conjoint analysis for smart phones

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The Smartphones in

India – A Conjoint

Analysis and Simulation

CFPP Project Report Date of submission: 17-Feb-2012

Group 16 Dhruv Dhruv Dhruv Dhruv Anand Anand Anand Anand ((((FT12425FT12425FT12425FT12425)))) SudhanSudhanSudhanSudhanvavavava DDDD VVVV ((((FT12264FT12264FT12264FT12264)))) Anamika Anamika Anamika Anamika Roy Roy Roy Roy ((((FT12477FT12477FT12477FT12477)))) BikramBikramBikramBikram Satapathy Satapathy Satapathy Satapathy ((((FT12417FT12417FT12417FT12417)))) SrinivasSrinivasSrinivasSrinivas Dhenuvukonda Dhenuvukonda Dhenuvukonda Dhenuvukonda ((((FT12467FT12467FT12467FT12467)

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Contents

Introduction ....................................................................................................................................................... 3

Objectives ........................................................................................................................................................... 4

Methodology ...................................................................................................................................................... 5

Pre-study and selection of attributes ............................................................................................................. 5

RESULTS AND ANALYSIS ..................................................................................................................................... 7

Participation Level .......................................................................................................................................... 7

Conjoint Analysis ................................................................................................................................................ 8

Introduction ................................................................................................................................................... 8

Part worth utility curves ............................................................................................................................... 10

Demographic Analysis ...................................................................................................................................... 11

Benefit segment Analysis ............................................................................................................................. 13

Market simulation ............................................................................................................................................ 16

Sensitivity Analysis ....................................................................................................................................... 17

Issues and recommendations to full-scale study ......................................................................................... 19

Conclusions .................................................................................................................................................. 19

Exhibit-1 ........................................................................................................................................................... 20

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Introduction

A smart phone is can be found in every second hand nowadays – from a white collared

professional to a student! This pervasive entry of smart phones into our lives is due to two

primary reasons. One reason being the rapid advancement in technology and R&D which

makes the present technology redundant within weeks. The second is the drastic drop in

prices which occur every few days.

So the question now is why is the Smart Phone such an in demand product when compared

with our traditional Feature phone; for a multitude of reasons. A smart phone is a mobile

phone built on a mobile computing platform, with more advanced computing ability and

connectivity than a feature phone. The presence of Application Programming Interface (APIs)

on the smart phones is used for running the third party applications which bring in life to the

mobile phone.

The first smartphones were devices that mainly combined the functions of a personal digital

assistant (PDA) and a mobile phone or camera phone. Smart phones now have well-developed

touchscreens, web browsers that can access any page on the web and not just sites designed

specifically for mobile phones, and high-speed data access via Wi-Fi and mobile broadband.

We were immensely interested in understanding the consumer’s preferences in this ever

changing dynamic market. The cell phone from being a product of utility at the beginning

turned into an accessory and a hand held device with multiple features. We are in a very

crucial phase for a country like India where the purchasing power of the consumer is

increasing and they don’t think too much about spending a little more.

We designed our project with the intention to understand how the different attributes and

features provided by the manufacturers hold how much value to the consumer. When there is

a tradeoff between different attributes the consumer makes a choice based on the attributes

he considers the most favorable to his taste. This process is as much a scientific process as is

psychological.

The study is conducted among urban individuals who are part of the workforce and among

students who are old enough to own mobile phones. The Smart phone manufacturers can

enhance their products and better position their phones to the consumers. After all, the

customer is the king!

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Objectives

The objective of this study is to understand the consumers preference in the purchase of

Smart Phones and the attributes he/she thinks are of importance during the time of purchase.

We are trying to understand how the five attributes – price, design, brand, shape and user

friendliness, interact with each other to shape the purchase decision of the consumer.

We hope this conjoint project will help prioritize the most desired attributes of the Smart

Phone so as to maximize revenue by understanding the consumer utility. By using this

conjoint analysis, we can conclude what are the most significant attributes and what is each

attributes’ relative value. This study gives us insights into what are the consumer’s

preferences in a Smart phone and how changes in each attribute effect the likelihood of

purchase.

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Methodology

Pre-study and selection of attributes

In order to select the attributes of the service, we conducted qualitative research. Two Focus

group discussion and 6 interviews were conducted to understand the participants’

preferences. This formed as a good base to arrive at attributes and further frame the levels of

the attributes. As the product is smart phones so the Participants were enthusiastic in

expressing about the latest trends and style of the smart phone.

