The power of conjoint analysis

The Power of Conjoint Analysis Insight into preference, choice and trade-off David Murray Download free books at David Murray The Power of Conjoint Analysis Insight into preference, choice and trade-off Download free eBooks at bookboon.com The Power of Conjoint Analysis : Insight into preference, choice and trade-off © 2012 David Murray & bookboon.com ISBN 978-87-403-0214-1 Download free eBooks at bookboon.com Contents The Power of Conjoint Analysis Contents 1 Introduction The Role of Statistics in Market Research An Outline of Conjoint Analysis 13 Sample Size Considerations: 15 Case Studies 28 Public Sector Case Study: Exercising Choice for Specialist Healthcare Treatment 35 Generic Conjoint Techniques Reflection and the Way Forward Bibliography 360° thinking Biography 360° thinking 40 49 54 55 360° thinking Discover the truth at www.deloitte.ca/careers © Deloitte & Touche LLP and affiliated entities Discover the truth at www.deloitte.ca/careers Touche LLP and affiliated entities © Deloitte & Touche LLP and affiliated entities Discover the truth 4at www.deloitte.ca/careers Click on the ad to read more Download free eBooks at bookboon.com © Deloitte & Touche LLP and affiliated entities Disc Introduction The Power of Conjoint Analysis 1 Introduction The Role of Statistics in Market Research Wikipedia defines statistics as ‘The study of the collection, organisation, analysis and interpretation of data’ Wordwebonline goes on to add the fact that data can be represented in numerical format, which will inform the basis of this particular insight Statistics is a branch of applied mathematics concerned with the collection, interpretation and to an extent, the manipulation of quantitative data These data can be compiled from a number of sources: • Secondary data – these are data that already exist in hard or soft documents where the analyst has the appropriate authority to extract and interpret Examples of these documents, freely available in the public domain, are Census Statistics, Regional Trends, Social Trends, Euromonitors and data available upon subscription from Mintel, the Target Group Index or National Shoppers’ Survey • Primary data – these are data necessitating fieldwork to derive meaningful information in the form of Market Research Surveys where data can be compiled through observation or communication using: Self-completion questionnaires Face-to-face interviews door-to-door or on the street Telephone interviews Online questionnaires completed upon an e-mail request or by an invite on a website The completed questionnaires will contain response codes to enable the analyst to input or data capture responses in numerical format For example a response to a dichotomous will create three codes: RESPONSE CODE YES NO DON’T KNOW Fig Dichotomous coding frame or a response to a semantic scale will create either three codes: RESPONSE CODE AGREE NEITHER AGREE OR DISAGREE Fig Three scale semantic coding frame Download free eBooks at bookboon.com Introduction The Power of Conjoint Analysis or five codes: RESPONSE CODE STRONGLY AGREE AGREE NEITHER AGREE OR DISAGREE DISAGREE STRONGLY DISAGREE Fig Five scale semantic coding frame Qualitative responses can also be formatted into a coding structure to permit quantitative data analysis, for example in a customer satisfaction survey, the question asks: “What one thing would improve the service Company X provides its customers?” A randomly selected ten questionnaires from the hundred carried out reported the following answers: • “Answer the telephone within five rings” • “Staff to be polite and courteous” • “Quicker response to my telephone call” • “Replenish stock levels” • “Shelves run out of stock very quickly” • “More staff to be available to answer queries” • “When I phone up, not to be passed from one staff member to another” • “More accurate invoices” • “Phone me back quicker with an answer” • “Invoice not clear” Within these open-ended responses there are some key words: • Tele(phone) • Stock • Staff – polite • Staff – available • Invoice If codes were to be affixed to these key words or statements, we can now quantify open-ended responses to this very important question and analyse the data: Download free eBooks at bookboon.com Introduction The Power of Conjoint Analysis KEYWORD CODE RESPONSE(S) % RESPONSE Tele(phone) 40 Stock 2 20 Staff – polite 10 Staff – available 20 Invoice 20 Base 10 Fig Hard coding open-ended analysis Please note one respondent referred to two service aspects, (telephone and staff available) These codes have been set up based on a random selection of open-ended responses from every nth, or in this case, 10th questionnaire These codes together with their interpretation, or coding frame, can now be used to code all one hundred responses with a default ‘others’ for any responses which not apply to the descriptions within the coding frame You will have noticed a simple analysis of