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Design of New Products Through Conjoint Analysis

Design of New Products Through Conjoint Analysis. Role of design in new product development Conjoint Analysis for product (offering) design. Value of Good Design. 80% of a product’s manufacturing costs are incurred during the first 20% of its design (varies with product category).

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Design of New Products Through Conjoint Analysis

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  1. Design of New ProductsThrough Conjoint Analysis • Role of design in new product development • Conjoint Analysis for product (offering) design

  2. Value of Good Design 80% of a product’s manufacturing costs are incurred during the first 20% of its design (varies with product category). Conjoint Analysis is a systematic approach for matching product design with the needs and wants of customers, especially in the early stages of the New Product Development process. Source: Mckinsey & Company Report

  3. Based on a study of 203 products in B2B -- Robert G. Cooper, Winning at New Products (1993) . Success measured using four factors: (1) whether it met or exceeded management’s criteria for success, (2) the profitability level (1-10 scale), (3) market share at the end of three years, and (4) whether it met company sales and profit objectives (1-10 scale). ME Conjoint Analysis 2006 - 3

  4. Source: Robert G. Cooper, Winning at New Products (1993) ME Conjoint Analysis 2006 - 4

  5. Source: Robert G. Cooper, Winning at New Products (1993) ME Conjoint Analysis 2006 - 5

  6. Source: Robert G. Cooper (1993) ME Conjoint Analysis 2006 - 6

  7. What Does Conjoint Analysis Do?(Measure Importance by Assessing Preferences) • The basic outputs of conjoint analysis are: • A numerical assessment of the relative importance that customers attach to attributes of a product category • The value (utility) provided to customers by each potential feature of an offering • Identification of product designs that maximize market share or other indices.

  8. Why is Customer Value Assessment through Conjoint Useful? • Design new offerings that enhance customer value. • Forecast sales/market share/profit of alternative offerings. • Identify market segments for which a given concept/offering has high value. • Identify the “best” concept/offering for a target segment. • Explore impact of alternative pricing and service strategies. • Plan production in flexible manufacturing systems.

  9. Conjoint Analysis in Product Design Should we offer our business travelers more room space or a fax machine in their room? Given a target cost for a product, should we enhance product reliability or its performance? Should we use a steel or aluminum casing to increase customer preference for the new equipment?

  10. Measuring Importance of Attributes When choosing a restaurant, how important is… Circle one Not Very Important Important Decor 1 2 3 4 5 6 7 8 9 Location 1 2 3 4 5 6 7 8 9 Quality of food 1 2 3 4 5 6 7 8 9 Price 1 2 3 4 5 6 7 8 9

  11. Measuring Importance of Attributes

  12. Measuring Importance By Measuring Utility • For single-attribute products, an underlying preference or utility scale can be constructed as follows: • If a customer tells you she prefers Blue to Red, Red to Yellow, and Blue to Yellow (transitivity), then you can create an underlying numeric scale with the following “utiles” to represent customer preferences for the three colors: assign 3 to Blue, 2 to Red, and 1 to Yellow; or you could assign 10 to Blue, 9.95 to Red, and 1 to Yellow. From this can we say whether this customer would prefer Orange to Red? Note: Preferences represent a higher-order construct than Utility, i.e., utility comes from preferences. • How do we come up with an underlying scale to represent customer preferences for multi-attributed products?

  13. Conjoint Study Process Stage 1 —Designing the conjoint study: Step 1.1: Select attributes relevant to the product or service category, Step 1.2: Select levels for each attribute, and Step 1.3: Develop the product bundles to be evaluated. Stage 2 —Obtaining data from a sample of respondents: Step 2.1: Design a data-collection procedure, and Step 2.2: Select a computation method for obtaining part-worth functions. Stage 3 —Evaluating product design options: Step 3.1: Segment customers based on their part-worth functions, Step 3.2: Design market simulations, and Step 3.3: Select choice rule.

  14. Simple Example ofConjoint Analysis

  15. Simple Example ofConjoint Analysis

  16. How to Use in Design/Tradeoff Evaluation • Example: Italian vs Thai = 20 – 16 = 4 util units $10 vs $15 = 22 – 14 = 8 util units • So “Thai”is worth $2.50 more than “Italian” for this customer: ÞCan use to obtain value to customer of service (non-price) attributes.

  17. Conjoint Study Process Stage 1—Designing the conjoint study: Step 1.1: Select attributes relevant to the product or service category, Step 1.2: Select levels for each attribute, and Step 1.3: Develop the “product” bundles to be evaluated. Stage 2—Obtaining data from a sample of respondents: Step 2.1: Design a data-collection procedure, and Step 2.2: Select a computation method for obtaining part-worth functions. Stage 3—Evaluating product design options: Step 3.1: Segment customers based on their part-worth functions, Step 3.2: Design market simulations, and Step 3.3: Select choice rule.

