Slides of a course given at the Solvay Business School about Big Data potential in the Retail Business.

1. Big Data in the Retail Business.
Laurent Kinet
CEO of Swan Insights

2. Who am I?
My goal in my
professional life has
always been to deliver
strategic value to my
customers through the
potential of new
technologies.
DIGITAL-IN
FU
AND ENTREPREUNEUR
SED

3. Who am I?

4. Data is not new. Big is not new.
The first and most
beautiful data
visualization on
earth.
Galileo Galilei, On Saturn.

5. Data is not new. Big is not new.
The first and most
beautiful data
visualization on
earth.
“The best statistical graphic ever drawn”, Edward Tufte.

6. The new in Big Data is…

7. A couple of figures.
$600
buys you a disk drive that can
store all of the world’s music
7 billion
mobile phones in
use in 2012
40 billion
Source: McKinsey
pieces of content shared
on Facebook every month

8. A couple of figures.
12
10
8
Data Growth
6
IT Spending
4
2
0
2013 2014 2015 2016 2017 2018 2019
Source: McKinsey
20

9. A couple of figures.
Source: McKinsey
Big Data: The next frontier for
innovation, competition and
productivity

10. Meet the demand.
Data have swept
into every
industry and
business function
and are now an
important factor
of production.
Big Data creates
value in several ways.
Transparency.
Expose variability and
Improve performance.
There will be a
shortage of talent
necessary for
organizations to
take advantage
of Big Data.
Segment populations to
customize actions.
Supporting human
decision making with
automated algorithm.
Innovate new business
models and P&S.
Source: McKinsey
Big Data: The next frontier for innovation,
competition and productivity

11. Background observations on Big Data.
The first and most
beautiful data
visualization on
earth.
“The best statistical graphic ever drawn”, Edward Tufte.

12. Use cases.
Source: SAP 2013

13. Big Data?

15. Big Data for Retail?

16. Big Data: the next big thing in Retail?

17. Fact. We entered a data-driven society.
All decisions will soon be made
out of data.
WE SWITCH FROM “GUESS” TO “KNOW”.
We entered the age of information. Human
information is growing three times faster than
structured, corporate data. We can’t ignore
them both anymore.
However, tons of data
are still under-exploited.
Huge opportunities are
missed. Companies
need help to take the
most of external data,
delivering strategic
insights as the fuel for
decision-making and
targeted actions.
Today,
companies can’t ignore
those facts to ensure their
business sustainability and
competitiveness.
>

18. Holistic Data-driven Business.
External Data Sources
are the
KEY
to sustainable performance
HOW DO WE DO THAT

19. CM Tools are here
Analytics
Prediction
OUTSIDE
Social Web Data
Open Data
VIEW
Historical Social
Data Analysis
INSIDE
VIEW
Corporate Cockpits
Standard B.I.
PAST
Machine-learning
Algorithms
Corporate Data
Machine-learning
Algorithms
HOW DO WE DO THAT
FUTURE

20. How can we do that?
You need three things
MULTIPLE
DATA SOURCES.
POWERFUL
DATA ANALYSIS.
HUMAN
INTELLIGENCE.
HOW DO WE DO THAT

21. DATA SOURCES
WEB DATA
SOCIAL DATA
OPEN DATA
ACQUIRED DATA
YOUR DATA
BIG DATA ANALYSIS METHODS
MICRO
SEGMENTATION
CUSTOMER
INTELLIGENCE
PREDICTIVE
MODELING
PRESCRIPTIVE
ANALYSIS
BEHAVIORAL
OUTLOOK
WHAT-IF
SCENARIOS
SENTIMENT
ANALYSIS / NLP
DATA-DRIVEN OPERATIONS
DATA-DRIVEN
CAMPAIGNS
ACTIVATION
PROJECT
MANAGEMENT
INFORMATION
SYSTEMS LOOPBACK
SPECIFIC
ACTIONS
STRATEGIC
CONSULTING

25. The DataGraph in 90 seconds.
See it online on swaninsights.com/video

26. The DataGraph in 90 seconds.
See it online on swaninsights.com/video

27. The DataGraph in action.
Data Sources
Extended
range of
data
sources
DataGraph
Proprietary DataGraph
Most advanced Data
Analysis Methods
Actions
Needs
Strategic Consultancy
Background &
Approach
Sectorial Knowledge
It delivers drastically better results than “mere” software.

