25 min presentation given at London Business School, to the OR Society's Analytics Network. Summarising Laughlin Consultancy's 9 step model of Softer Skills for Analysts.

1. Paul Laughlin, Managing Director, Laughlin Consultancy
The Softer Skills
Analysts need to make
an impact in their
businesses

2. Client-Side Background
❖ Created and lead customer
insight teams for all the major
insurance brands, products &
channels used by Lloyds
Banking Group over 13 years
❖ Added over £11m incremental
profit to bottom line annually
❖ Developed team of 44 analysts &
mentored future leaders

3. Laughlin Consultancy Background
“Helping businesses make money from customer analytics”
Laughlin Consultancy helps companies maximise sustainable
value from their customer insight, for example by growing their
bottom line, improving customer retention and demonstrating to
their regulator that they treat customers fairly.

4. Customer Analytics can improve Finances
© Laughlin Consultancy Ltd, not to be used without permission.

5. Businesses need Analytics that works
© Laughlin Consultancy Ltd, not to be used without permission.

6. Focus is often on technical skills
© Laughlin Consultancy Ltd, not to be used without permission.
An SMA Perspective © 2017 SMA All Rights Reserved | www.strategymeetsaction.com 4
Business intelligence and analytics are becoming increasingly vital to every part of the insurance business. Insurers
need capabilities that address a wide variety of questions across marketing, sales, and service as well as enterprise
operations. The general types of questions raised are illustrated in Figure 1. At a high level, insurers want to explore
questions like the ones above the orange boxes: How do we gain new insights from historical data? What are our
What happened? Why is it happening? What can we do about it?
There are a variety of technology tools and approaches to address these questions. They generally fall under the
categories of business intelligence, advanced analytics, and emerging analytics, and these technologies can be
applied to answer the types of questions posed in each of the sections.
Figure 1. The BI and Analytics Spectrum for Insurers
Source: Strategy Meets Action 2016
As can be seen from the diagram, the questions to be answered range from the traditional, more operational
data enables reporting on the state of the business (What happened? What is happening now? Where is the
(Why is it happening? What if it continues?). Towards the middle and the right of the diagram, more complex
new opportunities, and ultimately, through emerging analytics, move to human augmentation and automated
decisioning. It should be noted that big data is an overlay onto this diagram, providing a set of approaches and
technologies to answer these questions when the volume, variety, and velocity of the data cannot be addressed in
a timely manner by traditional analytics.
How do we gain new insights
from historical data?
BUSINESS INTELLIGENCE
What are our new
opportunities?
How do we capitalize on new
opportunities?
How do we leverage
human intelligence?
ADVANCED
ANALYTICS
EMERGING
ANALYTICS
Analytic
Collab-
oration
Predictive
Modules
Predictive
Analytics
Data & Text
Mining
Advanced
Statistical
Analysis
ScenariosAnalysis
Ad-hoc
Queries
Dashboards
&
Scorecards
Reporting
Cognitive
Computing
What can we do about it?What is likely to happen?
What if it
continues?
Why is it happening?
Where
is the
problem?
What is
happening?
What
happened?
EDSF Release 2: Part 1. Data Science Competence Framework (CF-DS)
Table 4.2. Identified Data Science skills related to the main Data Science competence groups
SDSDA
Data Science
Analytics
SDSENG
Data Science
Engineering
SDSDM
Data Management
SDSRM
Research Methods
and Project
Management
SDSBA
Business Analytics
SDSDA01
Use Machine Learning
technology,
algorithms, tools
(including supervised,
unsupervised, or
reinforced learning)
SDSENG01
Use systems and
software engineering
principles to
organisations
information system
design and development,
including requirements
design
SDSDM01
Specify, develop and
implement enterprise
data management and
data governance
strategy and
architecture, including
Data Management Plan
(DMP)
SDSRM01
Use research methods
principles in developing
data driven applications
and implementing the
whole cycle of data
