Welcome to Powerpoint slides for Chapter 15 Conjoint Analysis for Product Design

1. Welcome to Powerpoint slides for Chapter 15 Conjoint Analysis for Product Design Marketing Research Text and Cases by Rajendra Nargundkar

2. Slide 1 1. Marketing managers frequently want to know what utility a particular product feature or service feature will have for a consumer. 2. Conjoint analysis is a multivariate technique that captures the exact levels of utility that an individual customer puts on various attributes of the product offering. It enables a direct comparison between say, the utility of a price level of Rs. 400 versus Rs.500, a delivery period of 1 week versus 2 weeks, or an after sales response of 24 hours versus 48 hours. 3. Once we know utility levels for every attribute (and at every level), we can combine these to find the best combination of attributes that gives him the highest utility, the second best combination that gives the second highest utility, and so on. 4. This information can be used to design a product or service offering. 5. If this is done across a sample of customers say, segment-wise, it can also be used to predict market-share, and the response of customers to changes in the competitive strategy through changes in the marketing elements.

3. Slide 2 • Method • 1. The researcher determines a set of attributes and their levels, say 3 attributes, each at 2 levels, which he feels are critical decision-making variables for his consumers. Now, all possible combinations of these levels are listed out. • 2. For example, in a readymade shirt, price could be one factor, at levels Rs. 300 or Rs. 350, stores could be exclusive or non-exclusive, and design could be checks or solid colours. We would then take all the possible combinations as follows - • 1. Rs. 300 Exclusive Store - Checks • 2. Rs. 300 Exclusive Store - Solid Colours • 3. Rs. 350 Exclusive Store - Checks • 4. Rs. 350 Exclusive Store - Solid Colours • 5. Rs. 300 Non-exclusive Store - Checks • 6. Rs. 300 Non-exclusive Store - Solid Colours • 7. Rs. 350 Non-exclusive Store - Checks • 8. Rs. 350 Non-exclusive Store - Solid Colours • 3. These eight combination can be presented to the respondent of our survey, and he is asked to rank the combination he prefers from rank 1 to rank 8. • 4. This forms the input data for conjoint analysis.

4. Slide 3 1. The objective, as stated earlier, is to convert these rankings into utilities, so we know how this respondent’s utility varies with any change in the level of any of the attributes. 2. In other words, the output of conjoint analysis will generate utility levels for combinations given above. For example, the computer output after conjoint analysis may generate a utility table that looks like this :- a. Rs.300 – Utility 5 b. Rs.350 – Utility 1 c. Checks – Utility 10 d. Solid Colours – Utility 6 e. Exclusive Stores – Utility 4 f. Non-exclusive Stores – Utility 2 3. Thus this table indicates that relatively, checks have the highest utility of 10, and solid colours, 6. Price at the given price points has lower utility, but still, Rs. 300 has a much higher utility than Rs. 350.

5. Slide 3 contd... 4. Relatively, exclusivity or otherwise of the stores has less utility. But exclusive stores have 4, and non-exclusive stores have 2. 5. The best combined utility can also be calculated for the original eight combinations. For example,for this consumer, the best utility combination would be a price of Rs 300, checks and exclusive stores – 5+10+4=19 points. 6. The second best would be 5+10+2=17 points. (Rs 300, checks, non exclusive stores). The third,fourth,fifth….eighth best combination and their utilities can similarly be found.

6. Slide 4 Recommended usage 1. The usage of conjoint analysis can be at three levels - i. Individual consumer. ii. Segment level. iii. Across segments. 2. For industrial marketing usage, the author recommends individual level usage. This is because the industrial marketing consumers are usually smaller in number, and larger in importance individually, as compared with consumer goods. Each significant consumer may be a segment in itself. 3. In the case of consumer goods or services, it is advisable to do the exercise segmentwise. Income, age, or other relevant variables can be used to segment the sample. If stratified sampling is done, natural segments would be already available, and these could be used. 4. If it is done across segments, much of the value of Conjoint Analysis is lost, because we end up aggregating utility levels of segments which have different needs. This is therefore not advisable.

7. Slide 5 • Number of attributes and levels • 1. To avoid creating masses of data , the researcher has to be careful in selecting both the number of attributes and the number of levels of each . Only those attributes and levels must be used, which are feasible offerings from the manufacturer’s / marketer’s view point. • 2. Another point of interest is that the number of combinations being offered for ranking by respondents should not be too high . For example, beyond about 25 or 30 combinations, respondent fatigue would probably induce inaccurate or disinterested responses, affecting the validity of the procedure . • 3. In such cases, a partial list of combinations (you can specify, for example, that you want only 16 combinations) can be chosen. An orthogonal design employing a subset of the full list of attribute combinations can be generated by many of the statistical packages. This pruned list can be offered for ranking by respondents for the input data. (In the SPSS package, for example, the commands DATA, ORTHOGONAL DESIGN, GENERATE from the main menu are used to do this, as described in the chapter-end SPSS commands section).

