Mm3 project ppt group 1_section a
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This document describes the results of a survey conducted to understand preferences for features of a work-life balance app. 366 respondents participated in a conjoint analysis survey, rating their preferences for different app profiles that varied by operating system, customizability, and display resolution. The key findings are: 1) Customizability was found to be the most important feature, influencing 32% of preferences. 2) Operating systems like Windows and iOS were preferred over Android. 3) Higher display resolutions like 720p and 1080p were preferred over lower resolutions like 480p. 4) A regression equation was developed to predict preferences based on feature levels.
Mm3 project ppt group 1_section a
- 1. Submitted by Section – A group – 1 UM14001 ABHIJEET DASH UM14014 ANUPRAS JAIN UM14017 AYUSHI GILRA UM14042 PRITAM RANJAN ROUL UM14053 SUBHAM MEHRA UM14060 VIJENDRA KUMAR
- 2. Work-life Balance : Ironical isn’t it for a MBA student??? • This App performs 3 major functions • Build a daily expected timeline that is customized according to your choices • Prepare a daily schedule syncing Gmail, WhatsApp, Intranet notice board • Gives a weekly report gauging your target achievement based on time spent on all activities in the entire week. Why should you install this app?? • Responds swiftly to changing circumstances throughout the day. • Actively encourages a balance among work, home and play. • Helps you prioritize your work
- 3. Profile Operating System Customizability Resolution 1 Windows Medium 1080p 2 Android Low 1080p 3 Android Medium 720p 4 iOS Medium 480p 5 Windows Low 480p 6 iOS Low 720p 7 iOS High 1080p 8 Android High 480p 9 Windows High 720p Conjoint Profiles
- 4. 4.80 4.85 4.90 4.95 5.00 5.05 5.10 5.15 5.20 5.25 0 50 100 150 200 250 300 350 400 Grp1 Grp2 Grp3 Grp4 Grp5 Grp6 Grp7 Grp8 Grp9 Grp10 Overall Rating Count1 Count2 Count3 Count4 Count5 Count6 Count7 Mean Univariate Analysis
- 5. 0.00 1.00 2.00 3.00 4.00 5.00 6.00 0 50 100 150 200 250 300 350 400 Grp1 Grp2 Grp3 Grp4 Grp5 Grp6 Grp7 Grp8 Grp9 Grp10 Relevance Count1 Count2 Count3 Count4 Count5 Count6 Count7 Mean
- 6. 4.60 4.65 4.70 4.75 4.80 4.85 4.90 4.95 5.00 5.05 0 50 100 150 200 250 300 350 400 Grp1 Grp2 Grp3 Grp4 Grp5 Grp6 Grp7 Grp8 Grp9 Grp10 Credibility Count1 Count2 Count3 Count4 Count5 Count6 Count7 Mean
- 7. 4.30 4.40 4.50 4.60 4.70 4.80 4.90 5.00 5.10 0 50 100 150 200 250 300 350 400 Grp1 Grp2 Grp3 Grp4 Grp5 Grp6 Grp7 Grp8 Grp9 Grp10 Uniqueness Count1 Count2 Count3 Count4 Count5 Count6 Count7 Mean
- 8. 4.00 4.20 4.40 4.60 4.80 5.00 5.20 0 50 100 150 200 250 300 350 400 Grp1 Grp2 Grp3 Grp4 Grp5 Grp6 Grp7 Grp8 Grp9 Grp10 Intention To Try Count1 Count2 Count3 Count4 Count5 Count6 Count7 Mean
