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)

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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.


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€2

Conjoint: stylised example All else equal: which of these 2 beer bottles would you buy?

Click on beer of choice (blue square)

€2


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Conjoint: stylised example

€2 €1

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


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€1 €2

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


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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.


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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



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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."


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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.


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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


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Formulating Attributes

•  Attributes…

• … should be actionable and all actionable attributes should be included

• … should cover all key decision drivers


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•  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


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•  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?


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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


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•  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


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•  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


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•  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


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•  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


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Why are utilities so useful?

•  Utilities enable us to simulate behavior regarding hundreds of different market situations and / or hundreds of different (new) products


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Ingredients of conjoint research

What’s

cooking?


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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 Analysis Simulation Run Business problem


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•  Define in advance: •  the results you expect •  the reason why

Got Brains?


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


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•  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


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•  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)


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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?


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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


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ACA

•  Example

• 

• 

•  3 Ghz

•  4 Ghz 4 GHz 3 GHz

prefer left prefer right indifferent

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


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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?


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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


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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?


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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


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CBC: “Shopping trips” (example screen)


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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


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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


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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?


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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


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That’s all for today Anything left to discuss?


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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 [email protected]

SKIM | Research Services & Software +31 10 282 35 00

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

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Dirk huisman training day - 2011