Data Quality BootcampMarketo User Group

19-Mar-15

Elliott Lowe

Dir, Marketing Ops

@elliottlowe

Institute for

Integrative Nutrition

Inga Romanoff

President/CEO

@ingaroma

Romanoff Consulting

Page 2

Inga RomanoffPresident, Romanoff Consulting

Elliott LoweDirector, Marketing Operations,Institute for Integrative Nutrition

With over 15 years of marketing experience in the U.S., Russia, and EMEA, Inga is no stranger to Marketing Automation. Inga is a Principal of a boutique marketing automation consultancy. She is passionate about helping clients implement and optimize Marketo, recruit talent, and get exceptional results. She is an award-winning Certified Marketo Expert & a multi-year Marketo Champion, and leads Marketo User Group in New York.

With over 30 years of experience at startups and large public companies, Elliott Lowe specializes in building solid operations foundations for rapidly growing companies. Presently, Elliott heads up Marketing Operations at the Institute for Integrative Nutrition, the world's largest nutrition school. He is a Marketo-Certified Expert, a multi-year Marketo Champion and a co-leader of the Marketo New York User Group.

Page 3

Agenda

• Why Should I Care About Data Quality?

• Where Dirty Data Comes From

• Your 6-Step Program to Clean Data

• Data Quality Saves

• Hands On Tips & Tricks

• Housekeeping

#NYMUG

#DataQuality

#MKTGNATION

Page 4

Why Should I Care About Data Quality?

“25 percent of the average B2B marketer’s database is

inaccurate and 60 percent of companies have an overall

data health of ‘unreliable’.”- SiriusDecisions study

COMPANIES DO NOT HAVE A SOPHISTICATED APPROACH TO DATA QUALITY1

74%

MARKETERS SAY DATA QUALITY IS THE BIGGEST OBSTACLE TO MARKETING AUTOMATION SUCCESS

36%

COMPANIES WITH CENTRAL DATA MGTMT HAD A SIGNIFICANT INCREASE IN PROFITS1

53%

RECORDS ANALYZED WERE LACKING FIRMOGRAPHIC DATA3

88%

1 2015 Experian The data quality benchmark report2 Ascend2 Marketing Automation Benchmark Survey, July 20143 2014 Netprospex Annual Marketing Data Benchmark Report

#DataQuality Facts

Page 5

Data Quality Horror Stories

Page 6

“Quality is never an accident…” ~William A. Foster

#NYMUG #DataQuality

Page 7

Where Dirty Data Comes From

• Systems• Flawed setup

• Poorly designed integrations

• People and Process• Manual input

• Lack of a data quality strategy

51%48%

44%

32%

0%

10%

20%

30%

40%

50%

60%

Most common data errors1

1 2015 Experian The data quality benchmark report

#NYMUG #DataQuality

Page 8

6-Step Program to Clean Data

1. Perform data audit

2. Complete systems audit

3. Revise data capture processes

4. Correct data errors

5. Implement email alerts and reports

6. Manage data quality across the organization

#NYMUG #DataQuality

Page 9

Data Quality Saves

• The wrong way to select leads for an email

• Thousands of leads without email addresses

• Incorrectly configured duplicate preventer

• Fix that caused data errors

• Where are these phone numbers from?

#NYMUG #DataQuality

Hands On Tips & Tricks

Data Capture

Sales Input

CRM Sync

Duplicate Prevention

Data Normalization

Page 11

Data Capture

• The value of the Marketo forms (+ server-side, SOAP, REST)

• Select Fields, Input Masking, Required Fields

• Field pre-population and locking email address change

• Progressive profiling

• Field validation

• List import (field aliases, import template, user access)

• Institute Sales process for data quality (deduplication & append)

#NYMUG #DataQuality

Page 12

Sales Inputs

• Require fields needed for marketing

• Managing duplicate records (Leads, Contacts, “Orphan” Contacts)

• Marketing attribution for Sales-owned leads

• [mktUnknown] created from Outlook MSI plug-in

#NYMUG #DataQuality

Page 13

CRM Sync

• Total Marketo leads vs. Salesforce (SFDC) leads/contacts

• Deleted in SFDC or deleted Email Address

• Sync failures

• Average sync time

• Top syncing fields

• Field visibility, do I need to sync all data?

• Syncing to SFDC campaigns

#NYMUG #DataQuality

Page 14

Duplicate Records

• QC Smart Lists, notifications and weekly reports

• Sales process for duplicate records and alerts

• Deleting records

• Removing duplicates and prioritizing data sources by quality

• Auto-merging based on cookies

• Mass merging with Marketo Easy Merge

#NYMUG #DataQuality

Page 15

Data Normalization

• Using drop downs vs text fields

• Data hygiene by source

• Normalization smart campaigns (State and Country)

• Phone validation

• Capitalization of the First and Last Name in SFDC

#NYMUG #DataQuality

Page 16

Takeaways

AuditClean-

Up

ProcessSustain

Page 17

Housekeeping

• Marketo Summit summit.marketo.com

• LinkedIn Group

• New Community

• Certification Challenge - $75 discount (CODE NEW YORK)

• Will post slides in LinkedIn Group & on SlideShare

#NYMUG

#DataQuality

#MKTGNATION

Thank You!