- 9. Model Summary Model R R Square Adjusted R Square Std. Error of the Estimate 1 .418a .175 .167 .83736 a. Predictors: (Constant), Uniqueness, Relevance, Credibility Coefficients Model Unstandardized Coefficients Standardize d Coefficients t Sig.B Std. Error Beta 1 (Constant) 2.688 0.310 8.682 0.000 Relevance 0.294 0.048 0.313 6.095 0.000 Credibility 0.144 0.041 0.184 3.512 0.001 Uniqueness 0.055 0.039 0.071 1.408 0.160 a. Dependent Variable: Overall Rating So, the equation is – OR = 2.688 + (Uniqueness*0.055) + (Credibility*0.144) + (Relevance*0.294)
- 10. So, the equation is – ITT = 3.070 + (Uniqueness*0.174) + (Credibility*-0.010) + (Relevance*0.396) Model Summary Model R R Square Adjusted R Square Std. Error of the Estimate 1 0.328a 0.107 0.100 1.28427 a. Predictors: (Constant), Relevance, Uniqueness, Credibility Coefficients Model Unstandardized Coefficients Standardize d Coefficients t Sig.B Std. Error Beta 1 (Constant) 2.222 0.475 4.680 0.000 Relevance 0.396 0.074 0.287 5.366 0.000 Credibility -0.010 0.063 0.009 -0.158 0.875 Uniqueness 0.174 0.060 0.153 2.913 0.004 a. Dependent Variable: Intention to try
- 11. intention to try Total 1.00 2.00 3.00 4.00 5.00 6.00 7.00 gender 1.00 5 6 13 38 76 41 29 221 2.00 2 3 6 17 38 26 14 108 3.00 0 1 0 0 0 0 0 1 4.00 0 0 0 1 1 0 0 2 6.00 0 0 0 1 0 1 0 2 Total 7 10 19 57 115 68 43 334 intention to try Total 1.00 2.00 3.00 4.00 5.00 6.00 7.00 btech/n on btech 1.00 4 8 14 48 98 55 34 261 2.00 0 2 6 4 12 10 7 41 3.00 0 0 0 1 0 0 0 1 5.00 0 0 0 1 0 1 0 2 Total 4 10 20 54 111 66 41 305 1 – Male 2 – Female Others – Neglected • Maximum no of males and females gave us the rating above 5 1 – B.Tech 2 – Non B.Tech Others – Neglected • Engineers as well as non engineers found our app suitable
- 12. intention to try Total 1.00 2.00 3.00 4.00 5.00 6.00 7.00 income 1.00 1 4 4 10 21 13 11 64 2.00 0 2 2 4 16 10 2 36 3.00 1 3 8 25 36 21 15 109 4.00 1 0 4 9 23 16 9 62 Total 3 9 18 48 96 60 37 271 intention to try Total 1.00 2.00 3.00 4.00 5.00 6.00 7.00 age 1.00 0 0 0 0 5 2 2 9 2.00 3 9 15 45 78 47 32 229 3.00 2 0 4 9 25 15 8 63 4.00 0 0 0 0 3 2 1 6 Total 5 9 19 54 111 66 43 307 1 – Less than 7 Lakhs 2 – 7-10 lakhs 3 – 10-15 Lakhs 4 – More than 15 Lakhs • Income level of less than 7 Lakhs and More than 10 Lakhs intent to try our app. 1 – Less than 22 Years 2 – 22-25 Years 3 – 26-28 Years 4 – More than 29 Years • People having age between 22 to 25 years intent to try the app.
- 13. intention to try Total 1.00 2.00 3.00 4.00 5.00 6.00 7.00 work ex 1.00 0 7 7 20 33 24 14 105 2.00 0 2 2 11 20 16 11 62 3.00 3 1 10 15 42 11 10 92 4.00 1 0 0 7 13 13 7 41 Total 4 10 19 53 108 64 42 300 1 – less than 12 months 2 – 12-24 months 3 – 24-36 months 4 – More than 36 months • People with work experience less than 12 months and 24-36 months found our app worth trying.
