Download - Future of Xiaomi in Indian Market

Xiomi Inc. is one of the biggest electronics companies in China, which manufactures and designs smartphones, mobile applications and produces consumer electronics. Recently this brand has tried to acquire the Indian market by the means of ecommerce website such as Flip kart. It does not own any physical store in India and does all the sales 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 their age, gender, occupation and level of income, on what are the different features they prefer is are important for any product. This includes the following:

Display of the phone.Sound featureMemory and ApplicationSpeed and NavigationPhysical features such as weight and screen and make of the phone.Battery life and robustness.After sales services and availability.Pricing

This was done my means of a questionnaire. The three major competitors for Xiomi in India are:

Motorola Micromax Samsung.

We found out the satisfaction level of the customers in terms of the various characteristics 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 they will buy the Phone or not, and the target market of our product.

Gap analysis

Display Sound Memory Speed OS Screen

Make of the phone

Importance 4.41818

4.01818 4.21818

4.49091

4.16364

4.10909

3.92727

Satisfaction 4.24545

3.84545 3.74545

3.83636

3.90909

4.03636

3.71818

The mean ratings on importance and satisfaction are presented diagrammatically. The chart shows the gaps between mean importance levels and mean satisfaction levels. On the parameters display, sound, memory, speed, operating system, screen and make of the phone which were significant to both satisfaction and importance we could conclude that there is a huge gap between satisfaction and importance perceived with speed and memory, followed by operating system. The survey shows that speed and memory are really important factors and the satisfaction level on these parameters is very low.So for Xiaomi to become a market leader it is important that it improves its microprocessor speed and memory better than its competitors to stay in the market as a leader.

Competitor Analysis

motorola

micromax

samsung

XIAOMI

XOLO

HTCGoogle Nexus

Asus

Apple

Sony Experia

Nokia

0

5

sat_Displaysat_Soundsat_Memorysat_Speedsat_OperatingSystemsat_Screensat_Makeofthephone

sat_Display sat_Sound sat_Memory sat_Speed sat_OperatingSystemsat_Screen sat_Makeofthephone

4.15385 3.84615 4.23077 3.84615 4.07692 4 3.84615

micromax 4 4 3 4 2 5 3samsung 4 3.61538 3.53846 3.48718 3.64103 3.87179 3.33333

XIAOMI 5 4 3 4 3 4 5XOLO 4 3 3 4 4 4 2HTC 4.25 4 4 2.75 3.75 4.5 4.25

Google Nexus 4.61538 3.76923 4.30769 4.46154 4.07692 4.46154 4.07692Asus 2 3 3 3 3 2 2Apple 4.55 4.3 3.65 4.45 4.45 4.2 3.95

Sony Experia 4 3.5 3 2.5 3.25 4 3.75Nokia 4 3.8 3.4 4.2 3.6 3.6 3.6imp 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

Brand performance

motorola 2.868701792micromax 2.56165998samsung 2.60731772XIAOMI 2.920518112XOLO 2.520714427HTC 2.780132964Google Nexus 3.099858178Asus 1.821443488Apple 3.087342572Sony Experia 2.455270661Nokia 2.725840193

Competitor Analysis For 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 and display 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 which Apple is the leader. Also it needs to focus on memory capacity, speed and sound attributes so that it can become market leader in India. The overall performance of Google nexus is the best.

Logistic Regression

Taking BUY as the dependent variable and 26 independent variables which include demographic variables such as Age, Gender etc. we ran the data set through binary logistic regression. The following is the output obtained.

Model Summary

Step -2 Log likelihood

Cox & Snell R Square

Nagelkerke R Square

1 142.117a .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 .425

a. Estimation terminated at iteration number 3 because parameter estimates changed by less than .001.b. Estimation terminated at iteration number 4 because parameter estimates changed by less than .001.c. Estimation terminated at iteration number 5 because parameter estimates changed by less than .001.

We see that there is a decreasing trend in value of -2LL , The Cox and & Snell R square and Nagelkerke R square term shows an increasing trend. This shows that the model is a good fit.

Classification Tablea

Observed Predicted

BUY Percentage Correct0 1

Step 1

BUY0 38 24 61.3

1 16 32 66.7

Overall Percentage

63.6

Step 2

BUY 0 48 14 77.41 23 25 52.1

Overall Percentage

66.4

Step 3

BUY0 43 19 69.41 16 32 66.7

Overall Percentage

68.2

Step 4

BUY0 47 15 75.81 20 28 58.3

Overall Percentage

68.2

Step 5

BUY0 49 13 79.01 18 30 62.5

Overall Percentage

71.8

Step 6

BUY0 48 14 77.41 15 33 68.8

Overall Percentage

73.6

Step 7

BUY0 50 12 80.61 14 34 70.8

Overall Percentage

76.4

Step 8

BUY0 49 13 79.0

1 14 34 70.8

Overall Percentage

75.5

a. The cut value is .500

Hit ratio is 75% for the model.Variables in the Equation

B S.E. Wald df Sig. Exp(B)

