Conjoint Analysis

What is Conjoint Analysis?

• CA is a multivariate technique used specifically to understand how respondents develop preferences for products or services. It is based on the simple premise that consumers evaluate the value or utility of a product / service / concept / idea (real or hypothetical) by combining the utility provided by each attribute characterizing the product / service / concept / idea

• CA is a decompositional method. Respondents provide overall evaluations of products that are presented to them as combos of attributes. These evaluations are then used to infer the utilities of the individual attributes comprising the products. In many situations, this is preferable to asking respondents how important certain attributes are, or to rate how well a product performs on each of a number of attributes

Managerial uses of Conjoint Analysis

After determining the contribution of each attribute to the consumer’s overall evaluation, one could

1. Define the object with the optimal combo of features

2. Predict market shares of different objects with different sets of features

3. Isolate groups of customers who place differing importances on different features

4. Identify marketing opportunities by exploring the market potential for feature combos not currently available

5. Show the relative contributions of each attribute and each level to the overall evaluation of the object

Commercial Applications

• Technique is widely used by consumer and industrial product companies, service companies, marketing research, advertising and consulting firms

• Over 400 commercial applications per year even in the mid 80s

• Types of applications include– Consumer durables: automobiles, refrigerators, car stereos, condos, food

processors, HDTV

– Industrial products: copy machines, forklift trucks, computer software, aircraft

– Consumer nondurables: bar soaps, hair shampoos, disposable diapers

– Services: car rentals, credit cards, hotels, performance art series, rural health care systems, BART

– Other: MBA job choice

A Survey

• Familiarity & usage of value assessment methods

• 58 industrial firms in the top 125 of the Fortune 500 list

• 16 market research firms from the top 40

Survey ResultsMethod Industrial Market Research

Familiarity % Usage % Familiarity % Usage %

Internal Engg.Assessment

61.3 42.5 - -

Field value-in-use 63.8 36.3 25 5

Focus group 92.5 60 90 60

Direct survey 91.3 48.8 85 55

Benchmarks 83.8 27.5 80 25

Conjoint 75 28.8 90 60

Compositionalmethods

45 10 40 5

P&G and Disposable Diapers

• P&G makes extensive use of CA to guide product modification

• Question: What value do consumers associate with two improved features in disposable diapers:

– Improved absorbency

– Elastic waistband

• Context: P&G had a patent on the elastic waistband, but a competitor imitated the modification. If the imitation was illegal, what damage should P&G claim?

• Potential answers:

1. Use market data to estimate the effect of the elastic waistband on market share. Problem: Elastic waistband + Increased absorbency were introduced simultaneously

2. Use CA to separately estimate the effects

Steps in CA

• Identification of respondents

• Identification and definition of attributes in customer language

• Specification of attribute variation and levels

• Creation of objects (experimental design)

• Creation of instrument, including socioeconomic, demographic and usage questions

• Sampling plan

• Data collection

• Data analysis: Typically, regression analysis separately by respondent

• Market simulation: exploration of “what-if” questions

Preferences for Sports Cars

You are provided 18 hypothetical sports cars each described on 5 features:

Point of origin: US, Japan, Europe

Convertibility: Sunroof, Removable top (Manual), Removable top (Automatic)

Styling: Coupe (2-door), Sedan (4-door)

ABS: No, Yes

Acceleration: 0 to 60 in 5.5 secs, 0 to 60 in 8.5 secs

Assume all 18 cars are roughly equivalent on attributes not mentioned above such as gas mileage, safety, price, etc.

Selecting the stimulus set of profiles

• In the above example, there are 72 possible profile combos or “cars”. Typically, not all combos of attribute-levels are required to estimate the conjoint model, i.e., fractional factorial designs may be adequate

• How many profiles to include in design?

– Degrees of freedom to estimate individual level parameters

– Data collection costs and respondent load

• Criteria for profile selection

– Look out for dominated profiles and unrealistic profiles

– Most software do the appropriate selection

Steps in the analysis

• Each of the 18 selected profiles is presented to respondent• Respondent indicates her/his preference for each of the profiles by:

– Rank ordering the profiles, or– Rating them on a 1-100 scale, or– Choosing the most preferred alternative

• Depending on the above, an ordinal regression (LINMAP), a regular regression or a logit model is fitted to the data

• Dependent variable is the preference measure. Independent variables are dummy variables, i.e., presence / absence of each of the attribute-levels

• Estimated coefficient are called part worths

Profile Origin Convertible Style ABS Accel Euro Japan Auto Manual Coupe ABS Y Fast1 US Sun Sedan No 8.52 Japan Sun Coupe Yes 5.53 Euro Manual Sedan Yes 8.54 Euro Auto Coupe No 8.55 Japan Sun Sedan No 8.56 Euro Sun Coupe No 8.57 US Manual Coupe No 5.58 Japan Manual Sedan No 8.59 Euro Sun Sedan No 5.510 US Maual Sedan No 5.511 Japan Manual Coupe Yes 8.512 Euro Manual Sedan No 8.513 US Auto Sedan Yes 8.514 Japan Auto Sedan No 8.515 US Sun Sedan Yes 8.516 US Auto Coupe No 8.517 Japan Auto Sedan No 5.518 Euro Auto Sedan Yes 5.5

Interpreting the Coefficients or PART WORTHS

18K 17K 16K Sun Manual Auto No ABS ABS

PRICE CONVERTIBLE BRAKING

UTILITIES UTILITIES UTILITIES

30 40

10

40

20

Simulating aggregate choices

Objective is to forecast likely market shares of attribute combos which represent potential management actions, in a defined competitive scenario

Translating Utilities into Choice Predictions

First Choice RuleHighest utility profile chosen

by each respondent

Share of Preference RulePredict choice probabilities using a model such as Logit

Both methods ignore marketing variables such as advertising weight and distribution which are typically not in the conjoint design. Fix: “Adjust” the market shares using this additional information

Using CA for segmentation

Two-Stage Approaches

A prioriResearcher selectsspecific attributes

Post hocFull set of

attributes used

Clustering (K-means)

Relate clusters to background variablessuch as demographics using techniques

like discriminant analysis

One-Stage Approach

Concomitant variableLatent Class Conjoint

Simultaneous clusteringand profiling using

background characteristics

CA with large numbers of attributes

• Full profile models are unrealistic with a large number of attributes

• Two alternatives

– Self-explicated models: Respondent provides

• a) Rating of desirability of each level of each attribute

• b) Relative importance of each attribute

• Part-worths are given by (a) * (b)

• Compositional, not decompositional approach

– Hybrid models: Combine self explicated with part worth conjoint approaches. Self explicated info is used to pare down the number of attributes / profiles. Then a fractional factorial design is used on the remaining. Hence, needs to be customized for each respondent

• Sawtooth software’s ACA

Choice Based Conjoint

Motivation: Using conjoint judgment studies to forecast choices is theoretically unappealing because of the ad hoc assumptions required

In choice based conjoint, the respondent chooses one profile from the set of alternative profiles known as the choice set. The stated choices are used to estimate the parameters of the choice model such as the logit model.

Advantage: Greater realism of respondent’s task

Disadvantage: Given limited information on each respondent, individual level estimation is precluded. Hence, individual differences (heterogeneity) needs to be accounted for in other ways