Key Questions In The Vendor Selection Process
Performance Measurement
Training & Adoption
Systems & Data Integration
System Deployment
Project Management
Resourcing
Alignment & Expectations
Resourcing Considerations
Internal –vs – Vendor – vs – Agency
5.
Unify Data Sources4.
Monitor & Refine
3.
Clean & Segment
2.
Define Data Standards
6.
Append & Validate
1.
Assess Data Quality
MarTech Data Management Methodology
Common Mistakes #1: Data Hoarding
Common Mistakes #2: Boil The Ocean Paralysis
Common Mistakes #3: Wrong Tool for the Job
• Establish a baseline
• Input to the maturity model /
roadmap
• Know areas of weakness
• Know where low-hanging fruits are
• Monitor quality metrics
Start With Data Quality Assessment
Clean, Accurate, & Updated Data != Quality Data
Do you have the RIGHT
leads?
Do you have USEFUL
segmentation?
• Profile based scoring
• Buyer persona
• Account based marketing
• Personalization
• Campaign execution & ROI analysis
Segmentation Starts With Why?
Common B2B Dimensions Custom Dimensions
• Job function / sub-function
• Job level
• Buyer persona
• Industry
• Company size
• Region / territory / metro
• Language
• Number of job openings
• Size of vehicle fleet
• Number of mobile devices
• Number of stores
• Competitive products
• Complimentary products
• Channel
Which Dimensions & How Many Segments?
Common Dimension != Standard Segments
= = Vending
• Difficult to correct false positives
and overlaps
• Even more difficult to identify
false negatives
• Can’t analyze and report on
segmentation results
• Can’t be used by other systems
But I’m Already Doing Segmentation With Filters
The Problem With Filters You Can’t Do Analysis Like This
Add Segmentation As
Part of Your Data!
What You Will Need To Do Segmentation Right
Segmentation Mapping Data
A Data Automation Platform
Lists & Databases
PredictiveSolutions
Help Desk
Marketing Automation CRM
Social, SalesAdvertising
The Big Marketing Data Challenge Is Unification
This even assumes data from the different sources are clean.
CA
vs.
California
Normalize data standards
+1 415-555-1212
vs.
(415) 555-1212
Normalize data format
Toyota Motors U.S.A.
vs.
Toyota Motor Sales USA
Correlate non-exact data values
Normalize segmentation
11-50 employees
vs.
20-100 employees
Unification Challenges
Unification Tips
You don’t have to have a central data warehouse
Leverage the systems you
already have
Avoid a master data schema at all cost
Translate and map what you
need as required
There is no “perfect” or even “best” data vendor
Know which data parts you
want from which vendor(s)
Have a unification strategybefore buying any data
Digest data immediately
after acquisition
Data Automation for Marketing & Sales
Analyze Clean Enhance Unify