presentation nicole huyghe (advanced analytics) get inspired 2012
DESCRIPTION
On Get Inspired 2012, Nicole Huyghe (solutions-2) gave a presentation about analytics and innovation: new statistical techniques in market research.TRANSCRIPT
ANALYTICS & INNOVATION
NEW STATISTICAL METHODS IN
MARKET RESEARCH
solutions-2, Nicole Huyghe June 27, 2012
GET INSPIRED 2012
2
New methods Re-instating existing methods
Pushing boundaries
4
MENU BASED CONJOINT
CLUSTER ENSEMBLES
K. CHRZAN MAKE/BREAK MODEL
KANO RESEARCH
MULTI / SPARSE / EXPRESS MAXDIFF
CBC + MAXDIFF
TURF (+ SURF)
What are customers looking for when buying a product?
How are we doing and how can we
increase satisfaction/loyalty?
Are there customer segments with
different needs?
How is my brand positioned versus the competition?
NPD/Voice of customer Dashboard/Score card
Brand positioning Perceptual mapping
Segmentation
Menu based conjoint
Conjoint objective
Understanding which product
features drive purchase
decision
Business outcome
Maximising sales and profit
through optimising product
characteristics
9
There are different types of
conjoint methods
Choice-Based-Conjoint
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Choice-Based-Conjoint
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Assumption: compensatory approach
Natural choice task
None option makes it even more realistic
Easy for the respondent
Fairly short exercise
Choice-Based-Conjoint
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All respondents do get to see all attributes
and levels
Risk of focusing on a few attributes only
Might result in poor data
Less engaging exercise
Assumptions: features are pre-bundled
Choice-Based-Conjoint
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Adaptive CBC (ACBC)
Menu based conjoint (MBC)
Non compensatory approach
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CBC tasks are tailored
But still bundles
Adaptive CBC (ACBC)
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Extension of CBC
For multi-check/configurator choice tasks
A la carte product and service configuration
ACBC and CBC: pre-bundled
Examples: Restaurant menus, Cars, Telecom
bundling, Insurance policies, Banking options
Menu Based Conjoint (MBC)
Below you will see the Land Rover L560 together with all of the additional features at different prices. For each feature, please indicate whether or not you would subscribe to that feature at that price.
If you are not interested in any of the features at any of the given prices, please tick ‘None of the above’.
Dual View £800
Bluetooth with seat belt microphones £1,100
Bluetooth phone audio connection £900
Rear seat phone with cordless handset £800
None of the above
TOTAL PRICE: £71,400
£70,500
Land Rover L560
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CBC ACBC MBC
Assumption Compensatory
approach (Non) compensatory
approach (Non) compensatory
approach
Principle
Choice between pre-bundled
concepts
Choice between pre-bundled
concepts
No bundling – respondent can chose
between levels
Mimicks real purchase process
Average length ~7 ‘ ~15 ‘ ~7’
Engaging
Programming flexibility of Sawtooth
Ease of programming design/questions
Analysis options
Simulator: What if, price sensitivty, optimum product
Simulator: Optimising bundling
Programming and analysis
cost average average expensive
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Share of
preference,
revenue, profit
Price sensitivity,
value of features
Impact of or on
competition Product/Portfolio
optimisation
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Cluster ensembles
Segmentation objective
Identifying groups of customers
with different needs, attitudes
and behaviour
Business outcome
Optimising products and
communication by better
understanding the (potential)
customers
Segmentation approach: cluster ensemble answers the who and the why question
Segments differ on few
dimensions
Less diffentiated on
tangeable aspects. More
focus on the why than the
who
Difficult to target segments
Traditional
segmentation
Cluster ensemble
segmentation
Segments differ on many
dimensions
Differentiation on both
tangeable (who aspect)
and less tangeable aspects
(why aspect)
Targeting segments is
essential and possible
Theme 1
Theme 2
...