responses in this table - this is what is known as Univariate Analysis Univariate analysis is carried out using a single variable, in this case percentage response to the question, “What one thing would improve the service Company X provides its customers?” The analysis depicted is a frequency analysis of the distribution of responses So we can conclude that 40%, or in every 10 responses made a comment pertaining to the company’s telephone service The standard output of Univariate Analysis is a frequency distribution table and for a more visual impact, a chart or a graph Let us now assume that the analyst has coded all one hundred responses to this open-ended question The data table now is as follows: KEYWORD/PHRASE RESPONSES TELE(PHONE) 37 STOCK 26 STAFF – POLITE 12 STAFF – AVAILABLE 18 INVOICE 11 Fig Integrating hard coded open-ended questions into the coding frame (Some respondents will have given more than one answer) Download free eBooks at bookboon.com Introduction The Power of Conjoint Analysis Most market research questions ask a number of classification questions This enables the analyst to find out more about the respondents in terms of: • Gender • Age • Income • Social grade • Household size • Employment status Let us now have a look at the gender distribution of these responses: KEYWORD/PHRASE RESPONSES MALES FEMALES TELE(PHONE) 37 28 STOCK 26 20 STAFF – POLITE 12 STAFF – AVAILABLE 18 12 INVOICE 11 Fig Gender distribution of hard coded responses We are now in a position to add more detail and insight into the analysis by carrying out Bivariate Analysis Bivariate Analysis determines the relationship between these two variables, improvement and gender Bivariate Analysis can be helpful in testing simple hypotheses of association, for example: • Males are more likely to query invoices • Females are more likely to complain about items being out of stock Let us now look at testing these hypotheses: • Query Invoices: RESPONSES PROFILE % 81.8 FEMALES 18.2 TOTAL 11 100 MALES Fig Gender profile of hard coded responses Download free eBooks at bookboon.com Introduction The Power of Conjoint Analysis • One can see that males are more likely than females to focus on invoice accuracy Furthermore, let us assume that the overall sample of one hundred respondents was split fifty males and fifty females We can now add value to this analysis and state that of all males who took part in the survey, 18% or almost in 5, are more likely to query an invoice whereas only 4% or in 25 females were likely to query invoice accuracy Therefore males are 4½ times more likely to query an invoice The hypothesis ‘Males are more likely to query invoices’ has been tested and proved accurate • Out of stock: SAMPLE SIZE RESPONSES PROFILE % PENETRATION% MALES 50 23.1 12.0 FEMALES 50 20 76.9 40.0 100 26 100.0 Fig Comparative profile and penetration of hard coded responses Increase your impact with MSM Executive Education For almost 60 years Maastricht School of Management has been enhancing the management capacity of professionals and organizations around the world through state-of-the-art management education Our broad range of Open Enrollment Executive Programs offers you a unique interactive, stimulating and multicultural learning experience Be prepared for tomorrow’s management challenges and apply today For more information, visit www.msm.nl or contact us at +31 43 38 70 808 or via admissions@msm.nl For more information, visit www.msm.nl or contact us at +31 43 38 70 808 the globally networked management school or via admissions@msm.nl Executive Education-170x115-B2.indd 18-08-11 15:13 Download free eBooks at bookboon.com Click on the ad to read more Introduction The Power of Conjoint Analysis By applying the same procedure as in the previous analyses, three in every four complainants about outof-stock items are female Furthermore, four in every ten females voiced their opinion about this problem compared with only one in eight males In terms of the hypotheses, we can now accept the hypothesis that females are more likely to complain about items being out-of-stock than males Let us now assume that the questionnaire contained five point semantic scales regarding their satisfaction with the company’s service The questionnaire asks the respondents to either give a score of between and 5, with being very satisfied and being not at all satisfied, or just a tick box under the appropriate satisfaction heading for each of these service aspects: Aspect of service provided Very Satisfied Satisfied Neither nor Dissatisfied (5) (4) (3) Very Dissatisfied (2) (1) Convenient opening hours The company are market leaders Company X is very professional Their product range is extensive 5 When I ring the telephone is answered quickly Staff are approachable Staff are polite All things considered, I am happy with the service provided Fig Five scale very satisfied to very dissatisfied semantic scale pertaining to key business