The LCG Digital Transformation Maturity Model

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Oi, Visionário,
Vamos conversar sobre
Transformação Digital
13 SET 2018
PAUL LIMA
TWITTER: @PAUL_LIMA
PLIMA@LIMACONSULTING.COM
SERVING DIGITAL MARKETING VISIONARIES

2
Paul Lima
plima@limaconsulting.com
@Paul_Lima
The Digital
Transformation Maturity
Model
Lima Consulting Group
Managing Partner

Mobile adoption changed
3

4

5
Rome, 2010

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Rome, 2010
Philippines 2015Philippines 2015

About Paul

About Paul
The Institute for Operations Research
and the Management Sciences

Our Agenda Today
1. Set the Context for DX
2. Introduce the Digital Transformation Maturity Model
3. Data Readiness Posture → X Axis → Get Smart
4. Agility Posture → Y Axis → Get Return
5. Putting it together with the Law of Accelerating Returns

New Ways … New Answers
ACCELERATING PACE OF
INNOVATION
EXPLOSION OF COMPUTING POWER
MORE ACCESSIBLE THAN EVER
BIG
DATA
FLOOD OF INFORMATION FROM
DEVICES, SERVERS AND THE CLOUD
DIGITAL
BEHAVIOR
BOTH CONSUMERS AND COMPANIES
PREFER DIGITAL ENGAGEMENT

13
Exponential Growth of Computing for 110 years
Source: Ray Kurzweil, DFJ

The Law of Accelerating Returns – Ray Kurzweil

15
We tend to overestimate
the effect of a
technology in the short
run and underestimate
the effect in the long
run.
- Roy Amara (1968)
Co-founder of the Institute for the Future
RAND Corporation Spinoff

16
What do the next 100 years look like?
Source: Ray Kurzweil, DFJ

https://techcrunch.com/2016/08/01/pokemon-go-passed-100-million-installs-over-the-weekend/
How long to reach 100MM users?
17
Pokemon Go = 44 days
https://techcrunch.com/2016/08/01/pokemon-go-passed-100-million-installs-over-the-weekend/
Source: BCG

Porter’s Sustainable Competitive Advantage is dead
▪ Transient competitive
advantage is the New Norm
▪ Learning how to frequently
Jump Curves is the only
sustainable, competitive
advantage
Source: The End of Competitive Advantage, Rita Gunther McGrath
18

Get Ready to Jump
Curves

Marketers are
drowning in data

They didn’t teach us how to count like this in school
Name number of zeros groups of 3 zeros
Trillion 12 4 1,000,000,000,000
Quadrillion 15 5
Quintillion 18 6
Sextillion 21 7
Septillion 24 8
Octillion 27 9
Nonillion 30 10
Decillion 33 11
Undecillion 36 12
Duodecillion 39 13
Tredecillion 42 14
Quattuordecillion 45 15
Quindecillion 48 16
Sexdecillion 51 17
Septendecillion 54 18
Octodecillion 57 19
Novemdecillion 60 20
Vigintillion 63 21
Centillion 66 22
Name Bits Binary (base 2)
Bit 1 0 or 1
Byte 8 bits "A" = 0100 0001 or ASCII 65
Kilobyte 1024 210
Megabyte 10242
220
Gigabyte 10243
230
Terabyte 10244
240
Petabyte 10245
250
Exabyte 10246
260
Zettabyte 10247
270
Yottabyte 10248
280
Brontobyte 10249
290
Geobyte 102410
2100
Zotzabyte 102411
2110
21
•An exabyte hard drive would be about the size of Connecticut
•A zettabyte hard drive would be about the size of Antarctica
•A yottabyte hard drive would cover the earth 23 times
•A brontobyte hard drive would cover the earth 23,000 times
•A gegobyte hard drive would cover the earth 23,000,000 times
•A zotzabye => 23 billion times

5 V’s of Big Data
Volume
Velocity
Value
Veracity
Variety
Value:
• Statistical
• Predictive
• Prescriptive
• Cognitive
• Integrated
• Trustworthiness
• Authenticity
• Reputation
• Accountability
• Availability
• Probabilistic and Interpolated
• Structured vs Unstructured
• Forms and Sources
Velocity:
• Streaming data collection
• Batch vs real-time
• Data Collection
• Data Processing
• Still naming big numbers
• Data Layer
• Governance
• Accepted Nomenclature w/i Enterprise
22

Growth of the marketing technology landscape
24

Growth of the marketing technology landscape
25
LCG predicts 100,000
MarTech & Ad Tech solutions
by 2022