Theme 3
Theme 9
Theme 10
Cluster
Ensemble
The cluster ensemble process
Traditional
segmentations are
run on each of the
different
dimensions (such
as behaviour,
needs, attitudes,
demographics, …)
All individual
segmentation
results are used as
input to an
ensemble
methode to get
segments differing
on all dimensions
Stability Integrity Accuracy Size
A succesful segmentation fulfills 4
characteristics
Stability People do not move around much from one solution to
another
Cluster Integrity – Heterogeneity The segments do differ significantly on key dimensions
Cluster Integrity – Homogeneity The people within a segment are very similar on key
dimensions
Accuracy segment membership can be easily predicted – i.e. targetable segments
1 2
3
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7
9
4
5
6
1
2
3 8
7
9
4
5 6
Reality Prediction
Size Ideally, there are no very large, nor very small segments
K. Chrzan’s make or
break model
Model objective
Quantifying the drivers of
overall satisfaction/loyalty/NPS
Business outcome
Increasing sales through
improved performance/loyalty
/advocacy scores
***** hotel
Drivers of the customer experience
Staff
Room
Cleanliness
Room size
Breakfast Reservations
Restaurant
Lounge area
Hotel atmosphere
Internet/Wifi
Price
***** hotel
Drivers of the customer experience
4 Staff
9 Room
9 Cleanliness
9 Room size
8 Breakfast 7 Reservations
8 Restaurant
9 Lounge area
7 Hotel atmosphere
2 Internet/Wifi
6 Price
+ Overall experience score: 6
Regression/correlation analysis - drivers of overall customer experience - non-compensatory impact is not measured
Keith Chrzan’s Make or Break model
x Staff
Room
Cleanliness
Room size
Breakfast Reservations
Restaurant
Lounge area
Hotel atmosphere
x Internet/Wifi
Price
Overall experience score: 6 If < 7 ask if there were aspects so bad that they made the whole experience awful If > 8 ask if there were aspects so good that they made the whole experience wonderful
2
1
x Staff
9 Room
9 Cleanliness
9 Room size
8 Breakfast 7 Reservations
8 Restaurant
9 Lounge area
7 Hotel atmosphere
x Internet/Wifi
6 Price
3
4 x Staff
9 Room
9 Cleanliness
9 Room size
8 Breakfast 7 Reservations
8 Restaurant
9 Lounge area
7 Hotel atmosphere
x Internet/Wifi
6 Price
1
1
RESULTS – FOR EACH ASPECT
Standard weights Penalty weights for bad experiences Bonus weights for wonderful experiences
Richer and more accurate model
KANO
RESEARCH
Kano objective
Identifying the delighters and
dissatisfiers of the customer
experience
Business outcome
Increasing sales through
improved performance/loyalty
/advocacy scores
Understanding how performance
drives satisfaction/loyalty
Kano Theory
allows us to
derive how
performance
in an area
drives overall
satisation
Traditional
Methods assume that
there is
always a
linear impact
Performance
– Poor
Overall Satisfaction
Satisfied
Overall Satisfaction Dissatisfied
Performance -
Outstanding
Identify the ‘Must Haves’….
Must haves
No extra points
if you get it
perfect BUT
people will be
upset if it
doesn’t work.
Critical to fix if
performance is
poor
DISSATISFIERS
Performance –
Poor
Overall Satisfaction
Satisfied
Overall Satisfaction Dissatisfied
Expected / Must haves
Dissatisfiers
Performance -
Outstanding
Identify the ‘Added Bonuses’….
Added Bonus
People don’t
expect it, so
there is no
dissapointment
if it is lacking
BUT it delights
people when it
happens
Create/
Identify USPs
DELIGHTERS
Overall Satisfaction
Satisfied
Overall Satisfaction Dissatisfied
Attractive / Added bonuses DELIGHTERS
Performance –
Poor
Performance -
Outstanding
….and the ‘Key Desired’ elements
Desired
Fall into both
categories.