drivers Based on the answers given by the one hundred respondents, we can now correlate the overall satisfaction, (all things considered, I am happy with the service), scores with each of the seven aspects of the service delivery Correlation analysis is the statistical relationship, in this instance, between two variables, the dependent variable (all things considered), and each of the independent variables (e.g opening hours) Output is expressed as the ‘coefficient of correlation’ or the strength of the association between the dependent and independent variables The coefficient is always 1.0 or less with 1.0 equating to perfect correlation The closer the coefficient is to 1.0, the better the correlation Should three independent variables result in similar coefficients of correlation of 0.7 or greater, then we need to test variations of these or combinations of these three variables using a multivariate statistical technique called Regression Analysis 10 Download free eBooks at bookboon.com Generic Conjoint Techniques The Power of Conjoint Analysis Scalar Conjoint Analysis: - Scalar Conjoint Analysis is a method used to ensure a real ranking of importance This method forces respondents to work through alternative choice sets rather than just applying rankings or ratings in a conventional conjoint way, which not differentiate enough By that I mean, many things may emerge as important in an absolute sense and it is not until respondents have to choose between one aspect and another that a real hierarchy emerges Respondents could be asked to firstly indicate on a scale from ‘extremely important’ to ‘not important at all,’ their attitudes towards a number of statements Following the use of this rating scale, respondents are then presented with statements in pairs and asked to say which of the two statements is more important This technique of trading off one against another is a scalar conjoint technique which forces respondents to choose from pairs of statements which is the more important to them personally The pairs of statements are modelled by the computer in a way that means that each respondent does not have to be exposed to all combinations of pairs Once respondents have traded off the statements, a ranking is obtained which shows the importance of the statements One example of this technique available in the public domain relates to Car Servicing and Repairs (DTI 2001) Respondents were asked to rate varying selections of the minimum standards that make up a national ‘Good Garage’ scheme Respondents were given two alternative minimum standards and asked which was the more appealing of the two The results were then analysed to rank twenty minimum standards in order of appeal to respondents Respondents were also asked to rate how likely they would be to comply with a random set of proposed minimum standards, using a ‘very likely’ to the ‘not at all likely’, 5-point semantic scale Mean scores for those minimum scores were then calculated by affixing scores from +2 being very likely to -2 being not at all likely The Scalar Conjoint Analysis permitted not only an evaluation of all possible combinations of features but also the assignment of costs to enable combining those features with the highest value and thereby giving them combinations with high value to low costs As a result the DTI were able to propose a matrix outlining the prices to join the scheme by number of employees’ bands, (1 employee, - employees, 5+ employees) Brand – Price Trade-off: - The trade-off technique only has two variables – brand and price It is typically used to measure price elasticity A group of products are initially presented to respondents Each product is priced at its lowest level The respondent is asked to select one product The price of the selected product is then increased to the next level, no other prices change The respondent is then asked to select a product from the group which includes the original product selected at the new price This process is repeated until the product reaches its maximum price or the respondent selects another product This study is continued through all the competing products 42 Download free eBooks at bookboon.com Generic Conjoint Techniques The Power of Conjoint Analysis b) Choice based: Choice Based Conjoint Analysis (CBC): – The main characteristic distinguishing Choice Based Conjoint Analysis from the other types, is that the respondent expresses preferences by choosing concepts from sets of concepts, rather than ranking or rating them Instead of asking respondents to rank a series of product scenarios, they are shown several at a time and asked to pick the one they would buy CBC interviews closely mimic the purchase process Instead of rating or ranking product concepts, respondents are shown a set of products on the screen (in full-profiles) and asked to indicate which one they would purchase As in the real world, respondents can decline to purchase in a CBC