This Stuff Wasn’t
Made to Fit Together

Digital Transformation Barriers
Legacy technology is
our biggest barrier.
It’s not just the
technology, it’s the
policy, training &
behaviors that
surround it.
27

Digital Transformation is about Strategy.
Not Technology.
28

Preparing for the DX Journey
❑ 1. Understand VOC & anticipate trends (Design Thinking)
❑ 2. Build multi-disciplinary, cross- departmental teams (think
Collaboratively)
❑ 3. Harness the power of data (think data-driven)
❑ 4. Adapt quickly (think Agile)
❑ 5. Execute on-time & on-budget (think PMO & Six Sigma)
❑ 6. Take controlled risks & fail fast (think Entrepreneurially)
❑ 7. Plan GDPR path as an opportunity to improve (Think Globally)
❑ 8. Use proven frameworks for Digital Transformation
29

Remember this scene from Minority Report?

Digital Maturity Defined
Getting Smart AND Getting Return
Data Readiness Posture Action-ability and Agility Posture
▪ Technical and Data Operations
▪ Defining “Normal” (band of excellence)
▪ Segmentation (psycho, demo, firm, behavioral, geo,
techno, etc…)
▪ Video, Social, Mobile, Apps, iBeacons + innovations
▪ Real-time monitoring of operations
▪ Reports, Dashboards and Alerts
▪ Enhanced Data (from 3rd parties and Networks)
▪ 360 view of the customer
▪ Advanced Analytics (math & algo’s)
▪ Data Warehousing
▪ Data Visualizations
▪ Data Science
▪ Data Management Platform
▪ Data Layer
▪ Strategy
▪ Vision
▪ Governance
▪ Culture
▪ Marketing, Sales and Operations
▪ IT-Business Relationships
▪ Transformation Management
▪ Digital Platforms
▪ Human Resources and Org Design
▪ Financial Readiness
▪ Portfolio & Program Management
▪ Customer Journey Mapping
▪ Customer Engagement
▪ Preparing other form-factors, infrastructure
31

Data&Information
Gathering
Web Content Management
Capability Begins
LCG Digital Transformation Maturity Model Methodology
MarTech, Gap Analysis
Biz Requirement
Definition
Data Layer Design
Tag Management Capability Begins
Technical Requirements
& Architecture
Tag
Audit
Data Warehouse
Capability Begins
Dashboards & Alerts
by Stakeholder
Group
Data Management Platform
Capability
Integrations of Enterprise
Customer Data
Pre-Click → Post-Click
Closed-Loop Integration
End-to-End Closed Loop
Conversion Attribution
Data Quality Practice
Begins
Data Visualizations Capability
Begins
Attribution Modeling
Capability Begins
Rules-Based A|B Testing
Begins
Portfolio Based MVT
Testing Begins
Manual A|B Testing
Begins
Portfolio Based Ad
Buying
Programmatic Based Ad
Buying
Digital Asset Manager Capability
Required for Machine Creation
of Creative
E-mail Service Provider
Optimization Begins
Cognitive UIs accessible to all
user groups
Chief Data Responsibility, Data Governance
Policies & MarTech Implementation Team
Conversion Optimization
Capability
Advanced Analytics
Practice Begins (Data
Science Team)
Call Center Conversions
data Tracking Capability SEO Platform Capability
SSO/Audience
Profiling 3rd Party
Profile Capability
Customer Analytics &
Heat Maps Capability
Begins
Voice of the Customer / Chat
/ Survey Feedback Integrated
with Respondent's
Engagements
Lead Scoring &
Sales Automation
Integration with Database
Marketing Platforms
Social Marketing via ads,
listening, publishing, e-care &
analytics
Community Building Capability
Diagnostic Descriptive Predictive Prescriptive Cognitive
Cognitive
Analysis
Marketing
Action
Agility&Action-abilityPosture
Data Readiness Posture
Establish Program
Management Office
Digital Marketing
Platforms
Responsibility
Translation Mgmt
Platform Integrated
Develop Customer Journeys
Establish Digital Transformation
Steering Committee
Develop Personas
Single Digital Asset Mgmt
Solution
Deploy
Computational
Creative Capability
Reports by
Stakeholder Group
Digital Balanced
Scorecard
KPI Definition
32

