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Uni > Marketing Analytics > Flashcards

Marketing Analytics Flashcards

1
Q

What is a hypothese?

A

A prediction from a theory

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2
Q

What is a dependent variable?

A

the outcome variable, the propsed effect (example: sales)

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3
Q

What are the characteristics of a dependet variable?

A

it’s measured and not manipulated in experiments

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4
Q

What is an independet variable?

A

the proposed cause (predictor variable)
(example: type of advertising)

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5
Q

What are the characteristics of an independet variable?

A

it’s manipulated in experiments

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6
Q

What is the approach for qualitative research?

A

testing theories using language (interviews etc.)

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7
Q

What is the approach for quantitative research?

A

testing theories using numbers

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8
Q

What is the objective of quantitative research?

A

to quantify the data and generalize the results from the sample to the population of interest

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9
Q

What are the samples of qualitative research?

A

small number of non-represenative cases

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10
Q

What are the samples of quantitative research?

A

large number of representative cases

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11
Q

What is descriptive reserach?

A

aimed at capturing the structure of your data and representing it in a compact manner

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12
Q

What data does descriptive research collect?

A

user characterstics (age, gender etc.), product characteristics etc.

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13
Q

What is an example of analytics for descriptive research?

A

Cluster analysis

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14
Q

What is predictive research?

A

it uses statistical modeling techniques with the aim of predicting new or future observations based on a training data set

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15
Q

What data does predictive research collect?

A

output: making a purchase within the next month
input: age, gender, frequency of past purchases, past ad exposures etc.

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16
Q

What is an example of analytics for descriptive research?

A

Regression, decision trees, dandom forests, etc.

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17
Q

What is a causal inference research?

A

the focus is almost exclusively on research that tests causal hypotheses

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18
Q

What data does causal inference research collect?

A

outcome: making a purchase within the next month
treatment: initation of targeting campaign
confounders: for non-experimental settings (interest in product categroy etc.)

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19
Q

What is an example of analytics for causal inference research?

A

experiments with random assigment, regression, instrumental variable etc.

20
Q

What is a correlation?

A

When we observe changes in the input variable (X) and a change in the outcome variable (Y)

21
Q

What is causality?

A

When we change the input variable (X) and we observe the resulting changes in the outcome variable (Y)

22
Q

What is a spurious correlation?

A

a connection between two variables that appears causal but is not

23
Q

What are the conditions ofr causality?

A
  1. concomitant variation (a cause and an effext should vary together in the way predicted by the hypothesis under consideration)
  2. time order of occurrence (the causing event must occur befire the effect)
  3. absence of other possible causal factors (the factor or variable being investigated should be the only possible causal explanation)
24
Q

What is systematic variation?

A

Differences in the dependent variable created by a specific experimental manipulation

25
Q

What is unsystematic variation?

A

Differences in the dependent variable created by unknown factors (age, gender, IQ etc.)

26
Q

What does randomization do?

A

Minimize unsystematic variation

27
Q

What does spillover mean?

A

The treated individual affects the outcome of the untreated individuals

28
Q

What is the crossover effect?

A

When an individual was exposed to more than one treatment

29
Q

What are the scale characteristics?

A
  1. Description (unique labels)
  2. order (relative sizes of the descriptiors like „greater than“)
  3. distance (between the scale descriptors)
  4. origin (true zero point)
30
Q

What is a nominal scale?

A

This variable has a number just for identification - it can have numeral values

31
Q

What is an ordinal scale?

A

The numbers indicate the relative position of objects (eg. 1st place)

32
Q

What is an interval scale?

A

Differences between objects can be compared (eg. performance rating from 0 to 10)

33
Q

What is a ratio scale?

A

Possesses all properties of nominal, ordinal and interval scales (eg. time to finish in minutes) - has an absolut zero point

34
Q

What is a measurement?

A

It’s not the true value of the characteristic of interest but rather an observation

35
Q

What is the measurement error?

A

The Variation in the information sought by the researcher and the information generated by the measurement process

36
Q

What is reliability?

A

The consistency

37
Q

What is validity?

A

The truthfulness

38
Q

What to indicators measure?

A

Indicators (items) measure the cause for the change of the not directly observable construct

39
Q

When is a multi-item measurement particularly important?

A

for the increase in the reliability of the measurement

40
Q

How does a between-subjects design look like?

A

We manipulate the independent variable (treatment) using different persons for each group

41
Q

How can the effect of the treatment on the treated in DID models be written as?

A

(Yt,post - Yt,pre) - (Yc,post - Yc,pre)

42
Q

What is the main concern in predictive modeling tasks?

A

overfitting

43
Q

What do we primarily care about in predictive modeling tasks?

A

the predictive ability of the model

44
Q

Is the magnitude of difference between scale points is known for ordinal scales?

A

No

45
Q

What is the goal of cluster analysis?

A

Reduce number of observations

Uni (1 decks)

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