8. Slide 6 • Let us take the example of an industrial product – a CNC machine tool which is used to perform a variety of manufacturing operations – to illustrate the application of conjoint analysis . • Similar to the brief example of a branded shirt discussed earlier, we first identify the attributes of the product which are important to customers, and then the levels for each attribute that we are willing to design and offer to a customer. • Thus, this will be an application of conjoint analysis for product design of an industrial machine tool. Let us assume that three attributes of a CNC machine tool are important – • 1. Setup time in minutes . This is the time it takes to prepare or setup the machine for operations . • 2. Delivery period in days . this is the time the manufacturer needs to deliver after the customer has placed an order . • 3. Number of different tools the machine can accommodate. This is a measure of machine flexibility in performing different operations .

9. Slide 6 contd... These are the three attributes . The levels of these attributes are 1. Setup time - 3 minutes, 6 minutes, 9 minutes, 12 minutes (4 levels) 2. Delivery period - 18 days, 22 days, 28 days (3 levels) 3. Number of tools - 4, 8 or 10 (3 levels) These levels are the options that we (the manufacturer) are willing to consider in design and delivery of the machine.

10. Slide 7 Since we have 4, 3 and 3 levels of the three attributes , we get a total of 4x3x3 = 36 different combinations of attribute levels . The next stage of the input process is to collect from a respondent his ranking for all the 36 combinations of attribute levels . This table would look like Fig . 1 . Table contd on next slide

11. Slide 7 contd… Remaining part of Fig 1

12. Slide 8 Running Conjoint as a Regression Model For those who do not have a conjoint analysis module on their statistical package, it isquite easy to convert the conjoint analysis input into an equivalent regression model and run it as a regression . The coding of the attribute levels for this purpose is known as “Effects coding “ and Fig. 2 shows our machine tool example coded for a regression run. In this input data matrix (Fig. 2), which is similar to coding of dummy variables, the four levels of Setup Time recorded as shown in the following table. Set up time in Var 1 Var 2 Var 3 minutes 1 0 0 S3 0 1 0 S6 0 0 1 S9 -1 -1 -1 S12 . Thus, 3 Variables, Var 1, Var 2, Var 3 are used to indicate 4 levels of setup time, as per the coding scheme above

13. Slide 8 contd... Similarly , the coding scheme for the 3 levels of the attribute Delivery Period is as shown below: Delivery Period in Var 4 Var 5 Days D18 1 0 0 1 D22 D28 -1 -1 Finally, the coding scheme for Number of Tools is as shown below - Number of Var 7 Var 6 Tools 0 1 T4 1 T8 0 T10 -1 -1

14. Slide 9 Thus, seven variables var 1 to var 7 are used to represent the 4 levels of Setup Time (S3,S6,S9 and S12), 3 levels of Delivery Period (D18, D22 and D28), and 3 levels of Number of Tools (T4, T8 and T10). All the 7 variables are independent variables in the regression run. Var 8 is the rating of each combination given by a respondent, and forms the dependent variable for the regression run. If the conjoint analysis is run as a regression model, the rating (which is a reverse of ranking) is used as a dependent variable. All combinations from the first to the thirty- sixth were ranked by the respondent. Rank 1 can be considered as highest rating and given a rating of 36. Rank 2 can be given a rating of 35, and so on. Strictly speaking, this is not an interval scale rating, and should have only ordinal interpretation.

15. Slide 9 contd... The complete input data recoded for a regression run on any package (EXCEL or SPSS, etc.), is in Fig. 2 (reproduced below) Table contd on next slide

16. Slide 9 contd… Remaining part of Fig 2

17. Slide 10 Output and its Interpretation If run as a regression model, the output is shown in fig 3. (partly shown below). ------------------ Variables in the Equation ------------------ Variable B SE B Beta T Sig. T VAR00001 5.500000 .656419 .374372 8.379 .0000 VAR00002 4.166667 .656419 .283615 6.348 .0000 VAR00003 -1.055556 .656419 -.071849 -1.608 .1190 VAR00004 3.333333 .535964 .261992 6.219 .0000 VAR00005 1.250000 .535964 .098247 2.332 .0271 VAR00006 -10.333333 .535964 -.812177 -19.280 .0000 VAR00007 1.583333 .535964 .124446 2.954 .0063 (Constant) 18.500000 .378984 48.815 .0000

