Big Data for the Retail Business I Swan Insights I Solvay Business School

Big Data in the Retail Business.

Laurent Kinet CEO of Swan Insights

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-INFUSED AND ENTREPREUNEUR

Who am I?

Data is not new. Big is not new.

Galileo Galilei, On Saturn.

The first and most beautiful data visualization on earth.

“The best statistical graphic ever drawn”, Edward Tufte.


Data is not new. Big is not new.

The new in Big Data is…

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 pieces of content shared on Facebook every month

Source: McKinsey


0

2

4

6

8

10

12

2013 2014 2015 2016 2017 2018 2019 20

Data Growth

IT Spending

Source: McKinsey


Source: McKinsey Big Data: The next frontier for innovation, competition and

productivity

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. Segment populations to customize actions. Supporting human decision making with automated algorithm. Innovate new business models and P&S.

There will be a shortage of talent necessary for organizations to take advantage of Big Data.

Source: McKinsey Big Data: The next frontier for innovation,

competition and productivity

Background observations on Big Data.

“The best statistical graphic ever drawn”, Edward Tufte.


Use cases.

Source: SAP 2013

Big Data?

Big Data for Retail?

Big Data: the next big thing in Retail?

Fact. We entered a data-driven society.

We entered the age of information. Human information is growing three times faster than structured, corporate data. We can’t ignore them both anymore.

All decisions will soon be made out of data.

WE SWITCH FROM “GUESS” TO “KNOW”.

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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.

However, tons of data are still under-exploited. Today,

companies can’t ignore those facts to ensure their business sustainability and

competitiveness.

Holistic Data-driven Business.

External Data Sources are the

KEY to sustainable performance

HOW DO WE DO THAT

HOW DO WE DO THAT

CM Tools are here

INSIDE VIEW

OUTSIDE VIEW

PAST FUTURE

Corporate Cockpits

Standard B.I.

Historical Social Data Analysis

Analytics

Machine-learning Algorithms

Prediction

Social Web Data Open Data

Machine-learning Algorithms

Corporate Data

How can we do that?

You need three things

HOW DO WE DO THAT

MULTIPLE DATA SOURCES.

POWERFUL DATA ANALYSIS.

HUMAN INTELLIGENCE.

DATA SOURCES

BIG DATA ANALYSIS METHODS

WEB DATA SOCIAL DATA OPEN DATA ACQUIRED DATA YOUR DATA

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

The DataGraph in 90 seconds.

See it online on swaninsights.com/video

The DataGraph in action.

DataGraph Data Sources Actions Needs

It delivers drastically better results than “mere” software.

Extended range of

data sources

Proprietary DataGraph

Most advanced Data Analysis Methods

Strategic Consultancy Background &

Approach

Sectorial Knowledge

The DataGraph in action.


WEB DATA

OPEN DATA

OTHER DATA

CORPORATE DATA

SOCIAL MEDIA SEARCH ENGINES GOOGLE TRENDS BLOGS / FORUMS

GOVERNEMENTS UNIVERSITIES INSTITUTIONS

DATA SUPPLIERS PARTNERS

CRM / ERP INDUSTRIAL DATA

DATAGRAPH

Insights

DATA ANALYSIS

GRAPH DATABASES

RELATION DATABASES

PROPRIETARY ALGORITHMS

ADVANCED ANALYSIS METHODS



INFO SYSTEMS LOOPBACKS

DECISION-MAKING

SPECIFIC ACTIONS

IDENTIFIED NEED

From data sources to tangible results.

Types of tangible benefits.

Data Products.

SAMPLES OF BENEFITS YOU CAN DRAW FROM THE DATAGRAPH.

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Lead Generation.

YOU CAN GET A LIST OF LEADS THAT ARE MOST LIKELY TO PURCHASE YOUR PRODUCT.

Lead Ranking.

YOU CAN RANK YOUR LEADS BASED ON THEIR PROPENSITY TO CONVERT.

Client Segmentation.

YOU CAN GET NEW, UNSUSPECTED INFORMATION ON YOUR CLIENT BASE.

Churn Prevention.

YOU CAN GET A LIST OF CLIENTS THAT ARE ABOUT TO LEAVE YOUR COMPANY.

Sociography.

YOU CAN MAP AND DEFINE GROUPS AGAINST ANY GIVEN TOPIC.

Example 1: Simple Lead Ranking for Automotive.


WEB DATA

OPEN DATA

OTHER DATA

CORPORATE DATA





DATAGRAPH

Insights

DATA ANALYSIS

GRAPH DATABASES

RELATION DATABASES






DECISION-MAKING

SPECIFIC ACTIONS

L E A D R A N K I N G

INCREASE CONVERSION

RATE

LEAD RANKING

Example 2: Advanced Lead Ranking for Automotive.


WEB DATA

OPEN DATA

OTHER DATA

CORPORATE DATA





DATAGRAPH

Insights

DATA ANALYSIS

GRAPH DATABASES

RELATION DATABASES






DECISION-MAKING

SPECIFIC ACTIONS

L E A D R A N K I N G

INCREASE CONVERSION

RATE

LEAD RANKING

Example 3: Churn Prediction for Telco.


WEB DATA

OPEN DATA

OTHER DATA

CORPORATE DATA





DATAGRAPH

Insights

DATA ANALYSIS

GRAPH DATABASES

RELATION DATABASES






DECISION-MAKING

SPECIFIC ACTIONS

C H U R N P R E D I C T I O N

DECREASE CHURN RATE

IDENTIFY POTENTIAL CHURNERS

Example 4: 360 Client View for Retail.


WEB DATA

OPEN DATA

OTHER DATA

CORPORATE DATA





DATAGRAPH

Insights

DATA ANALYSIS

GRAPH DATABASES

RELATION DATABASES






DECISION-MAKING

SPECIFIC ACTIONS

S E G M E N T A T I O N & C H A R A C T E R I Z A T I O N

RECOM-MENDATIONS

CROSS-SELL

UP-SELL

KNOW CUSTOMERS

360

Example 4: 360 Client View for Retail.


WEB DATA

OPEN DATA

TWITTER STREAM GOOGLE TRENDS

SOCIO-DEMOGRAPHICS & CARTOGRAPHY

LOYALTY CARDS CLIENTS / GOODS

1 LOYALTY CARD

PRODUCT GRAPH A- People/Product

affinity B- Cross-buying

DIRECT MARKETING

SUPPLY CHAIN

PLANNING

CRM ENRICHMENT

DECISION-MAKING

S E G M E N T A T I O N & C H A R A C T E R I Z A T I O N

RECOM-MENDATIONS

CROSS-SELL

UP-SELL

KNOW CUSTOMERS

360

2 MAPPING

SOCIAL GRAPH A- Segmentation

B- Characterization Lifestyle/Interests

Lifestage Psychology traits Professional info

3 INTEGRATION

180* VIEW WHAT, WHEN, TO WHOM

4 MATCHING WITH SOCIO-DEMO/

CARTOGRAPHY 360* VIEW

WHAT, WHEN, TO WHOM AND WHERE

CORPORATE DATA

Potential of Big Data: examples.

Swan Insights’ internal work note (December 2013).

Potential of Big Data: 10 examples.

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Data Graphization.

BY THE GRAPHIZATION OF YOUR DATA, IT IS POSSIBLE TO DERIVE AFFINITY LEVELS AND RUN PREDICTIVE MODELS

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

6.  Purchase prediction

7.  Bundle-purchase identification

8.  Smart Couponing

9.  Anticipate cash desk congestion

10. Real-time pricing changes

Ethics & Privacy.

One must declare the activities to the appropriate Privacy Commissions.

It is essential to comply strictly with Privacy regulations and follow

a Code of Conduct.

Privacy Commissions

Service Contracts and NDA’s must foresee privacy clauses and confidentiality.

Master Contracts & NDA

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.

Security Policies & Delivery

Open Discussion.

What kind of Big Data initiatives has your organization started?

Let’s keep in touch.

Laurent Kinet.

[email protected]

CEO Swan Insights sa/nv

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Swan on LinkedIn.

company/swan-insights

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Big Data for the Retail Business I Swan Insights I Solvay Business School

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