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Conjoint analysis - A business case

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Conjoint analysis - A business case

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This analysis has been done applying the knowledge developed during the course of statistic methods and applications to a real business. A conjoint analysis was conducted to estimate the partial worths of the different features of running shoes. This analysis helps to figure out which attribute and level is most important according to the constumer's view and costumize the offer of the firm considering the different interests of the market. It allows to orientate the product to a target market.

This analysis has been done applying the knowledge developed during the course of statistic methods and applications to a real business. A conjoint analysis was conducted to estimate the partial worths of the different features of running shoes. This analysis helps to figure out which attribute and level is most important according to the constumer's view and costumize the offer of the firm considering the different interests of the market. It allows to orientate the product to a target market.

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Conjoint analysis - A business case

  1. 1. 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
  2. 2. 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 :
  3. 3. The data was collected using a questionnaire through Internet. It has allowed to pick up a sample with different demographic features 3 PRELIMINARY ANALYSIS 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. 4. 4 PRELIMINARY ANALYSIS 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. 5. 5 INTRODUCTION The questionnaire was created using Google survey
  6. 6. 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 PRELIMINARY ANALYSIS 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. 7. 7 PRELIMINARY ANALYSIS Descriptive analysis: Demographic Informations The sample does not rappresent the whole population but mainly male and young people
  8. 8. 8 PRELIMINARY ANALYSIS
  9. 9. 9 PRELIMINARY ANALYSIS Descriptive analysis: Attributes Summery 𝑥 = 6,38 SD = 2,61 𝑥 = 7,87 SD = 2,42 𝑥 = 6,54 SD = 2,01
  10. 10. 10 PRELIMINARY ANALYSIS 𝑥 = 7,19 SD = 2,12 𝑥 = 8,67 SD = 2,14 𝑥 = 7,41 SD = 2,04
  11. 11. 11 PRELIMINARY ANALYSIS 𝑥 = 7,61 SD = 1,82 𝑥 = 6,19 SD = 2,59
  12. 12. 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. 13. 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. 14. 14 PRELIMINARY ANALYSIS 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
  15. 15. 15 PRELIMINARY ANALYSIS 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.
  16. 16. 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
  17. 17. 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
  18. 18. 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
  19. 19. 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. Frequency Weight (gr.) 1. Identification of attributes and LEVELS 19
  20. 20. 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 2. Definition of profiles and conjoint analysis method Each combination of the factors’ levels generates one profile that is evaluated by responders It consists in a Full Factorial Design 20
  21. 21. 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 2. Definition of profiles and conjoint analysis method 21
  22. 22. Caracteristic of our Conjoint Analysis: • Metric C. A. • Part-worth model • Orthogonal plan 2. Definition of profiles and conjoint analysis method 22
  23. 23. 3. Drawing an appropriate paper and pencil format 23
  24. 24. 28 runners answered the conjoint questionnaire 3. Drawing an appropriate paper and pencil format 24
  25. 25. 3. Drawing an appropriate paper and pencil format Disaggregate overall results Aggregate overall results Collected data were elaborated by SPSS software, obtaining different types of results: 25
  26. 26. CONJOINT QUESTIONNAIRE General info Runner’s attitudes 26
  27. 27. CONJOINT QUESTIONNAIRE 28 runners answered the conjoint questionnaire 27
  28. 28. CONJOINT QUESTIONNAIRE 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 conjoint questionnaire 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
  29. 29. CONJOINT QUESTIONNAIRE 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
  30. 30. CONJOINT QUESTIONNAIRE 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
  31. 31. 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
  32. 32. 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
  33. 33. Conjoint analysis – Conclusions RESULTS AND CONCLUSION 33
  34. 34. 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
  35. 35. 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
  36. 36. Conclusions: Overall utilities The preferred levels of the factors are: COMPLETE cushioning, MEDIUM weight, NEW BALANCE as brand, design B 36
  37. 37. Conclusioni 37
  38. 38. 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 23,3523,73 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
  39. 39. 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
  40. 40. 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
  41. 41. 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
  42. 42. 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
  43. 43. THANK YOU FOR YOUR ATTENTION 43

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