How to Advance Your Data Readiness Posture
Past Present Future
Consumer
Insights
What happened?
(Reporting)
What is happening now?
(Alerts)
What will happen?
(Extrapolation)
Actionable
Intelligence
How and why did it happen?
(Modeling, experimental
design and cluster analysis)
What’s the next best action?
(Recommendation)
What’s the best/worst that
can happen? (Min/Max)
(Prediction, optimization,
simulation)
Take note of the questions asked in your meetings, they will tell you where you are.
33

LCG Digital Analytics Maturity Lifecycle
What will happen on your next
experience? (1:1 Personalization)
Cognitive Computing. Computational Creativity.
Personalization @ Scale.
360 view of customer interactions meets computational
creativity performed in milliseconds. Machines perform all
stages of this model + all stages of the Agility Lifecycle.
(Learning + Doing). All chess moves are known and
prepared for presentation.
Descriptive Analytics Diagnostic Predictive Prescriptive Cognitive
What happened (within a silo)?
Data Gathering.
Focus on verifying and validating data and generation of
reports from individual data sources. Datawarehousing and
data visualization tools begin.
Why did it happen?
Closed Loop Marekting. Information @ 1:1 Causality.
Information contextualized and compared into learning
& Insights w/ focus on documenting equations &
segments. Profile enrichment/enhancement using 3rd
party solutions begins.
What’s happening for my customer?!
Information Causality commences 1: ∞. Real-Time.
Manual Analysis. Multi-channel analytics begins. Focus on
intelligence, and determining “what’s a good #?” Alerts and
exceptions based processing begins.
What is the next best action (1:∞)? What to do?
Automating the equations.
Focus on past behavior driving future actions, propensity modeling and lead scoring. What
should we do for customers like you? A bias towards action. Automation and decision
engines/sciences begin to shift engagement and profile enhancements from segments to
individually created experiences across all channels & content types along manually crafted
customer journeys executed in mostly a linear fashion.
34

LCG Digital Analytics Maturity Lifecycle
Descriptive Diagnostic Predictive Prescriptive Cognitive
Typically an
Investment
Consumer Insights
gained and an
improved
understanding of the
business
Typically Cash Neutral
investment
Manual Deployment of
learnings
+ ROI – Some projects can have
10,000% ROI and break even
points as early as 14 days
35

The LCG Analytics Maturity Roadmap
➢ The roadmap provides the connection between strategy and action
➢ Practical plan of action to accomplish business objectives
Organizational Analytics Capability Data & Infrastructure
People Analytics Framework Health
Leadership Roles & Skills Access
Measures Analytics Services Traceability
Processes Analytics Processes Analytics Architecture
36

37 | Copyright © 2018 Deloitte Consulting Group S.C. All rights reserved.
Sample Data Readiness Posture Work Product.
Org. Gente
D&I:
Arquitectura Analítica
D&I:
Trazabilidad
D&I:
Acceso
D&I:
Salud
Límite Analítico:
Procesos
Límite Analítico: Servicios
Límite Analítico:
Roles y Habilidades
Límite Analítico:
Marco Analítico
Organización:
Procesos
Organización:
Medidas
Organización:
Impacto de Liderazgo
10
09
08
07
06
05
04
03
02
01
00
06
05
04
03
04
03
02
01
07 06 05 04 02
08
07
05
02
02
04
06
01
04
05
06
01
04
07
03
04
03
04
05
06
04
04
Año 2 Futuro (Y-3)
Año 1Actual
DIMENSIONES
• Organización
• Capacidad analítica
• Datos e Infraestructura
DISCOVERYVISIONGAPANALYSISRECOMENDACIONES
YPRIORIZACIÓN
DERECURSOS
DECISIONESY
APROBACIONES
EJECUCIÓNESTADOFUTURODE
LOSOBJETIVOSDEL
NEGOCIO
EVALUACIÓN

Web Analytics, VOC, Online Survey and Customer Analytics Data
First Party Enterprise Data
2nd and 3rd Party Data
Appetite for additional Data will grow as Action-ability Posture Matures
Cognitive
Analysis
Marketing
Action
Data
Data&Information
Gathering

Computing Power
Man Power
Where Computing Power ends, Man Power begins
Cognitive
Analysis
Marketing
Action
Data&Information
Gathering
Conducted by
People
Conducted by
Computers
39

Getting Return → Y Axis → Actionable Insights
Data
Advice and Action
Information Gathering
Value Chain Analysis revealed: .00012 - $12 USD / hour
$12 USD / hour - $45 hour
$45 -500 USD / hour
$1,000 USD / second –
comes in 6 second bursts
Cognitive Analysis
40