Attributes and Levels

Price

• Rs 40000

• Rs 30000

• Rs 20000

User-friendliness

• Low

• Medium

• High

Brand

• Nokia

• Apple

• HTC

• Samsung

Design

• Trendy

• Sleek

• Changeable Skin

Shape

• Touch screen

• Qwerty

• Slider

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Survey Development and Design

In order to make the survey effective, a two part conjoint survey was designed. The survey asked

the survey takers to first rate the importance of various attributes. The survey takers were then

given various options to choose between mock purchase scenarios, in this case job offerings. This

was developed using ASEMAP which is a computer adaptive survey generator. This survey was

linked to the demographic survey which was developed using Survey gizmo. The purpose of linking

both was to have consistency in data and also not to break the flow of the survey for the survey

takers. The survey was then tested by the team and a few participants to gauge the user-

friendliness and usability. Based on the feedback, changes were made in the survey design.

Survey Administration.

Our target response size was 75 in order (15 per member) to complete our survey. The

challenge was to get the respondents fill all the four sections of the survey. We tracked the

validity of the responses based on the Rank order correlation, Adjusted R-Square, Logit

Adjusted R conversation and made sure that 50 valid responses are available for analysis.

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RESULTS AND ANALYSIS

Participation Level

Validity of Data We got 65 responses for the survey. On analysis of the data, it was found that few of the

responses were not valid. Following criteria was used to determine the validity of the data

• Rank order should be greater than 0.5

• Adjusted R square should be greater than 0.25

• Logit Adjusted R square should be greater than 0.25

Only those responses were considered which satisfied all the three conditions mentioned

above. It was found that 23 responses did not satisfy at least one of the conditions mentioned

above and hence they were removed. The demographic data for these respondents was also

removed. Analysis was done for the remaining 54 valid responses.

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Conjoint Analysis

Introduction

Based on the ASEMAP output, we calculated the mean utilities and importance levels for the

various attributes and individual levels. All 5 attributes were then ranked according to their

importance levels. Table 1 shows the importance rankings of different attributes as well as

the mean part worth utilities of a given attribute level.

A review of Table 1 shows that price is the most important attribute, across all participants

with its importance level being 37.3%. Brand, User-friendliness, shape & design formed the

next set of important attributes with the important percentages being 25%, 21.7%, 8.7% and

7.3% respectively.

It is also prevailed and common observation that brand and price are the two most important

attributes for buyers in the consumer market before purchasing any smart phone.

Table 1

Sl No Rank Attributes Levels Mean utilities

Mean importance

1 2 Brand

Apple 10.83

25.0% Nokia -1.00

Samsung 1.14

HTC -10.97

2 1 Price

20000 17.12

37.3% 30000 -1.67

40000 -15.45

3 5 Design

Sleek 2.93

7.3% Trend 0.49

Changeable skins -3.42

4 4 Shape

Slider -3.52

8.7% Qwerty -0.58

Touch screen 4.10

5 3 User

friendliness

Low -9.56

21.7% Medium 0.15

High 9.40

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From the conjoint analysis one can says that consumers are more interested in price of the

smartphone and followed by brand. This could be the reason that Samsung captured market

from Nokia in smartphone segment due to Samsung decreased prices for the same phone

segment.

The next best attribute is brand with nearly 25% importance for consumers in the market. It

will also tell that consumers will prefer brands to other attributes while purchasing a mobile.

User-friendliness is the third most important for consumers of softphone segment. As the

most of the consumers go for smartphones only for the sake of sophistication, versatility and

fully functional. Only user-friendly mobiles will have all those qualities.

The utility curves of conjoint analysis are given in the figures

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Part worth utility curves

The mean part worth utility curves are drwan for the 5 attributes and as given above

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Demographic Analysis

We collected demographic data in 12 categories:

• Age

• Gender

• Family income

• Profession

• Usage Level per day (hours)

• Location

• Brand

• Persons influencing brand selection

• Price

• Style

• Features

• User friendliness

Gender based preferences for Brands:

Of all female respondents 78% preferred Apple brand of smart phones. For males the

preferences vary with Apple being preferred by 46% of respondents, 33% preferring

Samsung.

Preferred Brand

Apple Samsung

Female 78% 22%

Male 46% 33%

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Gender based Usage level patterns:

The level of usage per day for females suggests that 67% use it for more than 4 hours a day.

The percentage of males that use it for less than 4 hours is 67 %. Of these 50% preferred

Apple brands of smart phones.