- 14. Here are the facts about the responses – • Out of 334 responses, 221 were male & 108 female. • 261 are Engineers & 44 are others. Age Group Frequency Less than 22 9 22-25 229 26-28 63 More than 28 6 Work Experience (in Months) Frequency Less than 12 105 12-24 62 24-36 92 More than 36 41 Household Income Frequency Less than 7 Lakhs 64 7-10 Lakhs 36 10-15 Lakhs 109 More than 15 Lakhs 62 Region No of Respondents Belonging to that region East 170 West 27 North 54 South 22 Central 15
- 15. Utility and importance of features winner concept of Section A The interpretation of results for Conjoint Analysis of Group 3 are- Conjoint Analysis Regression Eqn- Preference = 4.75 – 0.11DP1+ 0.11DP2+ 0.13DL1 + 0.07DL2 + 0.11DA1+ 0.01DA2 PRICE Life Support Features Academic Support Features α60 – α20 = -0.11 αL2 – αL1 = 0.13 αA2 – αA1 = 0.11 α95 – α20 =0.11 αL3 – αL1 = 0.07 αA3 – αA1 = 0.01 α20 + α60 + α95=0 αL1 + αL2 + αL3=0 αA1 + αA2 + αA3=0 Equations for calculating part worth
- 16. LEVEL ATTRIBUTE Level Description Utility Importance PRICE 1 20 (Basic) ( P1) -0.111 0.48062 60 (Standard) (P2) 0.109 3 95 (Premium) 0.002 LIFE SUPPORT FEATURES 1 Insta services +Expense Tracker (L1) 0.06163 0.2822 NewsCenter (L2) 0.00600 3 Health and Fitness -0.06763 ACADEMIC SUPPORT FEATURES 1 Interview Preparation (A1) 0.069 0.237 2 Academic & Committee Planner (A2) -0.028 3 Insta services +Expense Tracker -0.040 N=366*9 = 3294 data points
- 17. Utility and importance of features of our App Our conjoint analysis is used for the purpose of app- product development research. We have taken the operating system, customizability of the app and the resolution supported by the app The regression equation: Preference= 4.791+0.037*DO1+0.203DO2-0.080DC1-0.160DC2+0.172DR1-0.069DR2 • When operating system changes from android to windows, preference decreases by 0.033 • When resolution is increased from 480p to 720p, it goes down by 0.027 • Customization is the most important feature of the app. Operating System Customizability Resolution Supported by App αI-αA = 0.037 αM-αH = -0.080 α720-α480 = 0.172 αW- αA= 0.203 αL- αH = -0.160 α1080 - α480 = -0.069 αI+αA+ αW = 0 αM+αH+ αL = 0 α720 + α480+ α1080 = 0
- 18. Levels Attribute No. Description Utility Range Importance Operating System 1 Android -0.08 0.203 32.27% 2 iOS -0.043 3 Windows 0.123 Customizability 1 High 0.080 0.16 25.43% 2 Medium 0.000 3 Low -0.080 Resolution Supported by App 1 480p -0.051 0.266 42.28% 2 720p 0.146 3 1080p -0.12 Sum of part worth 0.629 100% N=366*9 = 3294 data points
- 19. The following factors were identified and explained 65.62 % of variance – • Factor 1 – Incomplete life without luxury (0.656), incomplete life without International Travel (0.712) and Exotic Food (0.650) . Factor name is Luxury • Factor 2 – Spouse (0.559) , Social Work (0.780) , India’s Development (0.803). So the factor can be named as Networking. • Factor 3 – Power (0.735), Success (0.820). So the factor can be named as success. • Factor 4 – Work-life Balance (0.866), Enrich parent’s life (0.754). So this factor can be named as Importance • Factor 5 – Small Cities (0.832), Career Opportunities in Mega Cities (0.895). So this factor can be named as Lifestyle. *The brackets show the factor loadings for various variables in a factor.