These are the
key areas for a
company to
focus and
perfom on
DELIGHTERS &
DISSATISFIERS
Overall Satisfaction
Satisfied
Overall Satisfaction Dissatisfied
Desired
Expected / Must haves
Attractive / Added bonuses
Dissatisfiers
Delighters Performance
– Poor
Performance -
Outstanding
Establish customer driven action plan
Identify Critical Fixes
Tailor Offering to Customer Needs
Create USPs
Optimise Investment
More satisfied, loyal & profitable
customers
Kano Analysis Creating a better customer experience
Multi, sparse, express
maxdiff
Maxdiff objective
Understanding which product
features are most important for
customers
Business outcome
Maximising sales and profit
through optimising product
characteristics
Average number of
tested items : 15 – 30
if 5 items on a screen
9 to 18 screens
WHAT IF 120 ITEMS ??
EXPRESS MAXDIFF
Each respondent only ~ 30 items
The 30 items are seen 3 times
Each respondent a different set of 30
Fully randomised sets
Full utility set for each respondent
Sample size >>
Ideal for list with > 60 items
SPARSE MAXDIFF
Each respondent sees all items
All items are only seen once
Full utiliy set for each respondent
Shorter questionnaire
Ideal for list with < 60 items
Only asks about persuasiveness
What about uniqueness, believability?
MULTI MAXDIFF
Persuasive
Unique
Believable
Average High
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2
2
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3
3
3
4
4
4
5
5
5
6
6
6
Maxdiff
score
Maxdiff + CBC
Weekday
Weekend
Both
Headline news National news
Financial news Economic news
Sports Stock exchange
Cultural news Blogs
Local news …
International news
On ipad
On iphone
On pc
On all
1 month archive
1 year archive
1 week archive
Paper copy week
Paper copy WE
No paper copy
Weekday
On ipad
1 month archive
No paper copy
Financial news
International news
Sports
Local news
Blogs
£9/month
Weekday
On ipad
1 month archive
No paper copy
National news
Blogs
Cultural news
Local news
Sports
£13/month
Weekday
On ipad
1 month archive
No paper copy
Financial news
Local news
Sports
Local news
Eonomic news
Stock Exchange
£15/month
Online paper option 1 Online paper option 2 Online paper option 3
Weekday
On ipad
1 month archive
No paper copy
Financial news
International news
Sports
Local news
Blogs
Weekday
On ipad
1 month archive
No paper copy
National news
Blogs
Cultural news
Local news
Sports
Weekday
On ipad
1 month archive
No paper copy
Financial news
Local news
Sports
Local news
Eonomic news
Stock Exchange
Online paper option 1 Online paper option 2 Online paper option 3
Best to reduce list
before conjoint
Headline news
Financial news
Sports
Cultural news
Most likely
subscribe
Least likely
subscribe
Headline news
Financial news
Sports
Cultural news
Most likely
subscribe
Least likely
subscribe
The top 5 items for each respondent are then passed to the conjoint tailored
Turf + Surf
TURF objective
Identifying the set of products /
product characteristics which
will reach most customers
Business outcome
Maximising sales and profit
through optimising the product
range
Gelati & Sons
76
Gelati & Sons
Which 4 our of these 8 should we chose?
There are 70 different ways to
choose 4 flavours from these 8!
R5
R4
R 1
R 2
R 3
1 1
1 1 1 0
1
0 0 0 0
0 0 0 0 0 0
0
0 1 0
1 1 0 1 1 0 0
0
0
0
1
0 0 0 0 0 1 0 0
Respondents have a 1 if they would buy the flavour.
77
Re
# Flavours
Unduplicated
Reach Flavours
1 65%
2 80%
3 90%
4 95%
5 100%
Gelati & Sons
Results from all 100 respondents
= with this selection of 5 flavours, they please all respondents
< 30 items TURF
If ≥ 30 TURF + SURF
(SURF: Successive Unduplicated Reach and Frequency)
