Enhance Customer Contact Touchpoint Analysis 

Overview

As a trusted data advisor for a major insurance company, the CloudEQS team delivered a project to better analyze data from their call center.  

This case study examines the challenges faced by a call center due to fragmented reporting, siloed data, and duplicate metrics. Thanks to the expertise of the CloudEQS team and a modern enterprise data stack, our client was able to improve the customer experience, enhance call efficiencies, and improve agent performance through data-driven insights.  

Business Problem 

Operations & IT leadership needed to conduct deeper analysis of their call center data. Their current siloed reporting infrastructure, ad-hoc analysis against disparate systems, and incorrect metrics hindered their ability to perform their analysis. Specifically, the client needed to understand the types of calls to their call center, optimize agent performance, enhance the customer experience through quicker resolution times, reduce technical debt, and standardize their reporting infrastructure. 

Simply put, leadership needed answers to the following questions from their data: 

• What types of calls does the call center receive?  

• Who is calling? 

• How well are our agents performing? 

• What are the outcomes of the calls? 

• How long does it take for us to resolve a customer inquiry? 

Solution Approach 

As experts in guiding clients through the insights journey, the expertise of the CloudEQS team enabled deep data analysis. Here was our multi-faceted approach to tackle the client’s challenges: 

• Data Integration: The CloudEQS team worked with leading data integration provider, Matillion, to ingest data from SQL Server, FIS (formerly Macess), Gensys, and the client’s Interactive Voice Response platform (IVR). 

• Metric Resolution & Consolidation: To report off a central source of truth, we had to refactor the correct metrics and data models in both the ETL & PowerBI layers. 

• Implement standardized reporting platform: As apart of the clients enterprise reporting strategy, the CloudEQS team implemented PowerBI on top of Snowflake.  

Architecture  

Here’s a high-level architecture of the project. 

Results 

With the new architecture, refined metrics, and ability to conduct deeper analysis, the IT & Operations teams have real-time data to report on the following: 

• Call KPIs: number of call backs, call durations, types of calls. 

• Agent KPIs: max call time, average handle time, percent of calls answered within 30-seconds, 45-seconds, 60-seconds, transfer rate, number of transfer legs.  

• Service Form KPIs: number of Service forms opened within a specific period and the time to resolution. 

Conclusion 

This case study highlights the power of a modern enterprise data stack and collaboration between CloudEQS and Matillion. By integrated siloed data and standardizing metrics using Matillion, the customer can conduct deeper insights into call types, agent performance, and customer interactions. As a result, they improved operational efficiencies, reduced resolution times, and significantly enhanced the overall customer experience.