interview by choosing “None.” If the aim of conjoint research is to predict product or service choices, it is natural to use data resulting from choices According to surveys of Sawtooth Software customers, Choice Based Conjoint became the most widely used conjoint-related method in about 2000 The reasons for its popularity are: • The task of choosing a preferred concept is similar to what buyers actually in the marketplace Choosing a preferred product from a group of products is simple and a natural task • Choice Based Conjoint Analysis lets a researcher include a ‘none’ option for respondents to select It might read ‘None: I wouldn’t choose any of these’ If this option is selected, information can be inserted by the respondent regarding the decrease in demand to be expected if, for example, prices of all offered products increased or the products became unattractive in other ways Challenge the way we run EXPERIENCE THE POWER OF FULL ENGAGEMENT… RUN FASTER RUN LONGER RUN EASIER… READ MORE & PRE-ORDER TODAY WWW.GAITEYE.COM 1349906_A6_4+0.indd 22-08-2014 12:56:57 43 Download free eBooks at bookboon.com Click Click on on the the ad ad to to read read more more Generic Conjoint Techniques The Power of Conjoint Analysis • Because Choice Based Conjoint Analysis data are analysed by pooling information across respondents, it is feasible to quantify interactions Here is a quick example of Choice Based Conjoint Analysis: Question – “If you were at the off-license to buy lager and these were your only choices, which would you choose?” Visuals of the brand and logo would be made available to enable brand recall: Brand Strength Price PLEASE TICK PREFERENCE Skol Carling Becks 3% 3.20% 4% £1.50 £1.80 £2.00 ο ο ο Wouldn’t choose any ο Fig 25 Product description – Choice based conjoint analysis Another presentational method would be to ask: “Please assume you wanted to purchase 10 bottles of lager for yourself If these were the only options, how many would you purchase of each? Please allocate 10 points Your responses must add up to 10.” Brand Strength Price PLEASE TICK PREFERENCE Skol Carling Becks 3% 3.20% 4% £1.50 £1.80 £2.00 ο ο ο I wouldn’t buy 10 bottles from the options shown I’d purchase this many from another source: ο Fig 26 Alternative product description – Choice based conjoint analysis The simplest analytical method is to access the relative impact of each attribute level by counting ‘wins’ Each attribute level is equally likely to occur with each level of every other attribute Therefore the impact of each level can be assessed just by counting the proportions of times concepts including it are chosen That method of analysis can be used not only for main effects, but for joint effects as well Discrete Choice Modelling (DCM): - Discrete Choice Modelling looks at choices that customers make between products and services By identifying patterns in these choices, DCM models how different consumers respond to competing products DCM allows decision makers to examine the impact of product configuration, service bundling, pricing and promotion on different classes of consumers 44 Download free eBooks at bookboon.com Generic Conjoint Techniques The Power of Conjoint Analysis Discrete Choice Modelling has been used to: • Configure new products • Ascertain adoption patterns for new products • Bundle and design new rate structures • Configure financial packages • Forecast attendance at sports and cultural events • Develop techniques for translating model results into potential profit • Forecast market share • Give insight into branding issues and loyalty marketing • Enable customer retention Understanding what lies behind a purchase decision is difficult but one can at least draw inferences from the pattern of choices that groups of people make By observing many consumers’ choices, one can infer the probability of purchasing a given product based on the product’s characteristics, pricing and the demographic profile of the consumer, (Statwizards LLC 2005 – 2010) With DCM there is a need to identify the product’s key buying factors Focus Groups may enable this understanding in generating subjective data regarding product benefits or developing creative product concepts The Focus Group will generate a range of hypotheses for testing at a quantitative level This can be carried out in two ways: Particularly useful for existing products – surveying potential customers to observe what they buy and have bought Choice experiments – a hypothetical marketplace is described containing a set of products Respondents are asked what they would – they can choose to buy a product, choose not to buy, or choose to buy at a later date Pricing and other product attributes are varied and respondents are asked again for their course of action Through the careful design of these choice experiments, an analyst can learn about how consumers make trade-offs between product characteristics and budget available to purchase 