Avoiding Data Quality Pain in Customer Relationship Management (CRM) applications. The Impact on User Adoption...

1. CRMUG SIG
Avoiding Data Quality Pain in CRM
The Impact on User Adoption
Be Certain. Be Trillium Certain.
August 6, 2013

2. Agenda
Introductions & Expectations
Trillium Software Overview
Facts on CRM User Adoption
Impact of Data Quality on User
Adoption
Steps to Avoid the Pain Associated
with Poor Data Quality
Questions & Answers
2

3. About Dave Pietropaolo
With over 25 years experience in the Information
Technology industry, Dave has held numerous
customer and partner-facing positions with Trillium
Software for the past 17 years. He is a member of
Trillium Software’s Executive Council and currently Vice
President of Global Alliances.
Trillium Software
A Division of Harte Hanks, Inc.
300 Concord Road, Suite 200
Billerica, MA 01821
978-671-6008
davep@trilliumsoftware.com
3

4. Who is Trillium Software?
Trillium is a global provider and innovator of data quality solutions
• A business unit of Harte-Hanks (HHS-NYSE) - $1+B revenue & 6000 employees worldwide
• Over 2 decades in business with specific focus on data quality solutions market
• Solution platform (technology + domain expertise) addresses business critical, enterprise data
quality requirements
• Our Mission - “To enable our clients to develop data quality management programs that
support on-going improvements in business performance”
Best in class, proven technology leader…
..with deep data quality expertise
• Gartner
• CRM, CDI & MDM
• 2012 Magic Quadrant: Leader
• Rated as leader for over 10 years
• Forrester
• Forrester Wave – Leader
• Current Analysis: Very threatening to
competition
• Bloor Research (UK):
• Best in Class
• Single Customer view
• Vertical applications
• Business Intelligence

5. Trillium Software Customers
Insurance and Healthcare
Financial Services
Retail and Energy
Manufacturing
Hospitality/Transportation
Technology
Pharma/Life Sciences

6. Trillium/Microsoft Partnership
Recognized leader – Gartner Magic Quadrant & Forrester Wave
Courted by Microsoft
Used by Microsoft
internally
Managed in
“Top 200 ISVs”
Complimentary
Technology
Expertise
Oracle & SAP compete
Best in class solution for enterprise data quality leveraged internally
by Microsoft for over a decade
CRM Orion TAP Beta Participant
“We are pleased to be working with Trillium Software to deliver
strong value to current customers while enabling expansion into
new growth markets.” – Mark Albrecht, Microsoft Dynamics
Director, ISV Strategy
Accurate data increase adoption rate of CRM & other Microsoft
technologies
Proven Enterprise Data Quality technology
30+ Years experience in data management
Integration w/Dynamics CRM Online & On Premise
Over 2,000 implementation around the globe
Silver Application Development Partner

7. Data Quality Defined
Data Quality Basics?
Data can be seen as high quality if it is fit for its intended uses
in operational, decision making/analytical and planning processes
Factors that influence Data Quality?
The number of potential definitions of “the customer” defined by user communities;
departments, channels, business units, regions, etc.
Business rules controlling cleansing and matching logic must be flexible and
powerful
Co-mingled, global data; diverse business standards, languages, character sets,
addressing formats, naming conventions, business rules and expectations
Integration within CRM must provide real-time interactive and high-volume batch
processing
Data Quality should be integrated as a natural part of the CRM user’s experience

8. Industry Research: Just the Facts
Companies implement CRM systems so they can
answer important questions about their customers.
Many companies believe that investing in a CRM solution, in
and of itself, will result in the much-sought-after 360° view of
customer relationships.
However 50% or more of companies with CRM programs are
not satisfied with the customer views they get, citing distrust of
the data among those who are supposed to use them.
They are finding that investing in data integrity, reliability, and
completeness is also critical—both for integrating diverse
data for use in CRM systems and keeping that
valuable information up-to-date.
Source:

9. Industry Research: Just the Facts
At least 25% of most companies’ data is probably inaccurate,
according to industry analysts at Gartner.
If your data was cleansed at integration, you may breathe a
little easier—but not much. Even clean data can degrade.
Consider:
2% of all customer data becomes stale monthly, for an annual rate of 25%.
70% of people change at least one item on their business cards each year.
7-10% of consumers and businesses relocate every year.
Given the data challenges, it’s not surprising that companies
report having as many as 40 - 400 disjointed records for one
corporate customer.
Source:

10. Customer Data Cleansing and Matching
Across Multiple User Communities
Sales Contact
Service Contact
Marketing Lead
John Nicoli
Jack Nicoli
John Nicole
Alpha Imports, Inc.
Alpha Importers Co.
AIC
501 6th Avenue, Suite 700
501 Avenue of the Americas
501 6th Ave.
New York, NY 10071
Manhattan, NY 10071
New York, NY
Direct: 212-345-5000,ext.101
Direct: 212-345-5101
Main: 212-345-5000
Mobile: 917-625-4546
jnicoli@alphaimports.com
jbn55@yahoo.com
jnicoli@alphaimports.com
Service Id# 87005
Service Contact
John (Jack) Nicoli
Alpha Importers, Inc.
501 Avenue of the Americas
Suite 700
Manhattan, NY 10071
Direct: 212-345-5000, ext.2101
Mobile: 917-625-4546
Service Id# 87005
jnicoli@alphaimports.com
jnicoli@yahoo.com

11. Industry Research: Just the Facts
Changing data — An accurate, up-to-date, and integrated record
for each customer is difficult to achieve because the facts themselves keep changing.
Every transaction registers some change. In addition, about 2% of all customer data becomes
stale each month.
Integrated, clean data is what successful companies strive for, yet only 15% of businesses
report full integration among their customer data systems.
Analysts believe poor data quality is a primary reason that 40% of all CRM business initiatives
fail to achieve their targeted benefits.
Source:

12. Why Tackle Data Quality
Along With CRM?
Because Data Quality degrades due to normal,
everyday CRM business processes
Real-time data entry by diverse sets of users
Different departments and channels have different data needs and standards
Integration with other applications and systems, e.g. ERP
Mergers/Acquisition require CRM integration
List imports/external data
Reference data feeds

13. Adoption Defined:
1.
To take and follow by choice or assent:
for example a course of action or a new technique
2.
To take up and make one's own: adopt a new idea
3.
To take on or assume: adopt as important
4.
To vote to accept: agree to a resolution
5.
To choose as standard or required: adopt a new line of thinking or process
to pursue

14. CRMUG Whitepaper
Driving User Adoption, August 2012
Top Five Common Causes of Poor
User Adoption
1. Poor data accuracy and lack of a
management plan
2. The application is not set up to work the way
an organization does
3. Insufficient training
4. Missing executive sponsor
5. Several things can lead to confusion and
frustration when learning new software
Top Five Excuses
1. I don’t trust the data.
2. I don’t know how to use
the software.
3. It takes too much time.
4. It doesn’t follow our
business process or work
the way we work.
5. Management doesn’t use
it, why should I?
http://www.crmug.com/member-resources/crm-online/drivinguseradoption.pdf

15. Data Quality Impacts All Areas of CRM
Need to Involve all User Communities
CRM objectives
Increase revenue per
sales rep
Shorten sales cycle
Increase average
order size
Increase close rate
Increase conversion
rate
Increase revenue
per customer
Increase campaign
response rate
Decrease leadgeneration cost
Decrease customer
acquisition cost
Increase marketing
sourced revenue
Improve targeted
prospecting
Accelerate lead
maturation
Increase customer
retention
Increase customer
service productivity
Reduce customer
service costs
Decrease service
response times
Decrease call waiting
times
Decrease request
solution time
Sales
Business
function
Marketing
Service
Source: Forrester Research

16. Sales
•
Data collection and content management
•
Correspondence processing
•
Opportunity management
•
Pipeline tracking
•
Sales team collaboration
•
Task and activity management
•
Order processing and delivery
•
Proposal and sales history
•
………

17. Sales Operations & Management
• Pipeline Management
• Opportunity inspection & strategy
• Activity analysis and trending
• Revenue forecasting
• Reporting
• Analytics
• Channel Management
• Partner analysis, promotion and growth
• ……….

18. Marketing
• Target marketing and demand creation
• Data acquisition and management
• Lead nurturing and development
• Campaign management
• Multi-channel marketing
• Customer retention and growth
• Reporting & analytics
• Senior management dashboarding
• ……….

19. Services
• Customer care and lifecycle management
• Contact management and correspondence
• Case management and service history
• Escalation
• Collaboration with professional services
• Closed loop w/product management and
development
• Reporting, trending and analytics
• ……….

20. Aberdeen Group Research Study
Leveraging Data Quality Best Practices and Technologies to Improve Sales Effectiveness
Net result: better data created significant efficiencies and time is money, thus best-in-class
had more opportunity to succeed.
Data quality had direct impact on user adoption based on the realized value of being able to
maximize “quality customer facing time”.

