Multiattribute decisions in marketing: A measurement approach

Tags: product features, number of cards, plan file, variable labels, utility procedure, Attribute importance, holdout, Card format, fractional factorial designs, SPSS system file, simulation methods, list of values, data mining techniques, SPSS Conjoint, alternative products, customer loyalty, product attributes, attribute, customers, data analysis, Simulation results, experimental data analysis, Conjoint analysis
Content: SPSS ConjointTM 12.0
Win new business Find out which product features are important to potential customers. Increase customer loyalty Discover what product features are most important for your current customers. Increase cross-selling Determine the influence of product attributes on customer preference.
SPSS Conjoint enables you to understand what drives purchasing decisions. In this example, you quickly see the importance each factor, brand name, seal of approval, money-back guarantee, package design, and price, plays on a customer's product selection.
Easily discover what your customers value with SPSS Conjoint
By better understanding consumer preferences with SPSS Conjoint, you'll launch successful products and price them more effectively Conjoint analysis gives you a realistic way to measure how individual product attributes affect consumer preferences. Easily measure the tradeoff effect of each attribute in the context of a set of attributes -- as consumers do when making purchase decisions. When you use both conjoint analysis and competitive research for your new product, you'll be less likely to overlook important product dimensions and more likely to develop products that sell. Answer your critical questions: What product attributes do my customers care about? What are the most preferred attribute levels? How can I most effectively perform pricing and brand equity studies? And, you can answer all your questions before you spend valuable resources trying to bring successful products to market. product development is expensive -- use SPSS Conjoint to ensure you expend your efforts on products that succeed.
A three-step plan for new product research SPSS Conjoint gives you all the tools you need for developing product attribute ratings. You can use SPSS Conjoint's three procedures to: Generate designs easily. Use the design generator -- Orthoplan -- to produce an orthogonal array of alternative potential products that combine different product features at specified levels. Print "cards" to elicit respondents' preferences. With Plancards, you quickly generate cards that respondents can sort to rank alternative products. Get informative results. Analyze your data with the conjoint procedure, a specially tailored version of regression. You'll get results you can act on, such as which product attributes are important and at what levels they are most preferred. You can also perform simulation, which tells you the market share of preference for alternative products. These steps save you time and money, by generating a set of conjoint experimental trials that are a fraction of all possible combinations of attribute levels. You'll quickly learn how your respondents rank their preferences when you create and print cards they can sort. And, with the results from your conjoint procedure, you'll learn how your respondents rank product attributes.
Using SPSS Conjoint 12.0 in a real-life study Green and Wind present a classic conjoint analysis example in which a company wants to market a new spot remover for carpets and upholstery. Management has identified five attributes it believes will influence consumer preference: package design, brand name, price, Good Housekeeping seal of approval and a money-back guarantee. Three of these attributes have three levels, while the others have two. The resulting full factorial design would have 108 alternative product bundles, making for an unfeasibly large study. Using Orthoplan, the researcher can generate 18-run alternatives such as those in Figure 1 and still receive all the information he or she needs to make a good decision. Green and Wind present a set of preferences for the 18 alternative attribute bundles. SPSS Conjoint produces the results shown in Figure 2. Two attributes stand out as important -- package design and price -- while the Good Housekeeping seal of approval is relatively unimportant. The Utility and Factor columns in Figure 2 show the relative preference for each level of each attribute. Consider packaging: Package B is the most preferred, Package A is the least preferred. In the Factor column, Package B has tick marks extending to the right, while Package A has them extending to the left.
SPSS Conjoint features* Figure 1. Save time and money with SPSS Conjoint by using Orthoplan to present a fraction of all possible alternatives. Here, Orthoplan generates an 18-run orthogonal array instead of all 108 possible combinations. Figure 2. Easily see which attributes consumers prefer. Here, the large bars associated with Package and Price indicate that design and price are most important.
Citation: Green, P.E., and Y. Wind. 1973. Multiattribute decisions in marketing: A measurement approach. Hinsdale, IL: Dryden Press.
SPSS Conjoint 12.0 features ORTHOPLAN Generate orthogonal main effects fractional factorial designs; Orthoplan is not limited to two-level factors Specify variable list, optional variable labels, a list of values for each variable, and optional value labels Specify the desired number of cards for the plan; Orthoplan will try to generate a plan in the desired minimum number of runs Generate holdout cards to test the fitted conjoint model Mix the training and holdout cards or stack the holdout cards after the training cards Save the plan file as an SPSS system file PLANCARDS Use this utility procedure to produce printed cards for a conjoint experiment; the printed cards are used as stimuli to be sorted, ranked or rated by the subjects Specify the variables to be used as factors and the order in which their labels are to appear in the output Choose a format ­ Listing-file format: differentiate holdout cards from experimental cards and list simulation cards separately following the experimental and holdout cards ­ Card format: holdout cards are not differentiated and simulation cards are not produced Write the cards to an external file or the listing file Specify optional title and footer
Specify pagination so that each new card in single-card format begins on a new page CONJOINT Perform an ordinary least squares analysis of preference or rating data with this procedure Work with the plan file generated by Plancards, or a plan file input by the user using DATA LIST Work with individual level rank or rating data Provide individual level and aggregate results Treat the factors in any of a number of ways; Conjoint indicates reversals ­ Discrete: factor levels are categorical ­ Linear: scores or ranks are linearly related to the factor ­ Ideal: a quadratic relationship is expected between the scores or ranks and the factor; this method assumes that there is an ideal level for the factor, and that distance from the ideal point in either direction is associated with decreasing preference ­ Antideal: a quadratic relationship is expected between the scores or ranks and the factor; this method assumes that there is a worst level for the factor, and that distance from this point in either direction is associated with increasing preference Work with experimental cards that have one of three scenarios ­ Training ­ Holdout ­ Simulation
Select from three conjoint simulation methods ­ Max utility ­ Bradley-Terry-Luce (BTL) ­ Logit Print controls ­ Print only the results of the experimental (training and holdout) data analysis ­ Print only the results of the conjoint simulation ­ Print results of both the experimental data analysis and the conjoint simulation Write utilities to an external file Show print results with ­ Attribute importance ­ Utility (part-worth) and standard error ­ Graphical indication of most to least preferred levels of each attribute ­ Counts of reversals and reversal summary ­ Pearson R for training and holdout data ­ Kendall's Tau for training and holdout data ­ Simulation results and simulation summary System requirements SPSS Base 12.0 1MB hard disk space Other system requirements vary according to platform * Features subject to change based on final product release.
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