45 Download free eBooks at bookboon.com Generic Conjoint Techniques The Power of Conjoint Analysis There are two basic forms of discrete choice: classic and exploding data (Chapman and Staelin 1982) Classic discrete choice involves showing a respondent a series of sets of products (as described above) In exploding data discrete choice, respondents are asked to rank order a set of products based on purchase This rank-ordered data set can be transformed into a format suitable for model estimation as was outlined in chapters and Exploding data discrete choice has the advantage of more efficient data collection over classic discrete choice The exploding data approach creates many times more data points (or cases) than the classic approach with the same interview length Based on these quantitative tools, model(s) are developed to give insight into a particular model to predict the outcome of various scenarios The statistical input required to build these models are beyond the scope of this paper, but, for example, forecasting techniques are based on micro simulations, elasticities, pivot point predictions and transferability of parameters, (Larson & Max 2001) c) Hybrid: Adaptive Conjoint Analysis: – This ‘flavour’ is defined as: This e-book is made with SetaPDF SETASIGN PDF components for PHP developers www.setasign.com 46 Download free eBooks at bookboon.com Click Click on on the the ad ad to to read read more more Generic Conjoint Techniques The Power of Conjoint Analysis “Adaptive Conjoint Analysis, (ACA), is a computer-administered, interactive conjoint method designed for situations in which the number of attributes exceeds what can reasonably be done with more traditional methods” (Sawtooth Software 2012) In other words, ACA focuses on the attributes that are more relevant to the respondent and avoids information overload by focusing on just a few attributes at a time The scope of many studies is constrained by limitations in respondents’ time and attention span ACA overcomes these constraints by adapting the interviews for each respondent Respondents are firstly walked through a range of feature-importance ratings and rankings; second, through a series of pairwise trade-offs of different product configurations The product configurations shown to any one respondent may not include all of the attributes being tested The configurations to be paired are based on the answers to the importance questions and rankings asked in the beginning of the interview Items that are considered of little importance show up in the comparisons less often Items that are considered of greater importance show up in the comparisons more often For each pair of products being tested, the respondent is to indicate which product they prefer and the degree to which they prefer it The software continues prompting with pairwise comparisons of product configurations until enough data has been collected to estimate conjoint utilities for each level of each feature At the early stage of the interview process, the computer learns enough about each of the respondents’ values and behavioural characteristics, to focus on those areas of importance to that respondent Since the procedure is adaptive, only a fraction of the total number of possible product combinations are tested This results in a much broader framework as more attributes can be included in the market study, with attributes not of interest or relevance being excluded to focus on a set of richer ones This will of course, lead to the creation of higher quality data as respondents are more interested or involved in the task Software licences for ACA can handle up to thirty attributes, each having as many as fifteen levels Adaptive Conjoint Analysis therefore combines the design of the conjoint tasks, data collection, data analysis and market simulation in a single piece of software (Johnson 1987) One key application this analysis method has is its unique computation of a reservation price, which is defined as: “The reservation price is the highest price that a given person will accept and still purchase the product or service” (Varian 2003) In other words a person’s reservation price is the price he/she is just indifferent between purchasing or not purchasing the product or service 47 Download free eBooks at bookboon.com Generic Conjoint Techniques The Power of Conjoint Analysis The major approach in pricing studies by conjoint analysis is incorporating the price as an additional attribute, (Green & Srinivasan 1990) The attribute price is then assigned a part-worth utility as the other attributes are applied ACA was incorporated in a study designed for the Nokia online-shop in the German market for mobile phones In this shop customers are offered suitable phone enhancements at a discounted price on the purchase of a phone To enable the online-shop to formulate its pricing strategy, the reservation prices of customers were estimated at the individual level