From Customer Insights to Action

From Customer
Insights to Action
Ruurd Dam, November 2015

2Copyright © 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 ……
IoTMobile
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
BusinessTechnology
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
7654

Master Governance, Security and Privacy of your
Data Assets
H
U
R
D
L
E
S
HOWTOGETTHERE?
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
HOWTOGETTHERE?
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
HOWTOGETTHERE?
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 SellAdvice/Need
Renew
Explore/
Experience
Lead Generation/
Campaign
Management

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
CompetitiveAdvantage
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 matrixBUSINESSVALUE
Phase 1Phase 2
Phase 3
High
Low
Difficult EasyEASE 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
2117
39
37
36
35
31
3042
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 Value1
3 weeks
Proof of
Value2
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 /
DATA CHALLENGES
SOLUTION BENEFITS
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
Insuranc
e
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 /
DATA CHALLENGES
SOLUTION BENEFITS

From Customer Insights to Action

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