Advanced Analytics Training: Sydney
Part II: Segmentation & CBC
Jane Tang
December 16, 2010
Segmentation
Segmentation
Segmentation
Overview
Background
• The segmentation results enable the client to target its product
development, positioning, and messaging to the most promising
audience, as well as tailoring marketing strategies to enhance
appeal across multiple market segments.
• Often can be part of a U&A study.
• The criteria for assessing the practical value of a segmentation
scheme include:
– Statistical criteria – clear and significant quantitative differences
across the segments.
– Business value – clear distinctions and linkage regarding key
business metrics/actions.
– Clarity – segments that are easy to understand and to communicate
with or about.
– Findable– the ability to easily identify and locate segment members.
Segmentation
• Pre-determined (a priori)
– Pre-select criterion variable and segments
– Cross-tabs to profile segments on demographics, attitudes;
etc.
– A typing tool that predict segment membership
• Derived
– Pre-select basis of segmentation and criterion variable, based
on understanding of role product plays in consumer’s life
– Factor analysis is conducted to reduce number of attitudinal
predictor variables
– Cluster analysis is used to group respondents into segments
– Cross tabs with significance testing and discriminant function
analysis to profile groups
Cluster Analysis
• We let the data determine what technique are appropriate for
segmentation analysis.
– Convergent Cluster Analysis (CCA) is more suitable for scalar-based
attitudinal data include
– Latent Class segmentation (i.e. finite mixture model) is more suitable for
behavioral categorical data is.
•
Respondents are divided into groups based on their scores on a set of
behavioral, attitudinal, psychographic, and demographic variables.
– The respondents are segmented into groups where rating
differences within a group are minimized and rating differences
between groups are maximized.
– This results in several groups containing people who are similar in
their attitudes and behaviors. Each group forms a market segment.
Statistical Methods
• Factor Analysis
– A Factor Analysis often proceeds the actual clustering to determine the
dimensionality involved in the variables used.
• Latent class segmentation/Finite Mixture Model
– Latent class assigns each respondent a probability of belonging to each
cluster. Segment membership can be hard coded by assigning
individual to the segment with the highest probability.
– If a respondent exhibits characteristics of more than one segment, they
will have a positive probability of falling into each one.
• Convergent Cluster Analysis: K-Means clustering
– A CCA segmentation assigns each respondent to one segment.
• Discriminant analysis/CHAID & CART:
– Used to develop typing tool to classify future respondents into
segments.
Segmentation Output
• A Segmentation produces three types of output:
– Segment characteristics (behaviors, attitudes, psychographics,
and demographics),
– Segment sizes expressed as a percentage of the target market,
– Probabilities of each respondent being in each market segment.
• There are a number of ways we can represent the
segmentation output. The most straightforward approach
is with simple cross-tabs of the segments by other
determining variables in our study.
– Correspondence analysis
Segmentation Output – Algorithm
• We will develop an excel based algorithm that can be used by
the client to identify the segment of future respondents.
HSDD Consumer Segmentation
January 2009
Please use the following scale: 1-Disagree Strongly, …., 7- Agree Strongly
Answer
Below are various statements people have made about how they feel about themselves and decreased sexual desire. For each statement, please indicate how much you agree or disagree.
My decreased sexual desire makes me feel like there is something wrong with me.
2
My decreased sexual desire makes me feel like a failure.
2
I feel really guilty about my decreased sexual desire.
3
I feel like a piece of me is missing because of my decreased sexual desire.
1
Below are various statements people have made about how they feel about themselves and decreased sexual desire. For each statement, please indicate how much you agree or disagree.
Enter "9" for this statements if respondent does not have partner
My decreased sexual desire [is driving/drove] my partner crazy.
3
I [am/was] more distressed about the impact of decreased desire on my relationship than I [am/was] about the impact on me.
3
This respondent is in:
Segment 2:
Unconcerned
Conjoint Analysis
-All the flavors of conjoint analysis
- History
- What we do today, comparison of methods
-“Calibration”
- Market share vs. Choice Share
- Forecasting – when do we need it?
- Pricing for choice models
- Input from clients
- Key impact areas
History of Conjoint
• 1970s – Full Profile Conjoint
– Rating/Ranking based Conjoint (Paul Green)
– Dan McFadden introduced Choice theory in Transportation
• 1980s – ACA & CBC
– Rich Johnson invented Adaptive Conjoint Analysis (ACA) –
launching Sawtooth Software
– Jordan Louviere introduced Choice Based Conjoint to Marketing
• 1990s – HB estimation
• 2000s – CBC Becomes Most Widely Used
• 2008/9 – Adaptive CBC (A/CBC) was introduced by Sawtooth.
Overview of Conjoint Analysis:
• Conjoint analysis is a popular marketing research
technique that marketers use to determine what
features a new product should have and how it
should be priced.
• Conjoint analysis became popular because it was a
far less expensive (smaller sample size) and more
flexible way to address these issues than concept
testing.