ACA was used to estimate the part-worth utilities of all attribute levels excluding the price information The Price Estimation Scene, (Breidert et al), was then added at the end of the ACA The Price Estimation Scene is a choice-based scene where, on several occasions, a different product or a dynamically set price is offered and the respondent has the option to accept the offer or reject it With these questions, we search for two reservation price points - one an observed upper, the other an observed lower price These upper and lower prices are where the algorithm tries to box the respondent’s utility/price exchange ratio by locating two points where the respondent would purchase for a given utility, and one at which the respondent would decline to purchase 48 Download free eBooks at bookboon.com Reflection and the Way Forward The Power of Conjoint Analysis Reflection and the Way Forward For companies to understand which aspects of their product or service are most valued by the customer, conjoint analysis is considered one of the best tools available (Kotri 2006) Conjoint analysis, in essence, consists of generating and conducting specific experiments among customers with the purpose of modelling their purchase decision It enables the estimation of value created to customers with remarkable accuracy It is also useful for market segmentation decisions and other improvements that create value for the company Its key advantages cover seven main areas: Efficient study of customer value – it defines precisely attribute performance levels The relative importance of each attribute is statistically calculated, not directly asked in the questionnaire Accurate estimation of needs – value creating factors are revealed on an individual customer level and the amount of value will be gauged from attributes’ performance levels Basis for pricing – points out the price sensitivity and level of acceptance of customers Basis for market segmentation – results are valid on single customer or micro level Basis for New Product Development – creates a simulation model to test customer preferences for alternatives, and helps to make compromises in product development through the power of trade-off By combining customer preferences with corresponding cost data, the profit maximising configuration can be found Realism – comparing concept cards is analogous to comparing products in the real marketplace Insight - generating a real idea of the psychological trade-offs consumers are making when evaluating several attributes together Limitations to conjoint modelling: The small number of product or service attributes that can be effectively analysed Experience has shown that a four attribute product or service each comprising four levels (on average) will necessitate the evaluation and ranking of sixteen options is sufficient to retain a respondent’s attention span before interviewee, and interviewer, fatigue creeps in Conjoint analysis is statistically very complex Unless the analyst is familiar with multiple or, at least, linear regression analyses and probability theory, we recommend you securing the services of a consultancy or analyst who has the confidence and competence to implement this for you 49 Download free eBooks at bookboon.com Reflection and the Way Forward The Power of Conjoint Analysis Restrictions on questionnaire length - as this depends on the number of options to be evaluated, a typical Adaptive Conjoint Questionnaire with 20 – 25 attributes may take more than 30 minutes to complete leaving very little time or opportunity to capture other meaningful data For New Product Development, there may only be partial knowledge of all the key attributes applicable to the product or service Poorly designed studies may overemphasise the importance of a single feature leading to indifferent decision-making Conjoint analysis does not take into account the number of items per purchase so it can give a poor reading of market share So as to ensure the respondent carries out sorting or ranking tasks so that no two scores are identical or all options have been evaluated, we would not recommend using an online methodology where there is very little control over the respondent A face-to-face approach using professional Market Research Interviewers is much preferable and will lead to higher quality results Despite these limitations, if one is able to overcome the complexity of this analytical approach, conjoint analysis can be an important tool for helping a company to answer the following questions: • What price will maximize my profits? • What features should my product have? • How many of these will I sell? • Who will buy them? • Why will they buy them? • What will happen to my sales if my competitor alters his product line? • What market action can I take that will be most devastating to my competitor? Merits of each conjoint approach: • Full profile conjoint: Limited to the number of attributes and levels