2. Production of consumer goods Research Design Manufacturing
Sales Effective market research is integral to the design,
manufacture, and sale of successful products.It identifies the
needs and wants of target markets, ensuring that products will sell
because they meet the needs of buyers. 3. Consumer Behavior ? But
what is the best?
Conjoint analysisis a statistical technique used in market
research to determine how people value different features that make
up an individual product or service.
The objective of conjoint analysis is to determine what
combination of a limited number of attributes is most influential
on respondent choice or decision making. A controlled set of
potential products or services is shown to respondents and by
analyzing how they make preferences between these products, the
implicit valuation of the individual elements making up the product
or service can be determined. These implicit valuations (utilities
or part-worths) can be used to create market models that estimate
market share, revenue and even profitability of new designs.
Conjoint Analysis CONJOINT:- Joined together; combined 5. Types
1). Two-factor-at-a-time2). full-concept methodLets Consider a
conjoint analysis problem with three attributes, each with levels
as follows:BrandColorPrice ARed$50 BBlue$100 C$150 There are 18
possible product concepts or cards that can be createdfrom these
three attributes:3 brands 2 colors 3 prices = 18 cards Further
assume that respondents rate each of the 18 product concepts on a
scale from 0 to 10, where 10 represents the highest degree of
reference. 6. Full-factorial experimental design Data coding The
first card is made up of the first level on each of the attributes:
(Brand A, Red, $50). 7. Orthogonal Design 8.
we can specify the preference score (column Y) as the dependent
variable (Input Y Range) and the five dummy-coded attribute columns
(columns T through X) as independent variables (Input X range). You
should also make sure a constant is estimated; this usually happens
by default (by not checking the box labeled Constant is zero).
The mathematical expression of the model is as follows:
Y = b0 + b1(Brand B) + b2(Brand C) + b3(Blue) + b4($100) +
b5($150) + e
where Y is the respondents preference for the product concept,
b0 is the constant or intercept term, b1 through b5 are beta
weights (part-worth utilities) for the features, and e is an error
term. In this formulation of the model, coefficients for the
reference levels are equal to 0. The solution minimizes the sum of
squares of the
errors over all observations.
BrandColorPrice
A = 0.00Red = 0.00$ 50 = 0.00
B = 1.67Blue = 1.11$100 = -2.17
C = 3.17$150 = -4.50
Statistical Analysis & Interpretation 9. Summary 10.
Interpretation 11. 12. 13. 14. 15. 16. Decide to lead
