Dirk huisman training day - 2011

Training
Day
–
31st
October,
2011

Introduction to Conjoint and DCM / CBC
Dirk Huisman - SKIM

A
Presenta*on
from
the
Fes*val
of
NewMR
Training
Day
–
October
31,
2011

expect great answers
Introduction to Conjoint and DCM / CBC
NewMR training 31-10-2011
Dirk Huisman
SKIM | Research Services & Software

Dirk Huisman, SKIM, The Netherlands
Training Day - Festival of NewMR 2011 - Session 3 Schedule: 7:40am – 9:00am (GMT)
3
What is conjoint analysis?
•  Technique initially developed by psychometrists in early 70s, in parallel
economists developed the Utility Value theory (von Neumann, Morgenstern)
•  Academics were interested in understanding how people made decisions
•  By just asking, people tend to say what
•  they thought the interviewer wanted to hear (politically / socially correct answers)
•  was top-of-mind
•  So the answers didn’t reflect what they actually
would do / choose / buy. It was noticed however that
choices involve trade-offs
and compromises.

4
€2
Conjoint: stylised example
All else equal: which of these 2 beer bottles would you buy?
Click on beer of choice (blue square)
€2

5
€2 €1

6
€1 €2

7
Index of added value
Carlsberg Heineken
€2 €1
The choices indicate that:
• S/he prefers Heineken.
• Offering a price reduction of €1 is ENOUGH to change his / her mind.
The added value of Heineken is SMALLER than the added value of a price
reduction of €1.

8
The choices indicate that:
• S/he prefers Heineken.
• Offering a price reduction of €1 is NOT enough to change his / her mind.
The added value of Heineken is LARGER than the added value of a price
reduction of €1.
Index of added value
Carlsberg Heineken
€2 €1

9
When to use conjoint analysis?
•  So conjoint is a technique to measure what people
prefer: by controlling the choice tasks; observing what
the choose and analyzing what is driving them. "
•  The power of conjoint lies in capturing what really
drives people when choosing a product or service
instead of another."

10
Basic idea of Conjoint Analysis
1.  Mimic the actual choice process
2.  Respondents will show their actual choice behaviour
3.  Determine the importance of, and preferences for
different product features by analysing the
respondent’s choice behaviour
These three steps--collecting trade-
offs, estimating buyer value
systems, and making choice
predictions-- form the basics of
conjoint analysis.

11
What about the features?
The key to using Conjoint Analysis is to think about
products as a collection of different features:
•  Mobile Phone:
•  Brand + Stand-by time + Games + Price + Design
Include all
important
features!
Brand
Camera
Size
Price
Screen type
Ring tones
GPRS
Games
Design

12
Formulating Attributes
•  Attributes…
•  … should be actionable and all actionable attributes should be
included
•  … should cover all key decision drivers

13
•  Levels
•  … should be independent / mutually exclusive
(e.g. car accessories)
•  … should span the relevant range and somewhat beyond
(e.g. price)
•  … should be stated in terms of consumer benefits

14
•  Some other important questions about attribute
formulation are:
•  How many Attributes are appropriate?
•  Do we have to use visual / sensory aids?
•  Should we prohibit pairing of certain Attributes?

15
Utility of the features
•  Each attribute level has a certain value for every
respondent / buyer
•  This relative value is called ‘utility’
à Utility of product = combined utility of all attribute levels
of that product
à Uproduct = Ulevelfeature 1 + Ulevelfeature 2+ Ulevelfeature 3

16
•  Example: respondent 1 has the following utilities for
three mobile phone attributes
Brand
Nokia +20
Samsung -15
Siemens - 5
Games
No games -10
3 games + 3
6 games + 7
Price
€100 +30
€200 - 5
€300 -25

17
•  The utilities for the following products are thus as
follows
•  Product 1: Nokia, 3 games, €200 20 + 3 - 5 = 18
•  Product 2: Siemens, 6 games, €100 -5 + 7 + 30 = 32
•  Product 3: Samsung, no games, €100 -15 -10 + 30 = 5

18
•  The utilities are the key output of Conjoint Analysis
•  They represent the ‘added value’ of the different attribute levels
(per respondent)
•  E.g. Nokia is valued more than Siemens by the respondents
•  They determine which attributes are most important (impact
choice most)
•  E.g. brand is more important than number of games
•  Be aware utilities are raw material NOT the
answer

19
• The utilities are used to simulate market scenarios:
•  We know for each respondent what they value
-  i.e. we know the individual utilities per attribute level
•  Based on this, we can determine what products in the market
are valued most per respondent
-  i.e. which product has the highest ‘total utility’ per
respondent?
•  For every market situation that we define, we can forecast
the choice of every respondent
-  Assuming that they choose what they value most
•  This will result in total ‘share of choice’ per product

20
Why are utilities so useful?
•  Utilities enable us to simulate behavior regarding
hundreds of different market situations and / or
hundreds of different (new) products

21
Ingredients of conjoint research
What’s
cooking?

22
Planning a Conjoint Study
•  Conjoint Chain
•  Business problem drives research question
•  Research question drives research needs
•  Equal importance of simulation, brainstorm and attribute definition
Research question
Simulation Brainstorm
Attributes and Levels
Design and Data Collection AnalysisSimulation Run
Business
problem

23
•  Define in advance:
•  the results you expect
•  the reason why
Got Brains?