Data to Information to Decision to Action across the Curve of Automation
Cognitive
Analysis
Marketing
Action
Data Collection
Data&Information
Gathering
The Curve of Automation
Information Gathering
Decision
Decision Making
Action
Data Processing
Launch
Computing Power
Man Power
41

Exemplo: Prontidão da postura da agilidade (Y Axis)
Estado actual Estado futuro
Dimencao de
evaluación
Atual 12 meses 18 meses 24 meses
Estrategia 1 2 2 2
Operaciones de marketing
Gobernabilidad, adopción y aplicación
Preparación de datos
Gente, Lugares, Empoderamiento
Procesos
Tecnología
DISCOVERYVISIONGAPANALYSISRECOMENDACIONES
YPRIORIZACIÓN
DERECURSOS
DECISIONESY
APROBACIONES
EJECUCIÓNESTADOFUTURODE
LOSOBJETIVOSDEL
NEGOCIO
EVALUACIÓN
42

LCG Digital Transformation Maturity Roadmap
Cognitive
Analysis
Marketing
Action
Data&Information
Gathering
Benchmarking,
Reporting,
Cleansing, QA &
Preparation
Opinion Driven
Decision Making
over Months
Decision
Action
Data Processing
Launch
Data Collection
43

➢Rank beats Opinions

Without the right materials, architecture and planning,
the weight of the data will crush most initiatives.

In God we trust;
all others
bring data
- EDWARD DEMING
American engineer, statistician, professor, author, lecturer, and
management consultant & founder of the Deming Institute
“
”

Establish a Data Layer
Tennis Match Soccer Team 3-5-2A Military Platoon Blue Angels Missing Man
Formation
47

Establish a Data Layer
Tennis Match Soccer Team 3-5-2A Military Platoon Blue Angels Missing Man
Formation
48

Bring Data to Decision Briefs
Challenge
• All 5 V’s of Big Data
• Management didn’t
trust our data
Solution
• Tag Auditing in sprints
• Established a Tiger
Team
• Sprint based “look-
aheads” and “look-
behinds”
Result
• Changes to how we
organized ourselves +
our sprints
• Value of our team
elevated to Trusted
Advisors
• All analysts spent less
time cleansing and
more time analyzing
• Insights into how to
better organize our
data layer with
uniformity
• We identified piggy-
back tags
Learning
• Use data in decision
briefs
• It’s harder than we
thought to fight
entropy of the 5 Vs
• Management has more
questions now that
everyone is an analyst
• Everyone can be an
analyst – and inquire at
the speed of thought
• Enabling analysts
required more project
requests and training
49

Cognitive
Analysis
Marketing
Action
Data&Information
Gathering
Benchmarking,
Reporting, Cleansing,
QA & Preparation
Data Driven
Decision Making
over Weeks
Data Warehousing, Omni-
Channel Integration, Data
Visualizations, Dashboards by
Stakeholder/Objective
Opinion Driven
Decision Making
over Months
Decision
Action
Data Processing
Launch
Data Collection

➢Data beats Rank

Let 1,000 flowers bloom
52

Segment, then analyze
53

Acquisition
Convert
Customer
Experience
LCG Engagement Funnel Development Methodology
REGISTER /
PURCHASE
This model becomes
your blueprint for
Analytics
implementations
54

Comparing segments reveals consumer insights
Examples on segmenting customer journeys
▪ Trigger events (paid a fine < 30 days)
▪ Attribution Source
▪ Region/Geo (urban vs rural)
▪ Mobile device vs desktop
▪ Registered users vs non-registered
▪ Paperless statement vs paper
▪ Automatic billing vs manual
▪ Called w/i last 30 days vs over 30
REGISTER
Segment: A B C

Cognitive
Analysis
Marketing
Action
Data&Information
Gathering
Data
Benchmarking,
QA & Preparation
Opinion Driven
Decision Making
over Months
Automated Segmentation
Analysis, Look-Alike Modeling,
Cluster Analysis, Enhancing
Customer Profiles
Data Driven
Decision Making
over Weeks
Information
Driven Analysis
Over Days

➢Job Title beats Opinions
➢Data beats Job Title
➢Information beats Data
➢Data beats Rank

