Stage 1: Developing product profiles to collect data on consumers’ preferences
R&D team of a company has been developing a new product whose attributes can be
customized to meet the demands of different consumers. Specifically, the new product has 5
key attributes that have different options as follows:
Product package: there are 3 options including A style, B style and C style.
Brand image: there are 3 options including luxury (LUX), common (COM) and cheap
(CHE).
Price: there are 3 levels of price including 200$, 500$ and 700$.
Delivery: Consumers can choose home delivery service for this product or buy it at
company store.
Money-back guarantee: Consumers can choose whether to participate in money-back
guarantee program for this product or not.
Before launching this product to the market, marketing team tries to investigate how product
profiles are different in terms of the utility they provide to the consumers. Thus, they need to
identify possible product profiles that will be used to collect consumers’ preference. Help the
team to identify the number of product profile that is feasible to collect data.
Stage 2: Launching new product to the market
After having the preference data, ranking from the most preferred (1) to the least preferred (xx)
(File: ProductDesign_prefs) for the feasible product profiles (File: ProductDesign_plan), the
team needs to know which product profiles should be offered to the consumers. Help the team
to identify the product profiles that can potentially meet consumers’ demands.
Tip: The syntax is as follows:
CONJOINT PLAN="address where the plan file is located"
/DATA="address where the data file is located"
/SEQUENCE=PREF1 TO PREF22
/SUBJECT=ID
/FACTORS=PACKAGE BRAND (DISCRETE)
PRICE (LINEAR LESS)
DELIVERY (LINEAR MORE)
MONEY (LINEAR MORE)
/PRINT=SUMMARYONLY.
In which:
SEQUENCE: Specify the ranking preferences from the most to the least. Must be
exact variable names in the data file and no blanks in between. (PREF1 TO
PREF22)
SUBJECT: Specify the ID of participants in the data file
FACTORS: Specify the factors (exact factor names in the data file)
DISCRETE: DISCRETE is assumed if a factor is not labelled with one of the four
alternatives (DISCRETE, LINEAR, IDEAL, ANTIIDEAL)
LINEAR: LINEAR is used for the factors indicating that the data are expected to
be linearly related to the preference.
LESS: LESS indicates an expected direction for the relationship between the
factor and preference: higher preference for lower factor. For instance: higher
preference for lower price
MORE: MORE indicates an expected direction for the relationship between the
factor and preference: higher preference for higher factor. For instance: Higher
preference for higher quality
