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An Introduction to Advanced Analytics
(Without Equations!)
Paul Carney, Bonamy Finch
We know we need to
improve our
understanding of the
‘stats stuff’

What our clients say after
Analytics training courses

...
Bonamy Finch’s Team of 10 Advanced Analysts

Dr. Leigh Morris MMRS
Managing Director

Paul Carney MMRS
Deputy Managing
Dir...
Objectives of the session
» Overview of some useful analytical techniques:
» What they are
» When they can be used
» How t...
4 Main Topic Areas:

1

Segmentation

2

3

Key Driver
Analysis

Conjoint

4

MaxDiff &
TURF
Segmentation
Segmentation – what is it?
» Segmentation analysis is used to identify groups of:
» Consumers (most often based on attitud...
Aim of segmentation:
To create groups that contain similar items…

…and the groups are different from each other
Segmentations impact many business streams – sometimes they conflict!

Target group
development

Product
development

Segm...
A Typical Segmentation Process:
Factor analysis groups a longer list
of statements into ‘themes’

1
2
3

This makes the se...
Factor Analysis – Pros & Cons


Can be useful in summarising large sets of attributes into a
more manageable number



I...
Segmentation Outputs
» Our Segment Profiling outputs allow you to
understand the segments as quickly as possible,
without ...
Key Driver Analysis
Key Driver Analysis – what is it?
» Key Driver Analysis is used to establish the relative influence of
an attribute or att...
Many different types of KDA – all with different strengths and weaknesses,
and suitable to different types of variables
» ...
Kruskal’s Analysis avoids many of the problems with ‘old’ KDA…
Correlations

Kruskal’s

Regression

B

B

B

Outcome

Outc...
Some example KDA deliverables
% Affected
Attribute 5

26.4

Attribute 4

18.5

Attribute 1

16.1

Attribute 2

14.6

Attri...
Conjoint
Conjoint – what is it?
» Measuring the influence of different product
or service features (including price) on
consumer be...
Benefits of Conjoint
» Exceptional flexibility in use of the findings –
able to analyse impact on brand preference
of any ...
Conjoint – main outputs
» Market Drivers - impact of each attribute
in driving preference /choice
» Relative value of each...
MaxDiff & TURF
Maximum Difference Scaling (MaxDiff) – what is it?
» Maximum Difference Scaling (or MaxDiff) uses traditional trade-off me...
Why & when should we use MaxDiff?

» Benefits of MaxDiff:
» Straightforward & fast process
» Simple exercise for the respo...
TURF – what is it?
» TURF = Total Unduplicated Reach and Frequency
» "Where should we place ads to reach the widest
possib...
TURF – what does it do?
» Individual products through a simple metric such as purchase intention
» TURF looks at all combi...
advanced analytics

p.carney@bonamyfinch.com
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Introduction to Advanced Analytics (without equations!)

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Introduction to Advanced Analytics (without equations!)

  1. 1. An Introduction to Advanced Analytics (Without Equations!) Paul Carney, Bonamy Finch
  2. 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. 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. 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. 5. 4 Main Topic Areas: 1 Segmentation 2 3 Key Driver Analysis Conjoint 4 MaxDiff & TURF
  6. 6. Segmentation
  7. 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. 8. Aim of segmentation: To create groups that contain similar items… …and the groups are different from each other
  9. 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. 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. 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. 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. 13. Key Driver Analysis
  14. 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. 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. 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. 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. 18. Conjoint
  19. 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. 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. 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. 22. MaxDiff & TURF
  23. 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. 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. 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. 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. 27. advanced analytics p.carney@bonamyfinch.com

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