Predictive Analytics for Call Center

Table of Contents

Introduction
What is Call Center Predictive Analytics?
Use Cases of Call Center Predictive Analytics
Metrics used for Call Center Predictive Analytics
Examples of Call Center Predictive Analytics
Final Words
Topics in Predictive Analytics

Introduction

The call centers have opened the realm of opportunities for business enterprises by allowing them to focus on their core competencies. On Contrary, reports of American Express found that 78% of customers deny purchasing products and services owing to the poor service experience. Additionally, an analyst firm CEB stated that 56% of the customers have to explain themselves to the agents multiple times, 62% have to reconnect with the help centers and 59% experience long waits due to call transfer. Therefore, in order to continue being the firm support system of companies across the world, call centers have turned towards Artificial Intelligence and Machine Learning algorithms.

What is Call Center Predictive Analytics?

Predictive call center analytics is a robust mechanism to identify training needs and predict customer behavior. As a result, brought a paradigm shift in the way call centers operate in today’s dynamic space. The analytics include a major focus on customer retention, revenue generation, performance assessment, customer satisfaction, and effort scoring of team members. As a result, call center predictive analytics helps to bridge the common challenges service executives experience in the daily course. These challenges include system outage, customer complaints, lack of employees’ coaching and training.

Evidently, call center predictive analytics has brought a big boost to the global customer experiences by leveraging an immense amount of benefits in analyzing gaps.

Understanding the agent’s knowledge and awareness of the concerned subject.
To ensure that set rules and regulations of the company are duly followed.
Analyzing customer’s sentiments and feedback.
Detecting the process outliers.

Thus, call centers analytics help businesses to make intelligent decisions as and when required by the market environs.

Use Cases of Call Center Predictive Analytics

The call center analytics establishes a data-driven approach to conduct effective outbound campaigns and improve the overall performance of the team. The prominent use cases of predictive analytics in call center operations can be explained using six crucial analytical tools which are as follows.

Use Case	Description
Text Analytics	The use of predicting analytics helps to translate unstructured text into valuable insight and understand market trends and patterns. The extended role of predictive analytics focuses on analyzing social media posts and comments too. As a result, it works effectively on social platforms by monitoring and assigning value to each relatable word and phrase.
Speech/ Voice Analytics	The widely used analytics focuses on identifying common problems of the customers by deriving meaning out of their tone and intonation over the call. There is AI-driven speech analytics software that acts as the digital channel to analyze a large volume of recorded calls by facilitating trigger keywords search options in the system.
Desktop Analytics	Generally, desktop analytics is combined with real-time call monitoring softwares to analyze the performance of the agents. The continuous scanning of the process helps to manage the agent’s activities and implementation of suitable improvement strategies. Alongside, the repetitive and simpler tasks are automated and save extra resource consumption leading to improved productivity of the agents.
Customer Predictive Analytics	The advanced predictive tool allows agents to pay attention to customer’s basic information and past call history if any. It helps to establish an effective flow of conversation. Also, it helps to optimize customer-oriented campaigns for surveys, sales, and collecting feedback. Thus, predictive analytics plays a vital role in contact centers and allows performance tracking of customer care departments.
Self-Service Interaction Analytics	It is the on-demand service where customers can conveniently solve their problems without interacting with agents over the call. Such tools include AI- enabled Chatbots, IVRs, FAQ page, and customer portals, help centers, etc. The prominent use of such advanced technology helps to reduce chances of human error and gain long term customer association.
Cross Channel Analytics	The Omni-channel customer software has brought a significant revolution in the call center ecosystem. Such software focuses on creating value for the customers by serving across all social platforms, calls, Chatbots, etc. Thus, it is referred to as cross-channel analytics and helps to develop well-equipped agents and satisfied customers.

Metrics used for Call Center Predictive Analytics

The continuous monitoring of KPIs is critical to enhancing customer experience. Thus, the selection of call center KPIs and metrics requires a thorough understanding and sincere implementation. Since, performance measurement begins with the call initiation and agent’s performance; therefore there exist several metrics to make it worthwhile.

Call Initiation Metrics

Peak hour traffic
Percentage of calls blocked
Average call abandonment rate
Cost per call
Calls handled by IVRs or agents
Average length of the calls
Channel mix

Agent’s Performance Metrics

Agent utilization rate
Adherence to schedule rate
Average speed of answering calls
Average handling time
Transfer rate
Average after-call work time
Average holding time

The call center metric concept revolves around customers’ experience and satisfaction at large. Thus, these metrics act as the measurement mechanisms.

Customer Experience Call Center Metric	Description
First Contact Resolution (FCR)	It measures the efficiency of the call centers to solve customer’s problems in the first interaction itself. A lower FCR indicates better customer satisfaction. Thus, it is calculated as a total number of calls resolved in the first attempt divided by the total calls received.
Net Promoter Score (NPS)	NPS is popularly used to determine customer satisfaction and loyalty. It is calculated as the percentage difference between promoters and detractors. On the 0-10 rating scale, promoters are rated 9 to 10, passives at 7 to 8, and detractors lie between 0-6.
Customer Effort Score (CES)	The significance of the metric lies in answering one question – How much time and effort do customers have to spend for getting their issues resolved. Broadly, the companies use a scoring model, and here CES is calculated as the percentage difference of agreed and disagreed customers.
Customer Satisfaction (CSAT)	Being the commonly used metric, it helps to determine the rate of satisfied customers on the five-pointer scale. As a result, responses vary as highly satisfied and highly unsatisfied. Additionally, the percentage figure can be calculated by dividing the number of satisfied customers by the total number of survey responses.

Examples of Call Center Predictive Analytics

The call centers are leading the predictive analytics race by adopting the technology for a wide range of operations. Being the support system of business entities across the world, call centers are ought to deliver expertise by making the best use of technologies like predictive analytics.

Wiki Lawn, a lawn care agency uses predictive analytics to accelerate sales closing rate. The use of technology allows the company to understand the interest of clients and schedule follow-up calls accordingly. Additionally, it allows them to tailor the experience of each client to deliver better services in the future.
Privacy Canada, blends the predictive analytics technology to make accurate predictions and understand the change in business areas. As a result, it helps to retain customers in the long run. Moreover, the online security tools ensure a trusted relationship between the customer and the company.
Perfect Data, simplified the use of predictive analytics and transited it from email to live call system. It resulted in improved retention rate and lifetime value addition by 5-6 %. Thus, Perfect Data emerged as a responsive and satisfying support system for its customers.
Big Research and Consulting, used call center predictive analytics for predicting the agents’ performance and bolstering required attributes to ensure goal- attainment. Furthermore, it helped the company to plant a healthy resource mapping system and ensure better performance.
Get VoIP, implemented the algorithm of predictive analytics to predict the success rate of follow-up calls. It came up as the most effective way of determining customer intent and developing the script flow accordingly. As a result, the time and effort wastage reduced significantly leading to the desired outcomes.

Final Words

The call centers are often approached with great hopes within, thus each support center shall stand by the customer’s expectations. Many times these expectations remain unfulfilled owing to vast data size and lack of related sources. Thus, call center predictive analytics is a robust tool to bridge all the communication and resource gaps.

Topics in Predictive Analytics