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