On Get Inspired 2012, Nicole Huyghe (solutions-2) gave a presentation about analytics and innovation: new statistical techniques in market research.

1. GET INSPIRED 2012
ANALYTICS & INNOVATION
NEW STATISTICAL METHODS IN
MARKET RESEARCH
solutions-2, Nicole Huyghe
June 27, 2012

2. New methods
Re-instating existing methods
Pushing boundaries
2

4. MENU BASED CONJOINT
CLUSTER ENSEMBLES
K. CHRZAN MAKE/BREAK MODEL
KANO RESEARCH
MULTI / SPARSE / EXPRESS MAXDIFF
CBC + MAXDIFF
TURF (+ SURF)
4

5. NPD/Voice of customer Dashboard/Score card
How are we doing
What are customers
and how can we
looking for when
increase
buying a product?
satisfaction/loyalty?
Are there customer How is my brand
segments with positioned versus
different needs? the competition?
Segmentation Brand positioning
Perceptual mapping

6. Menu based conjoint

7. Conjoint objective
Understanding which product
features drive purchase
decision

8. Business outcome
Maximising sales and profit
through optimising product
characteristics

9. There are different types of
conjoint methods
9

10. Choice-Based-Conjoint
10

11. Choice-Based-Conjoint
11

12. 12

13. Choice-Based-Conjoint
 Assumption: compensatory approach
 Natural choice task
 None option makes it even more realistic
 Easy for the respondent
 Fairly short exercise
13

14. Choice-Based-Conjoint
 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
14

15. Non compensatory approach
Adaptive CBC (ACBC)
Menu based conjoint (MBC)
15

16. Adaptive CBC (ACBC)
CBC tasks are tailored
But still bundles
16

17. Menu Based Conjoint (MBC)
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
17

18. 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’.
Land Rover L560
£70,500
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

19. 19

20. 20

21. CBC ACBC MBC
Compensatory (Non) compensatory (Non) compensatory
Assumption
approach approach approach
Choice between Choice between No bundling –
Principle pre-bundled pre-bundled respondent can chose
concepts concepts 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
average average expensive
cost 21

22. Product/Portfolio Share of Price sensitivity, Impact of or on
optimisation preference, value of features competition
revenue, profit
22

23. 23

24. 24

25. Cluster ensembles

26. Segmentation objective
Identifying groups of customers
with different needs, attitudes
and behaviour

27. Business outcome
Optimising products and
communication by better
understanding the (potential)
customers

28. Segmentation approach: cluster ensemble
 answers the who and the why question

29. Traditional Cluster ensemble
segmentation segmentation
Segments differ on few Segments differ on many
dimensions dimensions
Less diffentiated on Differentiation on both
tangeable aspects. More tangeable (who aspect)
focus on the why than the and less tangeable aspects
who (why aspect)
Difficult to target segments Targeting segments is
essential and possible

30. The cluster ensemble process
Traditional
Theme 1 segmentations are
run on each of the
different
dimensions (such
Theme 2 as behaviour,
needs, attitudes,
demographics, …)
Theme 3
...
Theme 9
Theme 10
All individual
segmentation
Cluster results are used as
input to an
ensemble
Ensemble methode to get
segments differing
on all dimensions

31. A succesful segmentation fulfills 4
characteristics
Stability Integrity Accuracy Size

32. Stability
People do not move around much from one solution to
another

33. Cluster Integrity – Heterogeneity
The segments do differ significantly on key dimensions

34. Cluster Integrity – Homogeneity
The people within a segment are very similar on key
dimensions

35. Accuracy
segment membership can be easily predicted – i.e. targetable
segments
Reality Prediction
5 5
6
4 6 4
2
1 2 1
3 7 7
3
8 8
9 9

36. Size
Ideally, there are no very large, nor very small segments

37. K. Chrzan’s make or
break model

38. Model objective
Quantifying the drivers of
overall satisfaction/loyalty/NPS

39. Business outcome
Increasing sales through
improved performance/loyalty
/advocacy scores

40. ***** hotel
Drivers of the customer experience
Staff Restaurant
Room Lounge area
Cleanliness Hotel atmosphere
Room size Internet/Wifi
Breakfast Price
Reservations

41. ***** hotel
Drivers of the customer experience
4 Staff 8 Restaurant
9 Room 9 Lounge area
9 Cleanliness 7 Hotel atmosphere
9 Room size 2 Internet/Wifi
8 Breakfast 6 Price
7 Reservations
+ Overall experience score: 6

42. Regression/correlation analysis
- drivers of overall customer experience
- non-compensatory impact is not measured
 Keith Chrzan’s Make or Break model

43. 1 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 x Staff Restaurant
Room Lounge area
Cleanliness Hotel atmosphere
Room size x Internet/Wifi
Breakfast Price
Reservations

44. 3 x Staff 8 Restaurant
9 Room 9 Lounge area
9 Cleanliness 7 Hotel atmosphere
9 Room size x Internet/Wifi
8 Breakfast 6 Price
7 Reservations
4 1 x Staff 8 Restaurant
9 Room 9 Lounge area
9 Cleanliness 7 Hotel atmosphere
9 Room size 1 x Internet/Wifi
8 Breakfast 6 Price
7 Reservations

