How Contact Center Analytics Improve Customer Experience

How Contact Center Analytics Improve Customer Experience

Contact center analytics is the term used to describe the analysis of the best contact center software metrics that help business leaders identify emerging trends, their causes, and implications. In this article, we will explore different metrics used to determine the performance of a call center, the importance of customer data analytics, and how contact center analytics can improve the overall customer experience. 

What is Contact Center Analytics?

Call center analytics is the detailed analysis of contact center metrics, using tools and processes to gain business performance insights. For thorough contact center analytics, business management tracks various service metrics to identify areas for improvement and enable a stronger CX strategy overall with improved customer experience.

Business organizations use contact center analytics to process unstructured data and formulate customer-centric strategies. For more complex customer data reporting and analysis, many companies also choose to integrate their contact center with business intelligence (BI) tools. The different types of contact center analytics are: 

  • Contact Center Cross-Channel Analytics
  • Contact Center Desktop Analytics
  • Contact Center Self-Service Analytics
  • Contact Center Speech Analytics
  • Contact Center Text Analytics
  • Proactive Analytics
Need help finding the right software?

Tell us what you're looking for and we'll offer you personalized software recommendations.

How Do You Analyze a Call Center Performance?

Contact center performance is analyzed by measuring specific key performance indicators and taking corrective action dependent on the value of these metrics. Essential performance metrics and KPIs for contact center analytics that will help create a unique customer experience are:

  • Average call answering speed
  • Average call handling time
  • Average call-in-queue time
  • Average rate of call abandonment 
  • Percentage of blocked calls
  • Average post-call work time
  • Percentage of first call resolutions
  • Agent occupancy rates
  • Agent service level 

What Metrics are Measured in a Call Center?

Measuring performance-indicating metrics is essential when conducting customer service contact center analytics. The most frequently used metrics in contact center analytics are: 

  • Average After-Call Work Time (ACW)
  • Average Handle Time (AHT)
  • Average Time to Abandon (ATA)
  • Average wait time (AWT)
  • Call Arrival Rate
  • Customer Satisfaction (CSAT)
  • Net Promoter Score (NPS)
  • The number of blocked calls.

What are the Most Important Metrics in a Call Center?

Contact centers are mainly the only communication channel for customers and prospects. Therefore, closely tracking metrics for contact center analytics is crucial for business. Some of the most critical performance metrics, especially  in omnichannel contact center analytics are:

Rate of Call Arrival

This contact center analytics metric tracks the number of daily customer calls in a month and aids in identifying the time slots with the highest call volume. With such insights, it is easier to schedule agent work accordingly. 

Average Wait Time

This metric provides insight into the average call waiting time for handling customers and requests. It is an essential metric in contact center analytics that highlights the times customers have to wait before an agent caters to their queries and complaints. It is calculated by dividing the total call queue waiting time by the total number of answered calls. This contact center analytics metric establishes whether the current number of agents is enough for the contact center. Otherwise, it addresses the shortage to avoid negative customer feedback. 

Average Abandonment Rate

This metric tells about the percentage of calls hung up by the customers before being connected to an agent. A value higher than 10% calls for attention and corrective action. Clients that hang up are missed opportunities for the business. Therefore, this problem can be identified and fixed through integrated contact center AI analytics before more business is lost. 

Average Call Handle Time

This metric measures the time is starting from the call initiation, including the call queue time, as well as the post-call agent work time, until the agent is ready to switch to a different call. In contact center analytics, this metric assesses agent efficiency and performance. 

Average Post-Call Work Time

In contact center analytics, the time spent by agents in reporting and solving customer issues, completing transaction reports, updating customer info databases, and updating the team about the call details, is also tracked under this metric. A higher value of this metric suggests undertrained agents or overburdened by post-call agent work.  

What is Customer Data Analytics?

Contact center analytics uses customer data by collecting and analyzing customer data across all communication channels to enable businesses to make more informed decisions. Hosted contact center analytics include surveying customer data to allow companies to devise better strategies, build improved products, and offer superior services to customers. 

Essential steps in contact center analytics when investigating customer data include data collection, segmentation, visualization, modeling, etc. Businesses analyze customer data to improve marketing and brand positioning and manage optimized customer relationships. Broad categories of customer data include:

  • Transaction data such as customer payments and transactions, containing the customer ID, details of date and time, the amount spent, product purchased, etc.
  • Product or service usage data varies with the products or services but provides valuable insights into contact center analytics. It indicates the popularity or unpopularity of the product or service. This enables customers to make decisions regarding improvements or considering discontinuation.
  • Customer-created text data such as product or service reviews or comments can help understand customer sentiments. 

How Does Contact Center Analytics Improve Customer Experience?

Detailed and thorough contact center analytics can clarify business leaders’ prevailing customer sentiment about their products and services. Since customers are the defining force in making or breaking a brand, contact center analytics are crucial for understanding customer requirements and providing a wholesome and satisfying customer experience. Comparing contact center software and understanding features, pricing, and reviews from users can help ensure you choose the best option with the contact center analytics you need in your service center.

Contact center analytics provides an overview of customer habits and enables organizations to devise carefully planned strategies for customer acquisition, customer retention, and active engagement. Contact center analytics develop an improved understanding of customers' behavior and requirements, enabling businesses to deliver the right experience that will keep them coming back. In turn, this will increase sales and generate higher revenues.

Posted in:
Share Article:
The right software for your business

Get your personalized recommendations now.