Concept Testing Vs. Conjoint Analysis

Concept tests: Selection at the global or bundled level No extrapolation beyond the concepts tested

Conjoint Analysis Selection in terms of attribute and attribute level

(decomposition) Extrapolation to any combination of the attributes and

levels tested

The only feasible way to test Nk combinations of attributes and levels, for N, k > 2

Conjoint Analysis—Basic Template Product conceptualized as a bundle of attributes, with

each attribute conceived as having 2 or more levels The product space holds Nk possibilities, where N is the

number of levels and k is the number of attributes Consumer responds to X full profiles, where each

profile is a specific combination of attributes and levels Knowledge of experimental design allows X to be a fraction of

Nk, so that consumer fatigue is minimized Consumer’s response is a straightforward “I like/dislike this

profile about this much,” I.e., a multipoint rating scale The X profiles rated have been systematically selected so that

the consumer’s global rating can be decomposed to show the relative contribution of each level of each attribute

Decomposition Performed by Conjoint Analysis

Assume a 9 point rating scale, two attributes A and B, with 2 levels Consumer rates all 4 profiles

Results Which attribute

is more influential?

Preference Level of A Level of B

9 1 1

7 2 1

4 1 2

3 2 2

Decomposition II

A regression equation is used to determine ‘part-worth’ for each level of each attribute desirability or preference ratings typically serve as the

dependent variable Each profile rated provides one case The presence or absence of each attribute/level is

indicated by dummy variables Beta coefficients of the dummy variables are translated

into part-worths (see example) Part-worth ranges across the levels for an attribute are

used to calculate the relative importance of that attribute

Issues in Designing a Conjoint Analysis

Where do the attributes come from? If the attributes are not those actually used by consumers to

make choices, then the conjoint will not produce the optimal product design

Errors of commission and omission equally troublesome

Where do the levels come from? Should be meaningful to the consumer and actionable by the

manager Out of range values spuriously inflate the importance of that

attribute; omitted levels create the same problems as omitted attributes

Fractional designs presume that different attributes do not interact I.e., that only additive effects exist

Issues in Designing a Conjoint Analysis II

Conjoint analyses can be conducted at the individual or aggregate sample level Aggregation presumes rough homogeneity of choice

factors across individuals If homogeneity is questionable—very different utilities

across individuals—then it is better to cluster individuals first and perform a segment by segment analysis

Uncovering such segments may be one of the most important contributions of a conjoint analysis

Sample size requirements can be computed for conjoint analysis However, the analysis can be conducted on single

individuals Segment analyses drive sample size up

Issues in Designing a Conjoint Analysis III

There are many technical issues that managers need not be involved in What dependent variable to use Whether to use paired comparisons or ratings Whether to use hybrid conjoint What simulation procedure to use What subset of profiles need to be rated

The key managerial responsibilities include: Understanding how consumers make choices—key

attributes and realistic levels to examine Understanding which aspects of product design &

configuration are actionable by management Having clear decision criteria for use of the data

Concluding Perspective on Conjoint Analysis

Some believe it to be the single most significant contribution to market research methodology in the past 30 years

The best method for quantifying trade-offs in consumer choice The only method in which different price levels can be

traded off against different features, allowing a dollar value to be placed on these features

As with other experimental methods, the output of conjoint analysis corresponds closely to the central managerial question: “If I change X, how much will that change Y?”