  18. (Stage 3) Goals of Conjoint Simulation • Generate directional guidelines • Identify priorities • Gain anticipatory intelligence • Generate buy-in for action • Improve communication within the organization

  19. Other Aspects to Consider • Find optimal products by segment • Cluster part-worth data and select segments for Conjoint Analysis • Adjust simulation to reflect reality (adjust for awareness and availability of product) • Revenue/profit potential of a new product • Market share  Incremental margin over base product • Assess cannibalization potential of new product (Do before-after analysis) • Exclude new product(s) before doing analysis • Do analysis with new product(s) included

  20. Frozen Pizza Example

  21. Designing a Frozen Pizza Attributes • Topping (4 varieties) • Amount of cheese (2 levels) • Type of crust (3 types) • Type of cheese (3 types) • Price (3 levels) Type of cheese Topping Crust Pineapple Veggie Sausage Pepperoni Romano Mixed cheese Mozzeralla Pan Thin Thick Price Amount of cheese $9.99 $8.99 $7.99 2 Oz. 6 Oz. Note: The example in the book also has a 4 oz option for amount of cheese. A total of 216 (3x4x3x2x3) different pizzas can be developed from these options!

  22. Designing a Frozen PizzaExample Ratings Data

  23. Designing a Frozen PizzaExample Part Worth Computation

  24. Designing a Frozen PizzaExample Part Worth Computation

  25. Conjoint Utility Computations k m j U(P) = S S aijxij j=1 i=1 P: A particular product/concept of interest U(P): The utility associated with product P aij: Utility associated with the jth level (j = 1, 2, 3...kj) on the ith attribute kj: Number of levels of attribute i m: Number of attributes xij: 1 if the jth level of the ith attribute is present in product P, 0 otherwise

  26. Customer’s Utility Cust 1Cust 2Cust 3 Base* 0 45 30 Thin crust 10 -5 0 Thick crust 15 10 0 Veggie 10 0 50 Sausage 25 5 0 Pepperoni 30 20 0 Mixed Cheese 3 -10 0 Mozzarella 10 10 -5 6 oz 10 15 -20 $ 8.99 20 -10 10 $ 7.99 35 -5 20 Utility Computation(Designing a Frozen Pizza) *Base product is: Pan pizza with pineapple, 2 oz of Romano cheese, and priced at $9.99.

  27. Define the competitive set – this is the set of products from which customers in the target segment make their choices. Some of them may be existing products and, others concepts being evaluated. We denote this set of products as P1, P2,...PN. Select Choice rule Maximum utility rule Share of preference rule Logit choice rule Alpha rule Software also has a “Revenue index option” wherein you can compute the revenue index of any product compared to the revenue index of 100 for a base product you select. Market Share and Revenue Share Forecasts

  28. Maximum Utility Rule (Example) Under this choice rule, each customer selects the product that offers him/her the highest utility among the competing alternatives. Market share for product Pi is then given by: K is the number of consumers who participated in the study.

  29. Other Choice Rules Share of utility rule: Under this choice rule, the consumer selects each product with a probability that is proportional to the utility of that compared to the total utility derived from all the products in the choice set. Logit choice rule: This is similar to the share of utility rule, except that it gives larger weights to more preferred alternatives and smaller weights to less preferred alternatives. Alpha rule: Modified version of share of utility rule. Before applying the share of utility, the utility functions are modified by an “alpha” factor so that the computed market shares of existing products are as close as possible to their actual market shares.

  30. Consider a market with three customers and three products: Market Share Computation (Designing a Frozen Pizza)

  31. Utility (Value) of each product for each customer. Market Share Computation (Designing a Frozen Pizza) Maximum Utility Rule: If we assume customers will only buy the product with the highest utility, the market share for Meat Lover’s treat is 2/3 and for Veggie Delite is 1/3. Share of preference rule: If we assume that each customer will buy each product in proportion to its utility relative to the other products, then market shares for the three products are: Aloha Special (27.4%), Meat Lover’s Treat (27.8%) and Veggie Delite (44.8%).

  32. Identifying Segments Based onConjoint Part Worths

  33. Product Design for Specific Segments • Design optimal product by segment • Segment 1 (Value segment – 52% of the market): A thick-crust pizza with 6 Oz mixed cheese and pineapple (or sausage) topping priced at $7.99. This will get about 32% share and revenue index of around 100 (the same as the base product). • Segment 3 (Premium segment -- 27.5% of the market): A pan pizza with 2 Oz of Romano cheese and pepperoni or sausage topping priced at $9.99. This will get 31% share of this segment and have revenue index of about 100.