within attributes one can test Whenever a fractional factorial design is used, some information will be lost The analyst must ensure that the information being sacrificed does not compromise the project’s ability to answer the research objectives • Pairwise Conjoint Analysis: Attributes are traded-off in pairs Can be time-consuming for larger designs 50 Download free eBooks at bookboon.com Reflection and the Way Forward The Power of Conjoint Analysis Not very interesting – respondent fatigue will set in Not very realistic for products More relevant for services • Scalar Conjoint Analysis: The best method to ensure a real ranking of importance Costs can be added for each feature, thereby calculating value for £ and combining those features with the highest value This gives us combinations with high value and low cost • Brand – Price Trade-off: Can produce price elasticity charts 360° thinking Can show the effect of a price change on a brand in a static market, or with competitor price changes The robustness of the statistical applications used in this technique is questionable as it’s purely a procedural methodology Could be construed as a useful starting-off point but outcomes are ‘crudely’ arrived at in comparison with the aforementioned statistically based technique 360° thinking 360° thinking Discover the truth at www.deloitte.ca/careers © Deloitte & Touche LLP and affiliated entities Discover the truth at www.deloitte.ca/careers Touche LLP and affiliated entities © Deloitte & Touche LLP and affiliated entities Discover the truth51at www.deloitte.ca/careers Click on the ad to read more Click on the ad to read more Download free eBooks at bookboon.com © Deloitte & Touche LLP and affiliated entities Disc Reflection and the Way Forward The Power of Conjoint Analysis • Choice Based Conjoint Analysis: Orme B (1996): CBC is similar to what buyers actually in the marketplace Choosing a preferred product from a group of products is simple and a natural task Results are analysed in aggregate since choices provide less statistical information per respondent than traditional approaches like full profile conjoint Bearing this in mind, CBC projects require larger sample sizes to achieve the same precision of estimates • Discrete Choice Modelling: Despite its all-embracing features and outcomes, its statistical applications are very complex Outputs are based on probabilities rather than derived or modelled utilities • Adaptive Conjoint Analysis: This hybrid approach is appropriate for building preference behavioural models with large numbers of attributes – certain software can handle up to thirty attributes Higher quality data as respondents are more interested or involved in the task It may not be as useful when price sensitivity, non-cognitive purchase decisions or interaction terms are to be modelled In real-life situations, the task would be some form of actual choice between alternatives rather than the more artificial ranking and rating originally used A word about software: The author is only really familiar with SPSS as a software provider This ‘bolt-on’ to the SPSS suite of multivariate programmes does presuppose a sound knowledge of the subject matter and help is best sourced by actually speaking to one of their advisers rather than consulting their manual or accessing their software-based help facility Other software we came across during our desk research for this book included: • Sawtooth Software • XLStat • Qualtrics • Superanalytics And what developments are there out there now? 52 Download free eBooks at bookboon.com Reflection and the Way Forward The Power of Conjoint Analysis Conjoint analysis has been with us since the early 1970s (if not earlier) There have been developments of the core concept by adapting its rudiments to satisfy market needs and, in particular, the market requirement to handle more attributes and levels within attributes This technique now encompasses a variety of experimental protocols and estimation models, for example rating-based or choice-based for predicting market share, as well as several probabilistic models for predicting market share A new model for market share predictions, FRC – BOLSE yields convergent results for different rating scales, and outputs predictions that match regression reliability (Guyon and Petiot, 2011) Conjoint Analysis is reliable to predict share of preference, but is not necessarily reliable to predict market share This new model offers a new approach for predicting shares of preferences in rating based conjoint analysis Furthermore, the probabilistic model is consistent with the various rating scales and the probabilistic shares of preference estimates agree with the reliability of the utility estimates 53 Download free eBooks at bookboon.com Bibliography The Power of Conjoint Analysis Bibliography • Wildstrom, Stephen H (1994), Business Week, “The fax gets a facelift” • Moy C (2001) Factor Analysis – Lecture and