– When there are just too many potential product combinations
for concept testing
– Need to understand the tradeoff respondents make
– Need to understand the competitive context
http://intranet/download/attachments/10027862/Discrete+Choic
e+Modeling+vs+Concept+Tests.pdf?version=1
Overview of Conjoint Analysis:
• Conjoint analysis involving showing respondent
potential product combinations.
• Products can be factored into parts, called factors.
Different options within each factor represents factor
levels.
• The basic premise of Conjoint Analysis that a
respondent makes purchase decision based on the
inherent value he places on factor levels.
– He will tradeoff the levels within different factors. E.g.
trade in his favourite color for lower price, etc…
– However, the recent development of A/CBC has
changed this where we non-compensatory rules are
allowed.
Overview of Conjoint Analysis:
• These three steps form the basics of conjoint
analysis:
– collecting trade-offs: questionnaire with statistical design
showing various options of the product, and respondents
input in terms of product preference.
– estimating buyer value systems: modeling by the analytics
team.
– making predictions: simulation based on the model
developed. Analytics team working with you for results best
suited to answer your client’s marketing question.
Different flavors of a conjoint: Rating based Conjoint
• We design conjoint cards that represent possible products
based on factor levels. Respondents are asked to rate each
cards in terms of purchase intent.
How likely would you purchase this computer?
Please use a scale of 1 to 10. 1 being very unlikely and 10 being very unlikely.
OEM
BADGE LOGO
BRAINS IMAGE
Badge
Processor Speed
OS
HD
Memory (RAM)
Graphics Card
Optical Drive
Size
Price
Compaq
Intel Celeron Dual Core Processor
1 Brains
Intel Celeron Dual Core Processor
1.8 GHz
Windows Vista Home Premium
160GB
1GB
ATI Radeon HD 4850 XT 512MB
DVD+/RW/Flash
15.4"
$399.99
• Alternatively we can show respondents a stack of cards and ask
him to rank all the cards in terms of his preference.
Different flavors of a conjoint: Rating based Conjoint
• Analysis: based on regression. Linear (ratings), Logistic
(ranking).
• Individual level estimate is possible, i.e., each respondents will
have a model based his own data: collect lots of information
from each individual.
• Problems:
– Ratings: scale usage issues, “yeah”ers vs. “nay”ers.
– Ranking: only works with very small problem
• Output:
– Preference for the various product options on the same
rating scale
• simulated preference rating
– Relative preference for the various levels within each factor
• Isotherm
Different flavors of a conjoint: Adaptive Conjoint Analysis
• Adaptive Conjoint Analysis (ACA): Sawtooth
proprietary technology. Only works within the
Sawtooth SSI Web interviewing interface.
• Most popular conjoint technique in the 1990s. Still
enjoys popularity among certain research area.
• The respondent task is adaptive. That is, rather than
a fixed statistical design, the respondent’s later
conjoint tasks are determined by his preference
selection made earlier.
• Claims to be able to handle large number of factors:
– by focusing respondents on a few factors that are
considered most important through direct
solicitation.
Different flavors of a conjoint: Adaptive Conjoint Analysis
• Output:
– Similar to rating based, except we can simulate
respondent’s share of preference for the product by
assigning each respondent to his most preferred
product.
– The model is produced for each individual separately.
It is possible at the end of the interview to then build an
ideal product for each respondent.
• E.g. Tailoring patient preference to treatment options.
Different flavors of a conjoint: Self-Explicated Conjoint
• The poor-man’s conjoint
• Use direction questioning to get at respondent’s factor
importance and preferences for the different levels within the
factors.
• allocate 100 points across all the factors
• Rate each level within each factor in terms of preference.
• Not recommended.
What we do today: Choice Based Conjoint
• Choice Based Conjoint: we design conjoint cards that represent
possible products based on factor levels. Products are grouped
into options within a card, and respondents are asked to choose
within the group.
• Over the last decade, academics and practitioners have favored
choice over ratings-based methods:
– Stronger mathematical theory (McFadden: MNL theory)
– Stronger psychological underpinnings
– Argued to be more accurate (comparison to market data)
option1
One month free supply of
medication
Activiation is required in order to obtain the
savings.
option2
option3
Receive savings over 3 months:
Pay no more than $22.50 in your
Receive 60% off your co-pay for 6
first month, $20 in your second
months
month, and $17.50 in your third
month.
Includes a one month free supply of
medication
Includes a one month free supply of
medication
Activation is optional. A small additional
financial discount per redemption will be
provided with activation.
Activation is optional. Additional disease
education and/or health and wellness
information will be provided with activation.
What we do today: Discrete Choice Model
• DCM is really just one type of CBC, where the focus is less on
optimizing the product offer, more on the market competitive
context.
DCM
CBC
•
•
•
Uses with multiple factors (6-10) to
describe products
Respondents are shown limited
number of options per card (4-6).
Usually come at the earlier stage
in product development for
–
–
–
Market potential
Best feature combination
Rough price level
•
•
•
Mostly use Brand/Price combo to
describe products
Respondents are shown many options
that represents most of the market
Usually at later stage in product
development to:
–
–
Test for various marketing inputs,
such as package, POS
Determine pricing scenario, product
lineup vs. competitions.