Step 1aGender 1.153 .402 8.215 1 .004 3.167

Constant -.865 .298 8.424 1 .004 .421

Step 2b

Gender 1.376 .434 10.043 1 .002 3.960sat_OperatingSystem

-.633 .219 8.378 1 .004 .531

Constant 1.475 .849 3.021 1 .082 4.372

Step 3c


-.596 .229 6.799 1 .009 .551

sat_Weight -.597 .249 5.735 1 .017 .551Constant 3.637 1.285 8.015 1 .005 37.969

Step 4d


-.694 .246 7.949 1 .005 .500

sat_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 16.352

Step 5e

Gender 1.655 .492 11.303 1 .001 5.232imp_Memory .629 .316 3.954 1 .047 1.876sat_OperatingSystem

-.859 .268 10.236 1 .001 .424

sat_Weight -.903 .289 9.758 1 .002 .405sat_Battery .483 .229 4.433 1 .035 1.621Constant 1.411 1.564 .814 1 .367 4.100

Step 6f

Age .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_OperatingSystem

-.886 .276 10.318 1 .001 .412

sat_Weight -.896 .290 9.508 1 .002 .408sat_Battery .396 .236 2.832 1 .092 1.487Constant -.730 1.842 .157 1 .692 .482

Step 7g

Age .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_OperatingSystem

-.901 .284 10.023 1 .002 .406

sat_Weight -.793 .293 7.336 1 .007 .453sat_Battery .367 .242 2.301 1 .129 1.443Constant 1.717 2.211 .604 1 .437 5.570

Step 8g

Age .762 .292 6.803 1 .009 2.143

Gender 1.695 .513 10.920 1 .001 5.445

imp_Memory 1.129 .361 9.757 1 .002 3.091

imp_Battery -.890 .391 5.176 1 .023 .411

sat_OperatingSystem

-.847 .274 9.582 1 .002 .429

sat_Weight -.707 .281 6.334 1 .012 .493

Constant 2.137 2.146 .991 1 .319 8.473

a. 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.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 a significance level greater than 0.05 : Age, Gender, Importance of Battery, Importance of Memory, Satisfaction of operating system, satisfaction of weight .

Model Summary

Model

R R Square

Adjusted R Square

Std. Error of the Estimate

1 .982a .964 .961 .05694066

a. Predictors: (Constant), sat_Weight, Gender, Age, sat_OperatingSystem, imp_Battery, imp_Memory

Coefficientsa

Model Unstandardized Coefficients

Standardized Coefficients

t Sig.

B Std. Error Beta

1

(Constant) .889 .049 18.073 .000

Age .135 .006 .412 21.422 .000

Gender .297 .011 .514 26.871 .000

imp_Memory .197 .008 .540 25.174 .000

imp_Battery -.159 .009 -.377 -18.384 .000

sat_OperatingSystem

-.147 .006 -.512 -25.794 .000

sat_Weight -.125 .007 -.378 -19.210 .000

a. Dependent Variable: Predicted probability

The above table shows coefficient of the predictor variables after running it

through linear regression.

Regression equation : Buy (Z) = 0.889+(0.135*Age)+(0.0297*gender)

+(0.197*Importance of memory)-(0.159*Importance of battery)-

(0.147*Satisfaction of operating system)-(0.125*Satisfaction of weight)

