mdm - the key to successful customer experience managment

Copyright © 2015 Earley Information Science1

MDM - The Key to Successful Customer Experience Management

Copyright © 2015 Earley Information Science

Tim BarnesDave ZwickerEarley Information Science

Foundations for Successful Digital Transformation

Click to view a recording of this webinar

http://info.earley.com/mdm-key-successful-customer-experience-management


Today’s Agenda

• Welcome & Housekeeping– Session duration & questions– Session recording & materials– Take the survey!

• Introduction– Dave Zwicker, CMO (@davezwicker)

Earley Information Science

• MDM – The Key to Successful Customer Experience Management

– Tim Barnes, Director, Professional Services, Earley Information Science

• https://www.linkedin.com/in/timothybarnes

• Questions & Answers


Requirements for a trusted 360-degree view of the customer to

enhance the customer experience (CX) are forcing information

leaders to initiate or expand master data management (MDM)

programs at an increasingly rapid pace.

[Gartner]

MDM and the Customer Experience…


Customer Experience – Why Companies CareBetter customer insights lead to better business outcomes:

• Sustained growth in customer acquisition

• Increases in revenue per customer

• Decreases customer acquisition cost

• Reductions in customer churn

• Enhancements to product offerings

Research findings from Gartner:

• 89% of companies will compete based on customer experience by 2016

• 65% have the equivalent of a chief customer officer (office of the CCO)

• 18% of marketing budgets in 2014 were spent on customer experience

• Customer experience is the top innovation project for 2015


Personalizedpromotions

Seamless multi-channel transactions

Streamlinedcustomer service

BUSINESS OUTCOMES

Product & serviceinnovation

BUSINESS OUTCOMES

Increasedcustomer value

Optimized pricing,availability & delivery

Contextualizedcross-sell/upsell

Higher Conversions

Improvedloyalty & retention

Reduced acquisition cost

Personaldata

Big Datasources

DATA SOURCES DATA SOURCES

Market data

Product data (PIM)

Purchasehistory

Customer data (CRM)

Operationaldata (ERP)

Clickstreamdata

Service history

Data warehouse

360° View of the Customer Experience

Customer Lifecycle by Forrester

VOC & loyalty programs

Onlinesupport

Social Networks

Site search& navigation

Mobilecommerce

EmailPromotions

TOUCHPOINTS

Internet search

Advertising

Online/in-storemerchandising

Warranty & registration

Call center agents


By 2020, 75% of those organizations that neglect

MDM and EIM while creating a 360-degree view of

their customers to support the CX will adversely

affect CX metrics via the use of inaccurate data

during customer interactions.

[Gartner]

MDM and the Customer Experience…

Copyright © 2015 Earley Information Science7Copyright © 2015 Earley Information Science

Overcoming the Challenges


Tim Barnes

• Over 25 years experience in consulting, corporate IT and corporate Finance.

• Consulted for Fortune 500 clients in the areas of strategy, working capital management and MDM.

• Managed several large, complex MDM implementations

Director, Professional Services

Earley Information Science

SPECIALTIES• Master Data Management• Business Intelligence• Customer Data Integration• Working Capital Management

INDUSTRIES• Insurance• Business Services• Telecommunications• Travel


• Overcoming the Process Challenges– Business alignment & process enablement

– Dataflow and workflow for master data

MDM Challenges: Two Sides of the Same Coin

Process

Data

• Overcoming the Data Challenges– Data integration

– Data quality

– Data governance


The Data Integration Challenge

Order Management ERP CRM Sales

Automation eCommerce

Data Data Data Data Data

Customer

Location

Contract

CustomerInteractions

Contacts

Account Customer

Personas

ProductProduct Contact

Info

Customer

Orders

ProductLocation

Customer

ProspectContacts


The Data Quality Challenge

Data compiled by Talend


The Data Governance Challenge

• Set up your governance framework

• Start small and build up capabilities

• MDM areas of focus– Data Architecture– Data Quality Management– Match/Merge Data Stewardship


Bridging the divide between IT practitioners and business stakeholders

The Business Process Challenge

IT cares about:• Data quality (de-duping)• Standardizing/centralizing data• Data governance and compliance• Data integration/synchronization• Meeting operational SLAs

Business cares about:• Revenue value of a customer• Campaign response rates• Cross-channel customer experiences• Customer support success• Customer loyalty and retention


• Identify producers, consumers and owners

• Map the data workflow to identify transformations and process gaps

• Determine how the data is used

The Data Flow and Workflow ChallengeBusiness Intelligence

Operational Integration

Master Data

Read/Write Application



Read-Only Application



MDMAdministration

MDMGovernance


Implementing MDM - A Systematic Approach


• Do you need an Operational or Analytical Customer Hub? Or both?• Have you identified the producers, consumers and owners of the data

and how the users will access it?• Have you identified the data sources?• Have you determined which data sources should be mastered?• Have you identified the Hierarchies, Relationships and Groupings that

need to be captured?• Have you created an implementation plan that will bring business

benefit quickly?• Do you have a framework for the data governance that’s required to

maintain a customer hub?

Have you answered these questions?