Usage Pattern per day

3-4

hours

4-6

hours

Female 30% 67%

Male 67% 21%

Inference from Importance Ratings:

The importance rating show the contrast in the preference when it comes to Price as a factor,

almost equal percentage of respondents have preferred Price as most and Least important

criteria.

This shows that Price is a critical factor and has extreme reactions from respondents.

Similarly, more than 50% of respondents have chosen Style as 2nd or 3rd in ratings indicating

that relative unimportance compared to Price.

Features comes across as a criteria in which there is almost equal distribution whereby the

respondents are divided and hence there may be a clarity required in terms of explaining or

understanding the meaning attached to what all is covered in features when respondents rate

it.

User-friendliness as criteria seems to the 1st preference for minimum number of respondents

and hence gives insight that even for a complex product like smart phones the user attaches

less importance to it.

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Importance (1 - high, 4 - Low Priority

1 2 3 4

Price 30% 12% 6% 33%

Style 12% 24% 30% 18%

Features 21% 15% 24% 24%

User-friendliness 10% 24% 27% 24%

Benefit segment Analysis

Table 2: Product category table

Demo

variable

1

2

3

4

5

6

Sex 0-female

1-male

Location NCR/DELHI Mumbai Chennai Bangalore Hyderabad Others

Income

monthly

<50000 50000-

100000

>100000

Profession Private

employed

Govt

employed

Others

We have Performed Cluster Analysis (Benefit Segmentation) and Pseudo-F calculations. The

value of Pseudo-F is max for 3 segment levels. The value of maximum Pseudo-F is 8.3704.

Cluster No Pseudo-F

2 clusters 7.662908

3 clusters 8.373043

4 clusters 8.119052

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Table 3: part worth utilities segment wise

Final Cluster Centers

Cluster Cluster(importance)

1 2 3 1 2 3

Rs 40000 -10.44 -9.28 -19.05

22.70 21.26 48.00 Rs 30000 -.46 -1.88 -2.03

Rs 20000 10.91 11.16 21.08

Slider -1.37 -4.32 -4.04

8.33 7.63 9.92 Qwerty -3.23 1.31 -.21

Touch screen

4.60 3.01 4.25

Low -7.22 -25.37 -5.63

17.33 49.41 13.56 Medium -1.84 3.25 -.08

High 9.07 22.12 5.71

Sleek .08 1.46 4.37

4.13 4.10 10.26 Trendy 1.90 1.02 -.17

Changeable skin

-1.98 -2.48 -4.20

Apple 19.08 6.65 9.19

47.51 17.59 18.26 Nokia -6.18 4.66 -.89

HTC -25.59 -10.25 -6.07

Samsung 12.68 -1.06 -2.24

Segment 3 is a of price sensitive than segment than segments 1&2 as its part worth utility importance is

more for price. Segment 1 is more of brand oriented. Mostly all segments are not much worried about

either design or shape of the smartphone. It is widely assumed statement that any normally available

smart phone must be a good shaped phone and will have touch screen compulsorily. Hence

consumers may not feel those two attributes are not as important as others such as brand, price and

user-friendliness.

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Table 4 : Anova table for segmentation

ANOVA

Cluster Error

F Sig. Mean Square df Mean Square df

Rs 40000 331.756 2 32.959 30 10.066 .000

Rs 30000 6.522 2 28.210 30 .231 .795

Rs 20000 398.522 2 53.657 30 7.427 .002

Slider 20.861 2 22.022 30 .947 .399

Qwerty 36.612 2 44.667 30 .820 .450

Touch screen 4.653 2 31.434 30 .148 .863

Low 923.569 2 33.384 30 27.665 .000

Medium 43.339 2 7.427 30 5.835 .007

High 621.743 2 36.544 30 17.014 .000

Sleek 55.791 2 8.424 30 6.623 .004

Trendy 12.172 2 12.170 30 1.000 .380

Changeable skin 16.034 2 12.186 30 1.316 .283

Apple 317.433 2 32.432 30 9.788 .001

Nokia 190.143 2 74.542 30 2.551 .095

HTC 989.509 2 57.211 30 17.296 .000

Samsung 594.829 2 57.754 30 10.299 .000

The F tests should be used only for descriptive purposes because the clusters have been chosen to

maximize the differences among cases in different clusters. The observed significance levels are not

corrected for this and thus cannot be interpreted as tests of the hypothesis that the cluster means are

equal.

