Pairing Kano and Conjoint to Streamline New Product Development

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1 Pairing Kano and Conjoint to Streamline New Product Development From PDMA Knowledge Wiki CONTENTS 1 Author(s) 2 Definition 3 Description 4 References AUTHOR(S) Satisfaction Management Systems (SMS) ( DEFINITION Initial efforts to define the features for a new product or service often yield a list that is broader than development resources can support (time, budget, technical feasibility, etc.). Further, not all of those features drive purchase decisions. Defining "the best" set of features, levels and price points is critical to a successful end result. Kano analysis provides a quantitative customer-guided tool for reducing the list to those features most likely to yield a successful product. When followed by conjoint analysis, potential customers can make tradeoffs between different features, between different levels of features, and between various price points. The results define an optimum set of features, levels and price points, and can lead to estimates of market size, market segmentation and revenue potential. PDMA-Wiki -1-5/9/2008

Kano Analysis Introduction The Kano 1 method provides an orderly and efficient way to sort through an initial new product feature list and identify a manageable set of features that will resonate

2 Kano Analysis Introduction The Kano 1 method provides an orderly and efficient way to sort through an initial new product feature list and identify a manageable set of features that will resonate with prospective customers. Kano brings fresh quantitative Voice of the Customer data to the final feature set decision process. From the initial new product list, Kano identifies features that fall primarily into one of four categories they: 1. Must-be included these are givens or table stakes to customers in evaluating the product, OR 2. Are Exciting provide unexpected excitement and satisfaction the wow factor. These features can delight the customer, OR 3. Are Desirable where more is better vehicle fuel mileage, for example, OR 4. Yield Indifference produce a who cares? reaction if the feature is there or not. The goal is to prune the initial list down to a mix of Must-be, Exciting and Desirable features, avoiding features that generate Indifference. The analysis also points out the specific features that are the most likely candidates for the final reduced list. Applications The longer the initial feature list, the more useful Kano becomes. The Kano method can be applied to tangible and intangible products and services across a broad array of industries. It is applicable to entirely new products or add-ons and refinements to existing products. It is applicable to B2B or B2C products and services. The Kano Measurement Model Kano measurement taps two dimensions of the prospective customer s perception of the feature the perceived importance of the feature and the individual s feeling about the feature. Importance can be measured by conventional anchored scales, e.g., 0 (Not Important At All) to 10 (Highly Important). Feelings 2 about the feature are assessed by presenting it in the positive (How do you feel if it has the feature?) and in the negative (How do you feel if it doesn t have or has less of the feature?). 1 See Walls, T. (Editor). Center for Quality Management Journal, 2, 4, Fall Special Issue on Kano s Methods for Understanding Customer-defined Quality. 2 The terms Functional and Dysfunctional are sometimes used in place of Positive and Negative, respectively. PDMA Wiki -2-5/9/2008

3 For both the positive and negative feelings questions, the respondent chooses a response from the following list: 1. Is a basic requirement for you a must have 2. Is desirable and helpful but not a basic requirement 3. You're neutral about 4. You don't expect or desire -- but you could live with it 5. You'd dislike and not accept An example of the Kano questions for one feature 3 : [Importance:] How important is it to you that it is easy for a staff member to identify the unit or department to which the new [Equipment] belongs? [Positive form:] How would you feel if it is EASY for a staff member to identify the unit or department where the new [Equipment] belongs? Is this something that: 1. Is a basic requirement for you a must have? 2. Is desirable and helpful but not a basic requirement? 3. You're neutral about? 4. You don't expect or desire -- but you could live with it? 5. You'd dislike and not accept? [Negative form:] Alternatively, how would you feel if it is DIFFICULT for a staff member to identify the unit or department to which the new [Equipment] belongs? Is this something that: 1. Is a basic requirement for you a must have? 2. Is desirable and helpful but not a basic requirement? 3. You're neutral about? 4. You don't expect or desire -- but you could live with it? 5. You'd dislike and not accept? Kano Data Collection Kano feedback, like many other forms of market research, can be collected in qualitative settings, such as focus groups or 1:1s or, more commonly, in quantitative survey processes. Web surveys are a popular way of conducting Kano studies, in part because images can be displayed to clarify various feature possibilities. 3 Much of the success of a Kano study rests on the clarity and precision with which the features are described and in particular on the positive and negative forms of the feature. A meaningful metric in the example above, in place of Easy and Difficult would provide a stronger question. PDMA Wiki -3-5/9/2008