CBC Choice Tasks
DCM Choice Tasks
What we do today: CBC & DCM
• Output: the basic output is still the similar as those
from ACA/Rating based conjoint
– CBC:
• Factor importance/Level preference - Isotherm
• Simulation: simulator, product optimization
• Individual level estimation allows you to further segment
the respondents.
– Potentially developing different optimized product for each
segment. Caution: no simple typing tools for these.
– DCM:
• Usually no isotherm except for impact of packaging
change, sale/promotions
• Simulator: line optimization, pricing optimization
• Unlike ACA, no individual level recommendation.
Variations on CBC
• MaxDiff/Best-Worst Scaling
– One factor CBC
– Often used for the stated importance question
– Output: isotherm – relative preference of the items
• Anchored MaxDiff
– Add a direction question at the end
– Turn relative preference to “absolute”/anchored preference
• Adaptive MaxDiff – when there are too many items.
• Other used in conjunction with TURF – optimal combo set
Variations on CBC
• Adaptive MaxDiff – when there are too many items.
– An approach in stages to narrow down the list of
desirable
• Anchored MaxDiff
– Add a direction question at the end
– Turn relative preference to “absolute”/anchored preference
• Often used in conjunction with TURF – optimal combo set
Variations on CBC
• Best-Worst Conjoint
– Standard CBC
– Ask respondents to choose both the most and least
preferred option to get more data out of each
respondent
• Respondent can do this additional task very easily and
quickly since they have already evaluated all the options
– The additional information improves the model
significantly.
• To be tested: could mean potentially smaller sample/less
number of tasks.
– Output: same as before
What we would like to do: Adaptive CBC
• We have only done one of these study – internal R
on R on technology product. Never been tried in
health care.
• Allows for non-compensatory decisions
– What process do the respondents go through to make
decisions? How likely will non-compensatory rules apply?
– More likely in patient based research
• Issues:
– Longer interview length, 50%-100% longer
– Sawtooth Proprietary software: respondents are routed out
of Sparq for this portion.
Market Share vs. Choice share
• Choice shares are NOT market shares
– 100% awareness,
– 100% availability
– “Overstatement” on the new products
– “Price is no object”
• In our experience, we generally under-estimate price
elasticity
– Other issues ….
Comparison should only be made to the “BASE CASE”
Not to current market share
“Calibration”
• When client insists on comparison to market
information:
– We calibrate the “Base Case” to market information:
external effects adjustment
– We apply the same adjustment to all the simulated scenarios
– Effectively we are doing the same comparison – only that we
have now moved the “Base Case”.
– However, even the calibrated choice shares are still NOT the
market shares.
Forecast
• Market Shares are NOT one-time measures. They
reflect decisions consumers make over time.
– Trial: first purchase - Would you buy it?
– Repeat: subsequent purchases – Would you buy it
again?
• Calibrated choice shares, adjusted for media spend
and marketing plans, can be used to assess “Trial”.
– We have no information on “repeat”.
DCM alone will not give you “forecast”
• Bring in the “forecast” expert.
• Dr. Lin
Pricing for CBC – Analytics cost
• A standard CBC is about 60 hrs in the PPE,
– $9K internal cost
• Analytics will bill you the actual hours only
– If it will be more than 60 hrs, you will get notified.
– Rosanna bills at a lower rate than Jane, but might take
more time, so the cost will be about same in the end.
– $15K external to the client
• Your SBU keeps the difference
CBC Pricing - what do we need from the client?
• Sample specs:
– Sample size
• Who’s in the sample? How interested are they in this product?
• Model specs:
– Factors and levels
• # of factors, how many levels in each
• Restrictions: none/some/lots
– How the factors go together.
• Can we show everybody everything?
– Or do we have to worry about scenarios?
• Task specs:
– What question is asked to respondents:
• How many product options can we show? How many fixed
competitive options?
• What type of answers are we asking for?
– Choose one vs. allocation
What is reasonable?
• If we can show multiple product options (3) per task to
the respondents,
– A sample size of 1500 respondents should support a model
with 6 factors, each with 5 levels, with no restrictions in the
model
• If we can show one product option per task to the
respondents,
– A sample size of 1500 respondents would only support a model
with 4 factors, each with 3 levels
Ask Jane or Sample planning paper or
http://intranet/pages/viewpage.action?pageId=10027862
What impacts pricing?
• Factors that impacts Analytics cost:
– Complexity of the model:
•
•
•
•
•
restrictions
scenarios
Choices in stages/ selection from menus
Large categorical factors: 8+ levels (except in MaxDiff)
“unusual” requirements: purchases/ virtual shopping
– Output requirement other than isotherm and
simulator:
•
•
•
•
•
“Calibration”
Optimization scenarios
Premium calculations/Willingness-to-pay
Standard error estimates on choice shares/ factor importance.
Segmentations
What impacts pricing?
• Factors that impacts Sample/Ops cost:
– Size of the model:
• larger model requires more sample,
• longer task length, more incentive
– More complex design may require more
programming and PM cost as well.
• Options enabled/disabled based on previous
choice on the same page
• Adaptive factor levels
– Virtual shopping
http://intranet/display/research/Analytics
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