Conjoint Analysis: To find an optimum combination of features for smartphones

19

ContentsIntroduction:3Types of Conjoint Analysis4Usages of Conjoint Analysis4Industry Analysis4Problem Statement7Methodology:7Literature Review8Questionnaire:10OverallStatistics12Conclusion & Limitations18References:19

Introduction:Conjoint analysisis a statistical technique used inmarket researchto determine how people value different features that make up an individual product or service.The objective of conjoint analysis is to determine what combination of a limited number of attributes is most influential on respondent choice or decision making. A controlled set of potential products or services is shown to respondents and by analyzing how they make preferences between these products, the implicit valuation of the individual elements making up the product or service can be determined. These implicit valuations (utilities or part-worths) can be used to create market models that estimate market share, revenue and even profitability of new designs.Conjoint originated inmathematical psychologyand was developed by marketing professor Paul Green at the University of Pennsylvania and Data Chan. Other prominent conjoint analysis pioneers include professor V. Seenu Srinivasan of Stanford University who developed a linear programming (LINMAP) procedure for rank ordered data as well as a self-explicated approach, Richard Johnson (founder ofSawtooth Software) who developed the Adaptive Conjoint Analysis technique in the 1980s and Jordan Louviere (University of Iowa) who invented and developed Choice-based approaches to conjoint analysis and related techniques such asMaxDiff.Today it is used in many of the social sciences and applied sciences includingmarketing,product management, andoperations research. It is used frequently in testing customer acceptance ofnew product designs, in assessing the appeal ofadvertisementsand inservice design. It has been used inproduct positioning, but there are some who raise problems with this application of conjoint analysis (see disadvantages).Conjoint analysis techniques may also be referred to as multiattribute compositional modelling, discrete choice modelling, or stated preference research, and is part of a broader set of trade-off analysis tools used for systematic analysis of decisions. These tools include Brand-Price Trade-Off,Simalto, and mathematical approaches such asAHP,evolutionary algorithmsorRule Developing Experimentation.

How Conjoint Analysis works ? Decompose the overall utility into its individual attributes part- worths Additive model- Overall utility = Sum total of all part- worths Total worth/ Utility = Part- worth of level i for factor 1+ Part- worth of level j for factor 2 + . Part- worth of level n for factor m Interaction model- Overall utility > Sum total of all part-worths Total worth/ Utility = Part- worth of level i for factor 1+ Part- worth of level j for factor 2 + . Part- worth of level n for factor m + I (Interaction effect between the attributes and their level) Generally, the Traditional Conjoint analyses use additive models whereas ACA and CBC use interaction models

Types of Conjoint Analysis Full Profile Method- Analysis carries on based on the respondents evaluation of all the possible combinations in the stimuli. Fractional Factorial Design- Method of designing a stimuli that is a subset of the full factorial design so as to estimate the results based on the assumed compositional rule. Pair wise Comparison Method- Method of presenting a pair of stimuli to the respondent for evaluation, with the respondent selecting one of the stimuli as preferred. Self-Explicated Model- Compositional technique where the respondent provides the Part- Worth estimates directly without making choices Adaptive (Hybrid) Conjoint Analysis (ACA)- ACA asks respondents to evaluate attribute levels directly, and then to assess the importance of each attribute, and finally to make paired comparisons between profile descriptions. Choice Based Conjoint (CBC)- An alternative form of conjoint analysis where the respondents task is of choosing a preferred profile similar to what he would actually buy in the marketplace. CBC analysis lets the researcher include a "None" option for respondents, which might read "I wouldn't choose any of these." Fractional Factorial Design- Method of designing a stimuli that is a subset of the full factorial design so as to estimate the results based on the assumed compositional rule.

