Future of Xiaomi in Indian Market

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Analyzed factors responsible for the future growth of Xiaomi in Indian market using gap analysis, logistic regression, structural equation modeling and correspondence analysis

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  • 1. Business Analytics ProjectThe factors that would significantly impact the future ofXiaomi in the Indian MarketGroup 11DS Ambareesh DM15116 |Shubham Sanghal DM15155 | Lakshay NangiaDM15126 | Rohita Jain DM15143 | Abhishek Prasad DM15101 | JaskiratSingh DM15122 | Suhasini Jain DM15256 | Isha Gulati DM15223|Soochna Sahu DM15158| Ridhima Arora DM15245

2. IntroductionXiomi Inc. is one of the biggest electronics companies in China, which manufacturesand designs smartphones, mobile applications and produces consumer electronics.Recently this brand has tried to acquire the Indian market by the means of ecommercewebsite such as Flip kart. It does not own any physical store in India and does all thesales using online stores.The purpose of this project is to find out:1) How well the product will perform in the Indian market.2) The target group who will prefer to buy this model.This is done by first understanding the various groups of customers in terms of theirage, gender, occupation and level of income, on what are the different features theyprefer is are important for any product. This includes the following: Display of the phone. Sound feature Memory and Application Speed and Navigation Physical features such as weight and screen and make of the phone. Battery life and robustness. After sales services and availability. PricingThis was done my means of a questionnaire. The three major competitors for Xiomiin India are: Motorola Micromax Samsung.We found out the satisfaction level of the customers in terms of the variouscharacteristics of the phone, which they currently own.We carried out the following analysis my means of SPSS:1) Gap Analysis: To find out the Product Market Fit.2) Logistic Regression3) Structural Equation Modeling.4) Perception Mapping.With this analysis we can find out the likability of the customers on whether theywill buy the Phone or not, and the target market of our product. 3. Gap analysisDisplay Sound Memory Speed OS ScreenMakeof thephoneImportance 4.418184.01818 4.218184.490914.163644.109093.92727Satisfaction 4.245453.84545 3.745453.836363.909094.036363.71818The mean ratings on importance and satisfaction are presented diagrammatically. Thechart shows the gaps between mean importance levels and mean satisfaction levels.On the parameters display, sound, memory, speed, ope rating system, screen andmake of the phone which were significant to both satisfaction and importance wecould conclude that there is a huge gap between satisfaction and importance perceivedwith speed and memory, followed by operating system. The survey shows that speedand memory are really important factors and the satisfaction level on these parametersis very low.So for Xiaomi to become a market leader it is important that it improves itsmicroprocessor speed and memory better than its competitors to stay in the market asa leader. 