  34. Air Pollution Control System Example • Dürr Environmental is developing a new air pollution control system (thermal oxidizer) to compete against existing offerings from Waste Watch, Thermatrix, and Advanced Air. • Key offering attributes: • Thermal efficiency • Delivery time • List price • Delivery terms • Q: What to offer? • Who will buy/who to target? • Where will share come from?

  35. Air Pollution Equipment Example

  36. ME Conjoint Analysis 2006 - 36

  37. An Example Conjoint Study:Air Pollution Control Equipment Attributes • Performance specs (4 options) • Delivery time (4 options) • Price (4 options) • Delivery_terms (4 options) Efficiency Delivery timeList Price Exceed by 9% 6 months $600k Exceed by 5% 9 months $700k Meets target 12 months $800k Short by 5% 15 months $900k Delivery terms Installed, 2-year guarantee Installed, 1-year guarantee Installed, service contract FOB seller, service contract A total of 256 (4x4x4x4) different offerings can be designed from these options!

  38. Data for Conjoint Analysis: Paired Comparisons Deluxe Mid-level modelmodel Efficiency Exceed by 9% Exceed by 5% Delivery time 12 months 6 months List Price 700k 700k Delivery terms Installed, 1 year Installed, service contract Which do you prefer? Which one would you buy?

  39. Data for Conjoint Analysis: Full-Profile Ratings or Ranks

  40. Example Part Worth for Attributes

  41. Example Part Worths for Attribute Options

  42. Conjoint Utility Computations k m j U(P) = S S aijxij j=1 i=1 P: A particular product/concept of interest U(P): The utility associated with product P aij: Utility associated with the jth level (j = 1, 2, 3...kj) on the ith attribute kj: Number of levels of attribute i m: Number of attributes xij: 1 if the jth level of the ith attribute is present in product P, 0 otherwise

  43. Customer’s Utility Sunoco Mattel ICI Base 0 0 0 Meets target 5 10 10 Exceed 5% 35 0 40 Exceed 9% 40 0 50 12 months 20 5 3 9 months 30 20 8 6 months 40 10 10 $800k 5 20 2 $700K 8 35 5 $600K 10 50 10 Inst_ser 6 5 10 Inst_1Yr 8 10 20 Inst_2Yr 10 20 30 Market Share Computation:(Air Pollution Control Equipment)

  44. Define the competitive set – this is the set of products from which customers in the target segment make their choices. Some of them may be existing products and, others concepts being evaluated. We denote this set of products as P1, P2,...PN. Select Choice rule Maximum utility rule Share of preference rule Logit choice rule Alpha rule Software also has a “Revenue index option” wherein you can compute the revenue index of any product compared to the revenue index of 100 for a base product you select. Market Share and Revenue Share Forecasts

  45. Maximum Utility Rule (Example) Under this choice rule, each customer selects the product that offers him/her the highest utility among the competing alternatives. Market share for product Pi is then given by: K is the number of consumers who participated in the study.

  46. Other Choice Rules Share of utility rule: Under this choice rule, the consumer selects each product with a probability that is proportional to the utility of that compared to the total utility derived from all the products in the choice set. Logit choice rule: This is similar to the share of utility rule, except that it gives larger weights to more preferred alternatives and smaller weights to less preferred alternatives. Alpha rule: Modified version of share of utility rule. Before applying the share of utility, the utility functions are modified by an “alpha” factor so that the computed market shares of existing products are as close as possible to their actual market shares.

  47. Market consists of three products and three customers Market Share Computation (Air Pollution Control Equipment) Product Waste watch Thermatrix Advanced Air Performance specs Exceed 5% Exceed 20% Meet Specs Delivery time 9 months 9 months 6 months List Price $800k $900k $700k Delivery terms FOB_ser Inst_1Yr Inst_ser

  48. Market Share Computation:(Air Pollution Control Equipment) Computed Utility for Products Waste Watch Thermatrix Advanced Air Sunoco 70 78 61 Mattel 40 30 75 ICI 50 7840 • Maximum Utility Rule: If we assume customers will only buy the product with the highest utility, the market share for Thermatrix is 2/3 and 1/3 for Advanced Air. • Share of preference rule: If we assume that each customer will buy each product in proportion to its utility relative to the other products, then market shares for the three products are: Waste Watch: 30.3% Thermatrix: 34.8 Advanced Air: 34.9

  49. Identifying Segments Based onConjoint Part Worths (Airpol.pwr)

  50. Members in Each Segment Segment 1. Companies in this Segment include • Cummins Engineering, Illinois Tools, Mattel, Neste-Resin, Ralston Purina, New World Technologies, Baltimore Gas, Applied Coatings, Pharmasyn, and Thermal Electric. • These are smaller companies that operate in industries without major pollution problems. They want an equipment that meets EPA efficiency target, medium delivery times, have high price sensitivity, and require installation and warranty.

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