Usages of Conjoint Analysis Breaking down customers overall utility from the product into values put in by him on the products individual attributes. Product planning and design Accommodating conflicting interests- Buyers want all of the most desirable features at lowest possible price Sellers want to maximize profits by: Minimizing the costs of features provided Providing products that offer greater overall value than the competitors. Market segmentation based on the utility structures

Industry AnalysisIndia is the second largest mobile handset market in the world after China and is poised to become an even larger market with unit shipment of INR 208.4 million in 2016 at a compound annual growth rate (CAGR) of 11.8 per cent from 2010 to 2016. The mature Indian mobile consumers increasing preference for high-end handsets and the younger demographics desire to use mobile Web 2.0 technologies could see the market revenues soar from INR 255.91 billion in 2010 to INR 350.05 billion in 2016. With end users need for converged devices and original equipment manufacturers (OEMs) accelerated adoption of open-source operating systems (OSs) such as Android and Symbian, the mobile handset-smartphone combination is emerging as the future growth engine of the telecom industry. The Indian Phone market has grown by 24 % YoY in 2013 Q1 whereas the smartphone market grew 74 % in 1st quarter 2013 Y-o-Y.In 2011, the mobile market is likely to witness almost a three-fold increase in the number of participants. This is expected to result in a price war and a consequent squeezing of profit margins. In 2010, the decline in the average selling price (ASP) of mobiles by 11.3 per cent opened up possibilities of market consolidation. The intense competition is also compelling handset manufacturers to enhance the application ecosystem and invent products. While market entrants are likely to be daunted by the sheer numbers in the market, they will identify abundant opportunities in the rural and semi-rural market for the next three to four years. For the mobile handset market, the rural market offers exciting prospects due to its significant uptake of feature phones, says the analyst of this research. The rural and semi-rural markets accounted for 65 per cent of sales for most Indian manufacturers in FY2010.In the case of smartphones, most existing global brands are concentrating on building application stores and improving service quality to offer an attractive value proposition and strengthen their market position. Entrants will be particularly focused on branding, as a smartphone is a high-involvement product and the brand is a vital purchase criterion. While smartphone consumers are also price conscious, it will not be possible for manufacturers to subsidise due to the operation of an open market rather than an operator-leveraged distribution model. Moreover, 70 per cent of the Indian population consumes only voice services, and mobile data services have not yet achieved large-scale popularity. However, this is set to change with the reduction of mobile Internet charges by telecom service providers and the introduction of 3G in India. The market is also witnessing the influx of a new class of affordable handsets, which are classified as smartphone-like devices, with touch screens and other smartphone features.In the next five years, the revenue share of smartphones in the mobile handset market is expected to rise steadily, as an increasing number of participants are targeting this space for higher margins, notes the analyst. This trend is likely to persist, as numerous handset manufacturers are strategizing to deploy more smartphone models in their portfolio. In the smartphone market, applications will be a key differentiating factor. The mid-tier price bracket is likely to experience high uptake and hence, manufacturers should strategize on deploying more models in this category.Market SectorsExpert Frost & Sullivan analysts thoroughly examine the following market sectors in this research:Mobile Ultra Low-cost Mobile Handsets (ULCMHs) Low-to Medium-cost Mobile Handsets (LCMHs) High-cost Mobile Handsets (HCMH)

India Smartphone Shipments by Operating System - 2012 Q1 and 2013

Top-Five India Smartphone Vendors Share,2013 Q1Problem StatementThe objective is to find an optimum combination of features most sought after by people looking to buy a smartphone so that companies can work on providing these features at an affordable price and improving customer satisfaction while reducing costs incurred from other redundant features.

Methodology:The following is the methodology that was adopted by us for finding out the utilities of various attributes:

We have used Fractional Factorial Design. It is a Method of designing a stimuli that is a subset of the full factorial design so as to estimate the results based on the assumed compositional rule.

Literature Review

Conjoint Analysis and MDS approach to brand improvement of an aerosol productAuthors: Vincent Charles, Mukesh Kumar & Tulika Anand, Journal of centrum Cathedra

