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Flashcards in L10, Data analysis 1 - quantitative Deck (28)
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1

Explain the differences between exploratory and confirmatory research in the early phases of product development

Explanatory research
— Understand why cause/effect relationships.


Confirmatory research
— Researchers has a theory - Hypothesis and the objective of the research is to find out if their hypothesis is correct

2

Process of quantitative data analysis

1. create a data set, in an excel worksheet or in a dedicated statistical analysis software

2. Clean up the data set

3. analyze basic metrics and diagrams

4. analyze relationship between two variables

5. analyze relationships between three or more variables

6. compare with data from interviews

3

Analysis of quantitative study?

1. computation of standardized metrics
2. visualisation
3. interpretation
—> what does the number mean?
—> what can we say about the views of non-respondents
4. What conclusions can be drawn?

Analysis of correlations and open-ended questions are essential to explain reasons for ratings and suggest improvements

4

Analysis of quantitative study?

1. computation of standardized metrics
2. visualisation
3. interpretation
—> what does the number mean?
—> what can we say about the views of non-respondents
4. What conclusions can be drawn?

Analysis of correlations and open-ended questions are essential to explain reasons for ratings and suggest improvements

5

Name some basics metrics

1. Measures of central tendencies
—> mean
—> median
—> mode
—> sum
—> N
—> Response rate

2. Measure of variability
—> range
—> inter-quartile range
—> variance
—> standard deviation
—> standard error
—> min
—> max

6

Name some basics metrics

1. Measures of central tendencies
—> mean
—> median
—> mode
—> sum
—> N
—> Response rate

2. Measure of variability
—> range
—> inter-quartile range
—> variance
—> standard deviation
—> standard error
—> min
—> max

7

name some types of analysis of relations between two variables!

1. Tabulating and cross-tabulating proportions
—> e.g. agreements with statements for owners of different brands

2. comparing means across items, groups of customers

3. correlation coefficients
—> predicting an outcome as a function of an antecedent variable
—> e.g. level of satisfaction as a function of length of relationship with vendor

8

What does survey monkey provide?

1. enables cross-tabulations
2. selection
3. evaluation of statistical significance
4. text analysis

9

Strengths and weaknesses with quantitative data analysis?

STRENGTH
1. provides an investigation with scientific status
2. Offers confidence in the findings
3. precise measurements
4. enables analysis of large volumes of data
5. provides concise presentation of data

WEAKNESS
1. quality of data may be poor even if quantified
2. you may be overloaded by data
3. quantitative analyses is not as scientific as it might seem on the surface

10

Why should you do chice modeling/ conjoint analysis, concept testing or experimentation in product planing?

1. what drives the choice of one product configuration over another?

2. What are appropriate attribute levels?

3. How much are they willing to pay?

4. How many would buy at the price?

11

Give some examples of choice modeling tasks!

EXAMPLE 1: Choose TV
Attributes:
1. type
— plasma
—> LCD
—> LED

2. size
—> 36""
—>40""
—> 46""

3. brand
—> sony
—> toshiba
—> philips

4. price
—> 499
—> 699
—> 899

Attributes:
1. type
2. size
3. brand
4. price

Product profiles:
Then different levels for each attribute

EXAMPLE 2: glases
Attribute:
1. Lens type
—> polarising
—> UV protector
—> prescription

2. Design
3. Price
4. Frame type
5. Lens color
—> brown
—> blue
—> yellow
—> black

6. Brand
—> rayban
—> Oakley
—> D&G
etc.

12

Describe the conjoint analysis procedure

1. Identify attributes of the product
2. Decide on how many levels that will be considered for each category
3. Create screen shots or cards fr each variant you want to examine
4. Determine judgement procedure
—> pairwise comparison?
—> preferential scale?
—> probability to purchase

5. Administr survey

6. Compute utility weights for levels of attributes and attribute importance for individual responses

7. Aggregate responses
—> in market segments?
—> cluster analysis

13

what can be done to minimize the number of permutations?

fractional factorial design of experiments can be used

14

Important to think about when administering a survey?

How?
—> web
—> e-mail?
—> postal?

Collect addiational information:
1. spending level
2. involvement with product
3. purchae plans
4. demographics

15

How do you calculate attribute range?

max utility-min utility

16

How do you calculate attribute importance?

attribute range/ sum range

17

Strengths and weaknesses of choose modeling?

STRENGTH
1. Can handle complex relations
—> which can be hard to estimate otherwise

2. Good beyond a customer's self-report by forcing the respondent to act
3. large number of product design alternatives
4. mix of confirmatory and explanatory research

WEAKNESS
1. limited scope
—> sample size
—> choice of attributes

2. time-consuming & costly
3. does not fit all buying situations
—> e.g. decisions made by groups
4. modellable product complexity is limited to
—> about 6 attributes
—> 2-3 levels

18

How can you expand conjoint analysis into controlled

1. compare alternative price points or product designs

2. Establish treatment groups exposed to different alternatives

3. An alternative to conjoint analysis

4. more predictive than surveys and focus groups

5. fewer differences than in conjoint analysis

19

Concept testing?

1. Testing of concept by soliciting a response to a description of the product concept from potential customers in the target market

2. Face-to-face sessions with open-ended interactive formats are suitable for early phases

20

In what phase of the product development is qualitative concept testing appropriate?

1. In the planning and concept development phase

21

In what phase of the product development is quantitative concept testing appropriate?

In the testing and refinement phase

22

Namn some purposes for concept testing!

1. which of several alternative concepts should be pursued?

2. How can the concept be improved to better meet customer needs

3. approximately how many units are likely to be sold?

4. Should development be continued?

23

Describe the concept testing process

1. define the purpose of the test
2. choose a survey population
3. choose a survey format
4. Select way to communicate the concept
5. Develop survey format
6. measure customer response
7. interpret the results
8. reflect on the results and the process

24

give example of concept testing set-up for electric scooter

1. Purpose of concept test
—> what market to be in

2. Survey population
—> college students who live 1-3 miles from campus
—> factory transportation

3. Survey format
—> face to face interviews

25

Name some different ways to communicate the concept

1. verbal descriptin
2. sketch
3. photos and renderings
4. storyboard
5. video
6. simulation
7. interactive multimedia
8. Physical appearance models
9. Working prototypes

26

Describe the steps of designing the survey format

PART 1: Demographics and behavior
1. How far do you live from campus
2. How do you currently get to campus from home
3. How do you currently get around campus

PART 2: Product description
—> Present the concept description

PART 3: purchase intent
—> id the product were prices according to your expectations, how likely would you be to purchase the scooter within the next year?
—> definitely not, probably not, might or might not, probably will, definitely will

PART 4: Comments
1. What would you expect the price of the scooter to be?
2. What concerns do you have about the product concept
3. Can you make any suggestions for improving the product concept?

27

How can you interpret the results from the concept testing?

E.g. estimation of sales volume

Q=N*A*(Cdef*Fdef+Cprob*Fprob)


check bulrush eppinger p.177-178

28

Name some conclusions for choice modelling

1. choice modeling methods include conjoint analysis and experimentation

2. conjoint analyses provides quantitative results and is mainly effective for optimizing an established concept

3. choice modeling can be coupled to purchasing intent questions and then be used to estimate sales volume