customer-centric transformation

1 © 2016 Merkle. All Rights Reserved. Confidential

PLACE IMAGE HERE

LEADING A CUSTOMER-CENTRIC EVOLUTION

Truths (and Misconceptions) of Transformation

Merkle & Adobe’s Research on Transformation

250 senior level executives in the US

Have recently completed

a broad scale marketing/technology

transformation initiative

Company revenue $1 billion or more

Spans industries as well as B2B & B2C

Who were the respondents?

Learn all of the truths and misconceptions of transformation in Merkle’s Customer-Centric Transformation white paper. Click here to read the full report.

http://bit.ly/2cRpfSP




Business impact is… Incremental Substantial

Strategy is… Improving what’s in place Challenging what’s in place

Tactics are… Defined upfront Refined as you go

Approach is… One and done Iterative

O P T I M I Z AT I O N T R A N S F O R M AT I O N

Optimization vs Transformation






of transformation initiatives fail70%

#30Percenters






Plan

Build

Sustain

Fail to InitiatePlan never proceeded to development

Fail to AdoptIs not embraced by organization, and abandoned

Fail to LaunchEnhancements never launch or significantly under deliver against expectations

Risks at Each Stage of the Transformation Process






Transformation Success Requires…

Planning

Detailed plan and formal PMO to oversee work streams and track against milestones

Vision

Precise and shared understanding of business objective and desired end state

Organization

Processes, team in place and ready to

support and sustain change.

Team Skills

Team with the right skill set and allocated at an adequate level

FPO

Sponsorship

Engagement, commitment and financial support of senior executive to the initiative

FPO

41% 42%39%

43% 43%

24% 23%

16%20%

17%

0%5%

10%15%20%25%30%35%40%45%50%

Vision Sponsorship Management Execution Operations

Key Competencies for Transformation

Initiative a Success Initiative Not a Success






“We don't need to waste time (and money) on planning, we know what we want to do.”

Getting Planning Right is Critical to Project Success






Planning: Vision Value Roadmap as the FoundationCustomer Journey Mapping Results

Roadmap

Value

Vision

Roadmap

Value

Vision






Planning: Vision Value Roadmap as the Foundation

Roadmap

Value

Vision

Roadmap

Value

Vision

Benefit analysis, at actual realization

VALUE LEVERS (AT ACTUAL REALIZATION) 2014 2015 2016 2017 2018 2019 TotalFULFILLMENT (new accounts)

BEN

EFIT

IM

PACT

85% $xx,036 $xx0,994 $3,598,034 $6,360,712 $7,982,265 $8,735,309 $xx,670,349 FULFILLMENT (existing accounts) 85% $13,620 $327,164 $xx07,419 $xx19,224 $xx43,134 $xx01,920 $9,412,480 CROSS-SELL (accounts) 74% $16,856 $xx0,975 $4,002,647 $6,183,391 $8,648,510 $8,933,262 $28,045,640 MIGRATED CALLS (volume) 55% $4,560 $50,766 $134,155 $xx7,894 $221,911 $xx2,845 $852,132 ATTRITION IMPROVEMENT (existing HH) 74% $xx,274 $xx5,794 $xx46,999 $5,043,113 $xx23,857 $7,048,532 $xx,517,569 NEW CUSTOMERS (accounts) 86% $16,142 $469,572 $1,758,113 $xx08,044 $3,900,386 $xx68,347 $13,520,605 ATTRITION IMPROVEMENT (new accounts) 86% $xx,168 $xx3,040 $xx32,234 $4,653,342 $5,839,632 $6,390,540 $xx,242,956

$xx7,657 $xx08,304 $xx,679,600 $xx,665,721 $xx,159,694 $xx,620,756 $xx2,261,731

VALUE DRIVERS (AT ACTUAL REALIZATION) 2014 2015 2016 2017 2018 2019 TotalNEW CUSTOMER (ACQUISITION + ATTRITION + FULFILLMENT) $xx,347 $2,133,605 $7,988,381 $14,122,098 $xx,722,283 $xx,394,197 $xx,433,911 EXISTING CUSTOMER (CROSS-SELL + FULFILLMENT) $30,476 $xx8,139 $5,210,065 $xx02,615 $11,391,643 $11,935,182 $37,458,120 EXISTING CUSTOMER (ATTRITION) $xx,274 $235,794 $3,346,999 $5,043,113 $xx23,857 $xx48,532 $xx,517,569 CONTACT CENTER COST SAVINGS $4,560 $xx,766 $134,155 $xx7,894 $221,911 $242,845 $852,132