- 20. Rotated Component Matrixa Component 1 2 3 4 5 To me success means money. 0.820 To me success means that I need to become powerful. 0.735 Without Luxury life doesn’t have any meaning. 0.656 For me work-life balance is the most important thing in life. 0.866 It is important for me that I enrich my parents’ life. 0.754 I want to get all those things in life which I could not get from my parents. Without a Luxurious car life is incomplete. My life will be incomplete if I don’t get a chance for frequent international travel. 0.712 Exotic food is a must for my life. 0.650 My spouse should add glamour to my life. 0.559 Without social work my life does not have any meaning. 0.780 My life will be incomplete if I do not get a chance to work for India’s development 0.803 I can not live in small cities. 0.832 Only Mega cities can give me ample career opportunities. 0.895
- 21. Number of Cases in each Cluster Cluster 1 173.000 2 95.000 3 57.000 4 33.000 Valid 358.000 Missing 6.000 Distances between Final Cluster Centers Cluster 1 2 3 4 1 2.984 6.055 3.250 2 2.984 4.149 3.428 3 6.055 4.149 5.666 4 3.250 3.428 5.666 The following clusters were identified and the tables show cluster membership & distance between cluster centres:
- 22. 3 discriminant functions were observed with the following data – Eigenvalues Wilks' Lambda Function Eigenvalue % of Variance Cumulative % Canonical Correlation Test of Function (s) Wilks' Lambda Chi- square df Sig. 1 3.725a 86.4 86.4 0.888 1 through 3 0.126 728.843 15 .000 2 0.326a 7.6 93.9 0.496 2 through 3 0.598 181.453 8 .000 3 0.262a 6.1 100.0 0.455 3 0.733 81.951 3 .000 Wilk‘s lambda is low for 1st and 2nd function, so they contribute significantly to group differences.
- 23. The coefficients of the 3 functions are shown in the table – Standardized Canonical Discriminant Function Coefficients Factors Function 1 2 3 Luxury 0.951 0.435 0.387 Networking 0.756 -0.711 0.290 Success 0.928 0.404 0.047 Importance -0.877 0.333 0.161 Lifestyle 0.658 0.040 -0.823 Structure Matrix Function 1 2 3 Networking 0.223 -0.748 0.321 Luxury 0.288 0.470 0.439 Success 0.253 0.392 0.048 Importance -0.229 0.310 0.157 Lifestyle 0.184 0.040 -0.862 From the structure matrix – • Luxury and success are associated with function 1 • Luxury and success are again associated with 2nd function • Luxury and networking are associated with 3rd function.
- 24. Cluster Number of Case Function 1 2 3 1 1.556 -0.096 -0.315 2 -0.845 0.836 0.325 3 -3.773 -0.548 -0.354 4 0.789 -0.959 1.329 Unstandardized canonical discriminant functions evaluated at group means The coefficients of discriminant functions at various cluster centres are shown in the table below – Inferences – • Cluster 1 gives more emphasis on function 1 as compared to others, i.e. the respondents belonging to cluster one favoured Luxury & Success. • Cluster 2 has evaluated Luxury & Success as most favourable. • Cluster 3 has evaluated Luxury & Networking as most favourable. • Cluster 4 has favoured Luxury & Networking.
- 25. Xquilibrio • Segmentation • Based upon the conjoint analysis, price is the most significant attribute so we divide our market based upon the buying power of the consumers into 2 offerings:- • Standard • Premium • Then we segregate the customers based upon the perceived utility into 4 categories:- • Scholars • Health conscious students • News seekers • Committee members • In order to cater to customers who want are both price and feature sensitive we would bundle them into various Combo offers
- 26. • Target market • MBA students of XIMB • Appeal to different categories of students in a customizable way.ie. tweak the app based upon the type of students we are targeting • Involve famous personalities like youth icons so that customers identify themselves with the ambassadors. • Positioning • Project as a value added app • Harness potential work life balance expectations • Focus on price and quality
- 27. • 5 Factors were identified – o Luxury o Networking o Success o Importance o Lifestyle Cluster Size Favoured Factors Discrimina nt Function 1 173 Luxury & Success Function 1 2 95 Luxury & Success Function 2 3 57 Luxury & Networking Function 3 4 33 Luxury & Networking Function 3 The target group, segmentation variables and positioning strategies were clearly stated.