they make. The customer’s satisfaction level tends to dip to a significant percentage every time they have to call back. Data analytics helps the agents to understand the underlying causes that churn the FCR ratio. Data-driven tools help contact centers reduce repeat calls, thereby increasing customer satisfaction. 

Securing data to grow Your Customer Lifetime Value (CLV) 

The expectation of the type of experience customers want is, to a deeper extent, derived from the data they provide. Data gathered is used in the profiling of the customers. Data analytics helps in understanding who the customers are, what their expectations are, and predicting their needs so they could be met to have a competitive edge. 

Using or exploiting customer’s data to a large extent reflects a lack of privacy to the customer. Driving data analytics needs to be at the level where the customer receives a delightful experience and should not hit the balance where the customer feels insecure. Better data security can elevate your customer lifetime value. Securing the privacy of the customer and their data could go a long way toward converting clients to loyal customers. 

High-end reporting and monitoring 

As much as the advent of big data and analytics has been a massive boom to the contact center business, they all face the challenges of data silos. Data silos can become huge barriers in compiling data and improving productivity. Combining various IT Systems such as Workforce Optimization (WFO), Interactive Voice Recognition (IVR), CRM, etc. and even data from other channels like social media analytics, can help generate a complete profile of the customer that acts as a catalyst for successful interactions.  

Advanced and upgraded data monitoring and reporting systems can help in breaking down the existing data silos and improve the quality of the contact center process. Studying Voice of the Customer (VoC) as it relates to the Voice of the Employee (VoE) gives insights into what works for your contact centers and what doesn’t, meaning advanced reporting can give you a detailed understanding of the pattern when call drops are high or when the percentage of the completed calls are high and what triggered those results. These can be understood clearly when data-driven contact centers employ high-end reporting and monitoring systems. 

 Upgrading the contact centers into a full-fledged data-driven software solution is mandatory in today’s scenario with the consistent advancement around big data analytics, AI, and machine learning. Though the market has a fair number of providers for such systems, It’s also essential to select intelligence–backed methods to keep up with their customer’s expectations and stay way ahead of their competitors in the global market.