$xx7,657 $xx08,304 $xx,679,600 $xx,665,721 $xx,159,694 $xx,620,756 $xx2,261,731

TIMING IMPACT WeightsNON-FMP COSTS -> 6% 50% 71% 92% 100% 110% 40%FMP -> 0% 10% 65% 100% 110% 120% 60%NET -> 2% 26% 67% 97% 106% 116%

CROSS-SELL -> 1% 7% 75% 95% 112% 116%

BENEFITS 2014 2015 2016 2017 2018 2019 TotalFULFILLMENT (NEW + EXISTING)

BEN

EFIT

IM

PACT

85% $xx,656 $xx88,157 $xx05,452 $8,479,936 $xx,725,399 $xx,737,229 $37,082,829 NEW CUSTOMER (ACQUISITION + ATTRITION) 86% $40,311 $1,172,611 $4,390,347 $xx61,387 $9,740,018 $10,658,888 $xx,763,561 EXISTING CUSTOMER (CROSS-SELL + ATTRITION) 74% $xx,130 $xx6,769 $xx49,646 $11,226,504 $xx,472,366 $xx,981,794 $50,563,209 CONTACT CENTER COST SAVINGS 55% $4,560 $50,766 $134,155 $xx7,894 $221,911 $242,845 $xx2,132

$xx7,657 $xx08,304 $xx,679,600 $xx,665,721 $xx,159,694 $xx,620,756 $xx,261,731

Recommendations - Mapping of recommendations based on effort and business impact

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5

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10987654321

Leve

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Level of Effort

Quick Wins

Longer Term

The recommendations are plotted according to the prioritization exercise to determine potential quick win candidates

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a b

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a

b

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d

a

b

c

Ensure Data ConsistencyEstablish Data Governance Guidelines

Define Customer Profile

Improve Data Capture & Integration

Centralized Marketing

System

Determine DMP Technology

Integrate Digital Data

into DMP

Syndicate Data From DMP Across

Digital Media

Consistent Consumer Profile

Integrate Digital Data into Event Stream

Increase Use of 3rd Party Data

Analytics & Insights

Environment

Cross Media Measurement

System

Media Scenario Planning

Data Visualization &

Reporting

Co-Marketing Environments

Improve Visibility Over Current Decision Management

Expand Rule Based Decisions Across Channels & Media

Content Management

Tool

Personalization Across Media

Utilize Addressable

Platforms

Expand Use of Display Media

Site Personalization

Change Content Approval Process

Technology Consistency

Across Brands


Technology Ownership

25






Planning: Vision Value Roadmap as the Foundation

Roadmap

Value

Vision

Roadmap

Value

Vision

Recommendations - Future state conceptual technology blueprint (Example 1)

© 2014 Merkle. All Rights Reserved. Confidential and Proprietary. 10

Fifth Third – Hadoop Sources

Media Execution (Experiential /Advertising)

Channel Execution (Commerce/Service/Sales)

Identity Management

Decision Platform

Data Management Platform (DMP)

Foundational Marketing Platform

· Full view of customer including longitudinal event stream and digital persona (anonymous/semi-anonymous) linked to a known individual

· Supports full li fecycle of an individual (Prospect & Customer)

· Inclusive of all relevant and granular sales and marketing data

· Single, conformed tool agnostic dimensional data model

· Dimensions , Facts, and Aggregates supporting key subject areas

· Central rationalization of permission , preferences, and suppressions

· Marketing metadata and cost· Supports Business Intelligence and Campaign

Management

Marketing Database

Core Market ing

Digital

Analytics Management Platform

Application Integration

360 Known 360 Unknown Segment Profile

Decision Engine(PEGA PRPC) Personalization Engine

Web Services & API’s

DSP Syndication

Meassaging Trigger

Forecasting/Simulation

· Media Mix Optimizaiton· Scenario Planning

Response Attribution

· Advanced Attribution Methods

· Attribution Engine

Advanced Analytics

· Analytic Models & Segmentation

· Automated Scor ing Framework & Metadata

AnalyticsResponse Attribution Media Mix

Business Intelligence

· Reporting· Visualization· Dashboards

Real-time Data Management

· Enables 360 customer profile to organization· Relevant Atomic level customer and marketing

data· Synchronized with subset of Marketing Database

to ensure accuracy of matching/keys· Informs Decisioning in real -time· Captures real-time transactions, behavior, and

interaction details required for triggered communications

Audience Management

· Data Exchange Integration

· Audience Syndication

Dem

and

Slid

e Pl

atfo

rmPl

atfo

rm A

udie

nces

Con

tent

Man

agem

ent

Dig

ital A

sset

Man

agem

ent

Dig

ital A

naly

tics

Event Managment

· Identity Resolution and XREF Management

· Event Stream Parsing and Rationalization

Digital Data Integration (DDI)