handling
SDSBA01
and Business
Intelligence (BI)
methods for data
analysis; apply
cognitive
technologies and
relevant services
SDSDA02
Use Data Mining
techniques
SDSENG02
Use Cloud Computing
technologies and cloud
powered services design
for data infrastructure
and data handling
services
SDSDM02
Data storage systems,
data archive services,
digital libraries, and their
operational models
SDSRM02
Design experiment,
develop and implement
data collection process
SDSBA02
Apply Business
Processes
Management (BPM),
general business
processes and
operations for
organisational
processes
analysis/modelling
SDSDA03
Use Text Data Mining
techniques
SDSENG03
Use cloud based Big Data
technologies for large
datasets processing
systems and applications
SDSDM03
Define requirements to
and supervise
implementation of the
hybrid data management
infrastructure, including
enterprise private and
public cloud resources
and services
SDSRM03
Apply data lifecycle
management model to
data collection and data
quality evaluation
SDSBA03
Apply Agile Data
Driven
methodologies,
processes and
enterprises
SDSDA04
Apply Predictive
Analytics methods
SDSENG04
Use agile development
technologies, such as
DevOps and continuous
improvement cycle, for
data driven applications
SDSDM04
Develop and implement
data architecture, data
types and data formats,
data modeling and
design, including related
technologies (ETL, OLAP,
SDSRM04
Apply structured
approach to use cases
analysis
SDSBA04
Use Econometrics for
data analysis and
applications
EDSF Release 2: Part 1. Data Science Competence Framework (CF-DS)
Table 4.3. Required skills related to analytics languages, tools, platforms and Big Data infrastructure 6
DSDALANG
Data Analytics
and Statistical
languages and
tools
DSADB
Databases and
query
languages
DSVIZ
Data/Applicatio
ns visualization
DSADM
Data
Management
and Curation
platform
DSBDA
Big Data
Analytics
platforms
DSDEV
Development and
project
management
frameworks,
platforms and tool
DSDALANG01
R and data analytics
libraries (cran,
ggplot2, dplyr,
reshap2, etc.)
DSADB01
SQL and
relational
databases (open
source:
PostgreSQL,
mySQL, Nettezza,
etc.)
DSVIZ01
Data visualization
Libraries
(mathpoltlib,
seaborn, D3.js,
FusionCharts,
Chart.js, other)
DSADM01
Data modelling
and related
technologies (ETL,
OLAP, OLTP, etc.)
DSBDA01
Big Data and
distributed
computing tools
(Spark,
MapReduce,
Hadoop, Mahout,
Lucene, NLTK,
Pregel, etc.)
DSDEV01
Frameworks: Python,
Java or C/C++, AJAX
(Asynchronous
Javascript and XML),
D3.js (Data-Driven
Documents), jQuery,
others
DSDALANG02
Python and data
analytics libraries
(pandas, numpy,
mathplotlib, scipy,
scikit-learn,
seaborn, etc.)
DSADB02
SQL and
relational
databases
(proprietary:
Oracle, MS SQL
Server, others)
DSVIZ02
Visualisation
software (D3.js,
Processing,
Tableau, Raphael,
Gephi, etc.)
DSADM02
Data Warehouse
platform and
related tools
DSBDA02
Big Data Analytics
platforms
(Hadoop, Spark,
Data Lakes, others)
DSDEV02
Python, Java or
C/C++ Development
platforms/IDE
(Eclipse, R Studio,
Anaconda/Jupyter
Notebook, Visual
Studio, Jboss,
Vmware, others)
DSDALANG03
SAS
DSADB03
NoSQL Databases
(Hbase,
MongoDB,
Cassandra, Redis,
Accumulo, etc.)
DSVIZ03
Online
visualization tools
(Datawrapper,
Google
Visualisation API,
Google Charts,
Flare, etc)
DSADM03
Data curation
platform,
metadata
management (ETL,
Curator's
Workbench,
DataUp, MIXED,
etc)
DSBDA03
Real time and
streaming
analytics systems
(Flume, Kafka,
Storm)
DSDEV03
Git versioning system
as a general platform
for software
developmentSource: EDISON Data Science Framework (2017)

7. But Leaders say otherwise

8. 9 Step Analysis Model
Question
Data Analysis Insight
Planning &
Design
Presentation &
Distribution
Solution
Buy - in Sign - off
“Contracting”
translating business
questions into
actionable,
analytical terms
“Delivering”
expressing analysis
& insight in
actionable business
terms
Addressing
business need
Transparency of
activity
Engagement with
key stakeholders

9. Getting a better
quality brief
Question

10. The problem with requests

11. Socratic questioning
❖ Help your clients get clarity on what
they need, not just what they want:
❖ Concept clarification questions
❖ Probing assumptions
❖ Probing rationale, reasons &
evidence
❖ Questioning viewpoints &
perspectives
❖ Probe implications & consequences

