Total summary
Lecture slides
HC 1 | Tue 3 feb
Why do we experiment?
In marketing (and other sciences) we seek to:
Describe
Predict
Explain behavior of (market-parties, employees, buyers)
→ Break up phenomena in variables and relations
between those variables
Conversion optimalisation
A structured and systematic approach to improving the performance of a website
Informed by data insights & psychology
Taking the traffic there and making the most of it
Behavioral Research
1. Descriptive research
Thoughts, feelings, ideas, behaviors
2. Correlational research: Identifying relationships
between different observed variables: measuring
Total summary 1
, thoughts, feelings, behavior
Examples:
“people save more during the economic crisis”
“smoking mothers have more often problem children
→ Measures the association between 2 variables
Correlation of -1 (negative association) to 1 (positive association)
r = .80 smoking mother – problem behavior child ?
r = -.80 smoking mother – problem behavior child ?
Problems with correlational research?
There is 2
1. Cofounding variables, Omitted variables & spurious correlation
2. The direction → bi directional. The effect en cause can go both ways
→ Relationship : direction
→ Spurious correlations
Total summary 2
, Correlations are often interpreted as causal
Examples
Is the number of murders committed caused by the number of ice creams eaten?
Do criminals celebrate their crimes with an ice cream?
More on correlational research:
Weaknesses
Direction of the relationship?
Third explaining variable => spurious correlation/omitted variable/confound
There is usually a third variable that has effect on the result, so you cannot just conclude based
on the correlation of two variables, you have to be critical.
Total summary 3
, From description to prediction
1. Description: careful observation
2. Correlation: relationship between observed
variables
3. Experimental: Testing causality, A à B?
Impact of loyalty programs on sales?
Impact of loyalty program A vs. program B on sales?
Impact of loyalty program AND amount of advertising for loyalty program on sales?
Experimental research: settings
Field experimentation
Real life setting
Mundane reality: natural behavior | setting | treatment
Less control
Laboratory experimentation → usually start with this
More control
Better able to manipulate variables
No natural setting
Practical example → Campina yoghurt
“Why did sales drop 25%? Taste change or redesign?”
Experiment 1 (blind taste test): New taste better liking
Experiment 2 (package liking & eye tracking): Redesign better
Field experiment (camera observation): search time increases → consumers unable to find
‘their’ brand due to redesign.
Experimental Research: Crux
Experimentation: only type of research that (potentially) can demonstrate that a change in one
variable causes a predictable change in another variable
Most difficult: making sure that a change in Y was not caused by something else than X
Experimentation & Causation
1. Needs to be correlation between 2 variables
2. Asymmetrical direction
Total summary 4
Lecture slides
HC 1 | Tue 3 feb
Why do we experiment?
In marketing (and other sciences) we seek to:
Describe
Predict
Explain behavior of (market-parties, employees, buyers)
→ Break up phenomena in variables and relations
between those variables
Conversion optimalisation
A structured and systematic approach to improving the performance of a website
Informed by data insights & psychology
Taking the traffic there and making the most of it
Behavioral Research
1. Descriptive research
Thoughts, feelings, ideas, behaviors
2. Correlational research: Identifying relationships
between different observed variables: measuring
Total summary 1
, thoughts, feelings, behavior
Examples:
“people save more during the economic crisis”
“smoking mothers have more often problem children
→ Measures the association between 2 variables
Correlation of -1 (negative association) to 1 (positive association)
r = .80 smoking mother – problem behavior child ?
r = -.80 smoking mother – problem behavior child ?
Problems with correlational research?
There is 2
1. Cofounding variables, Omitted variables & spurious correlation
2. The direction → bi directional. The effect en cause can go both ways
→ Relationship : direction
→ Spurious correlations
Total summary 2
, Correlations are often interpreted as causal
Examples
Is the number of murders committed caused by the number of ice creams eaten?
Do criminals celebrate their crimes with an ice cream?
More on correlational research:
Weaknesses
Direction of the relationship?
Third explaining variable => spurious correlation/omitted variable/confound
There is usually a third variable that has effect on the result, so you cannot just conclude based
on the correlation of two variables, you have to be critical.
Total summary 3
, From description to prediction
1. Description: careful observation
2. Correlation: relationship between observed
variables
3. Experimental: Testing causality, A à B?
Impact of loyalty programs on sales?
Impact of loyalty program A vs. program B on sales?
Impact of loyalty program AND amount of advertising for loyalty program on sales?
Experimental research: settings
Field experimentation
Real life setting
Mundane reality: natural behavior | setting | treatment
Less control
Laboratory experimentation → usually start with this
More control
Better able to manipulate variables
No natural setting
Practical example → Campina yoghurt
“Why did sales drop 25%? Taste change or redesign?”
Experiment 1 (blind taste test): New taste better liking
Experiment 2 (package liking & eye tracking): Redesign better
Field experiment (camera observation): search time increases → consumers unable to find
‘their’ brand due to redesign.
Experimental Research: Crux
Experimentation: only type of research that (potentially) can demonstrate that a change in one
variable causes a predictable change in another variable
Most difficult: making sure that a change in Y was not caused by something else than X
Experimentation & Causation
1. Needs to be correlation between 2 variables
2. Asymmetrical direction
Total summary 4