45. RESULTS – FOR EACH ASPECT
Standard weights
Penalty weights for bad experiences
Bonus weights for wonderful experiences
 Richer and more accurate model

46. KANO
RESEARCH

47. Kano objective
Identifying the delighters and
dissatisfiers of the customer
experience

48. Business outcome
Increasing sales through
improved performance/loyalty
/advocacy scores

49. Understanding how performance
drives satisfaction/loyalty
Overall
Satisfaction
Kano Theory
Satisfied allows us to
derive how
performance
in an area
drives overall
Performance Performance satisation
– -
Poor Outstanding
Traditional
Methods
assume that
there is
always a
Overall linear impact
Satisfaction
Dissatisfied

50. Identify the ‘Must Haves’….
Overall Must haves
Satisfaction
Satisfied No extra points
if you get it
perfect BUT
people will be
upset if it
Performance Performance
– -
doesn’t work.
Poor Outstanding
Dissatisfiers Critical to fix if
Expected
performance is
/ Must
poor
haves
DISSATISFIERS
Overall
Satisfaction
Dissatisfied

51. Identify the ‘Added Bonuses’….
Overall
Satisfaction Added Bonus
Satisfied
People don’t
Attractive expect it, so
/ Added there is no
bonuses DELIGHTERS dissapointment
Performance Performance
– - if it is lacking
Poor Outstanding BUT it delights
people when it
happens
Create/
Identify USPs
Overall
Satisfaction
Dissatisfied
DELIGHTERS

52. ….and the ‘Key Desired’ elements
Overall
Satisfaction Desired
Satisfied Desired
Fall into both
Attractive categories.
/ Added
bonuses Delighters These are the
Performance Performance
– -
key areas for a
Poor Outstanding company to
Dissatisfiers
Expected focus and
/ Must perfom on
haves
DELIGHTERS &
DISSATISFIERS
Overall
Satisfaction
Dissatisfied

53. Kano Analysis
Creating a better customer experience
Establish customer driven action plan
 Identify Critical Fixes
 Tailor Offering to Customer Needs
 Create USPs
 Optimise Investment
 More satisfied, loyal & profitable
customers

54. Multi, sparse, express
maxdiff

55. Maxdiff objective
Understanding which product
features are most important for
customers

56. Business outcome
Maximising sales and profit
through optimising product
characteristics

60. Average number of
tested items : 15 – 30
 if 5 items on a screen
 9 to 18 screens
WHAT IF 120 ITEMS ??

61. 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

62. 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

63. Only asks about persuasiveness
What about uniqueness, believability?

64. MULTI MAXDIFF

65. Persuasive 1 2
4 3 5
6
2 1
3
Unique 6 4 5
1
4 5
Believable 2 3
6
Average High Maxdiff
score

66. Maxdiff + CBC

67. Weekday On ipad
Weekend On iphone
Both On pc
On all
1 month archive Paper copy week
1 year archive Paper copy WE
1 week archive No paper copy
Headline news National news
Financial news Economic news
Sports Stock exchange
Cultural news Blogs
Local news …
International news

68. Online paper option 1 Online paper option 2 Online paper option 3
Weekday Weekday Weekday
On ipad On ipad On ipad
1 month archive 1 month archive 1 month archive
No paper copy No paper copy No paper copy
Financial news National news Financial news
International news Blogs Local news
Sports Cultural news Sports
Local news Local news Local news
Blogs Sports Eonomic news
Stock Exchange
£9/month £13/month £15/month


69. Online paper option 1 Online paper option 2 Online paper option 3
Weekday Weekday Weekday
On ipad On ipad On ipad
1 month archive 1 month archive 1 month archive
No paper copy No paper copy No paper copy
Financial news National news Financial news
International news Blogs Local news
Sports Cultural news Sports
Local news Local news Local news
Blogs Sports Eonomic news
Best to reduce list Stock Exchange
before conjoint


70. Most likely Least likely
subscribe subscribe
 Headline news
Financial news 
Sports
Cultural news

71. Most likely Least likely
subscribe subscribe
 Headline news
Financial news 
Sports
Cultural news
The top 5 items for each respondent are
then passed to the conjoint  tailored

72. Turf + Surf

73. TURF objective
Identifying the set of products /
product characteristics which
will reach most customers

74. Business outcome
Maximising sales and profit
through optimising the product
range

75. Gelati
& Sons

76. Gelati Which 4 our of these 8 should we chose?
& Sons
Respondents have a 1 if they would buy the flavour.
R1 1 1 0 0 0 0 0 0
R2 0 1 1 1 0 0 1 0
R3 0 0 1 0 0 0 0 0
R4 1 1 0 1 1 0 0 1
R5 0 0 0 0 0 1 0 0
There are 70 different ways to
choose 4 flavours from these 8!76

77. Gelati
Re Results from all 100 respondents
& Sons
Unduplic
# ated
Flavours Reach Flavours
1 65%
2 80%
3 90%
4 95%
5 100%
= with this selection of 5 flavours,
they please all respondents 77

78. < 30 items  TURF
If ≥ 30  TURF + SURF
(SURF: Successive Unduplicated Reach and Frequency)

79. Nicole Huyghe
nicole@solutions2.be
www.solutions2.be
If you have any
risingquestions