· Cookie/Session/Pixel Linking and Aggregation

· Anonymous /Known and On-line/Off-line Linking

Insight/Action

Data

Data to Drive Decisions and Capture Insight

AudienceProfile

IdentityAudience

Real -time

Response / Disposition Data

AudiencesTags

Data

ProfileDecisionsIdentity

Data

Data

Data

Scoring HistoryMetadataInsights

Data

Prospect

Event Stream

Score

Data

Scores

Data

ProfileEvents

Data

Real -time

Real-time

Real-time

Batch / Real-timeBatch

Batch

Batch

Batch

Real-time

Real-time

CampaignsLeads

Rea

l-Tim

e / B

atch

Preference Management

Real-time CDI

Profile/Offer Lookup

Tag Management

DSP

Mobile

Social

Web

Nea

r-tim

e &

Incr

emen

tal B

atch

Pro

cess

ing

Operational

Inquiries, Referrals, and Transactions

Products, Accounts, Customers, and Services

Quotes and Applications

Campaigns

Web and Mobile

Direct Mail and Email

Call Center , Branch, and ATM

Digital

Email Activity and Dispositions

Search and Display Responses/Impressions

Social Profile and Activity

Web and Mobile Profile and Act ivity

Lists & Overlays

Prospect Files & Vertical Lists

Data Overlays & Appends

Suppressions, Segments, and Scores

Other

Market/Consumer Research

Mass Media Buy Activity

Econometric Data

Consumer Checking

Savings

CDs

IRAs

Money Market

Wealth Management

Investment Services

Credit Cards

Mortgages

Auto Loan

Home Equity Loan

Student Load

Personal Installment

Load

Personal Lines of Credit

Protect ion Services

Life Insurance

Home Insurance

Auto Insurance

Credit Risk Management

Institut ional Services

On-line Banking

On-line Bill Pay

Direct Deposit Check Card Rewards Mobile

Banking

Premium Banking

ECIF

EDW

UDS

ACAP

TSYS

BCRD

FSER

ALS

Zoot

FNA

IB

SBATDEP

GPR

AGL

ACE

Unica

Exact Target

Eloqua

ATG

CAMP

Chordiant

Phoenix (ATM)

Genesis (IVR)

Aspect Dialer

MGIC, Argus

S2S Acces DB

Dial America

Exact Target/Eloqua

Operational Email Systems

Seach Partners (Spark)

Display Partners (DART )

Landing Pages (Harte Hanks/IT)Monetize and 53 Mobile (Mobile

Data Mart)Preference Center (TBD)

Digital Analytics (TBD)

CoTweet (Social Activity)

Harte Hanks

Agus

Acxiom

IXI

Central Opt Out DB

DSG Customer Analyt ics

Preference Center (TBD)

Media Buying Partners(Segment Level)

Internal Market Research

External Market Research

Integration Layer

Transformation

Data Quality

Normalization(ODS Centric)

Dimensionalized(Star Schema

Centric)

Business Rules

Technical

Subject Area

Loads

ETL

- Rea

l-tim

e &

Batc

h P

roce

ssin

g

Mobile Application

Optimize· Personalization· Targeted Offers· Product/Service

Recommendations· Remarketing

Collect· Viewing history· Contact History· Site Act ivity· Permission/Preference

Identify· Latitude/

Longitude· Device ID· Email Address· Customer ID

Inbound Call / Outbound

Optimize· Preference History· Purchase History· Offer History· Offer Management· Call Routing· Knowledge Bank· Chat Response

Collect· Contact History· Call Center Activities· Permission/Preference· Offer Disposition

Identify· 53 Account Number· E-mail· Name and Address· Phone Number

53.com & Internet Banking

Optimize· Personalization· Targeted Offers· Product / Service

Recommendations· Remarketing· Forum / FAQ· Online Application· eDocs with Deep

Linking

Collect· Site Activity· Landing Page Act ivity· Content Impressions· Permission/Preference· eSignature· Click to Chat / Call· Issue / Support / Self

