6. Conjoint analysis
Individuals don’t fully understand their own preferences
self-reports are unreliable
!
For example: “what do you want in a house?”
!
You don’t want people to say they care about X when they
really make their decisions based on Y
!
Will cost a lot of money!!
!6
7. Characterising demand
This is where conjoint analysis comes in
!
It can characterize customer preferences and product demand
!
Conjoint analysis is important for 2 reasons:
1. Foundation for a reliable estimate of product demand
2. Knowing the nuances of the demand —how much of one
attribute level are customers willing to give up to get more
of another attribute and at what price?
!7
8. Discover what your Market Values
• Conjoint analysis gives you a realistic way to measure how
individual product attributes affect consumer and citizen
preferences. With conjoint analysis, you can easily measure
the trade-off effect of each product attribute in the context of
a set of product attributes, as consumers do when making
purchasing decisions.
• When you use both conjoint analysis and competitive
product market research for your new products, you'll be less
likely to overlook product dimensions and more likely to
develop products and services that sell
!8
9. Goal of conjoint analysis
• Estimate the demand curve:
o For product as a whole
o For each potential product attribute
• To aid in identifying
o Optimal product configuration
o Optimal corresponding price
!9
10. Using Conjoint Analysis:
!
If the stimuli are realistic, the sample of
consumers is representative, the
consumer tasks are designed carefully,
and the appropriate statistical methods
are used to estimate partworths,
conjoint analysis accurately represents
how consumers will behave when
faced with new products.
The willingness to pay for the features
is sufficiently accurate to make
decisions on which features to include
in a product.
http://www.mit.edu/~hauser/Papers/NoteonConjointAnalysis.pdf
11. Underlying Principles
• Foundational premise - Individuals don’t know their
underlying decision structure. Thus self-reports of that
structure are unreliable.
• Self-reporting produces mismatches between:
o Attributes individuals claim are important in their decisions,
and
o Actual attributes that are evident in their decisions
• Conjoint analysis is a technique that allows a researcher to
decompose an individual’s judgement into its underlying
structure
!11
12. Conjoint is used to understand preferences:
for example in VC investment behaviour
Source: Shepherd & Zacharakis 1997
!12
13. The mismatch problem
• In new ventures the mismatch problem is between stated
preferences and ultimate purchases
• Rather than ask customers what attributes matter, ask them
to rate or choose between “products”
• Each “product” is characterised by a real physical prototype
or merely a description, comprising a specific bundle of
attributes
• Statistical analysis decomposes the ratings of products into
customer utilities for each attribute (for individual, segment
or market)
!13
14. Conjoint analysis in 5 steps
!
1. Identifying the various physical attributes of the product/
service together with “levels” for each of these attributes
!
perceptual dimensions are intangible aspects of products that
buyers use to discriminate one product from another
!
convert this perceptual dimensions elicited in focus groups into
physical attributes of the product that can be specified
!14
16. Conjoint analysis in 5 steps
Choosing levels for each attribute
- Discrete (max 2; include or not)
- Continuous (min 3)
!
!
!
!
Include attributes that:
you control and incur a cost
!
Tip: include location + distribution channel attribute and “brand”
attribute
!16
17. Conjoint analysis in 5 steps
2. Creating potential product configuration descriptions that
combine each level of an attribute with each level of every
other attribute
!
Rather than ask customers what matters to them, ask them to
evaluate a whole series of products, all of which are feasible
configurations of the ultimate product.
!
Each “product” can be
• a product description
• a real physical prototype comprising a specific bundle of
attributes
!17
18. Conjoint analysis in 5 steps
3. Designing and administering a survey that probes customer
preferences for each potential product configuration
!
Advantages of Web-based surveys
!
-Immediate response
-Flexibility
-Reliability
-Vividness
-Simplicity
!18
19. Conjoint analysis in 5 steps
Designing the survey
!
(1) specifying/obtaining the sample
• Mailing lists can also be bought
• <10% will complete survey
!
(2) designing the cover letter or contact script
• People are most interested in participating in a study if they
feel it is important or interesting and if they believe that their
participation is highly valued.
• More personal follow-up contact, sponsorship = ~25%
response rate
!19
20. Conjoint analysis in 5 steps
!
(3) creating the “ad copy”
• As much as possible, you want the survey to mimic the
process of a customer being exposed to an ad and then
making a purchase decision.
!
(4) writing the instructions
• The main goal of the instructions is to ensure that all
subjects are interpreting the survey questions correctly and
in exactly the same manner.
!20
21. Conjoint analysis in 5 steps
(5) creating the product configuration questions
• reliability—ensuring that inferences drawn from the survey
data reflect the purchase decisions that will ultimately be
made in the real world —needs to be high!
• Two approaches
o Ratings-based conjoint
• Rate the configuration on either an attractiveness scale or a purchase
likelihood scale
o Choicebased conjoint
• to identify which of two or three versions you like best
• Number of choices: 3 the best
• Rule of Thumb: 5 comparisons gives you the best results!
!21
23. !23
Create a Demand Question:
!
• How much of the product will you buy within the next xx days/
weeks/months?
!
• Compare the answer with existing product or
!
• How frequently would you purchase this product within the next
xx days/weeks/months?
!
-Use “worst configuration” of your product
-Correct for optimism
-Time frame important!
24. • (6) Write additional questions regarding demographics,
distribution channel and media exposure
!
!
• Also include questions you excluded before (e.g. colour)
!24
25. Executing your market research
!
!
• After designing the survey you can put it online
!
• Survey Analytics:
o Interactive guide illustrating each step
o Extensive help links
!
