The document outlines Paul Laughlin's experience and expertise in customer insight and data science. It discusses his work helping businesses maximize value from customer insight, such as growing profits and improving customer retention. It then details Laughlin's 9 step analysis model for conducting customer insight projects and effective strategies for communicating findings, including stakeholder mapping, generating customer insights, and ensuring recommended actions are designed and implemented.

1. Paul Laughlin, Managing Director, Laughlin Consultancy
The Softer Skills
Data Scientists also need to
make an impact in business

3. Career within Lloyds Banking Group
❖ Created & 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
❖ Pioneered work with Financial Conduct Authority on
Behavioural Economics in communications
❖ Developed capability in team of 44 & mentored next
generation of leaders

4. Helping Businesses
“Helping businesses make money from customer insight”
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.

5. Data Science at the Whitehouse

6. 9 Step Analysis Model

7. Breadth not Depth

8. Getting a better brief
Question

9. The problem with requests

10. Socratic questioning
❖ Aim is to help client have clarity on
need not just what they want:
❖ Concept clarification questions
❖ Probing assumptions
❖ Probing rationale, reasons &
evidence
❖ Questioning viewpoints &
perspectives
❖ Probe implications &
consequences

11. Failing to Prepare is preparing to Fail
11
Question
Planning & Design

12. Design Map
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 could start at any
point in the cycle but
you should look to
address all elements
in completing your
analysis

13. 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)?
Context questions

14. Achieving Stakeholder Buy-In
14
Question
Planning & Design
Buy-in

15. Stakeholder Mapping

16. Technical stages (Data & Analytics)
Question
Data Analysis Insight
Planning &
Design
Buy-in

17. Generating Customer Insights
Question
Data Analysis Insight
Planning & Design
Buy-in

18. Converging sources of evidence

19. Agreeing Sign-off and Tactics
Question
Data Analysis Insight
Planning & Design
Buy-in Sign-off

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

21. Hierarchies of communication
❖ Hierarchies of headlining
❖ Short, eye-catching, wording

22. Storytelling
❖ People are engagedby stories
❖ Psychological studies show better attention &
recall (esp. feelings)
❖ Common templates:
❖ “Just imagine, if…”
❖ “Wow! I’m going to tell… about this”
❖ The Burning Platform

23. Tufte’s rules for Data Visualisation
❖ Graphical integrity
❖ Data-Ink
❖ Chart Junk
❖ Data Density
❖ Small Multiples

24. Negative examples of Data Visualisation

25. Checkout Viz.WTF

26. Positive example of Data Visualisation

27. Influencing the Action
needed as a result
Question
Data Analysis Insight
Planning & Design
Presentation &
Distribution
Solution
Buy-in Sign-off

28. 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

29. 9 Step Analysis Model

30. Our Exercise = Back to Socrates
Concept Clarification:
❖ What exactly does X mean? Can you give an example?
Probing Assumptions:
❖ You seem to be assuming Y, please explain why?
Probing Rationale:
❖ What evidence is there to support what you are saying?
Questioning viewpoints & perspectives:
❖ Another way of looking at this is Z, is that better?

31. 9 Step Analysis Model

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

33. linkedin.com/in/paullaughlin
paul@laughlinconsultancy.com
+44 (0)7446958061
How to keep in touch
customerinsightleader.com
laughlinconsultancy.com
@LaughlinPaul