28. The DataGraph in action.
Data Sources
WEB DATA
DataGraph
GOVERNEMENTS
UNIVERSITIES
INSTITUTIONS
OTHER DATA
DATA SUPPLIERS
PARTNERS
CORPORATE
DATA
CRM / ERP
INDUSTRIAL DATA
Needs
DATA-DRIVEN
CAMPAIGNS
DATAGRAPH
Insights
SOCIAL MEDIA
SEARCH ENGINES
GOOGLE TRENDS
BLOGS / FORUMS
OPEN DATA
Actions
STRATEGIC
CONSULTING
GRAPH
DATABASES
RELATION
DATABASES
INFO SYSTEMS
LOOPBACKS
DATA ANALYSIS
PROPRIETARY
ALGORITHMS
ADVANCED
ANALYSIS
METHODS
DECISIONMAKING
SPECIFIC
ACTIONS
From data sources to tangible results.
IDENTIFIED
NEED

29. Types of tangible benefits.
Data Products.
SAMPLES OF BENEFITS
YOU CAN DRAW FROM
THE DATAGRAPH.
Lead Generation.
Lead Ranking.
YOU CAN GET A LIST OF LEADS
THAT ARE MOST LIKELY TO
PURCHASE YOUR PRODUCT.
YOU CAN RANK YOUR LEADS
BASED ON THEIR PROPENSITY TO
CONVERT.
>
>
Client Segmentation.
Churn Prevention.
Sociography.
YOU CAN GET NEW,
UNSUSPECTED INFORMATION ON
YOUR CLIENT BASE.
YOU CAN GET A LIST OF CLIENTS
THAT ARE ABOUT TO LEAVE YOUR
COMPANY.
YOU CAN MAP AND DEFINE
GROUPS AGAINST ANY GIVEN
TOPIC.
>
>
>

30. Example 1: Simple Lead Ranking for Automotive.
Data Sources
WEB DATA
DataGraph
GOVERNEMENTS
UNIVERSITIES
INSTITUTIONS
OTHER DATA
DATA SUPPLIERS
PARTNERS
CORPORATE
DATA
Needs
DATA-DRIVEN
CAMPAIGNS
DATAGRAPH
Insights
SOCIAL MEDIA
SEARCH ENGINES
GOOGLE TRENDS
BLOGS / FORUMS
OPEN DATA
Actions
STRATEGIC
CONSULTING
GRAPH
DATABASES
RELATION
DATABASES
LEAD
RANKING
INFO SYSTEMS
LOOPBACKS
DATA ANALYSIS
PROPRIETARY
ALGORITHMS
DECISIONMAKING
ADVANCED
ANALYSIS
METHODS
SPECIFIC
ACTIONS
CRM / ERP
INDUSTRIAL DATA
LEAD RANKING
INCREASE
CONVERSION
RATE

31. Example 2: Advanced Lead Ranking for Automotive.
Data Sources
WEB DATA
DataGraph
GOVERNEMENTS
UNIVERSITIES
INSTITUTIONS
OTHER DATA
DATA SUPPLIERS
PARTNERS
CORPORATE
DATA
Needs
DATA-DRIVEN
CAMPAIGNS
DATAGRAPH
Insights
SOCIAL MEDIA
SEARCH ENGINES
GOOGLE TRENDS
BLOGS / FORUMS
OPEN DATA
Actions
STRATEGIC
CONSULTING
GRAPH
DATABASES
RELATION
DATABASES
LEAD
RANKING
INFO SYSTEMS
LOOPBACKS
DATA ANALYSIS
PROPRIETARY
ALGORITHMS
DECISIONMAKING
ADVANCED
ANALYSIS
METHODS
SPECIFIC
ACTIONS
CRM / ERP
INDUSTRIAL DATA
LEAD RANKING
INCREASE
CONVERSION
RATE