Service· eDocuments

Identify· Authenticated UID· Session, Cookie, IP

Address· Device ID· Customer ID· Email· Name & Address· Phone

Branch

Email – EXACT TARGET & ELOQUA

Optimize· Personalization/

Landing Pages· Targeted Offers Based

on Web Behavior· Recommendations

Collect· Sent· Opens· Clicks· Forwards· Spam/Opt Outs

Identify· Phone· Email Address· Address· STMS Account

Number

Display - SPARK

Optimize· Retargeting· Look-alike Models· 3rd Party Segments· Direct Match List

Collect· Ad Impressions· Clicks· Media Activities· Profile

Identify· Cookie· IP Address· Tracking Pixal

Social – Hearsey or Sprinklr

Optimize· Retargeting· Look-alike Models· 3rd Party Segments· Direct Match List

Collect· FB Parent-Child

Relationships· Application Usage· Ad Impressions

Identify· User ID· Email Address· Zip Code· Cookie

Search – SPARK & LEO BURNETT

Optimize· Keyword Bidding by

Segment· Creative Weights· Remarketing

Collect· Search Redirects· Search Landing Page

Activity· Key words

Identify· Cookie· IP Address· Tracking Pixal

Direct Mail – HARTE HANKS

Optimize· Personalization· Targeted Offers· Recommendations

Data Collection· Contact History

Identity Attributes· Name & Address· Customer Number

ATM

Workflow

Unica Campaign

Email Service Provider

Content/Asset ManagementETL and Workflow

Management

· Data loading and syncing

· Automation & Job Control

· File Management· Data Audit, Monitor &

Alert

Process Metadata & Data Health

· Central identity store supports all customer recognition

· Individual, ECIF Customer, Household, Account Identity Map and History

· On-line and Off-line· Anonymous to Known

Recognition Database

Customer Data Integration (CDI)

· Parsing, Hygiene, Standardization· Address Enrichment/NCOA· 3rd Party Match ID· Digital Data Integration (DDI)

· Cookie/Session/Pixel Linking/Aggregation

· Matching (Hard Key & Fuzzy Logic) · Identity Resolution and XREF

Management· Event Stream Management· Channel and Media Event

Rationalization

CDI Known DDI

Anon.DDI

Data Exg.

Search

Insight/ Identity / Event Stream

Real-time

Responsition & Disposition Data

Insight

· Next best action· Product

recommendation· Availab le offers

· Profle/Demos· Personalization· Segment/Value· Event Trigger

Data

· Cookie/IP, ECIF ID, Name/Address, Device ID

· Events, Behavior, Session· Disposition, Interests

Audience Platforms

Hadoop Platform

Hadoop Pre-Process ing – using big da ta capabilities as a “landing zone” before determining what data should be moved to the data warehouse . Data Exploration – using big data capabilities to explore and discover new high value data from massive amounts of raw data and free up the data warehouse for more structured, deep analytics. Offloading – moving infrequently accessed data from data warehouses into enterprise-grade Hadoop allows organizations the ability to store transaction -level data for longer periods of time.

Digital Channel Event Stream

Real-Time / Batch

Optimize· Preferences· Purchase History· Offer History· Offer Management

Collect· Contact History· Call Center Activities· Permission/Preference· Offer Disposition

Identify· 53 Account Number· E-mail· Name and Address· Phone Number

Optimize· Personalization· Targeted Offers· Product / Service

Recommendations· Remarketing

Collect· Visit· Transaction· Ad Impression

Identify· 53 Account NumberKnowledge

Bank

Staging Exploration Event Streaming

Execution SystemsACH

WIRE TX

CHECK IMAGE

AUTO BILL PAY

SUPERMICR

RAFT / TPP

Product Execution Systems

UDS

AFS

ALS

XAA

FINDUR

BANKTRADEACBS

NFS

TSYS

INFOLEASE

TRUST

FSERGENERAL LEDGER

BUSINESS SOLUTIONS

CRM

CCAS

MDM

RAROC

FNA

CML PORTAL

Integration Layer

· Perform discrete transactions on behalf of a customer (deposits, funds, transfer, withdrawals )

· System of record for some financial service or product -level attributes

· Authoritative system of record for client product or service and it’s current state (balance)

· System of record for most product or service specific or characteristics (rate, term)