18. Slide 10 contd... Variables 1 to 7 are treated as independent variables. Now, the column titled ‘B’ (the regression coefficients column) provides the part utility of each level of attributes. For example, Setup Time of S3 (3 minutes) is represented by variable 1 as per our coding scheme. Its utility is equal to 5.5 (looking under column B of fig 3, for variable 1). Similarly, the utility for variable 2 representing S6 (Setup Time of 6 minutes) is 4.16 and for variable 3 representing S9, it is –1.05. The utility for the fourth level of Setup Time (S12), is not in the table, but is derived from the property of this coding, that all the utilities for a given attribute should sum to 0. Thus, utility for S12 should be equal to –(5.5+4.16-1.05), or –8.61.

19. Slide 11

20. Slide 11 Contd... Utilities Table for Conjoint Analysis Attribute Level Part Range of Utility Utilities y 1 Setup Time in S3 5.5 (Max .-Min.) minutes S6 4.2 =5.5-(-8.6) S9 -1.1 =14.1 S12 -8.6 for Setup Time 2 Delivery Period D18 3.3 (Max .-Min.) in Days D22 1.3 =3.3-(-4.6) D28 -4.6 =7.9 for Delivery Period. 3 Number of T4 -10.3 (Max .-Min.) Tools T8 1.6 =8.7-(-10.3) (Flexibility) T10 8.7 =19.0 for No.of Tools

21. Slide 12 Now, with the part utilities of every level of every attribute available to us, we can come to several conclusions. First, we can conclude that machine flexibility is the most important attribute for this customer. There are two indicators for this. One, the range of utility values is highest (19.0) for number of tools (flexibility). Two, the highest individual value of utility for any level of any attribute is 8.7, for T10 (number of tools = 10). Both these figures indicate that number of tools is the most important attribute at given levels of attributes. The Setup Time seems to be the second most important attribute, as its range of utilities is 14.1, as shown in the above table. The last attribute in relative importance is the Delivery Period, with a utility range of 7.9.

22. Slide 13 Combination Utilities We can also pick up one attribute level from each attribute and combine their part utilities to calculate the total utility of the combination. For example, S3, D18 and T4 have a combined utility of 5.5+3.3-10.3 = -1.5. Similarly, S3, D22 and T4 have a combined utility of 5.5+1.3-10.3 = -3.5. If we want the best combination, we pick the highest utilities from each attribute, and add them. S3+D18+T10 in this case is the most preferred combination with a combined utility of 5.5+3.3+8.7 = 17.5. The next best combination is S6+D18+T10, with a combined utility of 4.2+3.3+8.7, or 16.2.

23. Slide 14 Individual Attributes We can also check what difference in utility a change of one level in one attribute makes. For example, S3 to S6 (Setup time change from 3 to 6 minutes) induces only a 1.3 units drop in utility, but it gets progressively more at the next stage –S6 to S9 has a difference in utility of 5.3. Similarly, increase in Delivery Period from 18 to 22 days costs 2.0 units(3.3-1.3) of utility drop, whereas 22 to 28 days causes 5.9 units of drop in utility (1.3-(-4.6)). Finally, Number of tools causes a drastic change in utility of 11.9 units from T8 to T4, and a significant drop in utility by 7.1 units from T10 to T8.

24. Slide 15 Additional Comments 1. We have seen an example of conjoint analysis for a single respondent in an industrial marketing situation. The same process is useful in any consumer product/service situation when designing or redesigning the product offering. As we have seen, service aspects of a product can also be incorporated into the conjoint analysis. 2. As we saw earlier, any number of attributes and levels of these attributes can be tested, subject only to respondent fatigue. If the number of combinations is larger than about 25-30, it is advisable to use fractional factorial designs, using a subset of the total combinations. 3. The conjoint analysis module of the computer package would explain how to do this. For example, SPSS has a feature called Orthoplan in its conjoint analysis module which helps the researcher to generate a subset of all the possible combinations of attribute levels. This can generate a specified number of combinations, which is then used to collect data from respondents, and to perform Conjoint Analysis. 4. The input data matrix of fig.1 can be directly input into a conjoint analysis program if available in the package being used. If not, the approach we have used is recommended, with effects coding, to run the conjoint analysis using a regression model. The results are equivalent, and will be as useful.