UNDERSTAND HOW A CLEAN
CUSTOMER MASTER IS CRUCIAL FOR
ANY ANALYTICAL NEED WHERE
DISPARATE DATA SETS ARE BEING
COMBINED
July 27st 2016
Bharti Rai
Director, Commercial Data Management
Bayer US Pharmaceuticals
The following presentation materials and remarks reflect the
personal thoughts and ideas of the speaker and do not necessarily
reflect the opinions or practices of Bayer Corporation or any of its
affiliates.
PAGE 2
AGENDA
 Evolution of Pharma Commercial Models
 Need for a Clean Customer Master
 Challenges in HCP vs Account Based Customer Master
 3 Scenarios in Reviewing Challenges
 Strategies for Mitigating Challenges
EVOLUTION OF PHARMA COMMERCIAL
MODELS
Value Based Selling
Multi-Channel
Service Portals
Key Account Management
Emerging Models
Legacy Model
PAGE 4
Sales Force Detailing
EMERGING MODELS LEADING TO NEW
CHANNELS
New Channels
•
•
•
•
•
•
PAGE 5
Key account management (KAM)
Clinical sales forces/teams
Patient/physician portals
Social media promotion
Dynamic channel management
Digital tools
NEW CHANNELS & KAM STRATEGIES
LEADING TO ACCOUNT BASED MODELS
Foundation for Account Based Models
Complication –
A Clean Account
Customer Master
Account industry
data is more often
captured at the
most granular level
creating duplicates
if integrated as-is
1. Single clean view of the account
2. Seeding a robust account hierarchy
3. Diligently maintaining rollup of accounts in a Hierarchy
EXAMPLES
SUTTER - ACCOUNTS OVERVIEW IN OAKLAND, CA
7
EXAMPLES
SPECTRUM HEALTH - ACCOUNTS OVERVIEW –
FREMONT, MI
8
• Unique Identifiers
available across varied
Data Sources
• Name is consistently
written (most of the time
!)
Account
HCP
COMPLEXITY IN A CLEAN ACCOUNT VS
PHYSICAN CUSTOMER MASTER
• Industry Identifiers not
consistently followed and
no authoritative standard
(NPI, DEA)
• Multiple Account Names
– Bill To, Ship To, DBA,
Satellite Location, etc
• Individuals may call their
account name differently
every time they call
• Specialty Pharmacies are
not in the MDM Business
SCENARIOS
Explore challenges in
maintaining a clean
customer master for 3
Scenarios
SP Data
Buy &
Bill
Analytical
Data Sets
SP DATA
KEY CHALLENGES FOR MDM
SP Data
Buy &
Build
• SPs are not in data business
• Aggregators used for HIPPA reasons and to enhance SP data for missing
gaps – may also not follow MDM rules
• Sales may be reported at Physician level and needs to be linked or
converted to an Account
PAGE 11
Analytical
Data Sets
EXAMPLES OF BAD DATA
Tagged as relocated in the name
John Doe Relocated Physician
Hospital
Vernon Montoya Acct Relocated Hospital
000 Moved Away
Santa Maria CA
93455
1300 147th Dr
Alachua
FL
00034
Fabricated Physician Named Account
Carlos Munoz Acct
Hospital
Unknown
Lemont
IL
60445
Jeff Jones Acct
Hospital
Restricted
LA
CA
90002
Fabricated Physician Named Account
Delete
Hospital
Colonial Drive
Trenton
NJ
60445
Michael Delete
Hospital
13 Main Street
Nashville
TN
90000
BUY & BILL
Scenario
 Single distributor
DILEMMA
If the data is good enough for shipping
to the customer, why is it not good
enough for MDM?