4. Competitor Analysismotorola543210micromaxsamsungXIAOMIXOLOSony ExperiaNokiaAsusGoogle Nexus HTCApplesat_Displaysat_Soundsat_Memorysat_Speedsat_OperatingSystemsat_Screensat_Makeofthephonesat_Display sat_Sound sat_Memory sat_Speed sat_OperatingSystem sat_Screen sat_Makeofthephone 4.15385 3.84615 4.23077 3.84615 4.07692 4 3.84615 micromax 4 4 3 4 2 5 3 samsung 4 3.61538 3.53846 3.48718 3.64103 3.87179 3.33333 XIAOMI 5 4 3 4 3 4 5 XOLO 4 3 3 4 4 4 2 HTC 4.25 4 4 2.75 3.75 4.5 4.25 GoogleNexus 4.61538 3.76923 4.30769 4.46154 4.07692 4.46154 4.07692 Asus 2 3 3 3 3 2 2 Apple 4.55 4.3 3.65 4.45 4.45 4.2 3.95 SonyExperia 4 3.5 3 2.5 3.25 4 3.75 Nokia 4 3.8 3.4 4.2 3.6 3.6 3.6 imp 4.41818 4.01818 4.21818 4.49091 4.16364 4.10909 3.92727 imp index 0.15056 0.13693 0.14374 0.15304 0.14188 0.14002 0.13383 5. Brand performancemotorola 2.868701792micromax 2.56165998samsung 2.60731772XIAOMI 2.920518112XOLO 2.520714427HTC 2.780132964Google Nexus 3.099858178Asus 1.821443488Apple 3.087342572Sony Experia 2.455270661Nokia 2.725840193Competitor AnalysisFor satisfaction of the attribute Screen, Micromax is the best amongst all competitors.Xiaomi at the moment is best on the satisfaction level of make of the phone anddisplay which can be shown as the differentiating factor for all its future pitching.Xiaomi can do much better on the following attributes Operating system in whichApple is the leader. Also it needs to focus on memory capacity, speed and soundattributes so that it can become market leader in India.The overall performance of Google nexus is the best. 6. Logistic RegressionTaking BUY as the dependent variable and 26 independent variables whichinclude demographic variables such as Age, Gender etc. we ran the data setthrough binary logistic regression. The following is the output obtained.Model SummaryStep -2 LoglikelihoodCox & SnellR SquareNagelkerkeR Square1 142.117a .075 .1012 132.876b .150 .2013 126.953b .194 .2604 121.426c .234 .3135 117.200c .263 .3526 111.379c .301 .4037 106.378c .332 .4458 108.740c .317 .425a. Estimation terminated at iteration number 3because parameter estimates changed by less tha n.001.b. Estimation terminated at iteration number 4because parameter estimates changed by less tha n.001.c. Estimation terminated at iteration number 5because parameter estimates changed by less tha n.001.We see that the re is a decreasing trend in value of -2LL , The Cox and & SnellR square and Nagelkerke R square term shows an increasing trend. This showsthat the model is a good fit.Classification TableaObserved PredictedBUY Percentage0 1 CorrectStep1BUY0 38 24 61.31 16 32 66.7OverallPercentage63.6Step BUY 0 48 14 77.4 7. 2 1 23 25 52.1OverallPercentage66.4Step3BUY0 43 19 69.41 16 32 66.7OverallPercentage68.2Step4BUY0 47 15 75.81 20 28 58.3OverallPercentage68.2Step5BUY0 49 13 79.01 18 30 62.5OverallPercentage71.8Step6BUY0 48 14 77.41 15 33 68.8OverallPercentage73.6Step7BUY0 50 12 80.61 14 34 70.8OverallPercentage76.4Step8BUY0 49 13 79.01 14 34 70.8OverallPercentage75.5a. The cut value is .500Hit ratio is 75% for the model.Variables in the EquationB S.E. Wald df Sig. Exp(B)Step1aGender 1.153 .402 8.215 1 .004 3.167Constant -.865 .298 8.424 1 .004 .421Step2bGender 1.376 .434 10.043 1 .002 3.960sat_OperatingSyst-.633 .219 8.378 1 .004 .531emConstant 1.475 .849 3.021 1 .082 4.372Step3cGender 1.436 .451 10.120 1 .001 4.204sat_OperatingSyst-.596 .229 6.799 1 .009 .551emsat_Weight -.597 .249 5.735 1 .017 .551 8. Constant 3.637 1.285 8.015 1 .005 37.969Step4dGender 1.563 .474 10.885 1 .001 4.774sat_OperatingSyst-.694 .246 7.949 1 .005 .500emsat_Weight -.755 .269 7.877 1 .005 .470sat_Battery .509 .225 5.148 1 .023 1.664Constant 2.794 1.369 4.169 1 .041 16.352Step5eGender 1.655 .492 11.303 1 .001 5.232imp_Memory .629 .316 3.954 1 .047 1.876sat_OperatingSyst-.859 .268 10.236 1 .001 .424emsat_Weight -.903 .289 9.758 1 .002 .405sat_Battery .483 .229 4.433 1 .035 1.621Constant 1.411 1.564 .814 1 .367 4.100Step 6fAge .662 .288 5.284 1 .022 1.938Gender 1.596 .502 10.087 1 .001 4.931imp_Memory .815 .335 5.928 1 .015 2.259sat_OperatingSyst-.886 .276 10.318 1 .001 .412emsat_Weight -.896 .290 9.508 1 .002 .408sat_Battery .396 .236 2.832 1 .092 1.487Constant -.730 1.842 .157 1 .692 .482Step7gAge .696 .298 5.449 1 .020 2.006Gender 1.778 .529 11.294 1 .001 5.918imp_Memory 1.055 .367 8.270 1 .004 2.871imp_Battery -.859 .398 4.663 1 .031 .424sat_OperatingSyst-.901 .284 10.023 1 .002 .406emsat_Weight -.793 .293 7.336 1 .007 .453sat_Battery .367 .242 2.301 1 .129 1.443Constant 1.717 2.211 .604 1 .437 5.570Step8gAge .762 .292 6.803 1 .009 2.143Gender 1.695 .513 10.920 1 .001 5.445imp_Memory 1.129 .361 9.757 1 .002 3.091imp_Battery -.890 .391 5.176 1 .023 .411sat_OperatingSyst-.847 .274 9.582 1 .002 .429emsat_Weight -.707 .281 6.334 1 .012 .493Constant 2.137 2.146 .991 1 .319 8.473a. Variable(s) entered on step 1: Gender.b. Variable(s) entered on step 2: sat_OperatingSystem.c. Variable(s) entered on step 3: sat_Weight. 9. d. Variable(s) entered on step 4: sat_Battery.e. Variable(s) entered on step 5: imp_Memory.f. Variable(s) entered on step 6: Age.g. Variable(s) entered on step 7: imp_Battery.Logit Function -Z = (0.762*Age)+(1.695*Gender)+(1.12*importance of memory)-(0.89*importance of battery)-(0.847*satisfaction of operating system)-(0.707*satisfaction of weight)Using Forward LR the following variables gave minimum value of -2LL and asignificance level greater than 0.05 : Age, Gender, Importance of Battery,Importance of Memory, Satisfaction of operating system, satisfaction of weight.Model SummaryModelR RSquareAdjusted RSquareStd. Error ofthe Estimate1 .982a .964 .961 .05694066a. Predictors: (Constant), sat_Weight, Gender, Age,sat_OperatingSystem, imp_Battery, imp_MemoryCoefficientsaModel UnstandardizedCoefficientsStandardizedCoefficientst Sig.B Std. Error Beta1(Constant) .889 .049 18.073 .000Age .135 .006 .412 21.422 .000Gender .297 .011 .514 26.871 .000imp_Memory .197 .008 .540 25.174 .000imp_Battery -.159 .009 -.377 -18.384 .000sat_OperatingSyst-.147 .006 -.512 -25.794 .000emsat_Weight -.125 .007 -.378 -19.210 .000a. Dependent Variable: Predicted probability 10. The above table shows coefficient of the predictor variables after running itthrough linear regression.Regression equation : Buy (Z) =0.889+(0.135*Age)+(0.0297*gender)+(0.197*Importance of memory)-(0.159*Importance of batte ry)-(0.147*Satisfaction of operating system)-(0.125*Satisfaction of weight)DescriptivesN Mean Std.DeviationStd.Error95% ConfidenceInterval for MeanMinimumMaximumLowerBoundUpperBoundAge1.0022 2.18 .395 .084 2.01 2.36 2 32.0022 2.55 .800 .171 2.19 2.90 2 53.0023 2.48 .730 .152 2.16 2.79 2 54.0021 2.67 .966 .211 2.23 3.11 2 55.0022 3.32 1.041 .222 2.86 3.78 2 5Total110 2.64 .885 .084 2.47 2.80 2 5Gender1.0022 .05 .213 .045 -.05 .14 0 12.0022 .41 .503 .107 .19 .63 0 13.0023 .61 .499 .104 .39 .82 0 14.0021 .67 .483 .105 .45 .89 0 15.0022 .82 .395 .084 .64 .99 0 1Total110 .51 .502 .048 .41 .60 0 1imp_Memory1.0022 4.09 .750 .160 3.76 4.42 3 52.0022 4.18 .907 .193 3.78 4.58 2 5 11. 3.0023 4.13 .815 .170 3.78 4.48 2