An Introduction to Advanced Analytics

(Without Equations!) Paul Carney, Bonamy Finch

2

This is great – we’ve

never felt confident

talking to our clients

about it until now! What our clients say after

Analytics training courses

We know we need to

improve our

understanding of the

‘stats stuff’

What a lot of MR agencies say

about Advanced Analytics

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

Frances McCabe AMRS

Director

Giselle Hillman AMRS

Director

Dr. Anders S. Olsson MMRS

Director

Ieva Bouaziz

Analytics Manager

Jingxue Chen

Analytics Assistant

Haydn Swift

Analytics Executive

Jacqui Savage

Senior Analytics Manager

» Recently established the MRS Advanced Analytics Special Interest Group (ADAN) – with Leigh as

chairperson

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

4 Main Topic Areas:

Segmentation Key Driver Analysis

Conjoint MaxDiff & TURF

1 2 3 4

Segmentation

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

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!

Brand &

portfolio

management

Informed

price setting

PR & comms

development Tailored

channel

strategy

Customer

retention &

acquisition

strategy

Media

planning

Target group

development

Product

development

Segmentation

Objectives

Needs

Lifestyle

Behaviour Category

Involve-

ment

Occasion

Lifestage

Psycho-

graphics

Attitudes

Optimal

Segmentation

» We often recommend (and participate in) Kick-Off Workshops and Stakeholder Interviews, to

extract the most important reasons for the segmentation to exist

A Typical Segmentation Process:

Factor analysis groups a longer list

of statements into ‘themes’

This makes the segmenting

process more efficient 1 The factors are then used to

group the respondents into

distinct segments, based on their

answers to the factors 2 Using our proprietary software, we often ‘optimise’ the

segments on behavioural or demographic criteria, to give more

differentiation on other dimensions 3

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

Factor Analysis – Pros & Cons

Brand A

Brand D

Brand C

Brand B

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

Key Driver Analysis

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?

Many different types of KDA – all with different strengths and weaknesses,

and suitable to different types of variables

» Analysis of Variance (ANOVA)

» Correlations

» Gamma Association Metrics

» Gap Analysis/Impact Indices

» Regression Modelling

» Genetic Algorithms

» Structural Equation Modelling (SEM)

» Kruskal’s Relative Importance Analysis

» Canonical Correlation Analysis

» CHAID

» Random Forest

Bonamy Finch select the most

appropriate method, based on:

1. The data you have

2. The specific questions your

client wants answered

3. Your budget!

Kruskal’s Analysis avoids many of the problems with ‘old’ KDA…

A

Outcome

B

Outcome

B

A

Outcome

Outcome

Correlations Regression Kruskal’s

B

A

Outcome

Double Counting! FPTP – Unfair

& Unrealistic!

Fair Share of

Importance

Some example KDA deliverables

% Influence

Re

lati

ve P

erf

orm

ance

Bubble 1

Bubble 2

Bubble 3

Bubble 4

Bubble 5

Bubble 6

Bubble 7

Bubble 8

70

80

90

100

110

120

130

0 5 10 15 20 25 30

26.4

18.5

16.1

14.6

11.4

10.2

2.9

Attribute 5

Attribute 4

Attribute 1

Attribute 2

Attribute 3

Attribute 7

Attribute 6

100

100

100

100

97

97

90

% Affected

Conjoint

» 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

Conjoint – what is it?

Dell

2.2 GHz Processor

2 GB RAM

21 Inch Monitor

£1,299

Toshiba

1.8 GHz Processor

4 GB RAM

24 Inch Monitor

£799

Asus

2.4 GHz Processor

1 GB RAM

19 Inch Monitor

£699

None: I wouldn’t choose any of these

If you were in the market to buy a new PC today and these were your only options, which would you choose?

» 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

Benefits of Conjoint

}

All Possible Attribute

Combinations

Identify Optimal Range to

Maximise Reach

+

+= 30%

S

I

M

U

L

A

T

O

R

» 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

Conjoint – main outputs

MaxDiff & TURF

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.

» 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!

Why & when should we use MaxDiff?

» 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’

TURF – what is it?

» TURF = Total Unduplicated Reach and Frequency

» "Where should we place ads to reach the widest

possible audience?”

» “Which flavours should we launch, or claims should

we make, to appeal to the largest number of

consumers?”

» 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

28.5

12.9

6.8

4.5 2.1

Strawberry Vanilla Peach Raspberry Lime

100%

41.4%

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

advanced analytics

p.carney@bonamyfinch.com