Conjoint

Product design: 

Product design Working assumption: What is a product? A product is a bundle of attribute levels or features that have utilities to customer (price is considered as attribute as well) The meaning of : “Designing a product” Deciding and setting the levels of the attributes. Performance criteria 1) Sales 2) Revenues 3) Profit Consider elasticity, demand curve, cash flow-credit (starts up firms), quick cents v.s slow dollars. Elements to consider 1) What are the product attributes and their levels 2) “Where” is the product positioned in the perceptual map and where it should be positioned. 3) Where are the competitor for the same dimensions

The Conjoint Model: 

The Conjoint Model Conjoint is a compensatory multiattribute model - it assumes that weakness on one attribute can be compensated for by strength in another. It assumes that the utility or value for a product can be expressed as a sum of utilities for its features or attributes. Working assumptions: Utilities can be measured by consumers’ overall evaluation of products where customers make tradeoffs among attributes. Customers differs in their preferences and the value they place on different attributes. Estimates of the utilities can be used to make market share predictions about new products

An Illustrative Example(Lehmann, Gupta and Steckel, 1998) P. 541: 

An Illustrative Example (Lehmann, Gupta and Steckel, 1998) P. 541 Consider a situation of shopping a notebook computer, the attributes under your consideration are (ignoring other attributes such as price, for clarity): 1) Processing speed: 100mHz, 133mHz. 2) Hard drive: 2GB or 3GB 3) Memory: 32MB or 64MB RAM There are 8 different combinations of notebook - defined as product profiles:

Ranking the profiles: 

Ranking the profiles Next, we can ask customer to rank or rate, the profiles. The table below present rankings for a hypothetical customer, the profiles are coded as dummy variables. Not surprisingly, this customers prefers profile 8, It is the ranking for of other profiles that reveals the customer’s preference for various attributes.

Uncovering Attribute Utilities from Overall Utility: 

Uncovering Attribute Utilities from Overall Utility Recall The conjoint assumption: U=a+b1*Processor + b2*Hard Drive + b3*Memory Using the dummy variables from the table in the last slide for profiles 1-4 we can write down 4 equations with 4 unknowns (a, b1, b2, b3) and solve them: a, b1, b2, b3 are called part-worths, in practice they are derived by means of a computer algorithms (dummy variable regression, MONANOVA, etc.)

Uses of the Part-Worths: 

Uses of the Part-Worths 1) We can estimate the relative value our customer attaches to different attributes. In this example, processing speed (b1=4) is valued more than hard drive capacity (b2=2) or memory (b3=1). In fact b1>b2+b3. 2) We can use the part-worths to forecast the preferences of this customer for other notebook computers. Note that in the examples we used only the first 4 profiles to compute the part-worths. In order to estimate other profiles we have to plug in the dummy variables: 3) We can simulate the impact of new product introductions.

Conjoint Simulation - The Motivation: 

Conjoint Simulation - The Motivation Consider a market with two existing brands A and B with the attributes (and levels) specified in the table below (using dummy coding); A has a 133mHz (processor = 1) with 2GB (Hard Drive = 0) and 64MB (Memory = 1). Brand B has 1 133 MHz processor, with 4GB hard drive but only 32MB of memory. Assume that we want to introduce a new notebook with 100mHz, 3GB and 64MB. What share can the new brand obtain? and where this share will come from?

Conjoint Simulation - The Principle: 

Conjoint Simulation - The Principle From the part-worths estimated earlier, we can obtain ratings or rankings for each product profile, including the new concept. For each customer separately we can determine his choice. The table below presents part-worths and brand utilities for 10 customers. For each customer we can assign a choice of brand A, B or the new concept (assuming a choice rule). Before the introduction the market share is expected to be: A=0.4, B=0.6. When the new brand is introduced: A=0.2,B=0.5, and New=0.3. In other words, the new brand is expected to draw 20% of brand A and 10% of brand B.

Assessing Relative Importance of Each Attribute: 

Assessing Relative Importance of Each Attribute Relative importance of an attribute = utility range of that attribute divided by the sum of the utility ranges for all attribute. For example the relative importance of processing speed for a customer is: Relative importances for our 10 customers are given below; Customers 1,3 and to some extent customer 8 have similar preferences. This data allows segmentation (e.g., by using cluster analysis), and understanding the market structure.

Steps involved: 

Steps involved Designing the conjoint study: Select attributes relevant to the product or service category. Select levels for each attribute Develop the product bundles to be evaluated Obtaining data from a sample of respondents: Design the data collection procedure. Select a computation method for obtaining part-worth functions. Evaluating product design options: Segment customer based on their part-worth functions Design market simulations. Evaluate (and select) choice rules. Establish the best design for the product.