Data Governance provides our uniform foundation
Challenge
• Get me out of excel
• Non-uniformity of our
data across
• 25+ properties
• 1+ Million SKUs
• 4 channels
• We were spending too
much time cleansing
data
• Time to Insight
measured in months
• De-centralized processes
for governance
Solution
• Updated architecture
• Use Analysis Workspace
• Re-deploy Adobe
Analytics through Adobe
Launch
• Establish a centralized
data governance
steering committee and
policies
Result
• More flexibility and
greater efficiencies of
Adobe Analytics
• Total uniformity across
all properties
• Reduced the number of
variables by 40% while
still collecting the same
data
• Monthly data
governance meetings
• Time to insight measured
in minutes
Learning
• The nature of questions
have matured from
“Diagnostic” to
“Descriptive” (What
happened vs “Why did it
happen”)
• We feel ready to push
the data to use
algorithms, data science
and test new MarTech
• Bring periphery
stakeholders who hold
veto power into the Data
Governance process
58

Exception Based Processing Improves Results
• What is normal?
• Knowing when actions must be
triggered means we have the
“bands of excellence” defined
• What is the next best action?
For call centers, customer service centers,
regional operational centers, these
insights can mean significant savings

Cognitive
Analysis
Marketing
Action
Data&Information
Gathering
Data
Benchmarking,
QA & Preparation
Opinion Driven
Decision Making
over Months
Customer Profiles
Opinion Driven
Decision Making
over Weeks
Information
Driven Analysis
Over Days
Predictive
Driven Analysis
Over Minutes
Portfolio Optimization of content,
products & ads, Personalization,
Automated Design of Offering,
Experiences and Products

➢Data beats Rank
➢Advanced Analytics beats
Information

Cognitive
Analysis
Marketing
Action
Data&Information
Gathering
Data
Benchmarking,
QA & Preparation
Opinion Driven
Decision Making
over Months
Customer Profiles
Data Driven
Decision Making
over Weeks
Information
Driven Analysis
Over Days
Predictive
Driven Analysis
Over Minutes
Portfolio Optimization of
content, products & ads,
Personalization, Automated
Design of Offering, Experiences &
Products
Prescriptive Driven
Analysis Over
Milliseconds
Cognitive Interfaces
accessible to
business users
Decision Sciences
Overtake Human
Analysis
Point where Machine
Learning and AI
overtake human
capacity to perform
marketing actions
Point where AI is
entrusted to decide
and act

➢Data beats Rank
➢Advanced Analytics beats Information
➢AI Beats Applied Decision Sciences
➢Machine Intelligence enhances AI

Data&Information
Gathering
Cognitive
Analysis
Marketing
Action
The journey of 1,000 miles begins with the first step…
Diagnostic
Tag Audit and Tag Audit
Capability Begins
Business
Requirements
Definition
MarTech Capabilities
Assessment, Gap
Analysis, Roadmap,
Vendor Selection
Technical
Requirements
& Architecture
Tag Management
Capability Begins
Reports by
Stakeholder
Group
People
Technology
Process
Digital Marketing
Platforms
Responsibility
Assigned
PMO Office &
Responsibilitie
s Assigned
Digital Balanced
Scorecard
KPI Definition
Develop
Personas
Data Layer Design
Web Content Management
Capability Begins
Single Digital
Asset Mgmt.
(DAM)
Solution
Establish Digital
Transformation
Steering Committee
Chief Data Responsibility,
Data Governance Policies &
MarTech Implementation
Team
Develop Customer
Journeys
Dashboards & Alerts by
Stakeholder Group
Translation Mgmt.
Platform Integrated
Data
Warehouse
Capability
Begins
66

Digital Transformation Maturity Model Methodology
Discovery
Vision Formulation
Future State Business
Objectives
Gap Analysis
Recommendations &
Prioritization of Resources
Decision & Approvals
Execution
Evaluation
Initial Initiative Future Initiatives
67

LCG GDPR SurePathTM Engagement Methodology
Discovery
Awareness
Training
SurePathTM
Readiness
Assessment
SurePathTM
Architect
SurePathTM
Roadmap
Build Launch
Train Operate & Serve Govern
Programs
Projects
SurePathTM Foundation SurePathTM ServiceSurePathTM
Transform

DTMM Sample Work Products across the 4 areas
People Process Platforms
Agility Augmented Digital Clarity Innovation Applied
Performance
Data-Driven Results
69

It is not the strongest of
the species that
survive, nor the
most intelligent, but
the one most
responsive to
change.
-Charles Darwin
70 70

71
Paul Lima
plima@limaconsulting.com
@Paul_Lima
The Digital
Transformation Maturity
Model
Lima Consulting Group
Managing Partner

The LCG Digital Transformation Maturity Model

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