· Primarily internal -facing business systems that create or derive information about a customer or transaction that has relevancy elsewhere in the Organization (RAROC Score , Service Tier, etc)

Fifth Third Sources & Extracts

Unica DetectUpgrade

FileNet

Recommendations - Mapping of recommendations based on effort and business impact

10

9

8

7

6

5

4

3

2

1

10987654321

Leve

l of I

mpa

ct

Level of Effort

Quick Wins

Longer Term

The recommendations are plotted according to the prioritization exercise to determine potential quick win candidates

ab

c

d

e

f g

h

i

jk

a

b

c

d

e

a b

c

a

b

c

d

a

b

c

Ensure Data ConsistencyEstablish Data Governance Guidelines

Define Customer Profile

Improve Data Capture & Integration


System

Determine DMP Technology

Integrate Digital Data

into DMP

Syndicate Data From DMP Across

Digital Media

Consistent Consumer Profile

Integrate Digital Data into Event Stream

Increase Use of 3rd Party Data

Analytics & Insights

Environment

Cross Media Measurement

System


Data Visualization &

Reporting

Co-Marketing Environments

Improve Visibility Over Current Decision Management

Expand Rule Based Decisions Across Channels & Media

Content Management

Tool

Personalization Across Media

Utilize Addressable

Platforms

Expand Use of Display Media

Site Personalization

Change Content Approval Process

Technology Consistency

Across Brands


Technology Ownership

25

Initiative Description Objective

The management and association of identity attributes across all channels and media. The creation of a longitudinal view of the experience a consumer has with Fifth Third Bank (the consumer event stream)

• To enable accurate marketing to a customer/prospect with an intelligent solution to identity management and association of identity attributes across all channels and media.

• Provide the ability to enhance the marketing platform with the organization of customer event stream information in a chronological order of events for an individual, account, and household.

Benefits & Pain Points Remediation Systems Impacted / Replaced

• The ability to scale and address digital event and activity data, as well as anonymous and known prospect universe

• Increase the ROI of marketing programs by accurately identifying customers and prospects and reducing the costs involved with additional data hygiene and duplication

• Marketing Data Mart (MDM)• ECIF• EDW• Foundational Marketing Platform (To be developed)• Webtrends• Akamai \ Video Server

Estimated Costs and Duration – Cash View

• Total of 7 months across multiple phases (for onetime costs)• One Time Cost: $500k• Ongoing Cost: $416K until 2019

Risks, Assumptions, Dependencies

• Data availability and timeliness• Client staffing availability and Skillset• Business rules definitions • Hadoop: Consideration for other data streams that haven’t been included (chat histories, etc.)• Hadoop: Consistent follow on work for Merkle to sustain additional feeds

Identity Management Platform



























Roadmap -Multi-phase Future State Enablement Roadmap

Addressable Messaging and Targeting

Consumer Analytics and Insights

Comprehensive Display Program

Decision Management

Cross-Media Measurement

Technology Alignment, Roles, Responsibilities, and Ownership


Content Approval Refinements

Data Quality

Improve Data Capture and Integration

Identity

Insights

Personalization

Channel Execution

Content Management

Quick Wins3-6 Months

Phase 1: Foundation12 Months (FY ’16)

Phase 2: Activation12 Months (FY ’17)

Phase 3: Expansion12-18 Months (FY ‘18-’19)

Phase 4: Efficiency12-18 Months (FY ‘19-’20)

OngoingFY ’20-on

Operating Model

Foundational Marketing Database

Data Management Platform and Integrations

Data Visualization

Co-Marketing Platform (Wholesale & Affiliates)

Omni-Channel Personalization Capabilities

Site Personalization and Testing






“Getting the technology right will be the hardest part.”

Operationalizing the Changes to Sustain Transformation






Organizational Readiness: Organizational Strategy

» Organization structure to deliver integrated programs

‣ Does your current organization structure allow you to easily collaborate and coordinate across product/business unit silos to deliver a customer- (vs. product-) centric program?

‣ If not, is there a way to accomplish that without a huge “re-org” effort?

Organizational Readiness: Operating Model

» Mapping current state» Identifying gaps, pain points» Defining future state flow» Provide details about each step

including input, outcome, key players, skill set






Organizational Readiness: Roles & Responsibilities

» List of required rolebased on defined process

» Indicate RACI for each of thefunctional roles

» Develop position descriptionfor each role

» Map roles and staffing levelagainst current team






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