Call Center and Customer Service Metrics and Analytics

Call Center and Customer Service Metrics and Analytics

Call centers can be the key to high customer satisfaction but with disparate data sources it is difficult to see the complete picture behind service levels. With vast amounts of data being constantly pulled from multiple data sources it can be hard to accurately predict and optimize operations without Business Intelligence.

In this blog, we are going to discuss:

  • How the customer service industry is using data.
  • Improvements felt within functional areas like operations and quality control. 
  • Key Performance Indicators including Average Length of Call, Overall Service Level, Total Incoming Calls, and Calls Forecasted.  
  • The benefits of a streamlined view of all call center data with multiple granularities. 
  • How increased service rates are driven by management of low performance indicators. 
  • Real time quality control. 

What was the challenge?

Call centers are faced with quantifying service levels yet lack a centralized hub for all service metrics. Without a streamlined approach to calculating service metrics and supplementing data integrity, data insights may be misinterpreted or even gone unnoticed. 

What was the solution?

SME deploys a data analytics solution, provides training, and promotes user adoption. Providing a concise dashboard that measures overall service levels and identify lowest performing departments, as well as individuals. Utilizing historical data to track and predict service call metrics. 

Our subject matter experts are able to implement, deploy, and develop a Qlik Sense enterprise solution for your business. Paired with our guidance and the Qlik Sense associative model, you can gain hidden insights into your business data. We understand that call centers handle multiple types of service tickets and in high volumes creating rows and rows of data. To improve customer satisfaction and internal efficiency it is  vital to focus on understanding KPIs.

quantifying call center service levels

Creating an executive dashboard

With Qlik, we blend disparate data sources together to create centralized dashboards that track key metrics as part of an overall SLA score. Using a mix of KPIs, visualizations, and geospatial technology, we are able to find quick insights from data and get a heart-monitor view of call center performance.

For example, one of SME's utility clients utilizes business analytics tools to highlight customers that have made contact multiple times for the same request. The dashboard automatically prioritizes these projects, whereas before there was no way of knowing if a call or online request was a repeat.

qlik call center 62

Customer Service

Resolving severe customer service tickets is crucial to maintain high satisfaction scores. But how can we quickly refine our search across an entire dashboard? Qlik's Associative Engine brings the data together to be analyzed at the speed of thought. Filtering on a data field dynamically updates every piece of these analytic dashboards. This allows you to quickly identify the key drivers for your high-priority tickets and help improve the process of decreasing resolution time. The end result: increased customer satisfaction. In addition, bookmarking specific selections allows users to return to a filter that is customized for their needs. This does not change any data, it simply remembers what you want to focus on. 

An account manager can drill down to his/her accounts to ensure proper service has been provided. Create bookmarks so that each time the dashboard is revisited, his/her responsible accounts are readily visible.

qlik call center 63

AGENT PERFORMANCE

Most of the time call centers analyze multiple metrics to identify an effective agent. Designing multiple visualizations that cover all the bases can be time-consuming and difficult for the average user. Self-service dashboards make data exploration simple and intuitive for any skill level. Using design elements like Master Items allow users to create and reuse governed metrics to be used across the entire organization. And with Qlik's cognitive engine providing augmented intelligence and chart suggestions, designing the perfect dashboard has never been easier.

qlik call center 64

Insights & Data Storytelling

Call center dashboards produce actionable results by identifying performance trends, the most common reason for service tickets, trends in call volume, and overall call center performance.

In our sample data, March was identified as the lowest performing month. The low service level was directly related to a significantly higher percentage of calls being left unanswered compared to the other months. In this same data set, Saturdays are shown to have the highest call volume but also have the lowest service quality. A department manager now considers this information when allocating human resources and labor budgets. 

Finding insights is just one component, sharing those insights is a whole piece in itself. PowerPoint presentations are useful for capturing information at a single point in time. However, this information quickly becomes irrelevant with rapidly changing data, such as open ticket volume. But with built in Story capability, create living presentations by embedding entire dashboards that refresh data at your own schedule. Experience the full functionality of your dashboards in your presentations, including selections, filters, and drill-downs. By leveraging the power of stories, you will never miss a second of your data.

Open Ticket Dashboard and Data Analytics

If you would like to learn more about SME Solutions Group, Inc. and how Automating performance measurements can transform a culture about service tickets to a culture about service quality, email us at info@smesgroup.com.

 

Related Articles

Journey to the Cloud: 7 Tips for Selecting the Right Strategy & Tools

January 9, 2023
Moving your data to the cloud can help manage costs and increase agility. In the cloud, you can scale up or down as...

SME Tech Stacks

March 16, 2023
In essence, there are three tiers or layers to any business intelligence ecosystem. Bottom-up, they are the...

5 Billion to 1: Data Engineering for Fraud Analysis

September 8, 2021
In this case study, the SME Team addresses a bank's fraud analysis need by utilizing Azure Data Factory and Snowflake...

Get Started Today