Total Organizational Project Governance

Program objective & success criteriaBusiness case and charterAssess current stateIdentify data stakeholders (Producers, Consumers and Owners)Develop implementation plan

Understand current maturity state of data qualityDefine future state and roadmapBuild use casesSoftware selectionIdentify source systems

MDM functional requirementsIdentify data elementsIdentify match criteriaPerform data assessmentDetermine data cleansing and standardization rulesIdentify hierarchies, relationships and groupings

BI functional requirementsIdentify consuming systemsIdentify and prioritize customer 360 componentsReporting & analytical requirementsLogical data model

Analysis & DesignHigh level designDetailed design

Data source extractETLMDM hub configurationPublish & integrationPhysical data model

Development and unit testing

Match tuning

TestingIntegrationSystem (QA)Performance

Knowledge transferSystem support

Prioritized rolloutStaggered implementation

Release management and deployment

Assess Define Requirements Design & Build Deploy

Program Manager

Information Architect and Business Analyst

Development Architects & Team (ETL, MDM, Integration, BI)

Test Lead and Testers

Roles

Phases

Business Stakeholders

Provide strategic direction Provide functional requirements

Assist w/ match tuningPerform user acceptance testing


Staged Implementation

Design & Build – Stage 1

MDMSource Systems• Order Mgmt• ERP

BI360 Components• Demographic• Financial

Deploy

IntegrationOperational Systems• eCommerce• ERP

Assess Define Requirements

Business ObjectiveOperational HubAnalytical Hub

Source System PrioritizationGood data qualityHigh utilizationClear ownershipEasily integratedMost trusted

BI PrioritizationBiggest business benefitLow complexityHigh utilization

Design & Build – Stage 2

MDMSource Systems• CRM• Sales Automation

BI360 Components• Product Usage• VOTC

Deploy

IntegrationOperational Systems• CRM• Sales Automation

MDMSource Systems• eCommerce

BI360 Components• Third Party• Predictive

IntegrationOperational Systems• Order Mgmt

Design & Build – Stage 3 Deploy

The determination of the business objective and prioritization of source systems will provide guidance for a staged implementation to realize business value early and often


Summary of MDM Best Practices


Summary of MDM Best Practices

• Establish the business value that an MDM initiative will enable

• Keep the focus on the data and how the quality impacts match and merge processes

• Create a ‘data governance’ track concurrent to the MDM road map

• Focus on the day-to-day business scenarios, not the exceptions

• Keep the MDM data lightweight

• Keep data transformations simple

• Don’t underestimate the time and resources needed for the match tuning process

• Emphasize finalizing and creating the customer logical data model

• Understand the source system(s)


Customer Examples


Large Insurance Company | MDM Implementationin

su

ra

nc

e

Business Challenge

• Policy centric legacy systems resulted in customer information across policies.

• Customer information unreliable – no mechanism to identify customer’s across policies.

• Underwriting was manually creating a 360 degree view of the customer

Solution

• Match and merge customer information from three legacy source systems to create customer master records.

• Create households as well as party-to-party relationships.

• Build a custom user interface to expose data from the customer hub and integrate the master data to the legacy policy administration systems.

• Integrate the customer master data with marketing and actuarial data

Outcome

• Underwriters able to reduce underwriting time drastically with the ability to view a customer, their policies, their household and relationships to other customers.

• Marketing has a clear view of the customer and is better able to segment customers


Large B2B Service Company | Customer 360 ImplementationB

2B

Business Challenge

• Five million small, mid and large customer records with duplication due to acquisitions and the legacy system setup of one customer per service sold.

• Multiple invoices were sent and Accounts Receivable phone calls made to the same customer

• Customers viewed the company as separate business units selling different products.

• Multiple business units were selling to the same customer in different locations.

Solution

• Implement a customer hub by implementing MDM software and matching and merging customer data

• Add third party data sources to the customer hub (D&B and InfoUSA) to obtain the corporate hierarchy of the client.

• Build data marts for revenue, survey, billing, product and prospects

• Layer a BI tool on top of the data marts to provide a 360 degree view of the customer

Outcome

• A new tool was rolled out to sales, service, marketing and executives to provide a 360 degree view of the customer

• Sales was able to visualize where in the customer’s hierarchy they were selling enabling upsell opportunities

• Enabled predictive analytics for next most likely purchase by preparing better quality data

• Provided sales executives with a “cheat sheet” of a customer prior to a meeting


Large B2B and B2C Car Rental | Customer 360 ImplementationB

2C

/B2

B C

ar

Re

nt

al

Business Challenge

• 50 million B2B and B2C customers, with multiple brands sharing customers

• No method to link customer data across brands creating duplicate customer data.

• No way to market across brands or personalize their experience online

Solution

• Integrate customer data from the legacy customer system, Salesforce.com and the data warehouse creating a customer hub.

• Provide a mechanism for the website to consume the customer data using web services.

• Build a user interface for customer service and executives.

• Enable online marketing.

Outcome

• Marketing was able to personalize the customer’s online experience by offering targeted ads based on their purchases across brands.

• Internal users were able to quickly and easily view details of a customer’s buying habits.

• Customer segments were created based on the customer’s buying patterns


Your Question and Answers


Earley Information Science helps organizations establish a strong

information architecture and content management foundation

Realize your digital transformation vision with EIS.

Earley Information Science (EIS)Information Architects for the Digital Age

Founded – 1994

Headquarters – Boston, MA

www.earley.com

For more info contact:

[email protected]

http://www.earley.com/

mdm - the key to successful customer experience managment

Data & Analytics