Kano Data Analysis The specific classification of each feature by a respondent is determined by the combination of positive and negative responses, as shown in the table below: Must Be (Code M in the

4 Kano Data Analysis The specific classification of each feature by a respondent is determined by the combination of positive and negative responses, as shown in the table below: Must Be (Code M in the table) This product feature must be included in the product. Customers will not buy without it but you do not get any bonus for providing it, they simply expect the product feature to be there. This suggests that this product feature should be optimized to meet customers expectations but not maximized. One-Dimensional ( O ) The more functionality of this product feature that is in the product, the more satisfied customers will be. Attractive ( A ) Possibly a latent product feature. If this product feature is in the product, customers find it more appealing but the absence of the product feature is not a strong negative. If this product feature is not in the product, it does not stop customers from purchasing. Indifferent ( I ) Customers don t really care about this product feature. They do not care if the product feature is or is not present in the product. Questionable ( Q ) Customers think that both the functional and dysfunctional are improvements over what they have, or that both the functional and dysfunctional are not improvements. Reversal ( R ) Customers like the dysfunctional and dislike the functional. The feature classifications can then be used in a variety of other analytical procedures, along with the Importance results. PDMA Wiki -4-5/9/2008

consist of: 1. 3o r 4 Attractive features. 2.

5 Kano Data Display Final Selection of Product Features Assuming technical and financial feasibility, the final mix could well consist of: 1. 3o r 4 Attractive features. 2. Several Desired features. 3. All Must-Be features. 4. No Indifferent features. PDMA Wiki -5-5/9/2008

6 Conjoint Analysis The Kano process, in concert with other information available to the team, isolates a feature set that is likely to yield a successful product. However, Kano doesn t identify the specific set of features, which, if offered at specific levels or amounts of the features, and at a specific price point, will result in the greatest acceptance of the product. This is where conjoint analysis becomes useful. Conjoint analysis provides a statistical basis for modeling choice behavior. By drawing on the results of the Kano analysis, the product development team has customer and market evidence to support its decision on the final product feature set. Building from these results, the conjoint helps zero in on the optimum set of product features and their levels, and provides highly probable scenarios for market acceptance. Brief History of Conjoint Analysis Conjoint analysis dates to the 1970s 4 and has evolved over the years since then. There are several types of conjoint analysis: Adaptive Conjoint Analysis, Discrete Choice, Full Profile Choice Modeling, Hierarchical Choice, Trade-off Matrices, Preference Based Conjoint and Pairwise Comparisons. More recently, multi-variate testing (MVT) has become popular for testing a large number of alternatives (e.g., simultaneous website layout, design, content, offers, etc.). Each type of conjoint has its passionate advocates and harsh critics. Traditional conjoint analysis Full Profile Choice Modeling is the subject of this presentation. Highlights of Traditional Conjoint Analysis Full Profile Choice Modeling: Yields respondent-level utilities. A utility represents the degree of preference a respondent has for a particular feature at a particular level. The higher a utility, the more strongly the respondent prefers a feature at a specific level. Extends the reach and richness of subsequent analysis, particularly for market segmentation. The results can be added to an initial database and used to identify critical price points, market segments, market size and market share. Can be tested in an experimental design framework, so that a large number of features and price points can be analyzed. 4 Green, P.E. and Rao, V.R. (1971), Conjoint Measurement for Quantifying Judgmental Data, Journal of Marketing Research, Volume 8, pp See the following for a more statistical discussion of conjoint and some related methods ties to multidimensional scaling. de Leeuw, J., Young, F.W., and Takane, Y. (1976), Additive Structure in Qualitative Data: An Alternating Least Squares Method with Optimal Scaling Features, Psychometrika, Volume 41, pp Young, F.W., de Leeuw, J., and Takane, Y. (1976), Regression with Qualitative and Quantitative Variables: An Alternating Least Squares Approach with Optimal Scaling Features, Psychometrika, Volume 41, pp PDMA Wiki -6-5/9/2008

