Description

Power BI Training for Customer Service Professionals (2 Days)

The “Power BI Training for Customer Service Professionals” is a comprehensive two-day course designed to empower customer service teams with the skills needed to leverage Power BI for enhanced data analysis and decision-making. Participants will explore key data sources, learn to build and optimize data models, and master essential DAX formulas for calculating critical customer service metrics. The course also covers advanced topics such as predictive analytics, effective dashboard design, and best practices for report deployment and sharing. By the end of the course, attendees will be equipped to create insightful reports and dashboards that drive improved customer service outcomes.

COURSE OUTLINE

The Customer Service Data Ecosystem

Key Data Sources in Customer Service

In this module, you will explore various data sources critical to customer service operations, including:

• Call Center Logs and IVR Data
• Customer Feedback and Survey Data
• Social Media and Web Analytics

Data Attributes and Requirements

Learn about essential data fields such as Case ID, Customer ID, and Agent ID, and understand the importance of data quality, cleansing, and privacy considerations.

Data Modeling for Customer Service

Building a Customer Service Data Model

This section covers the creation of a robust data model for customer service, including the definition of fact tables (Cases, Interactions, Resolutions) and dimension tables (Customers, Agents, Time).

Common Data Modeling Scenarios

Tackle common data modeling challenges such as handling many-to-many relationships, using date tables for time intelligence, and managing geographic and product hierarchies.

Optimizing Performance of Customer Service Reports

Optimize your reports with data aggregation techniques, partitioning, and incremental load strategies to ensure smooth performance.



Essential DAX for Customer Service Analysis

Calculating Key Metrics with DAX

Master DAX formulas to calculate crucial metrics like Average Handle Time (AHT), First Call Resolution (FCR) Rate, Customer Satisfaction (CSAT) Scores, and Net Promoter Score (NPS).

Using DAX for Segmentation and Cohort Analysis

Learn how to use DAX for advanced customer and agent segmentation, as well as cohort analysis to track customer retention over time.

Advanced DAX Patterns

Explore advanced DAX patterns, including calculating moving averages for trend analysis and using time intelligence functions to evaluate performance over time.

Predictive and Prescriptive Analytics

Implementing Predictive Models in Power BI

Delve into predictive analytics, including customer churn prediction, sentiment analysis using text analytics, and forecasting call volumes to better manage staffing needs.

Designing Customer Service Dashboards

Principles of Effective Dashboard Design

Understand the principles of effective dashboard design, focusing on user-centric design, visual best practices, and incorporating interactive elements like filters and slicers.

Common Dashboard Use Cases

Explore common use cases for customer service dashboards, including executive overview dashboards, supervisor dashboards, and agent-specific performance dashboards.

Enhancing Dashboards with Custom Visuals and Themes

Learn how to use custom visuals to represent complex data, brand your dashboards with custom themes, and enhance interactivity through bookmarking and button navigation.

Deployment and Sharing of Customer Service Reports

Best Practices for Report Deployment

This module covers best practices for deploying reports, managing report versions and updates, and ensuring security with row-level security and data sensitivity labels.

Sharing and Collaboration in Power BI

Discover how to share reports and dashboards effectively within your team using Power BI Service features, and how to export reports for different formats including PDF, PowerPoint, and Excel.

Monitoring and Maintaining Customer Service Reports

Learn how to monitor report usage and performance, manage data refresh and gateway connections, and ensure data governance and compliance within your organization.