21. Aberdeen Group Research Study
continued
The study included strategies for integrating,
standardizing and cleansing customer data
For more details, download the study from our website

22. Industry Research:
Recommendations
1.
Recognize the value of 360°customer view
2.
Define data business rules across lines of business to establish a
360°customer view
3.
Implement customer data integration (CDI) tool to cleanse, de-duplicate,
merge and manage customer data
4.
Employ clean data to integrate sales, marketing and customer service
standards
5.
Increase user adoption rates through confidence in your data
6.
Data Quality Management is not a one-time event, it is a journey
Forrester Research:
Data Management
Fundamentals for CRM

23. What are enterprises doing to
realize value from CRM?
Source: Forrester Research

24. 10 Steps to Ensure
Data Quality Adoption
1. Establish clear objectives you can measure and are
aligned with organizational goals – outline your vision,
e.g. single customer views
2. Recognize that data quality is not just an IT issue,
business involvement and ownership required
3. Really understand the technology and how it will fit
short and long term
4. Senior management sponsor along with project
(and data) champions
Excerpts from: Nigel Turner,
Strategic Consulting Services, Trillium Software

25. 10 Steps to Ensure
Data Quality Adoption
5. Remember, Slow and Steady Wins the Race;
Change must occur - process, culture and behavior…
and don’t forget data will change!!
6. Ensure high quality data for both operational and
analytical purposes… and fit-for-purpose for each
constituent group
7. Training is vital, not only how…by why are we doing this?
8. Establish a governance approach
9. Apply intentional energy to adoption
10.Track and measure ROI
Excerpts from: Nigel Turner,
Strategic Consulting Services, Trillium Software

26. CRM Adoption = Strategic and
Operation Usage
CRM must be core to business process
Every day/hour/minute usage
Must provide significant, recognized value to the
user’s every-day role, activities and
responsibilities
Must be embraced by the user community based
on value, usability and purpose

27. Data Quality Value to CRM
Data Quality Helping Deliver Business Value to
CRM Programs will Foster User Adoption:
•
Deliver a complete understanding of total customer value
and total household value across accounts, products, and
divisions
•
Reduce risks and errors in data consolidation and data
migration for a CRM package through data discovery and
integrated cleansing
•
Resolve customer data anomalies before they enter
operational systems and impact relationship management
decisions
•
Form the basis for accurate analytics, reporting,
forecasting, profiling, scoring, segmenting, and targeting
•
Enforce your definition of customer, prospect, household,
contact, etc. and how that information is to be recorded and
reflected within your organization

28. Factors that Influence Successful
Data Quality Management
Business Rules and Processes – robust, best practice business rules delivered with the
software to leverage for rapid results
Tunable rules with traceable results – adapt to specific business requirements & defend
why results were produced
Deep data quality subject matter knowledge – provide thought leadership,
independence and over 30 years of experience
Context-sensitive processing – superior approach for cleansing and matching data
regardless of data domain
High performance for real-time and high volumes deployments – designed from
inception for complex, operational deployments
Global reach – wide & deep worldwide coverage
Architecture - Enterprise scalability & availability – single, fault-tolerant platform to
support all business processes, applications and platforms

29. Data Quality Management
When & Where
Data Quality Processes Applied to:
CUSTOMER
DATA
1. New CRM Implementations
•
Data Migration and Conversions
•
Initial Cleanse
CUSTOMER
DATA
CUSTOMER
DATA
2. Existing CRM Implementations with Data
Challenges
•
Data Assessments & Analysis
•
Initial Cleanse
•
Integrated within the CRM application
3. Integrated within the CRM Application
•
DQ processes embedded as filters within batch
and real-time CRM processes
Cleanse &
Cleanse &
Migrate
Migrate
Dynamics
CRM

30. Ideal Resource & Activities
Mix for CRM Initiatives
User Adoption has its own program track with major focus and effort to drive
value, usage and adoption
Adoption is derived from strategy definition, objective setting and defining of
new processes

31. Data Quality Management and CRM
In Summary:
• CRM is highly data centric and requires data quality management to
fully realize program potential
• Clear objectives must be established in order to set expectations,
manage change and measure success
• Collectively CRM & data quality must be leveraged at all levels
• Natural Progression =
Data Accuracy and understanding
Trust & Confidence
Derived Business Value
User Adoption!!

32. Recommended Reading
CRMUG Whitepaper, Driving User Adoption
Gartner, The Eight Building Blocks of CRM: Data and Information
Aberdeen Research, Leveraging CRM in the Pursuit of the Elusive “Single
View of the Customer”
Forrester Research; Best Practices: Getting The Most From Your CRM
Deployment

33. Questions?