• http://venturedesign.surveyanalytics.com
!25
26. • After posting: pretesting on possible customers
!
• If correct, release mail
o mail survey link to subjects
o whenever possible do personalise to increase
response rate
!
• Review responses right away, first 2-3 days provide the bulk
of your responses
!
• Reminder e-mail; preferably after a week
!26
27. Interpreting the responses
!
• View and save raw data
o Entire record of data per subject
o Useful to check quality of each survey
!
• Analytical tools:
o Aggregate Utilities summarises how much benefit does the
average subject obtain when attribute level is increased
!
• From the Aggregate Utilities a utility curve (utility vs. level)
can be created
!27
28. Translating utility into demand
Demand = The number of units sold over a period of time
(year)
!
1. Sales and utility for an existing product of a competitor
(most reliable)
o Sum of the conjoint utility for each of its attribute levels,
including brand utility. The sales for that product provide
the demand with that utility
o Demand to Utility scale factor = Unit sales for the existing
product / sum of utilities for all attributes
!28
29. Translating utility into demand
!
2. New to the world products
!
No basis for comparison product
• Combine utility data with answers to your demand question
for the worst configuration.
• Demand= Reported demand adjusted for optimism
(adjustment by taking 0.75 of projected demand)
• Scale factor= Demand / utility for the worst configuration
!
Demand conversion: How many units you expect to sell of
any given configuration !29
30. Choosing your optimal configuration
!
Profit maximizing configuration choices:
!
1. Compute demand for all product configurations + compare
total revenues with aggregate cost for each configuration
(potential configurations can be large, example 162)
2. Look at each attribute seperately: convert demand for
attibute into a price that customers are willing to pay for that
attribute level
!30
31. Willingness to Pay
!
WTP: Tells how much customers are willing to pay extra for
next higher level of attribute
!
Combine with attribute costs to decide:
• Is it profitable to add attribute
• What level is most profitable
!
Find utily by adding utilities at all attribute levels into baseline
configuration > Search for ultimate utility curve
!31
33. Demand Conversion
Corresponding utility > summing up attribute levels:
!
0.003 (colors)+0.004(themes)+0.513(chain)+0.487($35)= 1.00
Demand/utility= 4.04/1.00= 4.03 per unit of utility
!
Preliminary Demand for estimated baseline (4.03 rolls,
subjected to optimism bias)
Apply adjusted estimate to market size (15 million households)
!
Sales number: 4.03 x 15 = 60 million rolls in a year
!33
34. Epigraphs
Price (most important attribute, )
• 72% of the variance demand. Each dollar price increase
decreases demand by 0.04 rolls
!
Distribution channel: 21% variance demand.
• Internet has a negative impact of 0.020, 1.96 decrease in roll
demand for any given price
!
Product configuration: no added value
• Product needs to be simple, no colors or themes
!34
35. Market Segmentation
Segment analysis:
!
1. Income ( Peak at $75.000 - $100.000)
2. Married / not married (3.58 – 1.41 rolls)
3. Children (+ 0.9 rolls)
4. Gender (+ 1.49 rolls for women)
!
Market for Epigraphs is not segmented > This finding matches
mass market for home decorating. Hence we can use industry
wisdom
!35
36. Conjoint Analysis in Venture Design
Apply conjoint analysis to characterize demand
• Allows customers to compare products comprising different
combinations of attributes
o Defining attributes
o Determining attributes level
o Limiting the set of possible products configurations
o Deriving demand curve for the product configurations and
individual attributes
It saves significant amounts of time and money by carefully
developing a product consumers want and are happy to pay
for!
!36
37. M I T S L O A N C O U R S E W A R E > P. 1
Note on Conjoint Analysis
John R. Hauser
Suppose that you are working for one of the primary brands of global
positioning systems (GPSs). A GPS device receives signals from satellites and,
based on those signals, it can calculate its location and altitude. This informa-
tion is displayed either as text (latitude, longitude, and altitude), as a position
relative to a known object (waypoint), or, increasingly, a position on a map or
navigational chart.
GPSs come in many versions. Some mount in cars and trucks and pro-
vide driving directions. Others are used in navigation on the oceans or lakes.
And some are handheld, useful for hiking, camping, canoeing, kayaking, or just
walking around the city. We will suppose that it is your job to decide which
features the new handheld GPS will have. Each feature is costly to include. In-
cluding the feature will be profitable if the consumers’ willingness to pay
(WTP) for that feature exceeds the cost of including that feature by a comfort-
able margin.
Simplified Conjoint Analysis Illustration
We’ll simplify the problem for illustration. First, let’s assume that all
consumers have the same preferences – the same WTP for each feature. This
assumption does not hold in real markets, hence we will have to consider pref-
!37
Link to the file
38. Conclusions
• Conjoint analysis is centre piece of venture design
o Feasibility: Is Core Benefit Proposition profitable?
o Design:
• Demand curves to choose optimal price and product configuration
• Presence of market segments
• Distribution habits to choose channel
• Media habits to choose advertising
!38
40. For chapter on feasibility analysis
• Assess customer preferences & demand curves
• Predict likely demand
• Determine price point (for each segment)
• Identify product configurations (for each segment)
• Identify distinctive market segments
• Validate the feasibility of your business in a empirically
grounded and sophisticated way
o a strong approach to critical assumption testing!
!
Use: http://venturedesign.surveyanalytics.com
!40
41. Next session: Business modeling
• Outline the business model that you have in mind for your
venture
• Read the suggested readings: Amit & Zott, 2001; Magretta,
2002; Zott, Amit, & Massa, 2011
!
• What alternative business models could be enacted for your
business?
• How do they compare (value creation potential, operational
feasibility)?
!41
