Data Analysis & Processing for Enhanced Customer Experiences (CX) 

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. 

• 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.

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.

• 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