7 The Conjoint Analysis Process Here are the steps in a full-choice conjoint analysis: 1. Select the features you are planning to test. You ll want to include price. Brand can be a key issue. 2. Identify the levels to be tested for each feature. (Examples: 50 vs. 100 vs. 200 tablets is 3 levels, cherry vs. grape is 2 levels, $3.00 cost vs. $4.75 vs. $6.00 vs. $7.25 is 4 levels, etc.) 3. Finalize the number of profiles to test. There is one profile for each unique combination of features and levels. If you test 3 features with 3 levels each, there are 3x3 = 9 profiles. If you have 5 features 3 with 3 levels each, 1 with 4 levels and 1 with 2 levels there are 3x3x3x4x2 = 216 profiles. It is not uncommon to have thousands of potential profiles to test. An example of one profile is shown below: 4. Identify the specific profiles to best tested. The goal is to end up with an efficient set of profiles that respondents will respond to attentively. To limit respondent fatigue and increase cooperation, the number of profile choices to rated is usually kept to 25. Profiles can be limited by scaling back on the attributes and/or their levels. Alternatively, an efficiency analysis 5 can be used to specify a sub-set of profiles which provide results that are nearly as robust as when the respondent is presented with the full set of profiles. If more than 25 profiles is needed, the sample can be divided into separate groups. 5. Check for conflicts within the selected profiles. While a certain profile may be a logical combination of attributes and levels, it is not necessarily a meaningful profile to test. There may be sound technological, financial or company policy reasons why a certain profile shouldn t be tested. The final profiles developed in #4 above need to be reviewed on these criteria and substitutes provided where needed. What are the outputs of a conjoint analysis? Typically three different measures are relevant: The relative importance of each attribute included in the product or service that is being tested The optimum features for that attribute The size of the market that is attracted to the product 5 Mitchell, T.J. (1974), An Algorithm for the Construction of D-optimal Experimental Designs, Technometrics, Volume 16 (May), pp PDMA Wiki -7-5/9/2008

8 Sample Results Utilities Below is a sample of how utilities are often presented in a conjoint study. The higher the utility, the more important the feature has (in this example, price) for that segment of respondents Utilities sum to zero across the features within a sample group. PDMA Wiki -8-5/9/2008

9 Market Potential Because utilities are measured for each individual, the measures can be used to size the market. Ideal customers to target, as well as those to avoid, can be identified. The share of a market by customer type can also be shown: PDMA Wiki -9-5/9/2008

10 Along with the share of wallet of these customer types. PDMA Wiki -10-5/9/2008

Conjoint Simulator In some cases, there may be last-minute reasons why NONE of the profiles that were specifically tested are acceptable as the new product.

11 Conjoint Simulator In some cases, there may be last-minute reasons why NONE of the profiles that were specifically tested are acceptable as the new product. A conjoint simulator presents the likely market value of all possible combinations of features and levels, regardless of whether they were tested directly or not. The simulator makes it possible to find a close substitute and to proceed with product development. A partial example of the results from a conjoint simulator is shown below. Summary In the early stages of product development, a team often finds itself confronted with a wealth of choices. Voice of the Customer processes, internal priorities and other sources of product ideas can generate an abundance of choices for the new product. Kano analysis, combined with conjoint analysis, allows the team to continue using Voice of the Customer input throughout the remainder of the development process. Kano analysis helps the team reduce the initial feature set it provides a specific customer-based rationale for the feature s inclusion. Conjoint analysis further specifies the product, including price, and predicts the product s likely market success. REFERENCES de Leeuw, J., Young, F.W., and Takane, Y. (1976), Additive Structure in Qualitative Data: An Alternating Least Squares Method with Optimal Scaling Features, Psychometrika, Volume 41, pp One of the two articles that give a more statistical discussion of conjoint and some related methods ties to multidimensional scaling. Green, P.E. and Rao, V.R. (1971), Conjoint Measurement for Quantifying Judgmental Data, Journal of Marketing Research, Volume 8, pp This is the founding article for conjoint analysis. Mitchell, T.J. (1974), An Algorithm for the Construction of D-optimal Experimental Designs, Technometrics, Volume 16 (May), pp PDMA Wiki -11-5/9/2008

12 This article provides an introduction to the statistical process for deciding which subset of profiles provide a highly efficient test of all possible profiles it reduces the many to the few so that the research can proceed. Young, F.W., de Leeuw, J., and Takane, Y. (1976), Regression with Qualitative and Quantitative Variables: An Alternating Least Squares Approach with Optimal Scaling Features, Psychometrika, Volume 41, pp One of the two articles that give a more statistical discussion of conjoint and some related methods ties to multidimensional scaling. Walls, T. (Editor). Center for Quality Management Journal, 2, 4, Fall Special Issue on Kano s Methods for Understanding Customer-defined Quality. PDMA Wiki -12-5/9/2008

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