32. Example 3: Churn Prediction for Telco.
Data Sources
WEB DATA
DataGraph
GOVERNEMENTS
UNIVERSITIES
INSTITUTIONS
OTHER DATA
DATA SUPPLIERS
PARTNERS
CORPORATE
DATA
CRM / ERP
INDUSTRIAL DATA
Needs
DATA-DRIVEN
CAMPAIGNS
DATAGRAPH
Insights
SOCIAL MEDIA
SEARCH ENGINES
GOOGLE TRENDS
BLOGS / FORUMS
OPEN DATA
Actions
STRATEGIC
CONSULTING
GRAPH
DATABASES
RELATION
DATABASES
IDENTIFY
POTENTIAL
CHURNERS
INFO SYSTEMS
LOOPBACKS
DATA ANALYSIS
PROPRIETARY
ALGORITHMS
ADVANCED
ANALYSIS
METHODS
DECISIONMAKING
DECREASE
CHURN RATE
SPECIFIC
ACTIONS
CHURN PREDICTION

33. Example 4: 360 Client View for Retail.
Data Sources
WEB DATA
DataGraph
GOVERNEMENTS
UNIVERSITIES
INSTITUTIONS
OTHER DATA
DATA SUPPLIERS
PARTNERS
CORPORATE
DATA
CRM / ERP
INDUSTRIAL DATA
Needs
DATA-DRIVEN
CAMPAIGNS
DATAGRAPH
Insights
SOCIAL MEDIA
SEARCH ENGINES
GOOGLE TRENDS
BLOGS / FORUMS
OPEN DATA
Actions
STRATEGIC
CONSULTING
GRAPH
DATABASES
RELATION
DATABASES
KNOW
CUSTOMERS
360
INFO SYSTEMS
LOOPBACKS
DATA ANALYSIS
PROPRIETARY
ALGORITHMS
ADVANCED
ANALYSIS
METHODS
DECISIONMAKING
RECOMMENDATIONS
CROSS-SELL
SPECIFIC
ACTIONS
SEGMENTATION & CHARACTERIZATION
UP-SELL

34. Example 4: 360 Client View for Retail.
Data Sources
WEB DATA
TWITTER STREAM
GOOGLE TRENDS
DataGraph
1
2
LOYALTY CARD
PRODUCT GRAPH
A- People/Product
affinity
B- Cross-buying
MAPPING
SOCIAL GRAPH
A- Segmentation
B- Characterization
Lifestyle/Interests
Lifestage
Psychology traits
Professional info
OPEN DATA
SOCIODEMOGRAPHICS
& CARTOGRAPHY
CORPORATE
DATA
LOYALTY CARDS
CLIENTS / GOODS
Actions
3
INTEGRATION
180* VIEW
WHAT, WHEN, TO WHOM
4
MATCHING WITH SOCIO-DEMO/
CARTOGRAPHY
360* VIEW
WHAT, WHEN, TO WHOM AND WHERE
Needs
DIRECT
MARKETING
SUPPLY
CHAIN
PLANNING
KNOW
CUSTOMERS
360
CRM
ENRICHMENT
DECISIONMAKING
RECOMMENDATIONS
CROSS-SELL
SEGMENTATION & CHARACTERIZATION
UP-SELL

35. Potential of Big Data: examples.
Swan Insights’ internal work note (December 2013).

36. Potential of Big Data: 10 examples.
1. Increase the Average Basket Price
2. Increase the Customer Year Time
Value
3. Churn Detection
4. Increase the share-of-caddy
5. Segment most valuable customers
Data Graphization.
6. Purchase prediction
BY THE GRAPHIZATION OF YOUR
DATA, IT IS POSSIBLE TO DERIVE
AFFINITY LEVELS AND RUN
PREDICTIVE MODELS
7. Bundle-purchase identification
8. Smart Couponing
9. Anticipate cash desk congestion
>
10. Real-time pricing changes

37. Ethics & Privacy.
It is essential to comply strictly with
Privacy regulations and follow
a Code of Conduct.
Privacy
Commissions
Master
Contracts & NDA
Security Policies
& Delivery
One must declare the
activities to the
appropriate Privacy
Commissions.
Service Contracts and
NDA’s must foresee
privacy clauses and
confidentiality.
Infrastructure must be protected against
intrusion through the latest technologies,
and the delivery channels must be adapted
to corporate security policies. Master
Service Contracts always must include a
Security Appendix detailing all measures
taken to ensure data integrity.

38. Open Discussion.
What kind of Big Data initiatives
has your organization started?

39. Let’s keep in touch.
Laurent Kinet.
Swan on LinkedIn.
CEO Swan Insights sa/nv
Get our news and insights
about Big Data and Social
Web Analysis
laurent@swaninsights.com
>
company/swan-insights
>