conjoint analysis - a business case

CONJOINT ANALYSIS APPLIED IN RUNNING SHOES

PRELIMINARY ANALYSIS

CONJOINT ANALYSIS & SEGMENTATION ANALYSIS

COMMENTS AND CONCLUSIONS

Aqeel Aslam Paolo Balasso Alberto Ballan Alessandro De Lorenzi

ORTHOGONAL DESIGN & CONJOINT QUESTIONNAIRE

Masep is a shop that sells different kind of sport clothing, shoes and other accessories, in Thiene

(VI)

2

INTRODUCTION

The analysis, focused in running shoes, is especially

Inherited to the products sold by Masep :

The data was collected using a questionnaire through Internet. It has allowed to pick up a sample with different demographic features

3


According to the first step, a survey has been performed for

an exploratory analysis. The goal was inhereted to

investigate the factors that the costumers are interested in.

This step wants to find the variables that will be

implemented in the conjoint analysis.

Preliminary Procedure

4


Impermeability

Material

Weight

Suitable field

Exterior design

Life span

Brand

Cushioning

Age

Gender

Average weekly Runs

Weekly distance covered

Yearly shoes bought

Type of occupation

Diligence in the activity

Possible characteristics to analyze

Demographic informations

5

INTRODUCTION

The questionnaire was created using

Google survey

https://docs.google.com/forms/d/1lTnizSJFJFPJ9AD0nF2NkUMtylsXe4YdlYyG5_aDMgU/viewform

In order to rank the importance of the different attributes an ANOVA test was performed but the Levine test was not significant(p-value= 0.37904). The attributes implemented in CA were choosen

considering the owner’s issues and other considerations described later

6


The following slides want to describe the sample with

descriptive indicators such as Standard Deviation and

mean.

To sum up the demographic informations a pie charts is

used insted of the hystogramm used for summarizing

attribute informations.

Preliminary Analysis

7


Descriptive analysis: Demographic Informations

The sample does not rappresent the whole population but mainly

male and young people

8


9


Descriptive analysis: Attributes Summery

𝑥 = 6,38

SD = 2,61

𝑥 = 7,87

SD = 2,42

𝑥 = 6,54

SD = 2,01

10


𝑥 = 7,19

SD = 2,12

𝑥 = 8,67

SD = 2,14

𝑥 = 7,41

SD = 2,04

11


𝑥 = 7,61

SD = 1,82

𝑥 = 6,19

SD = 2,59

12

FACTORIAL DESIGN

Materials

Suitable field

Life span

Impermeability

ATTRIBUTES NOT

IMPLEMENTED IN

CONJOINT

ANALYSIS

Few runners interested in it

It does not influence buying intention, it is related to the kind of running activity

Pro runners run more than others, this is the reason why they buy more pairs yearly

It is not up to the kind of shoes ( ~ 800 km for

each shoes)

Runners were interested in them, but they were no sensitive to the technical materials that running shoes are made by

13

FACTORIAL DESIGN

Cushioning

Brand

External design

Weight

ATTRIBUTES

IMPLEMENTED IN

CONJOINT

ANALYSIS

The most important attribute according to runners

Runners do not consider it so much but important to detect if there are brand preference effects

Easy identification of three kinds of design: Thin, neutral, bulky

Considered important by the runners interviewed

14


Frequency analysis on Yearly shoes bought vs Running club’s members

We have to reject the hypothesis that classification of rows and columns are indipendent

The rating of a running club’s member becomes more important because their buying frequency is greater So we are interested in assessing if they evaluate attributes differently compered to no-members

Using chi-square test no significant dependence has been found between higher attribute’s values and running club’s members

datasetScarpe.sgp

15


Running club’s members vs. weekly distance covered

We have to reject the hypothesis that classification of rows and columns are indipendent.

In order to verify why members have an high buying frequency could be interesting evaluating if there is a relation between members and high weekly distance covered

Since shoes have the same life span ( about 800 km) and the most members run more than 20 km a week , they will buy more than 1 shoes a year.

STAGES FOR CONJOINT ANALYSIS

1. Identification of attributes and levels using the results of

explorative questionnaire.

2. Definition of profiles and conjoint analysis method

3. Drawing an appropriate paper and pencil format, with

demografical information and labels with the different profiles

4. Estimates of part-worth utilities and relative importance.

5. Segmentation analysis

6. Results

16

1. Identification of ATTRIBUTES and levels

Cushioning

Brand

Design

Weight

The most important attribute according to

runners

Runners do not consider it so much but

the owner of the shop was interested in

testing this attribute deeper

Easy identification of three kind of design:

Thin, neutral, bulky

Considered important by the runners

interviewed

CHOSEN

ATTRIBUTES

17

1. Identification of attributes and LEVELS

Cushioning

Brand

Design

Weight

How: 1. Complete

2. Partial

3. Only under the heel

1. Mizuno

2. New Balance

3. Asics

1. Tapered

2. Medium

3. Bulky

1. 225 gr.

2. 288 gr.

3. 335 gr.