12. “By failing to prepare, you are preparing to fail” Benjamin Franklin
12
Planning & Design stage
Question
Planning & Design

13. 13
What do we already
know?
What am I missing?
How could I fill
this gap?
How am I going to
communicate
findings?
What are the
Hypotheses?
You can start
at any point in the cycle
but you should look to
address all elements in
completing your
analysis
Design Map

14. Who
Use / Involvement / Experience
Shopping/Buying
Engagement / Influences▪What characterises the
customer/ segment
−demographics
−stage of life
−attitudes
−behaviours
−dissatisfactions
−routines
−etc
▪What are their chief concerns
in life (attitudinal data)?
▪What are their key needs and
aspirations?
▪What are their circumstances
and what is going on in their
lives that impacts how they
see the category?
▪What motivates them?
▪What are their preferred channels for
researching and purchasing products
and how does this compare with other
types of products?
▪How do they make a purchase decision
and what factors are important?
▪What are their attitudes to advice? What
prompts/triggers them to seek advice?
▪ How do they perceive your brand vs
other brands? (product category and
wider)
▪ How do they become aware of the
category?
▪ What is there attitude to planning for
the future? When do they think
ahead, what triggers this?
▪ When do they reconsider their
choices? What prompts change?
▪ What and who influences them
and their choices? Who do they
turn to for guidance/information/
recommendations?
▪ What competitive product holdings
do they have and why?
▪ When are they receptive to
messages?
▪What does the segment need and want
when buying your products?
▪What products are they most likely to
buy and why?
▪What is the current customer
experience?
▪What are the key dissatisfactions
(irritations, frustrations etc) with the
current process?
Pricing/Finances
▪How engaged/informed/involved are
they with regard to competitive pricing?
▪What products do they hold? How does
this contrast with other segments?
▪What triggered their purchase? What
stops them buying?
▪Do they go on to buy something else?
(us or competitor)
▪What are their goals and to what extent
do they plan their spend (budgets)?
14
Insightful questions

15. “Men often oppose a thing merely because they have had no agency in planning it, or because
it may have been planned by those whom they dislike.” Alexander Hamilton (American politician)
15
Buy-In stage
Question
Planning & Design
Buy-in

16. 2 stage Stakeholder Mapping

17. Different types of stakeholder
Spotting a Pioneer
Pioneer motto: Have
fun. It’s just work.
Spotting a Driver
Driver motto: And your
point is…?
Spotting an Integrator
Integrator motto:
Consensus Rules!
Spotting a Guardian
Guardian motto:
Changing the World, One
Spreadsheet at a Time

18. “A moment's insight is sometimes worth a life's experience”
Oliver Wendell Holmes, Sr. (American writer)
18
Insight stage
Question
Data Analysis Insight
Planning & Design
Buy-in

19. 1. Cross functional teams review data,
research and analysis to answer a set of
core questions about their target
customer and “map the evidence”
2. Those “Evidence Maps”
are reviewed to identify key
customer themes
3. Structured questioning
techniques are used to dig
deeper to develop insights
A “Customer Insight” is:
A non-obvious understanding
about your customers, which if
acted upon, has the potential
to change their behaviour for
mutual benefit
4. Insights are prioritised &
converged with opportunity
areas. to generate ideas
Insight Generation Workshops

20. Further detail on how to run a Customer
Insight Generation Workshop

21. “Think like a wise man but communicate in the language of the people”
William Butler Yeats (Irish poet)
21
Sign-off stage
Question
Data Analysis Insight
Planning & Design
Buy-in Sign-off

