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DecisionMaker sm A Simple and Effective Decision Analysis Tool from.

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1 DecisionMaker sm A Simple and Effective Decision Analysis Tool from

2 To design the best possible product or service, a marketer needs a method of getting consumer input that: Holds respondents’ interest Asks questions that are meaningful to respondents Is adaptable to different media (including the Internet) Is scientifically valid Gives results marketers can understand and use

3 Existing conjoint techniques can come up short on these criteria: The ranking task in traditional conjoint is burdensome on respondents, limits the size of the problem that can be tested, and is impossible to do on the Internet “Hybrid” methods used to reduce burden do not use an orthogonal design, and risk biasing the results. Traditional conjoint yields arbitrary scores that can tell what product respondents think is best, but not how good it is –Can’t make absolute statements about a product’s worth –Can’t be directly expressed in terms of purchase likelihood without giving respondents an additional task

4 DecisionMaker sm, because it is oriented to purchase intent, provides a number of answers to these problems: Avoids forcing respondents to rank large product sets –Reduces respondents’ burden –Allow use of orthogonal product set, resulting in clearer results –Adaptable to almost any medium, including the Internet Uses appropriate mathematical model (logit) for predicting decisions All results expressed in terms of purchase likelihoods –Has an absolute, non-arbitrary meaning –Can be immediately grasped, and acted upon, by the user

5 Data Collection Like Classical Conjoint Analysis, uses orthogonal set of combinations of product feature levels –Design can be blocked, to increase the feasible problem size even further Respondents shown (or read) a description of each product, asked how likely they are to buy it –Can use different scales: “Yes/No”, 5- or 10-point scale Ideal for administration on the Internet –Respondents can read the description, see illustrations –Internet allows better control of responses than mail surveys

6 Data Analysis Respondents’ purchase likelihoods for each product is dichotomized, if necessary Product’s purchase likelihood is regressed on its characteristics across all respondents, using logit modeling –Appropriate for predicting a likelihood or share Yields utilities (part-worths) analogous to Classical Conjoint: –The higher the utility of a feature level, the more desirable it is –Can be used to compute the total utility of a product The sum of the utilities of the levels of its features –Then, a product’s Total Utility in DecisionMaker sm is transformed mathematically into a purchase likelihood for that product

7 Product Simulation Utility values are used to estimate purchase likelihoods for all possible products in the feature set tested (including those not asked) Purchase likelihoods displayed in an Excel spreadsheet for easy reference Simulation results are then used to represent utilities in terms of leverage on purchase likelihood –The “leverage” of a feature level is the average purchase likelihood of all products containing that level –The “lift”, or importance, of a feature is the difference between its highest and lowest leverage –Scaled in percentage points –Has both a relative and an absolute meaning –Easier to understand and use than part-worths

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9 DecisionMaker sm Advantages Ease of administration Low respondent burden Orthogonal design Intuitive interpretation Actionable results

10 To design, execute, and analyze a DecisionMaker sm project, Call Paul M. Gurwitz, Ph.D., at , or at


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