1. An Introduction to Advanced Analytics
(Without Equations!)
Paul Carney, Bonamy Finch

2. We know we need to
improve our
understanding of the
‘stats stuff’
What our clients say after
Analytics training courses
What a lot of MR agencies say
about Advanced Analytics
This is great – we’ve
never felt confident
talking to our clients
about it until now!
2

3. Bonamy Finch’s Team of 10 Advanced Analysts
Dr. Leigh Morris MMRS
Managing Director
Paul Carney MMRS
Deputy Managing
Director
Paul Jackson AMRS
Head of Analytics
Ieva Bouaziz
Analytics Manager
Jacqui Savage
Senior Analytics Manager
Dr. Anders S. Olsson MMRS
Director
Frances McCabe AMRS
Director
Giselle Hillman AMRS
Director
Haydn Swift
Analytics Executive
Jingxue Chen
Analytics Assistant
» Recently established the MRS Advanced Analytics Special Interest Group (ADAN) – with Leigh as
chairperson

4. Objectives of the session
» Overview of some useful analytical techniques:
» What they are
» When they can be used
» How to show their value to clients
» How to talk knowledgeably about them
» Ultimately, to give you confidence!
4

5. 4 Main Topic Areas:
1
Segmentation
2
3
Key Driver
Analysis
Conjoint
4
MaxDiff &
TURF

6. Segmentation

7. Segmentation – what is it?
» Segmentation analysis is used to identify groups of:
» Consumers (most often based on attitudes/behaviour)
» Occasions (most often based needs on occasion)
» Brands
» These groups should have members that are as similar as possible
to each other, and as different as possible to other segments.
» Hierarchical, Latent Class, k-means, Ensemble Analysis, etc.
» It is used to provide clients with the desired level of granularity – to
effectively target key target groups
» The Bonamy Finch analysts have run over 600 segmentations in
the past 8 years – with a norms database to help you understand
the quality of your segmentation

8. Aim of segmentation:
To create groups that contain similar items…
…and the groups are different from each other

9. Segmentations impact many business streams – sometimes they conflict!
Target group
development
Product
development
Segmentation
Objectives
Media
planning
Customer
retention &
acquisition
strategy
Tailored
channel
strategy
Attitudes
Psychographics
Brand &
portfolio
management
Informed
price setting
PR & comms
development
Needs
Optimal
Segmentation
Lifestage
Occasion
Category
Involvement
Lifestyle
Behaviour
» We often recommend (and participate in) Kick-Off Workshops and Stakeholder Interviews, to
extract the most important reasons for the segmentation to exist

10. A Typical Segmentation Process:
Factor analysis groups a longer list
of statements into ‘themes’
1
2
3
This makes the segmenting
process more efficient
The factors are then used to
group the respondents into
distinct segments, based on their
answers to the factors
Using our proprietary software, we often ‘optimise’ the
segments on behavioural or demographic criteria, to give more
differentiation on other dimensions

11. Factor Analysis – Pros & Cons

Can be useful in summarising large sets of attributes into a
more manageable number

It identifies discrete dimensions that often improve the
stability of cluster analysis

Factors can also be used in reporting to simplify the story
BUT…
×
×
×
Factors can reduce the sharpness of a segmentation – by
grouping together attributes, one loses the ability to form
segments with different views on these attributes
Factors sometimes confuse end clients – so are often used
as means to an end
Can use other options such as dendrograms instead
Brand A
Brand D
Brand C
Brand B

12. Segmentation Outputs
» Our Segment Profiling outputs allow you to
understand the segments as quickly as possible,
without waiting for full data tables:
» Indices & percentages
» Automatic sorting of attributes & key profilers
» Visual representation of the migrations between
segment solutions
» Customised, auto-charted dashboard of any
segment, from any solution
» We also provide ‘golden question’ algorithms
» And can help with activation & ongoing client
support

13. Key Driver Analysis

14. Key Driver Analysis – what is it?
» Key Driver Analysis is used to establish the relative influence of
an attribute or attributes on a particular measure. It is assumed
that a causal relationship exists.
» It can be used whenever we have:
» A Dependent Variable
» A series of Predictor Variables
» For example:
» If my call centre staff are more helpful, then will customer satisfaction
improve?
» If my brand is perceived as more modern, then will it get into more
people’s consideration sets?
» If I’m under 25, am I more likely to be in a particular segment?