Descriptives

N Mean Std. Deviation

Std. Error

95% Confidence Interval for Mean

Minimum

Maximum

Lower Bound

Upper Bound

Age

1.00

22 2.18 .395 .084 2.01 2.36 2 3

2.00

22 2.55 .800 .171 2.19 2.90 2 5

3.00

23 2.48 .730 .152 2.16 2.79 2 5

4.00

21 2.67 .966 .211 2.23 3.11 2 5

5.00

22 3.32 1.041 .222 2.86 3.78 2 5

Total

110 2.64 .885 .084 2.47 2.80 2 5

Gender

1.00

22 .05 .213 .045 -.05 .14 0 1

2.00

22 .41 .503 .107 .19 .63 0 1

3.00

23 .61 .499 .104 .39 .82 0 1

4.00

21 .67 .483 .105 .45 .89 0 1

5.00

22 .82 .395 .084 .64 .99 0 1

Total

110 .51 .502 .048 .41 .60 0 1

imp_Memory 1.00

22 4.09 .750 .160 3.76 4.42 3 5

2.00

22 4.18 .907 .193 3.78 4.58 2 5

3.00

23 4.13 .815 .170 3.78 4.48 2 5

4.00

21 4.48 .750 .164 4.13 4.82 3 5

5.00

22 4.23 .752 .160 3.89 4.56 3 5

Total

110 4.22 .794 .076 4.07 4.37 2 5

imp_Battery

1.00

22 4.77 .429 .091 4.58 4.96 4 5

2.00

22 4.64 .727 .155 4.31 4.96 2 5

3.00

23 4.57 .590 .123 4.31 4.82 3 5

4.00

21 4.62 .669 .146 4.31 4.92 3 5

5.00

22 4.05 .785 .167 3.70 4.39 2 5

Total

110 4.53 .687 .065 4.40 4.66 2 5

sat_OperatingSystem

1.00

22 4.27 .827 .176 3.91 4.64 2 5

2.00

22 4.36 .848 .181 3.99 4.74 3 5

3.00

23 3.96 .825 .172 3.60 4.31 3 5

4.00

21 3.57 .926 .202 3.15 3.99 2 5

5.00

22 3.36 1.255 .268 2.81 3.92 1 5

Total

110 3.91 1.010 .096 3.72 4.10 1 5

sat_Weight

1.00

22 4.55 .671 .143 4.25 4.84 3 5

2.00

22 4.27 .935 .199 3.86 4.69 2 5

3.00

23 3.83 1.029 .215 3.38 4.27 2 5

4.00

21 3.67 .730 .159 3.33 4.00 3 5

5.00

22 3.50 .512 .109 3.27 3.73 3 4

Total

110 3.96 .877 .084 3.80 4.13 2 5

Result : As the age increases the propensity to buy a xiaomi smartphone increases.

Result: Males are more likely to buy a Xiaomi smartphone.

Result : People who give more importance to memory are more likely to buy a xiaomi smartphone.

Result : People who give less importance to battery life are more likely to buy a Xiaomi smartphone.

Result: People who are not satisfied with their current operating system are more likely to buy a xiaomi smartphone.

Result: People who are not satisfied with the weight of their mobile are likely to buy a xiaomi smartphone.

Perceptual Mapping

Operating System:With regard to the OS, our sample set prefers Motorola as a Brand and Xiaomi is the least preferred.

Memory:Even when it comes to Memory, Motorola appears to be the most preferred choice while Samsung is the least preferred choice.

Speed:In terms of speed, Xiaomi scores the highest perhaps owing to its Samsung operating system while Xiaomi scores the lowest.

Price:With respect to Price, evidently Xiaomi scores the highest and Samsung scores the lowest

Display:However in terms of Display, Samsung is the most preferred brand and Xiaomi is the least preferred one.

Structural Equation Modelling – Xiaomi

As we can see from the above tables, the model is CMIN/DF – Good FitP value – Good FitGFI – Acceptable FitAGFI – Acceptable Fit (0.002 difference from specifications)

The P values of external features and service quality is less than 0.05 hence making them significant on the consumer propensity to buy.

Final structural model shows a significant link between, external features and service quality of the mobile phones with the propensity to buy of the consumer and not a significant link between the marketing effort of the mobile company and the psychological impact of the consumer

Questionnaire

1. Name

2. Age

3. Gender

4. Income

5. Occupation

6. The following is a list of factors that are usually important to you willing buying a mobile phone. Please rate the factors according to their importance in a mobile phone:

i. Display ii. Sound

iii. Appsiv. Memory v. Navigation

vi. Speedvii. Operating system

viii. Robustnessix. Weightx. Screen

xi. Batteryxii. Ergo

xiii. Aestheticsxiv. After salesxv. Availability

xvi. Offersxvii. Warranty

xviii. Costxix. Retailer suggestionxx. Expert Review

xxi. Advertisementsxxii. Peer influence

xxiii. Self-imagexxiv. Make

7. Please rate the factors according to their satisfaction for your current phonei. Display

ii. Sound

iii. Appsiv. Memoryv. Navigation

vi. Speedvii. Operating system

viii. Robustnessix. Weightx. Screen

xi. Batteryxii. Ergo

xiii. Aestheticsxiv. After salesxv. Availability

xvi. Offersxvii. Warranty

xviii. Costxix. Retailer suggestionxx. Expert Review

xxi. Advertisementsxxii. Peer influence

xxiii. Self-imagexxiv. Make

8. Would you be willing to buy Xiaomi mobile phone?Willing Not willing

9. Rate your likelihood of referring Xiaomi to your family and friends? (1 being Strongly Disagree and 5 being Strongly Agree)

10. Rate your expectations for Xiaomi on a scale of 1 to 5? (1 being Strongly Disagree and 5 being Strongly Agree)

11. How likely are you to buy Xiaomi Smartphone? (1 being Strongly Disagree and 5 being Strongly Agree)

12. Please select from the following brand of phones? Xiaomi Micromax Motorola Samsung Xolo

13. Please select an attribute for the above selected brand? Display Price Memory Speed OS