This document summarizes the results of a conjoint analysis study examining consumer preferences for smartphone attributes. The study used a survey of 162 respondents to rank 16 smartphone profiles varying price, battery capacity, operating system, memory, camera, processor, resolution, and quick charging. The conjoint analysis found that battery capacity was the most important attribute, followed by operating system, internal memory, resolution, and quick charging. The results imply that a smartphone priced 10000-12000 INR that emphasizes battery capacity of 3001-4000 mAh, Android operating system, 16GB or 32GB memory, 8-12MP camera, resolution of 4K, and quick charging could appeal to price-sensitive consumers.

1. 8/29/2016
Analysis of Consumer Preferences
for New Smartphone
Report
Tushar Sharma, Utkarsh Mishra and Akriti
IMT GHAZIABAD

2. Contents
Introduction............................................................................................................................................2
LITERATURE REVIEW: .............................................................................................................................2
THE NATURE OF CONJOINT ANALYSIS...............................................................................................2
Approaches to conjoint analysis........................................................................................................3
Research Design .....................................................................................................................................4
Results of Conjoint Analysis...................................................................................................................6
Implications of the Empirical Findings...............................................................................................8

3. Introduction
Technology has been a constant force of change in human lives. In today's world, the development of
the smartphone has risen to prominence has one of the technologies to have radically transformed
people's lifestyles by allowing them to digitally connect with their lived environments. With the
development of mobile internet services, more and more consumers are adopting smartphones as
their primary communication device. A smartphone offers more advanced computing ability and
connectivity than a feature phone, and typically includes a high‐resolution touch screen and offers
wireless‐internet access to web pages through a built‐in web browser. Telecommunication companies
have recently begun to promote smartphone products in hopes of promoting mobile internet services
as a way to increase revenues. While Android mobile devices account for over half of the market for
smartphones, Android users only accounted for a 16 percent share of the mobile internet market in
2011. Recently, several studies have explored motivation for adopting smartphones and mobile
internet from a variety of perspectives, such as technology acceptance model (TAM), aesthetic design,
and perceived value. However, few of these studies have specifically investigated Android
smartphones in mobile internet context, which are relatively new to the market. Android is an open
source system which allows manufacturers to customize their devices, including hardware and
software. Moreover, perceived value is the main determinant of payment intention
LITERATURE REVIEW:
THE NATURE OF CONJOINT ANALYSIS
Description Attempts to construct consumer typologies are an enduring feature of retailing research
and frequently centre on economic and demographic characteristics. Such research highlights the
relatively poor understanding of real-life consumer behaviour and, in particular, the need to develop
more appropriate methods of examining the behaviour of consumers in real-life retail settings. By
using a conjoint study researchers could gain a better understanding of the real value consumers
attach to certain attributes when making purchasing decisions in a retail situation. The concept
conjoint analysis is described by Hair et al (1998:392) as follows: “Conjoint analysis is a multivariate
technique used specifically to understand how respondents develop preferences for products or
services. It is based on the simple premise that consumers evaluate the value of a product or service
by combining the separate amounts of value provided by each attribute.” Sudman and Blair
(1998:229-230) warn that it is not a data analysis procedure like factor analysis or cluster analysis. It
must be regarded as a type of “thought experiment” designed to show how various elements of
products or services (price, brand, style) predict customer preferences for a product or service. Kotler
(2000:339) defines conjoint analysis as”…a method for deriving the utility values that consumers
attach to varying levels of a product’s attributes.” Churchill and Iacobucci (2002:748) refer to conjoint
analysis as “…conjoint measurement, which relies on the ability of respondents to make judgments
about stimuli.” These stimuli represent some predetermined combinations of attributes, and during a
laboratory experiment, respondents are asked to make judgments about their preferences for various
attribute combinations. The basic aim, therefore, is to determine the features they most prefer. From
the definitions given above it is clear that conjoint studies centre on certain attributes of products or
services and also various levels within each attribute.