22. Politically Savvy Segmentation
noeuvres differ from those of the player of psychological games is that they are about knowing
w the organisation works rather than about an individual predisposition. Knowing the written and
written rules requires the ability to read ‘who cares? who knows? who will?' in the manager's
ironment. Managers can use that ability and maintain their integrity or they can use it and play
chological games.
chological game-playing' is taken from the Transactional Analysis model (see Harris, 1970;
ne, 1968). Integrity implies the absence of psychological game-playing based on a degree of
eptance of yourself and other people for what they are. Game-playing is self-oriented. Harris
cribes 'a recurring set of transactions, often repetitious, superficially plausible with a concealed
ivation'. The pay-off for playing a psychological game for the player is that the player's feelings
ut himself or herself and about the rest of the world are confirmed. In this process somebody
s up feeling bad. When people act with integrity in their dealings with others their behaviour
s not involve the compulsion to engage in exchanges with this kind of pay-off.
ing these two dimensions together gives us further insight into 'clever’ and 'innocent' behaviour.
ead of solely being at opposite ends of the two poles, we can see that 'innocent' is both
itically unaware' and has 'integrity', whereas ‘clever' is 'politically aware' and a 'game-player’.
gure 1.
Descriptive Model of Political Behaviour
Politically aware
Politically unaware
Psychological
game-playing
Acting with
integrity
CLEVER
INEPT
WISE
INNOCENT
CARR YING
READING
Figure 3. Owl, Fox, Donkey or Sheep
Conclusion
In a world which requires expertise exercised with integrity there is a predisposition among many
managers to steer clear of anything which appears 'political'. This is not a good stance for personal
survival. It is not good for one's profession nor for one's dearest projects and enthusiasms, nor for
the life of an organisation, nor indeed for Politics.
CARR YING
READING
Politically aware
Politically unaware
Acting with
integrityPsychological
game-playing
© Management Education and Development. Vol. 18. Pt. 1. 1987. pp. 3-19

23. Communicating
your analysis
Question
Data Analysis Insight
Planning & Design
Presentation &
Distribution
Buy-in Sign-off

24. Effective Communication
❖ Complete
❖ Concise
❖ Considerate
❖ Concrete
❖ Clear
❖ Courteous
❖ Correct

25. Hierarchies of communication from tabloid
print media
❖ Hierarchies of headlining
❖ Short, eye-catching, wording

26. Effective Storytelling from TV dramas
© Laughlin Consultancy Ltd, not to be used without permission.
Four elements of TV dramas that create effective
stories which engage audiences:
1. Proven narrative structure
2. Characters you care about
3. Good pace (brevity)
4. Visually attractive & easy to follow

27. Data Visualisation design principles, are you
misleading your audience?
❖ Graphical integrity
❖ Data-Ink ratio
❖ Chart Junk
❖ Data Density
❖ Small Multiples

28. Make more effective use of Basic Chart
types by avoiding pitfalls
Well Designed Simplicity
chart:
0 5 10 15 20 25
United States
West Germany
France
Japan
Britain
South Korea
Employment Costs for a Steelworker per Hour
Average of first 9 months of 1982 in U.S. Dollars
$23.99
$13.45
$12.37
$11.08
$9.32
$2.39
Copyright © 2011 Stephen Few, Perceptual Edge Page 8 of 1
-20
0
20
40
60
80
100
120
1976 1977 1978 1979 1980 1981 1982
Over Their Own Barrel
OPEC’s Current International Account Balance in Billions of Dollars
Year

29. Experiment with a wider range of chart
types to learn what works

30. Explore use of Small Multiples, leveraging
the power go human eye

31. Keep honing your craft, view Data
Visualisation skills as core part of CPD

32. Influencing the
Outcome
Question
Data Analysis Insight
Planning & Design
Presentation &
Distribution
Solution
Buy-in Sign-off

33. It’s all about taking action
❖ Ensuring request is for action
❖ Designing analysis for action
❖ Including recommended actions
❖ Progress updates on action
❖ Measure effect of actions
© Laughlin Consultancy Ltd, not to be used without permission.

34. Influencing at the Top Table
❖ What’s on their agenda?
❖ Bring Customer updates
❖ Bring Commercial updates
❖ Joint updates with
Marketing, Sales or Ops
(collaborate)
© Laughlin Consultancy Ltd, not to be used without permission.

35. 9 Step Analysis Model
Question
Data Analysis Insight
Planning &
Design
Presentation &
Distribution
Solution
Buy - in Sign - off
“Contracting”
translating business
questions into
actionable,
analytical terms
“Delivering”
expressing analysis
& insight in
actionable business
terms
Addressing
business need
Transparency of
activity
Engagement with
key stakeholders

36. Action orientated learning
One thing I will do
differently as a result of
this presentation is…

37. More detail available on blog
© Laughlin Consultancy Ltd, not to be used without permission.

38. linkedin.com/in/paullaughlin
paul@laughlinconsultancy.com
+44 (0)7446 958061
Any questions?
customerinsightleader.com
laughlinconsultancy.com
@LaughlinPaul
© Laughlin Consultancy Ltd, not to be used without permission.