From Anova table of cluster analysis, it is also found that, changing price from Rs 40000 to Rs 30000 is

a significant rather changing to Rs 30000. Also, Apple has been chosen by consumers as a significant

player as a brand in the smartphone market.

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Market simulation

Our primary objective of this market simulation is to find the product attributes that

maximizes the share of the product. Profitability cannot be an objective in this simulation

because we are not known of costs of features of the product.

The best segments from the benefit segment analysis is given below Table 5: Best features of the 3 segments

Segment Price Design User-friendliness shape Brand

Segment1 20000 T/s High Trend Apple

Segment2 20000 T/s High sleek Apple

Segment 3 20000 T/s High sleek Apple The following table gives the various attributes of various brands in the market. Table 6

Segment Price Design User-friendly shape Brand

Product 1 40000 Touch screen High Trend Apple

Product 2 35000 Touch screen High sleek Nokia

Product 3 25000 Touch screen Medium sleek Samsung

The choice shares of the products are calculated by conjoint simulator using the principles of

maximum choice or log it choice rules. If the three brands Apple, Nokia and Samsung launch

those products in the market, then the choice shares of the above products by the consumers

can be calculated as follow.

Table 7

Segment Brand

Choice share (Max choice rule)

Choice share (Logit choice rule)

Product 1 Apple 26.94% 24.34%

product 2 Nokia 14.18% 19.86%

Product 3 Samsung 30.56% 27.47%

No purchase None 28.32% 28.32%

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Samsung would emerge as a most preferable consumer choice, if the same configurations are

competing in the market. It is purely due to price advantage over the others. There is slight

change in choice shares from the both choice rules. It is most preferable to use log it choice

rule for FMCG kind of products.

Sensitivity Analysis

What if suddenly Google launches a phone with the following configuration? Let us see the

calculations of the choice shares of the consumers. It is assumed that, since Google is new to

smartphone market, it is brand is perceived as in between Apple and Nokia. Now let us see

how the dynamics of the smartphone market changes with the launch of Google smartphone.

Table 8

Segment Price Design Uf shape Brand

Product 1 40000 Touch screen High Trend Apple

product 2 35000 Touch screen High sleek Nokia

Product 3 25000 Touch screen Medium sleek Samsung

Product 3 36000 Touch screen High sleek (middle of Apple and Nokia)

The following table will give us the consumer choice shares of the various smartphones.

Table 9: Choice shares

Segment Brand

Choice share (Logit choice rule)

Product 1 Apple 18.54%

product 2 Nokia 14.88%

Product 3 Samsung 21.44%

Product4 Google 17.09%

No purchase None 28.05%

So, with the introduction of Google with the above specified configuration, there is huge

damage to Both Apple and Samsung by 8% and 13% approximately assuming no reaction of

competitors with the new launch of the smartphone by Google.

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Suppose, in reaction to the Google’s launch, if Samsung changed its one of the attributes price

to Rs 22000/-

Hence, the following table will give us the choice shares post Samsung’s reaction to Google.

Table 9: choice shares

Segment Brand

Choice share (Logit choice rule)

Product 1 Apple 17.89%

product 2 Nokia 14.27%

Product 3 Samsung 25.77%

Product4 Google 16.41%

No purchase None 25.66%

There is slight gain of choice share for Samsung, but Google loses slightly. Whereas Apple and

Nokia don’t get any affect out of Samsung’s change of price. The entire usage conjoint

simulator is captured in screenshot as an Exhibit1.

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Issues and recommendations to full-scale study

There are several limitations in the study including limited sample set. The sample size is

small and the lack of details regarding the cost the sample is limited only to regular students

which should have been extended to staff, weekend batch students the more diverse sample

would have given more robust and better results. One more concern in the survey is many

respondents did not complete the ASEMAP survey. Also we need to go for heterogeneous

samples to get perfect utility values.

Conclusions

1. The five attributes we chose as key features for a Smart Phone gave us insights into the

purchasing behavior of the consumers. The tradeoffs they had to make while making a

choice of a Smart Phone among the attributes force them to choose few and leave out

others. For certain individuals certain attributes were more important than others. The

sequence of questioning ensured the consumer’s picked the attributes which really

mattered to them.

2. Conjoint analysis helps us to understand the part worth utilities, there by importance

levels and choice shares of any products.

3. Conjoint simulator can be used to strategically position the product or introduce the

product into the existing market.

4. Apple remained undisputed leader in the smartphone market according to consumer

preferences. Samsung is emerging as an alternative to Nokia.

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Exhibit-1

Conjoint simulator for calculating choice shares