4. In conjoint analysis respondents indicate their preference for a series of hypothetical multi-attribute
alternatives, which are typically displayed as profiles of attributes. The responses to these profiles are
analysed to yield estimates of the relative importance of the attributes and to build predictive models
of consumer choice for new alternatives (Oppewal & Vriens, 2000). Conjoint analysis is a dependence
technique that has brought new sophistication to the evaluation of objects, such as new products,
services or ideas (Hair et al, 1998:15). The theory and methods of conjoint analysis deal with complex
decision-making, or the process of assessment, comparison, and/or evaluation. In this process
consumers decide which aspects of products or services are important, compare the products or
services on each of the important aspects, and decide which one to choose (Louviere, 1988:9). Schutte
(1999:90-92) lists the following to indicate the value of conjoint analysis in assisting marketers to
provide answers when strategic marketing and selling decisions have to be made: Understanding
market preferences When a product has, say five key attributes: price, quality, style, brand and
packaging, these attributes and their associated levels represent the factors that materially affect
consumer preferences. Predicting market choices conjoint analysis offers the researcher opportunities
to apply certain simulations. The simulation capability of conjoint analysis enables the analyst to
explore alternative market scenarios. The impact on market share or changes in the product can be
assessed and the impact of competitive moves can then be anticipated (Wyner, 1995). Developing
market strategies it can aid marketers to identify product concepts that are extremely attractive from
the consumer's perspective. Concepts that are not technically or financially feasible can be eliminated.
The best of the remaining products must be selected, and then the attributes of this product must be
fine-tuned to achieve the stated objective. A series of simulation tests must be run to identify the
point at which the product performs best (Wyner, 1995). Segmenting the market conjoint results are
very useful for segmentation purposes. Consumers may be segmented on the basis of utility values or
attribute important scores. Thus simulations can be viewed as segmentation analyses that group
people together according to their most preferred product among other substitutes or competitive
products (Wyner, 1995).
Approaches to conjoint analysis
There are two general approaches to data collection for conjoint – the two‐factor‐at‐a‐time trade‐off
method and the multiple factor full‐concept method. The two‐factor‐at‐a‐time trade‐off method is
now seldomly used. The full‐concept is more realistic as all factors are considered and evaluated at
the same time.
In the full‐concept (or full‐profile), the respondents are asked to rank or score a set of profiles
according to their preference. On each profile, all factors of interest are represented and a different
combination of factor levels (i.e. features) appears. The factors are the general attribute categories of
the product/service such as colour, size, or price. The factor levels (i.e. product/service features) are
the specific values of the factors, such as red, small, and expensive. The possible combination of all
factor levels can become too large for respondents to rank or score in a meaningful way. The full‐
concept approach in SPSS categories conjoint uses fractional factorial designs, which uses a smaller
fraction of all possible alternatives. This reduced size subset (orthogonal array) considers only the
main effects and the interactions are assumed to be negligible.
The factor levels can be specified as DISCRETE (when factor levels are categorical), LINEAR (when data
are expected to be linearly related to the factor), IDEAL, or ANTI‐IDEAL (for quadratic function models).
The SPSS conjoint procedure can calculate utility scores (or part‐worths) for each individual
respondent and for the whole sample. These utility scores, analogous to regression coefficients, can

5. be used to find the relative importance of each factor. SPSS permits the use of simulation profiles to
represent actual or prospective products to estimate or predict market share of preference.
Research Design
In order to generate an orthogonal design for the appropriate smartphone factors and factor levels,
some rounds of focus group, survey and discussions were held. The focus group consisted of six
persons from different occupational backgrounds and survey had 150 respondents. The focus group
members were selected on the basis that they had smartphone use experiences. The focus group
members discussed in detail their experience, either good or bad, from many different perspectives
and occasions.
S No. Attribute
1 Screen Size
2 RAM
3 Operating System
4 Internal Memory
5 Network Type
6 Battery Capacity
7 Sim Type
8 Primary Camera
9 Selfie Camera
10 Display Type
11 Processors (Cores)
12 Fingerprint Sensor
13 Waterproof Body
14
Network Type
(2G/3G etc.)
15
Resolution (HD, full
HD etc.)
16
Body Type (metal,
fibre etc.)
17 LED Flash
18 Quick Charging
19
USB OTG (to
connect USB)
20
Display Type (LCD,
OLED etc.)
Despite a careful selection of factors, there were still too many possible profiles for the respondents
to choose from. The SPSS generated a parsimonious orthogonal array of 16 profiles.
It was decided it would be useful to study the following attributes:-
1. Price
2. Battery Capacity
3. Operating System
4. Internal Memory