Computer Demo, February • Kuhfeld WF, Garratt M, and Tobias RD (1994) “Efficient Experimental Design with Marketing Research Applications” Journal of Marketing Research • Qualtrics (2012) “Conjoint analysis: Explaining Full Profile and Self-explicated Approaches” (accessed 11/07/12) • Hauser JR and Shugan SM (1980) “Intensity Measure of Consumer Preference” Operations Research,28, 2, 278 – 320 • Johnson, Richard M (1987) “Adaptive Conjoint Analysis” July, 253 – 265 • Varian HR (2003) “Intermediate Economics – A Modern Approach” W.W Norton & Co., New York • Green PE & Srinivason V (1990) “Conjoint Analysis in Marketing: New Developments with Implications for Research and Practice” Journal of Marketing, 54, – 19 • Breidert C., Hapsler M., Schmidt – Thieme L (2006) “Reservation Price Estimation by Adaptive Conjoint Analysis” University of Freiburg • DTI (2001) Task Force “Jacking up standards in car servicing” September, (URN 01/1171) www.statwizards.com/product/info/WhitePaper/DiscreteChoiceModels/pdf (accessed 18/07/12) • Larsen RJ and Marx ML (2001) “An Introduction to Mathematical Statistics and its Applications” 3rd edition, Prentice Hall (chapters – 6) • Kotri A (2006) “Analysing customer value using Conjoint Analysis” University of Tartu, Faculty of Economics and Business Administration, Tartu University Press • Guyon H and Petiot JF (2011) “Market Share Predictions – a new model with rating based conjoint analysis” International Journal of Market Research Volume 53 Issue 6, 831 – 858 • Randall G Chapman and Richard Staelin (1982), “Exploiting Rank Ordered Choice Set Data Within the Stochastic Utility Model”, Journal of Marketing Research, August • Orme B (1996) “Which Conjoint Method Should I Use?” Sawtooth Software (accessed 30/07/12) 54 Download free eBooks at bookboon.com Bibliography The Power of Conjoint Analysis Biography David C S Murray MBA, B.A (Hons), Dip M, M CInstM, Dip MRS is the Director of the awardwinning Murray Consultancy Ltd, a company he founded in 2000 David has gained over 30 years experience in research roles within Telewest Communications (formerly Southwestern Bell), The Liverpool Daily Post and Echo Ltd, and Littlewoods His company is a full service Market Research Consultancy covering all aspects of consumer, business-to-business and behavioural research The organisation undertakes a wide variety of research-based projects including face-to-face interviewing, telephone interviewing, business-to-business research, online interviewing, Focus Groups, in-depth interviewing, Desk Research, and tracking studies Over the last twelve years the company has built up an enviable client base of private, public and voluntary sector organisations, which has resulted in repeat business, referrals and company growth What sets the Murray Consultancy’s competitive advantages over generic consultancies is the application of multivariate statistics to provide actionable solutions For more information about the company, please contact our website www.murrayconsultancy.co.uk David is the UK representative for the pan-European Business Information Group and is currently the ambassador for Market Research for the local Chartered Institute of Marketing Merseyside branch, having been Chair for five years In 2006 David was invited to Paris to receive the prestigious BIG European Research award for a paper he submitted on how to successfully evaluate Employee Satisfaction in a call centre working environment using multiple regression analyses As can be seen from his qualifications, David is a firm believer in continuous improvement and currently accumulates over 30 hours per annum in Continuous Professional Development David also believes in putting something back into the business community and is currently a mentor for the Merseyside Special Investment Fund helping new businesses start up by advising how to effectively evaluate the market for their product or service 55 Download free eBooks at bookboon.com Bibliography The Power of Conjoint Analysis Outside his day job, David follows the current European champions, Chelsea FC (since 1967), is a linguist speaking four other languages and is an anorak of the 1970s progressive music scene David Murray, The Murray Consultancy Ltd., The Corn Exchange, Drury Lane, Liverpool, L2 7QL 0151 225 0220 56 Download free eBooks at bookboon.com ... Studies The Power of Conjoint Analysis Case Studies We have now seen how a travel company, with the benefit of conjoint analysis, can: • Position their offers based on the combinations with the highest... statistical analysis technique and its related models now form the basis of this book 12 Download free eBooks at bookboon.com An Outline of Conjoint Analysis The Power of Conjoint Analysis An Outline of. ..   An Outline of Conjoint Analysis The Power of Conjoint Analysis This means that as the sample size increases over 385 correspondents, the reduction in the margin of sampling error starts