Computing the part-worth functions (using dummy variable regression): 

Computing the part-worth functions (using dummy variable regression)

Conjoint results: 

Conjoint results The utility of a product j to customer i: Note that product j can be any product that can be designed using the attributes and levels in the study, including those that were not included in the estimation of the part-worths in the former equation. Design market simulation: A major reason for the wide use of conjoint analysis is that once part-worths are estimated from a representative sample of respondents it is easy to asses the likely success of a new product concept under various simulated market conditions. A typical question is what market share would a proposed new product be expected to achieve in a market with several specific existing competitors? To answer this we have to specify all existing products as combinations of attributes and their levels. Also we have to select the choice rules that transform part-worths into product choices that customers are most likely to make.

Choice rules - maximum utility: 

Choice rules - maximum utility

Choice rules - Share of utility: 

Choice rules - Share of utility

Logit choice rule: 

Logit choice rule

Detailed (Classic) Example - Household Cleaner. (Green and Wind, 1975): 

Detailed (Classic) Example - Household Cleaner. (Green and Wind, 1975) Spot removers (e.g., for carpets); the following attributes were analyzed: Package design (A, B, C) Brand Names (K2R, Glory, Bissell) Price (1.19$, 1.39$, 1.59$) Good Housekeeping seal (yes or no) Money back guarantee (yes or no). For the 3x3x3x2x2=108 possible profiles and orthogonal design (18 profiles) was selected. The design with one customer’s ranking is presented below:

Derivation of the Attribute Utilities: 

Derivation of the Attribute Utilities Assuming no interactions the regression model becomes: Rating = B0 + b1(package A) + B2(package B) + B3(K2R) + B4(Glory) + B5(Price 1.19) + B6(Price 1.39) + B7(Seal) + B8(Money back) With the dummy coding: Package - A=0, B=1, C=2; Brand name - K2R=0, Glory=1, Bissel=2; Price - 1.19$=0, 1.39$=1, 1.59$=2; Sea; - No=0, Yes=1; Money back - No=0, Yes=1. The dummy coding scheme is presented below:

Estimated Attribute Utilities in Various methods: 

Estimated Attribute Utilities in Various methods Simple sums: Estimation of the average value of the dependent variable for each level of each attribute (e.g., Package A appears in six profiles, the average score is (6+8+2+1+11+4)/6=5.33). This set is rescaled to a range of .1 - 1 (by a linear interpolation - 5.33=.1, …) The regression suggests that package design is important, with a range of 8 (-4.5 to +3.5), as is the price (range of 7.67). Strong preference for package design B and low price the money back guarantee and the seal are relatively unimportant We can now estimate any combination, for example: K2R with package design B with a seal, priced at 1,39 and no money back guarantee (4.833-1.5+3.5+4.83+1.5+0=13.16) Constant = 4.833, R2=.98

Exercise in Conjoint Analysis - Designing a frozen pizza (Marketing Engineering P. 189): 

Exercise in Conjoint Analysis - Designing a frozen pizza (Marketing Engineering P. 189) Assume that frozen pizza can be described by combination of attributes - type of crust, type of topping, amount of cheese and its type, price and other attributes. Suppose that a firm considers 3 types of crust (thin, thick an pan), four types of toppings (veggie, pepperoni, sausage and pineapple), three types of cheese (mozzarella, ordinary, and mixed cheese), quantity of cheese at three levels (regular, double and extra), and price at one of the three levels (32Nis, 36Nis, and 40Nis). The table in the next page enumerates 16 product bundles that form an orthogonal study. Rank these profiles according to your own preference (taste), obtain your own part-worth function, discuss your preference as comes up from the analysis, and define the “best design” that matches your own choice of preference. How close is it?

An orthogonal design for the frozen pizza analysis: 

An orthogonal design for the frozen pizza analysis

Segmentation : 

Segmentation Why do we segment? When it is mostly important? A Definition Market Segmentation is concerned with individual or intergroup differences in response to marketing mix variables. The managerial presumption is that if these response differences exist, can be identified, are reasonably stable over time and the segments can be efficiently reached the firm may increase its sales and profits beyond those obtained by assuming market homogeneity. Du-Pont’s Definition “A group of customers anywhere along the distribution chain who have common needs and values - who will respond similarly to our offerings and who are large enough to be strategically important to our business.”