Data Analysis & Processing for Enhanced Customer Experiences (CX)
Course Overview:
This course equips Customer Experience (CX) and Customer Service Management (CSM) professionals with the fundamental skills for data analysis and processing. You'll gain hands-on experience with essential tools and techniques to extract valuable insights from customer data, ultimately leading to improved customer experiences within your organization.
Learning Objectives:
Describe the importance of data analysis and processing for gaining insights into customer behavior and improving CX.
Identify different types of customer data relevant to CX initiatives (e.g., survey responses, call transcripts, website interaction data).
Utilize common data analysis tools (e.g., spreadsheets, SQL) for cleaning, organizing, and manipulating customer data.
Apply basic statistical techniques (e.g., descriptive statistics, hypothesis testing) to analyze customer data and identify trends.
Communicate insights from data analysis in a clear and concise manner through data visualization techniques.
Course Highlights:
1. The Power of Data-Driven Customer Experience:
Introduction to Data Analysis for CX: Understanding how data analysis can be used to identify customer needs, preferences, and pain points, leading to improved CX strategy.
Demystifying Customer Data Landscape: Exploring various types of customer data relevant to CX (structured, unstructured) and their potential applications for understanding customer behavior.
Case Study 1: Utilizing customer survey data analysis to identify factors influencing customer churn, enabling targeted retention strategies.
Hands-on Session: Working with a sample customer dataset in a spreadsheet to practice data cleaning and manipulation techniques.
2. Unveiling Data Analysis Tools & Techniques:
Introduction to Data Analysis Tools: Exploring popular tools like spreadsheets (e.g., Excel, Google Sheets) and their functionalities for data organization, manipulation, and basic analysis.
Introduction to SQL for Customer Data: Understanding the fundamentals of SQL queries for retrieving and filtering customer data from relational databases.
Case Study 2: Utilizing SQL queries to extract customer purchase history data from a database, enabling personalized product recommendations.
Guest Speaker Session: Inviting a data analyst who has worked on CX projects to share their experience with data analysis tools and best practices.
Group Discussion: Identifying the types of customer data accessible within your department and brainstorming initial analysis projects for improving CX.
3. Unveiling the Power of Statistics for CX Insights:
Leveraging Statistics for Customer Data Analysis: Understanding basic statistical concepts like measures of central tendency (mean, median) and dispersion (variance) for summarizing customer data.
Exploring Hypothesis Testing for CX Decisions: Learning basic hypothesis testing techniques to statistically validate assumptions about customer behavior and inform CX decisions.
Case Study 3: Utilizing statistical analysis to test if a new customer service initiative has a significant impact on customer satisfaction ratings.
Interactive Workshop: Working with a sample customer satisfaction dataset to perform basic statistical analysis and draw insights.
4. Communicating Insights for Actionable CX Strategies:
Data Visualization for Effective Communication: Exploring data visualization techniques (e.g., charts, graphs) to effectively communicate insights from data analysis to stakeholders.
Storytelling with Data for CX Improvement: Learning how to present data analysis findings in a compelling narrative that drives action and improves CX strategies.
Case Study 4: Creating data visualizations to showcase the impact of a customer loyalty program on customer retention rates, informing future program development.
Course Wrap-up & Project Presentations: Teams present their chosen data analysis project for a specific CX challenge, outlining the data used, analysis methods, key insights, and their potential impact on improving customer experience.
Resource Sharing: Discussing best practices and ongoing learning opportunities for staying up-to-date with data analysis tools and techniques relevant to CX.
Prerequisites:
Basic understanding of mathematics and statistics
Familiarity with programming concepts and a language such as Python or R
Knowledge of database systems and SQL is beneficial but not required