24
•  Setting up the conjoint module:
•  selection of attributes/features
•  definition of attributes/features
•  definition of target groups
•  definition of environment (options, competition)

25
•  Selection and definitions of the attributes
•  Things to think about: - which attributes?
- how many attributes?
- how to define the attributes?
- In what situation/context do I
measure?
Let’s
discuss

26
•  The choice of the conjoint data collection method
•  Choices: 1 ACA (adaptive conjoint analysis (preference)
2 CBC (Choice Based Conjoint (also called Discrete
Choice Modeling)
3 ACBC (Adaptive Choice Based Conjoint, which
belongs to the family of menu based
conjoint)

27
Motivation for ACA
•  Do you want to forecast what the likely acceptance is of a product that will
be brought to the market?
•  Do you want to measure the attractiveness of specific product features?
•  Do you want to model high involvement purchases?
•  Do you want to have a questionnaire which adapts and immediately
focuses on what holds value to your respondent?
•  Are you thinking about (re)designing a (new) product?

28
ACA
•  The term “Adaptive” in Adaptive Conjoint refers to the
interview adapting itself to the respondent’s
preferences
•  Answers provided are the input for subsequent
questions

29
ACA
•  Example
• 
• 
•  3 Ghz
•  4 Ghz
4 GHz 3 GHz
prefer left prefer rightindifferent
The level of information gained per task is high as
the custom pairwise comparisons are formulated in
such a way that both concepts are very similar in
preference
Note that not all attributes of a product are
shown at the same time

30
Adaptive Conjoint Analysis
Drawbacks
•  Partial-profile is less realistic than a real world
representation
•  Not appropriate for pricing research
•  Use of computer is necessary
•  no Paper & Pencil studies
What’s the
catch?

31
Adaptive Conjoint Analysis
Drawbacks
•  ACA uses a main-effects-only model
•  no attribute interactions measured
•  ACA interviews are long and can be taxing
•  individual utilities require elaborate input

32
Choice-Based Conjoint
When to use CBC?
If you have one of the following questions in an existing
or new market:
•  What is the optimal product design/portfolio?
•  What if we launch a new product, package, pack size, or
flavour?
•  What are consumers willing to pay for new products or
features?
•  What if we increase our prices? Will a higher return per sale
outweigh a loss in quantity and share?

33
CBC
•  Respondents have to choose from different product
offerings. Product compositions vary within a choice
task and per choice task so respondents start to reveal
their decision rules in purchase behaviour

34
CBC: “Shopping trips” (example screen)

35
CBC
Strengths
•  Choice tasks closely mimic what buyers do in the real
world:
•  Choose from available products.
•  Good for pricing research
•  You can investigate interactions

36
CBC
Strengths
•  You can include a “None” option, or (multiple) “constant
alternatives”
Examples of none-option:
•  I wouldn’t choose any of these products
•  I would stick to my current provider
•  Paper & Pencil, CAPI and Web based interviews
possible

37
CBC
Drawbacks
•  The recommended number of attributes to be used is
about 6
•  Low ratio of information gained per respondent effort/
task
•  Sample sizes needed slightly larger than with ACA
•  Aggregate utilities when not using CBC/HB for
individual level estimation
What’s the
catch?

38
So what are the differences?
Choice versus Rating
Aren’t we
the same?

What is Menu-Based Conjoint?
An exercise that replicates a specific kind of choice
situation by allowing consumers (respondents) to specify
their desired product by selecting single features or
bundled group of features.
Menu-based conjoint is the family name showing the
relation with other variations to conjoint analysis, a class
of discrete choice models.
You may also call it a build-your-own product exercise.

Whopper $3.50 California W. $ 4.50
Omega3 $3.75 Chicken Deli $ 3.50
Cheddar $0.50 American cheese
$ 0.75
Crispy Onions
$1.50
Bacon
$1.50
Curly fries $1.25
French fries
$1.05
✔ ✔
✔
✔Supersize + $0.25
✔
Total price $ 8.50
Now enjoy building your own

Or why don’t we build our own
computer, as if we’re Dell?

Applications are mainly found in areas
where combining items matters
•  Menu optimization in fast food/branded restaurant
chains
•  TLC services bundling
•  BYO computers (e.g. Dell)
•  Optional features pricing optimization in automotive
market
•  Add-on services in the financial and insurance
services industry
•  Mix and Match situations like in apparel

43
That’s all for today
Anything left to discuss?

44
Keep practicing
Train I must

Q & A
Dirk Huisman
SKIM
Pravin Shekar
krea

How can we help you?
Rotterdam | Geneva | New York
www.skimgroup.com
Dirk Huisman
d.huisman@skimgroup.com
SKIM | Research Services & Software
+31 10 282 35 00

Dirk huisman training day - 2011

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