3 types on

the market

The greatest

market share

Common

shapes

Statistical

analysis

18

A sample randomly collected from the

internet was analyzed using Statgraphics

Different classes

were individuated

The central

point of the

intervals are:

1. 225 gr.

2. 288 gr.

3. 335 gr.

Fre

qu

en

cy

Weight (gr.)

1. Identification of attributes and LEVELS

19

Full Profile Approach

Too many factors

Fractional Factorial

Orthogonal Design

It eliminates the interaction

between levels of different factors

evaluating only main effects

Design is orthogonal if each factor

can be evaluated independently

from all other factors

Hierarchical assumption


Each combination of the

factors’ levels generates one

profile that is evaluated by

responders

It consists in a Full

Factorial Design

20

Attributes Cushioning Weight (gr.) Brand Design

Levels

1 Complete 225 Mizuno

Tapered (A)

2 Partial 280 New Balance

Medium (B)

3 Only heel 335 Asics Bulky (C)

4 attributes with 3 levels each Total number of combinations:

3x3x3x3= 81 profiles !

“Orthoplan” procedure

of SPSS 81 9 profiles


21

Caracteristic of our Conjoint Analysis:

• Metric C. A.

• Part-worth model

• Orthogonal plan


22

3. Drawing an appropriate paper and pencil format

23

28 runners answered the

conjoint questionnaire


24


Disaggregate overall results Aggregate overall results

Collected data were elaborated by SPSS software, obtaining

different types of results:

25

CONJOINT QUESTIONNAIRE

General info

Runner’s

attitudes

26


28 runners answered the


27


student 46%

employee 29%

retired 3%

enterpreneur 11%

housewife 7%

manager 4%

male 75%

female 25%

Frequency of the age

In the following graphs are

described the general

information about the sample

that responded to the


Mean of the age: 33,2

Median of the age: 31 8

6

9

5

age < 24 24<= age <34 34<=age<44 age >= 44

28


less than 3 times in a week

57%

3 or 4 times in a week 32%

more than 4 times in a week

11%

less than 8 km in a week

32%

8 or 20 km in a week 43%

more than 20 km

25%

How many times

do you go

running in a

week?

How many

kilometres do you

run in a week?

29


Not members 64%

Members 36%

less than 1 pair of shoes

26%

1 pair of shoes 37%

more than 1 pair of shoes

37%

How many people

joined a club:

How many pair

of running

shoes do you

buy in a year?

30

CONJOINT ANALYSIS

Conjoint analysis results for

subject1 :

-Student

-Male

-Run 3 or 4 times a week

-Run between 8 and 20 km in a week

-Not member

-One running shoes in a year

31

5

7

INDIVIDUAL UTILITY FUNCTION

utility (brand* Asics ) + utility (weigth*335gr)+ utility

(cushioning*solo tallone) + utility (design*B ) +

constant= 5 predicted score

actual score

utility (brand* New Balance) + utility

(weigth*225gr)+ utility

(cushioning*parziale) + utility

(design*A) + constant= 8

actual score

1th respondent

predicted score

32

Conjoint analysis – Conclusions

RESULTS AND CONCLUSION

33

Conjoint analysis – Overall Results

week run commitment age buy in 1 year Job

overall < 3 3 or 4 > 4 not joined joined < 24 24<=x<34 34<=x<44 >= 44 < 1 1 > 1 Student Employee Manager

imp cushion 33.18 27.51 35.7 36.15 30.95 27.37 30.94 40.77 24 40.5 32.65 30.79 38.81 35.5 25.54 28.57

imp weigth 15.41 20.87 13.69 11.33 18.29 11.46 20.42 18.49 11.99 11.89 25.22 11.33 12.76 20.61 11.22 8.44

imp brand 26.94 23.73 28 29.25 27.49 32.04 24.91 16.8 38.61 23.35 24.81 30.49 23.66 23.07 36.25 28.3

imp design 24.47 27.9 22.61 23.27 23.27 29.14 23.73 23.94 25.4 24.26 17.33 27.4 24.78 20.83 26.99 34.69

cushion1 0.9563 0.8025 1.0278 1.0317 0.8827 0.8 0.9012 1.2667 0.4286 1.3889 1.0159 0.9394 1.0606 1.0855 0.5694 0.7222

cushion2 -0.2698 -0.0123 -0.1944 -0.7302 -0.0432 -0.1 0.0123 0.0667 -0.381 -0.778 0.0159 -0.1212 -0.6061 -0.0171 -0.3056 -0.5278

cushion3 -0.6865 -0.7901 -0.8333 -0.3016 -0.8395 -0.7 -0.9136 -1.3333 -0.0476 -0.6111 -1.0317 -0.8182 -0.4545 -1.0684 -0.2639 -0.1944

weigth1 0.0873 0.0617 0.1389 0.0317 0.0864 0.1 0.0864 0.2 0.0476 0.0556 0.1111 0.0909 0.0606 0.1111 0.1111 0.0556

weigth2 0.2063 0.4691 0.0278 0.1746 0.2716 0.0667 0.2716 0.4667 0 0.1667 0.5873 0.0909 0.0909 0.3675 0.0694 -0.0278