The purpose of this study is to identify feasible offerings of aerosols from a companys point of view and the positioning of a comparatively new brand of room air freshener among all other existing brands in the capital city of Bihar (Patna) in India. The study makes use of conjoint analysis and the multidimensional scaling technique to identify:1. The attributes of the room air freshener and their corresponding levels from the consumers perspective2. The importance of each attribute of the room air freshener and its contribution in influencing the consumers purchase decision3. the best and the worst combinations of attributes and their levels from the consumers point of view, and4. the potential opportunities for the new brand of room air freshener in the perceptual map of the consumers mindThe authors have tried to understand the requirements and needs on management as well as from customers point of view. From managements perspective they aim: To understand the feasible offerings of the aerosol product (room air freshener); To identify the position of the ZZZ room air freshener in the minds of consumers and to identify the potential opportunities for the brand ZZZ.From customers perspective they aim: to identify the attributes of the aerosol product and their levels; to identify the importance of each attribute and its corresponding levels; to identify the consumers preferences from all possible combinations of attributes of the aerosol product; to identify the brand positioning of current brands of room air fresheners available in the Patna region on the selected dimensions; to look for gaps in the spatial map in order to identify potential opportunities for the brand ZZZ room air freshener.Findings:In this study, the room air freshener attributes identified as important in influencing the consumers purchase decision are fragrance, longevity of the fragrance stay, spray point inclination, price, pack design, and bottle material. Of these attributes, fragrance was found to be the most important attribute in influencing the purchase decision, followed by longevity, price, pack design, spray point inclination, and bottle material. The best combination of the attributes, according to the consumers, is the combination of Sandalwood fragrance, fair price, longevity of at least 6 hours, and pack design with cap, forward spray point inclination, and glass bottle material. The study also revealed that fragrance, price, and longevity of the fragrance stay significantly influence the consumers purchase decision while the other attributes do not significantly influence such purchase decision.

The use of Conjoint Analysis to determine consumer buying preferencesAuthors: Ernest North and Retha de Vos

The main aim of this article was to provide overview of nature and use of CA as a research tool, and to indicate its value when, for example a researcher wishes to analyse female consumers apparel purchasing decision based on the value they attach to certain attributes when making a purchasing decision. Although the apparel industry has reached maturity and growth is very slow, fashion trend cycles are accelerating. The presence of more and more brands has created a competitive environment unheard of in the past.Findings:Multiple discriminant analysis helps to understand and explain research problems that involve a single categorical dependent variable and several metric independent variables. A mixed data set is also possible for the independent variables if the non-metric variables are coded (Hair et al, 1998:321). According to Hair et al (1998:436) conjoint analysis places more emphasis on the ability of the researcher or manager to theorise about the behaviour of choice than it does on analytical techniques. Thus it should be viewed primarily as exploratory, because many of its results are directly attributable to basic assumptions made during the course of the design and the execution of the study. The critical interplay between the assumed conceptual model of decision-making and the appropriate elements of the conjoint analysis makes this a unique multivariate method (Hair et al, 1998:436).

Consumer Preferences for Quality Attributes of RiceAuthor: B.R. Walisinghe and L.H.P. GunaratneIn this research consumer preferences for different quality attributes of rice were assessed based on conjoint methodology. The appropriate attributes and levels were identified from a focus group discussion and subsequently a conjoint questionnaire was administered using a sample of 185 consumers under a fractional factorial design. ANOVA and part worth utility models were estimated. The relative importance of attributes was calculated using part-worths. ANOVA results indicate that of the four attributes, type, color and purity were significant, but price was not significant. Part worth estimates revealed that the purity is the most important attribute when selecting a type of rice.

Findings:Conjoint analysis was used to examine the consumers trade-offs with respect to rice quality attributes by using ANOVA and part worth score models. Of the four attributes studied, price was not found to be significant when making purchasing decisions of rice as it is essential and less price sensitive commodity. The study revealed that, of the selected quality attributes, consumers place a high value for purity of than that of the other attributes. The particular market segment had a high preference for samba type compared to nadu and raw rice because of its grain size. Even though there were some consumers who eat red rice owing to health concerns, the most preferred grain color is found to be white. The preference is not placed to a single attribute, but it is the interaction of several attributes as tested in this study.

Questionnaire:

Q1). Age

Q2). Gender

Q3). Profession

Rate the following Mobile Phones on the scale of 1-5(5 being most preferred) Large screen size: 5 inches or more; Medium screen size: less than 5 inches but more than 3.5 inches; Small screen size: < 3.5 inches

Card IDOSScreenSizeCameraPrice

11WindowsLargeHigh Megapixel(12+MP)20K+

22IOS 7Smallmedium Megapixel(6-12MP)