1. Taking a deeper dive into your survey data with key driver analysis
Diana Allen, Head of Statistics, ORC International

2. TODAY’S AGENDA 01. 02. 03. 04. DEFINE THE BUSINESS CONTEXT
THE ‘KEY DRIVER ANALYSIS’ TOOLKIT
BEYOND THE STATISTICS
Q&A

3. WHAT’S THE BUSINESS QUESTION WE’RE TRYING TO ANSWER?
How do we keep loyal customers?
What matters the most to our customers?
WHICH FUTURE CUSTOMER ATTITUDES AND BEHAVIOURS ARE WE TRYING TO AFFECT?
Likelihood to recommend
Overall satisfaction
Likelihood to switch

4. TYPICAL CUSTOMER RESEARCH SURVEY
Customer research into perceptions of the brand/provider
Survey includes target outcome question(s), plus specific attributes, eg Easy to understand

5. REPORTING RESULTS FROM CUSTOMER RESEARCH
Summary statistics showing positive, neutral and negative perceptions

6. KEY DRIVER ANALYSIS OVERVIEW
Key Driver Analysis (KDA) is a statistical technique that helps us focus in on what thing or things (‘inputs’) have the biggest or strongest influence on others (‘outputs’)
This analysis helps take the guesswork out of determining what inputs we need to change or take action on in order to make a desired change in the output(s) by pinpointing which one or more of the inputs is going to have the biggest effect
The underlying principle is that if you do something that causes a change to these ‘key driver’ inputs, you are much more likely to experience a change in the outputs than if you made a change to something that is not a key driver
Input
INPUT ?
Output(s)

7. INDIRECT EFFECTS / INTERACTIONS TRADITIONAL STATISTICAL TECHNIQUES
OVERVIEW OF METHODS
SIMPLE
COMPLEX
BIVARIATE
MULTIVARIATE
INPUT-OUTPUT MODEL
INDIRECT EFFECTS / INTERACTIONS
TRADITIONAL STATISTICAL TECHNIQUES
MACHINE-LEARNING

8. Independent variables
CORRELATION
Dependant variable
Independent variables

9. MULTIPLE LINEAR REGRESSION
Modelling likelihood to recommend as a linear combination of potential drivers
Those drivers that are found to have a statistically significant effect are considered to be key drivers

10. ONE ISSUE WITH REGRESSION ANALYSIS
MULTICOLLINEARITY
highly correlated predictor variables in a multiple regression model
Volatile findings in tracking studies
One driver with a very large effect 9 4

11. HOW DO YOU SOLVE A PROBLEM LIKE MULTICOLLINEARITY?
FACTOR ANALYSIS
Fees are reasonable
Proactive in support
Knowledgeable staff
Product offers value for money
Problem resolution is effortless
Easy to understand
Product meets my needs
Application is easy
Timely communication
Product and price perceptions
Processes
Communications
Likelihood to recommend
Direction and strength of the relationships between Product and Price, Processes and Communications, and Likelihood to recommend

12. RELATIVE IMPORTANCE REGRESSION (“SHAPLEY VALUE”)
Proportionate contribution each predictor makes to R2, considering both its direct effect (i.e. its correlation with criterion) and its effect when combined with the other variables in the regression equation
Utilises the R package relaimpo (Relative importance of regressors in linear models) created by Urlike Groemping

13. MODELLING COMPLEX RELATIONSHIPS
Performs simultaneous estimation of multiple equations in order to understand a system of complex relationship
Models relationships between individual attributes, underlying core dimensions, and ultimately the dependent variables of interest

14. MACHINE LEARNING APPROACHES TO KEY DRIVER MODELLING
DECISION TREE TECHNIQUES
BAYESIAN NETWORKS
Classification and Regression Trees (CART)
Random Forest Trees
Capture nonlinearities, thresholds and interactions in the data
Based on the inference of probability distributions from the data

15. BEYOND THE STATISTICS
Data Visualisation

16. “The goal is to turn data into information and information into insight.”
/ Carly Fiorina, former CEO of HP/

17. Thank you.