Survey Analytics Reporting

By the end of this course, you will become familiar with established statistical methods for converting survey responses to insights that can support marketing decisions. Techniques discussed include factor analytics, cluster analysis, discriminant analysis and multi-dimensional scaling.

Learning Objectives:

During this course, you will have the opportunity to develop the following skills:
- Data quality
- Missing data
- Cluster sampling
- Survey data visualization
- Basic statistics -- sample mean and covariance

Topic Outline:

Building a strong hypothesis
   Why hypothesis?
   Visioning the final stories in the beginning

Framework of Survey Results Analysis
   What a successful data collection looks like?
   Questions to ask before analyzing
   Quantifying potential error (metrics to test collected data)
   Describing the quality of data sources
   Predicting common pitfalls

Quantitative research
   Statistical models for data-driven decisions
   Predicting ¿most-likely¿ outcomes

Sharing insights
   The 6 Ws
   Static vs. Interactive
   How to present results from open-ended questions
   How to present results from close-ended questions
   When to sharing raw data
   Sharing next steps and closing the loop

Best practices library
   Statistical relevance - Survey data
   Survey data visualization

Training material provided:  Yes (Digital format)