15. Many different types of KDA – all with different strengths and weaknesses,
and suitable to different types of variables
» Analysis of Variance (ANOVA)
» Correlations
Bonamy Finch select the most
appropriate method, based on:
» Gamma Association Metrics
1.
The data you have
» Gap Analysis/Impact Indices
2.
The specific questions your
client wants answered
3.
Your budget!
» Regression Modelling
» Genetic Algorithms
» Structural Equation Modelling (SEM)
» Kruskal’s Relative Importance Analysis
» Canonical Correlation Analysis
» CHAID
» Random Forest

16. Kruskal’s Analysis avoids many of the problems with ‘old’ KDA…
Correlations
Kruskal’s
Regression
B
B
B
Outcome
Outcome
FPTP – Unfair
& Unrealistic!
Double Counting!
A
Outcome
Fair Share of
Importance
A
A
Outcome
Outcome

17. Some example KDA deliverables
% Affected
Attribute 5
26.4
Attribute 4
18.5
Attribute 1
16.1
Attribute 2
14.6
Attribute 3
11.4
Attribute 7
Attribute 6
130
Relative Performance
Bubble 8
120
Bubble 7
110
100
Bubble 6
Bubble 5
90
Bubble 4
Bubble 2
80
Bubble 3
Bubble 1
70
0
5
10
15
% Influence
20
25
30
10.2
2.9
100
100
100
100
97
97
90

18. Conjoint

19. Conjoint – what is it?
» Measuring the influence of different product
or service features (including price) on
consumer behaviour
» Influence is often difficult to measure with
direct questions. Conjoint is a means of
obtaining information indirectly:
» Respondents consider and evaluate whole
products – not individual components
» It uses analysis to derive the influence of features
on preference / choice
If you were in the market to buy a new PC today and these
were your only options, which would you choose?
Dell
Toshiba
Asus
2.2 GHz
Processor
1.8 GHz
Processor
2.4 GHz
Processor
2 GB RAM
4 GB RAM
1 GB RAM
21 Inch Monitor
24 Inch Monitor
19 Inch Monitor
£1,299
£799
£699
None: I wouldn’t choose any of these

20. Benefits of Conjoint
» Exceptional flexibility in use of the findings –
able to analyse impact on brand preference
of any combination of brand, price and
features
» Extract a lot of information out of a
respondent, in a simple, intuitive exercise
» Powerful strategic tool to allow
understanding (i.e. not just observation) of
consumer behaviour, and therefore predictive
capabilities
All Possible
Attribute
Combinations
S
I
M
U
L
A
T
O
R
Identify Optimal
Range to
Maximise Reach
+
+
}
= 30%

21. Conjoint – main outputs
» Market Drivers - impact of each attribute
in driving preference /choice
» Relative value of each component utility attached to each attribute level
» Complex demand curves – impact of
price change on choice
» Market Simulator – ability to model
different concepts to identify optimum
package or portfolio of packages

22. MaxDiff & TURF

23. Maximum Difference Scaling (MaxDiff) – what is it?
» Maximum Difference Scaling (or MaxDiff) uses traditional trade-off methodologies to provide a relative
measure of importance (or appeal) across a number of attributes.
» A typical respondent task looks like this:
» Respondents are shown multiple screens,
showing different groups of attributes
» Typically 4 to 5 attributes would be shown on
each screen.
» The number of tasks required is calculated on
the assumption that each attribute should be
seen at least 3 times.
» So, with 20 statements, and 4 attributes seen on
each screen, the respondent would see 3*(20/4) =
15 screens.

24. Why & when should we use MaxDiff?
» Benefits of MaxDiff:
» Straightforward & fast process
» Simple exercise for the respondent
» More engaging than lots of separate
ratings scales
» No opportunity for top-boxing problems,
or cultural scale use bias
» Makes excellent segmentation data!
» Downsides to MaxDiff:
» Needs to be designed & inputted into a
script or P&P questionnaire
» More expensive than rating scales (but
much cheaper than conjoint)
» Difficult to use for ongoing segment
classification tools
» Relative measure, so needs ‘calibrating’

25. TURF – what is it?
» TURF = Total Unduplicated Reach and Frequency
» "Where should we place ads to reach the widest
possible audience?”
41.4%
» “Which flavours should we launch, or claims should
we make, to appeal to the largest number of
consumers?”
4.5
2.1
6.8
12.9
28.5
100%
» The best combinations aren’t always the top 2 or top 3
individual products!
» Incremental uplift in reach is key
» Niche target groups or products with niche appeal
Strawberry
Vanilla
Peach
Raspberry
Lime

26. TURF – what does it do?
» Individual products through a simple metric such as purchase intention
» TURF looks at all combinations of products, and finds which combinations would be most
successful based on the portfolio’s ability to reach the maximum consumer base
» These results can be included in an Excel simulator, to model all different combinations, by key
subgroups

27. advanced analytics
p.carney@bonamyfinch.com