Week 1 - Lecture 1
Consumer Preferences:
● (New) Product Design:
○ It all starts with understanding your customers…
■ goals/needs/preferences:
● Psychological needs (e.g., the social status associated with
products)
● Functional needs (e.g., does it fulfill its intended purpose?)
○ How do they trade off these preferences for money?
■ Would they rather keep their money in their pocket (or buy an
alternative product)?
● Customer process (from identifying needs to a purchase):
○
● How to measure customer value?
○
● A historical perspective:
○ Thurstone presented an idea to measure consumer preferences in 1931 at a
meeting of the Econometric Society.
■ “Perhaps the simplest experimental method that comes to mind is to
ask a subject to fill in the blank space in a series of choices of the
following type: eight hats and eight pairs of shoes vs six hats and __
pairs of shoes”.
, ○ One of the combinations, such as eight hats and eight pairs of shoes, is
chosen as a standard, and each of the other combinations is compared
directly with it.
○ The academic crowd heavily opposed that idea that non-market (non-actual)
behavior could teach us something about consumers.
○ In 1942, other academics summarized Thurstone’s ‘futile’ attempt as:
■ “The fundamental shortcomings probably cannot be overcome in any
experiment involving economic stimuli and human beings”.
○ Only from the 1960s onwards academics really continued to try to extract
valuable information from stated preferences.
● Stated vs. revealed preferences:
○ Survey, for example, intention to buy (stated preferences):
■ To hard a question
■ Overconfident about buying probability.
○ Self-reported importance weights
■ Lack of discrimination, everything is important (=unconstrained
measure)
○ Trade-offs:
■ Benefits and costs
■ What is desired most (=constrained measure)
○ Behavioral measures (revealed preferences):
■ Counts
■ Salesforce reports
● Intention vs. purchase:
○
● Difference between “what people say and do”:
○ The goal is to figure out what they will do, not what they say they will do.
○
,● Is there any value in asking people what they want an existing/ a new product to look
like?
○ Consumers face different types of decisions daily: consumers are good at
making trade-offs (decisions) between benefits and costs.
■ So, let’s make respondents make trade-offs!
○ Conjoint analysis: a survey-based statistical technique designed for
consumers to make trade-offs, forcing them to reveal their true preferences.
○ Produces a mathematical system of preferences.
● Unconstrained vs. constrained (scanners):
○
● Conjoint analysis:
○ Consumers evaluate products; they have to make trade-offs.
○ What is a product?
■ From a very functional perspective: a collection of attributes.
● Conjoint world:
○ In conjoint, we treat all products as a bundle of attributes (not quite that
different from the real world).
○ Examples of attributes for a new phone:
■ Size of phone, camera(s), CPU, price, color
○ Examples of levels of attributes:
■ Price: low (500), medium (850), high (1200).
■ camera : 12 megapixels, 32 megapixels…
■ Size of phone: 5.8 inch, 6.5 inch, 7.2 inch…
○ Does this only hold for phones?
■ Shoes:
● Type: sneakers, boots,...
● Color: black, white,…
● Price: …
■ Courses:
● Attendance: mandators, optional
● Assignments: 0,2,6
, ● Methods: qualitative, quantitative
● Conjoint: why does it work?
○ Relate the rating (or choice, see later slides) on the attribute levels:
■ We decompose the overall utility in partworths.
■ We uncover the relative importance of each attribute.
● Conjoint: the process
○ Step 1: attributes
○ Step 2: attribute levels
○ Step 3: profiles
○ Step 4: type of conjoint
○ Step 5: utilities/partworths
● Step 1: Attributes:
○ How to decide on which attributes to include:
■ Focus groups, interviews with the NPD team, analysis of the current
market, pre-tests.
■ Not too many attributes (keep it simple here)
● Pick the most important ones
● Consumers may get confused with too many
■ Pizza:
● Crust
● Toppings
● Price
● Cheese type
● Cheese amount
● Step 2: attribute levels:
○ Need to cover the whole range of attribute levels (e.g., price ranging from low
to high).
○ Can be:
■ low/medium/high (e.g., price)
■ yes/no (e.g.Is there pepperoni on the pizza?)
■ Best to limit the # of levels
■ Best to keep # of levels roughly the same for different attributes (e.g.,
3 different price levels, 3 different toppings…)
■
● Step 3: develop profiles:
○ Factorial design: all possible combinations of all levels of alternatives. In our
specific case, this means 3x3x3x3x4x3=324(!) theoretical bundles:
■ Example of a bundle:
