From Customer Insights to Action

From Customer Insights to Action

Ruurd Dam, November 2015

2 Copyright © Capgemini 2015. All Rights Reserved

Presentation Title | Date

In a world of connected people and things …

1,820TB of Data created

# Source: World Economic Forum

*Source: Gartner

168 Million+ emails sent 98,000+ tweets

11Million instant messages

217 new mobile web users

25 Billion Connected

"Things" in use in 2020*

3,5 Billion Cars

13,2 Billion consumer

devices

695,000 status

updates

698,445 Google searches

2.5 Billion social

network users in 2018



… the new data landscape is the centerpiece of digital change ……

IoT Mobile

Cloud Social

Media

New

Data Landscape

No limit to volume

No limit to structure

No limit to analyzing

No limit to timing



Worldwide we see a four strategic ‘data plays’ for businesses and organizations

Insights-as-a-Service

Platform

BI factories, MDM

1

Big Data

2

Insights

3

4

Bu

sin

ess

Tech

no

log

y

Reporting

(looking back)

Insights

(predictive, prescriptive)

Existing Data Landscape New Data Landscape



Today we will do a deep dive into 3 out of 7 Insights & Data Principles

Unleash Data and

Insights

as-a-service

Make Insight-driven

Value a Crucial

Business KPI

Empower your People

with Insights at the

Point of Action

Develop an Enterprise

Data Science Culture

Master Governance, Security and Privacy of

your Data Assets

Enable your Data

Landscape for the

Flood coming from

Connected People and

Things

Embark on the Journey

to Insights within your

Business and

Technology Context

1 2 3

7 6 5 4



Master Governance, Security and Privacy of your Data Assets

H

U

R

D

L

E

S

HO

W T

O G

ET

TH

ER

E ?

Source: Capgemini Consulting Report – Cracking the data

conundrum: How successful companies make Big Data

operational.

Lack of strong data management

and governance mechanisms is

one of the greatest obstacles to

the success of insight-driven

business.

54% no IT-business joint

projects for Big Data

initiatives

47% scattered pockets of

resources / follow a

decentralized model for

analytics initiatives

53% no top-down

approach for Big Data

strategy development

54% 47% 53%

Industrialize & mature

your data processes &

organization, using industry

best practices, to increase

productivity, agility &

manageability

Develop a healthy risk

appetite to ensure end-

to-end security and

privacy of your data

assets, while staying

outcome-focused Engineer a

governance mix that

fits your culture,

balancing central and

de-central, top-down

and bottom-up

Define policies

and procedures

for management

of data assets

Develop Big Data

competencies

Set up the

technological

base for

Big Data initiatives

Establish a

robust

governance

framework

Big Data Operating

Model

Data is an invaluable

asset. You need a

high-performance data

organization that

embraces privacy and

security, is equipped to

meet both current and

future enterprise needs

and mirrors the

dynamics of the

enterprise.

ORGANIZATION



• Save people life’s

• Better maintenance

• Higher traffic security

• Bigger yields

• Invest smarter

•…….

• Invasion of privacy

• Lack of transparancy

• Monetization of data

•…

•…

•…..

..however..we are strange people….

EU Privacy regulations: are data blessing or curse?

Source: Wij zijn Big Data,

Sander Klous, Nart Wielaard



The answer to privacy has perhaps something to do with profit ánd people and planet..

Source: Wij zijn Big Data,

Sander Klous, Nart Wielaard

Achmea geeft premiekorting voor

data van klant, FD, vandaag



Develop an Enterprise Data Science Culture

H

U

R

D

L

E

S

HO

W T

O G

ET

TH

ER

E ?

Data science –

a relatively unknown

area

Skills and competencies

are scarce

To change the enterprise

mindset to leverage data

science

Enterprise Data

Science Framework

SOLUTIONS

Value of analytics often stays

purely conceptual - to the

uninitiated

A data-embracing culture does

not come naturally, even when

it’s part of the strategy

Data Prep

Selection &

Cleansing

Modeling

Design &

development

Define

Objectives &

Levers

Simulation

optimization &

Evaluation

Data inventory

collection &

Understanding

Deployment

solution within

the system

Combine business

acumen, analytical

skills and

technology

expertise

Systematically build

and acquire the

required data science

capabilities Provide hands-on

experience with analytics

- in real action – to get

stakeholders involved

and committed

Speeding up business value through

affordable real-time analytics

Analytics Accelerator

Data science unlocks

insights.

Making everybody in

the enterprise a bit of a

‘data scientist’ requires

nothing less than

culture change. An

organization that has a

data science -led

culture, can truly

become

insight-led

CULTURE



Data Science is the interplay of data, business process, technology and statistics

1 Industry/Vertical

expertise and

Customer focused

2 Domain / Functional

Expertise (DCX,

Sales, Customer

Service, Marketing)

3 Business Analysis

4 Solution Design

5 Pre-sales skills/

Presentation and

Communication

Business

Skills

6 Ability to build

Prototypes

(Presales

engineer)

7 Machine Learning /

Modeling

8 Statistics

/Mathematics

9 Simulation and

optimization

10 Data profiling

11 Data Preparation

and Mining tools

(e.g. SAS Base,

EG, R, SQL)

12 Visualization and

design

(Microstrategy,

Tableau, SAS VA

13 Ability to perform

data science on

Big Data, Hadoop

(Pivotal/Cloudera)

14 Digital Analytics

(Adobe, Google

Analytics, IBM

digital analytics)

Research

Skills

Technology

Skills



Empower your People with Insights at the Point of Action

H

U

R

D

L

E

S

HO

W T

O G

ET

TH

ER

E ?

Making the right insights

available at the right time at the

right place to the right person is

critical

Organizations face the scarcity

of data science, deep sector

knowledge & technological

expertise

Perception of the digital

transformation ambitions and

challenges may not be uniform

across all business units

Understanding of how

insights can be

seamlessly integrated at

the point of action is rare

Make sure you leverage your

market and industry expertise

at the business side to select

key Insights

Validate the value of your

selected Insights by

piloting them quickly –

right at the point of action

Never stop your quest for

insights: Build on your

experience to find more and

better opportunities

Identify insights to incite action on the

spot to drastically improve customer

experience, optimize operations and

reinvent business models

All organizations are a

series of decision

points, both at the

macro and micro level.

Empower your people

with timely insights to

make those decisions

better and transform

your business.

Mastering ‘Insights

Inside’ is the essence of

the journey.

INSIGHTS



Customer Value Analytics (CVA) – to make a fact based and decisive impact on customer journeys

More selling, more effective marketing and improved customer experience

Global

Swedish

Retailer

ACQUIRE

Acquire new customers by

implementing innovative

customer sourcing/profiling

models

AVOID FADING AND

ATTRITION

Retain customers by

identifying churn drivers and

building churn propensity

models

GROW SHARE OF

WALLET

Increase profitability of the

existing customer base by

building cross sell, up sell

or next best action models

Digital Channel

Migration

Pre-qualification &

Customized Offering

Acquisition

Customer Service

Retention

Promotion

Awareness

Transactions

Information

Activation

Receive Service

Cross Sell/Up Sell Advice/Need

Renew

Explore/

Experience

Lead Generation/

Campaign

Management



13

CVA is the science and art of converting data into insight that can be used to drive a great customer experience

“I can explore my

customer data, but is it

correct?”

“How frequently and

recently are customers

buying?”

“How likely is a customer

to respond to my offer?”

“What is the next best

action for a customer?”

Value extracted from Information

Co

mp

eti

tive

Ad

va

nta

ge

The five stages of analytics maturity

“Why do the performance

reports from my teams

disagree?”

Reporting

Descriptive

Analytics

Predictive

Analytics

Prescriptiv

e Analytics



With clients we make a first selection of applicable use cases using Customer Value Analytics prioritization matrix

BU

SIN

ES

S V

AL

UE

Phase 1 Phase 2

Phase 3

High

Low

Difficult Easy EASE OF IMPLEMENTATION

5

23

2 11

1

9

4

8

7

6

12

3

19

18

15

14

13

20

16

10

43

38 29

28

27

26

25

24

22 21 17

39

37

36

35

31

30 42

41

40

32 34

Business Value Levers: Increase Sales, Reduce Cost, Improve Working Capital

Ease of Implementation Levers: Data / Tool readiness, dependency on 3rd party, organizational readiness & alignment etc

# Insights & Data Capability # Insights & Data Capability

1 Standardized Reporting 23 Program ROI

2 Ad-hoc Analytics 24 Shopper Targeting

3 Household Segmentation (Protect,

Recover, Develop Strategies) 25 Sweepstakes

4 Store Segmentation 26 Net Value Cost Analysis

5 Trip Mission / Market Basket 27 Exclusivity & Loyalty

6 Shopper Insights 28 Effective Pricing

7 Test & Control Identification 29 Promo Decomposition

8 Retail Labs (R&D) 30 Household Segmentation

9 Assortment Optimization 31 Category / HH Targeting

10 Brand & Pkg Switching 32 Assortment Optimization

11 Out-of-Shelf Analytics 33 Product Lifecycle Management

12 Plan-o-gram optimization,

Development & Space Management 34 Loyalty Analytics

13 New Product Introduction / Adoption 35 Customer Group Analysis

14 Replenishment Planning 36 Neural Net Demand Forecasting

15 Top Shopper Identification 37 SC Network Optimization

16 Product Affinities 38 Promotion Decomposition with ROI

17 Household Exclusivity & Loyalty 39 Vendor Performance

18 Digital Analytics (e-com, social

media etc). 40 Working Capital Analytics

19 Campaign Analytics 41 Store Layout Optimization

20 Customer Lifetime Value 42 Predictive Asset Maintenance

21 Customer Churn 43 Recruitment Effectiveness

22 Cross Sell / Up Sell



PALLAS-

Capgemini

selecting use

cases

0

1 day

Business &

Data Value 1

3 weeks

Proof of

Value 2

8 weeks

'Fail fast, fail early'

A Silicon Valley mantra

An Insights Driven Journey is “to think big, start small and grow fast”



Marketing Analytics & Campaign Management for UK Retail Client

The objective was to design an analytical platform that could enable business and statistical analysts to mine

data and derive insights for new marketing campaigns

New platform will drive market position

by enabling

Cross-functional data available in one

place

Analytics and data mining to understand

customer behavior

Complex campaign design and

management

Effective and efficient delivery of

analytics and campaign management

services via Capgemini’s Right Shore

framework

The client, wanted to enhance their

marketing analytics and campaign

management platform for two of their in-

house brands

Current campaign management platform

lacks analytical capability and can run

only basic campaigns. Campaign

tracking and response modeling is also

limited

No integrated source of marketing and

customer information. Several disparate

sources

Analytical insights and models to be

integrated with campaign management

platform

Integrated platform to design

campaigns, allocate budgets, coordinate

implementation and measure response

IBM SPSS for analytics; IBM UNICA

for campaign management, Oracle

11g for Marketing DB

Analytic enablers to build Response

models, Churn models and share of

wallet analysis

Analytic enablers in building behavior

analysis for effective target segment

identification

Analytic enablers to design end to end

campaign management platform

BUSINESS /

DATA CHALLENGES SOLUTION BENEFITS

Tools used

Model Development – IBM SPSS

Modeler

Model Deployment – Oracle, IBM

Unica Campaign

High level architecture design for

the new environment



Propensity Models and facilitated Event-Based Marketing to identify Home & Car Insurance Prospects for a leading Norwegian Insurer

The objective was to predict the propensity of a Bank/Pensions customer to purchase home or car insurance

products in order to enhance the conversion rate of cross-sales efforts

A set of 8 distinct logistic regression

models that predicted the probability for

each Bank and Pensions customer to

purchase a home or car insurance

product

The model indicated that 35% of non-

customers could be potential purchasers

of home or car insurance products

Data insights around the profile of

existing home and car insurance

customers vs. non- customers

Hypothesis testing based on variables

that reflect the customers’ decision

journey

The propensity to purchase was

calculated in the form of probability for

each customer using binary logistic

regression

BUSINESS /


22%

78%

No. of Customers (1000s) by Gender

Male Female

Holders

Non-Holders

9%

30%

40%

21%

No. of Customers (1000s) by Age

Group

<= 28 28 - 44

Data Cleaning &

Structuring

Missing Value

Treatment

Hypothesis

Testing

Active Only

Customers

Predicted

Non-

Customers

Predicted

Customers

Non-Customers 62% 35%

Customers 1% 2%

N = 1 Customer has Car/Home Insurance

N = 0 Customer does not have Car/Home

Insurance

0.5

0.55

0.6

0.65

0.7

0.75

0.8

0

0.5

1

1.5

2

2.5

3

Before I/A Grouping After I/A Grouping After Dropping Vars After Dropping Vars 2After Dropping Vars 3

Lift (Training)

Lift (Testing)

ROC

Model Performance Analysis

Feature Importance Model

Accuracy

17% 83% Car

Insurance

11% 89%

House Insuranc

e



Digital Analytics helped a leading Global CPG player achieve a 2x improvement in online campaign reach and effectiveness

The objective was to monitor online visitor behavior through real-tile listening and help the client team to fine-

tune its online messages and content. This helped the client to improve the campaign reach and engagement

The analysis enabled the client to fine-

tune website content in real-time and

thereby engage better with their target

audience

The analysis identified which segments

and geographies responded best to the

campaign

The analysis helped the client allocate

more content to sites with higher traffic

and also coordinate channel integration

Resulted in 2.5x increase in reach and

2x increase in engagement levels

Resulted in lowest cost per engagement

for online channel relative to traditional

media channels

The client was undertaking multiple

online campaigns across its micro site

and social media channels for the a

marketing event spanning 4 days.

The Analytics team had to work closely

with the client’s media-buying, creative

and brand teams to analyze real-time

online visitor communication and

suggest appropriate response strategies

The methodology involved creation of

customized profiles for the micro site

using Google Analytics & configuration

of social media listening tools to capture

visitor behavior on the micro site (with a

lag) and on social media channels (real-

time)

The listening tools also identified

potential influencers on a real time basis

BUSINESS /


Thank You

Services

From Customer Insights to Action