weigth3 -0.2937 -0.5309 -0.1667 -0.2063 -0.358 -0.1667 -0.358 -0.667 -0.0476 -0.2222 -0.6984 -0.1818 -0.1515 -0.4786 -0.1806 -0.0278

brand1 -0.127 -0.1605 0.0556 -0.3968 -0.0432 -0.1 0.0123 -0.1333 -0.2381 -0.1111 0.0635 -0.0606 -0.2727 -0.0427 -0.3889 0.0556

brand2 0.4802 0.358 0.5833 0.4603 0.4938 0.5667 0.5679 0.5333 0.381 0.3333 0.4444 0.5455 0.4242 0.5726 0.4028 0.3889

brand3 -0.3532 -0.1975 -0.6389 -0.0635 -0.4506 -0.4667 -0.5802 -0.4 -0.1429 -0.2222 -0.5079 -0.4848 -0.1515 -0.5299 -0.0139 -0.4444

design1 -0.1389 -0.0864 0 -0.4444 -0.0062 0.0667 -0.0247 0.2667 -0.1905 -0.6111 -0.0317 0.0303 -0.3939 0.0598 -0.3472 -0.1944

design2 0.2778 0.4321 0.1667 0.2698 0.2716 0.2333 0.2346 0.7333 0.2381 0.1667 0.254 0.2424 0.3333 0.265 0.5278 0.0556

design3 -0.1389 -0.3457 -0.1667 0.1746 -0.2654 -0.3 -0.2099 -1 -0.0476 0.4444 -0.2222 -0.2727 0.0606 -0.3248 -0.1806 0.1389

34

Conclusioni

Considering the overall

results:

The most important factor is

cushioning with the value of

33,18

The less important factor is

weight, with the value of

15,41

35

Conclusions:

Overall utilities

The preferred levels of the factors are: COMPLETE cushioning,

MEDIUM weight, NEW BALANCE as brand, design B

36

Conclusioni

37

Conclusioni

32,65 30,79

38,81

25,22

11,33 12,76

24,81

30,49

23,66

17,33

27,4 24,78

0

10

20

30

40

50

< 1 1 > 1

Importance vs. buying frequency

imp cushion

imp weigth

imp brand

imp design

30,94

40,77

24

40,5

20,42 18,49

11,99 11,89

24,91

16,8

38,61

23,35 23,73 23,94 25,4 24,26

0

5

10

15

20

25

30

35

40

45

< 24 24<=x<34 34<=x<44 >= 44

Importance vs. Age

imp cushion

imp weigth

imp brand

imp design

• Mostly customers are inspired by the importance of cushion except the age

between 34 and 44 and they prefer importance of brand.

38

Conclusioni

27,51

35,7 36,15

20,87

13,69 11,33

23,73

28 29,25

27,9

22,61 23,27

0

5

10

15

20

25

30

35

40

< 3 3 or 4 > 4

Importance vs. weekly running

imp cushion

imp weigth

imp brand

imp design

• Most important factor is cushioning but design also influence people who

run less then 3 days.

39

Conclusioni

35,5

20,61 23,07

20,83

25,54

11,22

36,25

26,99 28,57

8,44

28,3

34,69

0

5

10

15

20

25

30

35

40

imp cushion imp weigth imp brand imp design

Importance vs. Occupations

Student

Employee

Manager

30,95

18,29

27,49

23,27

27,37

11,46

32,04 29,14

0

5

10

15

20

25

30

35

imp cushion imp weigth imp brand imp design

Importance vs. not joined/joined

not joined

joined

• In first graph, cushioning and weight are important factors for students. On the other hand,

Employees prefer brand and design influence Managers.

• In second graph, Brand and design have great importance for the members of the clubs.

Cushioning and weight attract non-members.

40

Conclusioni

1,0606

-0,6061

-0,4545

0,0606 0,0909

-0,1515

-0,2727

0,4242

-0,1515

-0,3939

0,3333

0,0606

-0,8

-0,6

-0,4

-0,2

0

0,2

0,4

0,6

0,8

1

1,2

cushion1 cushion2 cushion3 weigth1 weigth2 weigth3 brand1 brand2 brand3 design1 design2 design3

Utilities vs. buying frequency > 1

> 1

• This slide is important to evaluate the attributes, that person with high

buying frequency consider more important.

• The complete cushioning is preferred as compared to others.

• The weight utilities is slightly higher in the light and neutral weights

instead of heavy ones. New balance and Design B have is also

preferred.

41

Conclusion

Summary

• According to the overall importance of attributes, the most preferred

attribute is cushioning. And the least preferred is weight.

• After the analysis of segmentation, there is clear evidence that

cushioning is the most important attribute.

• The summary of utility for the different levels of each attribute suggests

that the best profile is;

Complete cushioning + 288gr + Newbalance + Design B

• The above design is perfectly matched with the utilities of members and

the respondents with “buying frequency>1”.

42

THANK YOU FOR YOUR ATTENTION

43

conjoint analysis - a business case

Business