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Your most unhappy customers are your greatest source of learning.

Steve Jobs

Multivariate Data Analysis are powerful statistical techniques for analyzing data with many variables simultaneously to identify patterns & relationships

Observational Data draws inferences from a sample to a population where the independent variable is not under the control of the researcher.

Marketing without data is like driving with your eyes closed.

Dan Zarrella

Tests of Association

= statistical techniques that measure whether two variables are associated.

Difference between correlation and causation - A correlation between variables, however, does not automatically mean that the change in one variable is the cause of the change in the values of the other variable. Causation indicates that one event is the result of the occurrence of the other event; i.e. there is a causal relationship between the two events.

#association #correlation #causation

Data quality and sampling method are more important than sample size.

#sampling #method #marketresearch

Week 4, Chapter 10: Factor Analysis

= A technique to summarise information contained in a large number of variables into a smaller number of subsets or factors.

The aim of marketing is to know and understand the customer so well, the product or service sells itself.

Peter Drucker

Customer experienceresearch is worth a lot to a business. Lead qualification studies aim to understand key drivers to purchasing, identify profiles of customers, which allow sales to fall into place.

An simple explanation of the difference between Systematic and Random error.

Errors

Errors can be made in survey research. Below are some examples of each-

•Random sampling error

Anyone who reads polls on the internet, or in newspapers, should be aware that sampling errors could vastly influence the data and lead people to draw incorrect conclusions.

To further compound the random sampling errors, many survey companies, newspapers and pundits are well aware of this, and deliberately manipulate polls to give favorable results.

In any experiment where it is impossible to sample an entire population, usually due to practicality and expense, a representative sample must be used.

Of course, when you use a sample group, it can never fully match the entire population, and there will always be some likelihood of random sampling error.

Any researcher must strive to ensure that the sample is as representative as possible, and statistical tests have inbuilt checks and balances to take this into account.

•Systematic error

Systematic error results from some imperfect aspect of the research design or from a mistake in the execution of the research.

•Sample Bias

Sample Bias exists when the results of a sample show a persistent tendency to deviate in one direction from the true value of the population parameter.

Week 2, Chapter 5: Survey

Survey= Primary data collected by communicating with a representative sample of people.

#survey

“Without data you’re just another person with an opinion.

W. Edwards Deming

A simple way to view questionnaire design.

Questionnaire Design

1.Specify what information will be sought.

2.Determine the type of questionnaire and survey research method.

3.Determine the content of individual questions.

4.Determine the form of response to each question.

5.Determine the wording of each question.

6.Determine questions’ sequence.

7.Determine physical characteristics of the questionnaire.

8.Re-examine and revise steps 1–7 if necessary.

9.Pretest the questionnaire.

#design #questionnaire

The Measurement Process

1.Determine what is to be measured

2.Determine how it is to be measured

3.Apply a rule of measurement

4.Determine if the measure consists of a number of measures

5.Determine the type of attitude and scale to be used to measure it

6.Evaluate the measure

#measurementprocess #marketing #marketresearch

Week 3, Chapter 8: Measurement

Measurement is not an exact science, partially because many consumer measurements such as attitudes, motivation, personality and intention are not tangible aspects, but internalised conditions, which the researcher tries to measure in concrete terms.

I found the above statement particularly interesting as I was wondering how this topic would approach the more intangible aspects of consumer satisfaction. Having completed a psychology degree, I am naturally drawn to consumer behaviour and the more qualitative aspects of this.

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