 Central point of taking orders
 Physician Office calling directly
MDM Principles
Distributor focus is shipping, not
necessarily an accurate and
consistent customer master
 Capture data at the most granular level so it can be rolled up
 Business rules defined to match records – based on name similarity, IDs,
precedence rules
 Survivorship rules defined on which record wins
BUY & BILL
KEY CHALLENGES FOR MDM
• Physician Names listed instead of Account Name
SP Data
Buy &
Build
Analytical
Data Sets
• Multiple Names – Ship To, Bill To, Doing Business As, Rep Calling Location,
Contract Name – and they may change
• Quality of the Data poor from an MDM perspective but ok for Shipping (E.g. –
Attn. in the Customer Name)
• Data is always changing – customer may move, customer acquisitions
PAGE 14
EXAMPLES OF BAD DATA
Bad Name for an Account
Mr Tom Hanes Guest
Hospital
Phoenix
AZ
77568
Attn in Name
Spayment Inc Attn C Fein
Hospital
Unknown
Dayatona
Beach
FL
60445
University of WA Attn R Sand
Hospital
1300 147th Dr
Seattle
WA
98176
Physician Named Accounts could be legitimate or used as a placeholder if the actual
Account Name is not known
ANALYTICAL DATA SETS
KEY CHALLENGES
SP Data
Buy &
Build
E.g. - Building a call plan – Integrating Claims Data, with Hub Data and Call
Data
• Data is never static and changes over time therefore data maintained in
Excel sheets outside mastering process could get stale
• Identifiers may be inconsistent across sources
• Demographic information and Specialty/Account Type may vary across
sources
PAGE 16
Analytical
Data Sets
STRATEGIES FOR MITIGATING CHALLENGES
Newer
Matching
Methodolo
gies
PAGE 17
Automated
customized
checks over
Enterprise
MDM
Manual
intervention/
stewardship
over
automated
MDM
Governance
in creating
and
updating
data
NEWER MDM TECHNOLOGIES
Newer
Matching
Methodolog
ies
Automated
customized
checks over
Enterprise
MDM
Manual
intervention/
stewardship
over
automated
MDM
Business
Rules Driven
Probabilistic
Matching
Machine
Learning
• Defined set of
rules to account
for Bad Data
• Big data based
matching
algorithms
• Learning rules
based on human
behavior/
Stewardship
PAGE 18
Governance
in creating
and
updating
data
AUTOMATED CUSTOMIZED CHECKS OVER
ENTERPRISE MDM
Manual
intervention
Automated /
customized stewardshi
checks over p over
Newer
automated
Matching Enterprise
MDM
Methodol MDM
ogies
• Preserving “Accounts of Interest”
• Custom survivorship rules for a Business Unit
• Preventing merges of certain types of accounts
• “True” new accounts check entering the universe (from SPs,
Hubs, Distributors, SFA etc.)
PAGE 19
Governanc
e in
creating
and
updating
data
ONGOING DATA STEWARDSHIP
• Looking for data anomalies
• Looking for “bad data” phrases
• Responding to Field change requests
• Looking for potential duplicates
PAGE 20
Manual
intervention
Automated /
customized stewardshi
checks over p over
Newer
automated
Matching Enterprise
MDM
Methodol MDM
ogies
Governanc
e in
creating
and
updating
data
GOVERNANCE AND BUSINESS RULES
AROUND CHANGING INFORMATION
Lock Down
Changes Based on
Specific Criteria
Distributor
•
•
•
•
Creates truly new accounts
Creates duplicate new accounts
Changes profile information
Maintains an alternate alias name
•
Sales
Transactional
System
Manual
interventio
Automated n/
customized stewardshi
checks
Newer
p over
Matching over
automated
Methodol Enterprise MDM
MDM
ogies
SFA/CRM
•
•
Field can submit change
requests
Merge requests
New accounts
Customer
Master
(BHOID, Name,
Address)
•
•
Standardizes Names and
Addresses for better
Matching
Administers business rules
on who wins in case of
conflict
MDM
Contracting
SP Data
•
Must Reflect Customer
Master
Lack of integration with
commercial systems
Governan
ce in
creating
and
updating
data
THANK YOU !
Bharti Rai