6. 5. Primary Camera
6. Processor
7. Resolution
8. Quick Charging
The Following cards were created using the orthogonal design which were later sent in for
ranking from the respondents:
Price Battery
Capacity
Operating
System
Internal
Memory
Primary
Camera
Processo
r Cores
Resolutio
n
Quick Charging
10000-
12000
2001-3000
mAh
Android 16 GB
12 MP &
above
Octa HD Yes
12001-
15000
4001-5000
mAh
Windows 16 GB 8-12 MP Hexa Full HD Yes
12001-
15000
4001-5000
mAh
Android 32 GB
12 MP &
above
Octa HD Yes
10000-
12000
4001-5000
mAh
Android 64 GB
12 MP &
above
Hexa 4K No
10000-
12000
4001-5000
mAh
Windows 16 GB 8-12 MP Octa HD No
12001-
15000
2001-3000
mAh
Android 16 GB
12 MP &
above
Hexa Full HD No
12001-
15000
3001-4000
mAh
Android 64 GB 8-12 MP Hexa HD Yes
12001-
15000
2001-3000
mAh
Android 16 GB 8-12 MP Octa 4K No
10000-
12000
2001-3000
mAh
Windows 32 GB 8-12 MP Hexa 4K Yes
12001-
15000
2001-3000
mAh
Windows 64 GB 8-12 MP Octa HD No
10000-
12000
2001-3000
mAh
Windows 64 GB
12 MP &
above
Octa Full HD Yes
10000-
12000
2001-3000
mAh
Android 16 GB 8-12 MP Hexa HD Yes
12001-
15000
3001-4000
mAh
Windows 16 GB
12 MP &
above
Octa 4K Yes
10000-
12000
3001-4000
mAh
Android 32 GB 8-12 MP Octa Full HD No
10000-
12000
3001-4000
mAh
Windows 16 GB
12 MP &
above
Hexa HD No
12001-
15000
2001-3000
mAh
Windows 32 GB
12 MP &
above
Hexa HD No

7. To examine consumer preference for mobile devices, this study carried out a survey asking
respondents to rank a set of alternatives. Each respondent was asked to rank the 16 profiles describing
the form of mobile devices attributes as a tool according to their own usage intention on a scale from
1 to 16. The respondents were selected in an “A” class university in Ghaziabad, India as they form the
main target group. This study assumed that all students have mobile devices or they have used one
before. Therefore, respondents could evaluate the profiles considering the price of the mobile device.
In addition, most undergraduate students have more knowledge about mobile devices including smart
phones and tablet PCs. Thus, this work could obtain more meaningful results from the respondents
who were the main users of mobile devices. A total of 200 respondents were interviewed via a survey.
38 respondents were excluded because they failed to respond to some of the values. Thus, the analysis
is based on the data from the final 162 respondents, consisting of 124 males (77.2%) and 38 females
(22.8%).
Results of Conjoint Analysis
Model Description
N of Levels Relation to
Ranks or
Scores
Price 2 Discrete
Battery_Capacity 3 Discrete
Operating_System 2 Discrete
Internal_Memory 3 Discrete
Primary_Camera 2 Discrete
Processor_Cores 2 Discrete
Resolution 3 Discrete
Quick_Charging 2 Discrete
All factors are orthogonal.

8. Utilities
Utility Estimate Std. Error
Price
10000-12000 .023 .384
12001-15000 -.023 .384
Battery_Capacity
2001-3000 mAh .487 .512
3001-4000 mAh 1.029 .600
4001-5000 mAh -1.516 .600
Operating_System
Android 1.559 .384
Windows -1.559 .384
Internal_Memory
16 GB .563 .512
32 GB .279 .600
64 GB -.842 .600
Primary_Camera
8-12 MP .312 .384
12 MP & above -.312 .384
Processor_Cores
Hexa .240 .384
Octa -.240 .384
Resolution
HD -.003 .512
Full HD -.075 .600
4K .078 .600
Quick_Charging
Yes .467 .384
No -.467 .384
(Constant) 8.211 .443
Importance Values
Price 6.302
Battery_Capacity 21.419
Operating_System 19.525
Internal_Memory 16.258
Primary_Camera 8.760
Processor_Cores 6.424
Resolution 12.986
Quick_Charging 8.326
Averaged Importance Score
Correlationsa
Value Sig.
Pearson's R .935 .000
Kendall's tau .817 .000
a. Correlations between observed and
estimated preferences

9. Implications of the Empirical Findings
The empirical results have a number of interesting and meaningful implications for policy makers and
business players to understand the essential characteristics of mobile devices.
The correlation is significant which represents that the amount of correlation between the observed
preference scores and the conjoint model estimated preference score. The model does a good job of
predicting the respondent’s preference for different attributes towards the smartphone.
1. The model description table displays the No of levels of each attribute and relation to rank or
scores.
2. The utilities table showcases that the following attributes with described values are significant :-
S No. Attribute
1 Price 10000-12000
2 Battery Capacity 3001-4000 mAh
3 Operating System Android
4 Internal Memory 16 GB & 32 GB
5 Primary Camera 8-12 MP
6 Processors (Cores) Hexa
7
Resolution (HD, full
HD etc.) 4K
8 Quick Charging Yes
3. According to the importance values table Battery Capacity is the most important attribute for
the respondents followed by OS, Internal Memory, Resolution and Quick Charging.
As per the exploratory studies conducted it was observed that the price sensitive consumer favours
some other attributes such as metal body, finger print sensor, light UI etc. These attributes clubbed
with the above mentioned attributes which showcases resolution as 4K and quick charging as a unique
feature in this price segment of 10000-12000 INR can create a unique and appealing product.