Too often data myths lead to inefficient processes in CRM, broken systems and/or paralysis analysis. You don’t need to wield the hammer of Thor to make progress with your data management strategy, you just have to separate fact from fable. On this recorded webinar, Donato Diorio (@idonato) and Michael Farrington (@michaelforce), two experts in CRM and marketing automation technology, dispel several common data myths providing tangible and actionable advice to ensure good fortune for all who rely on your data.

1. Dispelling CRM Data Myths
Donato Diorio
Founder & CEO
Broadlook Technologies
www.broadlook.com
#DataMyths
Michael Farrington
Chief Product Officer
RingLead
www.ringlead.com

2. Key trends in CRM
Metrics/
Dashboards
Social
Cloud
Big data & sales
intelligence
#DataMyths
Mobile
Analytics
Collaborative
selling
Empowered
Users

3. The Foundation: Good CRM Data
Without…
Clean
Protect
#DataMyths
Enhance
Enhanc
e

4. The Foundation: Good CRM Data
Without…
Clean
Protect
Without
performing a
comprehensive
data cleanse, the
foundation is weak
#DataMyths
Poor
foundation
Enhance
Enhanc
e

5. The Foundation: Good CRM Data
Without a
protection
Without…
strategy, your data
will continually
decay
Clean
Decaying
data
Protect
#DataMyths
Enhance
Enhanc
e

6. The Foundation: Good CRM Data
Without enhancing
your existing data
you limit your
“data potential”
Without…
Clean
Limited
potential
Protect
#DataMyths
Enhance
Enhanc
e

7. MYTH
It’s ok to delete data
#DataMyths

8. Never delete CRM data - Score it Instead!
Notes:
11/21/13:
06/15/13:
04/10/13:
03/19/11:
Donato is the decision maker for CRM Add ons
Dan went to Kansas State, played football
April is Donato’s assistant
Matt Smith is the new Doctor, Donato is Dr. Who fan
#DataMyths

9. Scoring Data: Focus on Good Data
Notes:
11/21/13:
06/15/13:
04/10/13:
03/19/11:
Donato is the decision maker for CRM Add ons
Dan went to Kansas State, played football
April is Donato’s assistant
Matt Smith is the new Doctor, Donato is Dr. Who fan
#DataMyths

10. What can we learn/derive from the
existing data?
•
•
•
•
•
•
•
•
•
Domain is broadlook.com
Email pattern in first-initial(.)last-name
April reports to Donato
On broadlook.com, there are 15 additional contacts
Notes on Donato are 1 month old
Notes on April are 8 months old
Natalie is no longer at the company
“The Doctor” is a fictional character
Natalie is now a VP at another company - and an
additional prospect!
#DataMyths

11. MYTH
Using the stick works:
make all fields required!
#DataMyths

12. Using the stick works
• Determine carrot and/or stick on
field basis, not object
• Educate users on the importance
of everything you ask of them
(focus on selfish reasons)
• Don’t ask what they don’t know
#DataMyths

13. MYTH
Training works to
enforce data standards
#DataMyths

14. #DataMyths

15. Enforcing Data Standards is Optimal
#DataMyths

16. MYTH
My Data is Fairly
Complete
#DataMyths

17. My Data is Fairly Complete
• Superstition or fact? Find out!
#DataMyths

18. My Data is Fairly Complete
#DataMyths

19. MYTH
Buy as much data as
you can, all at once
(because it’s cheaper)
#DataMyths

20. Data decay happens
• Change in title, promotion
• Change of department
• Change in working location
• Change of area code
• Change of phone number
• Change of email format
• Add mobile phone number
• Merger or acquisition
#DataMyths

21. #DataMyths

22. MYTH
Bad Data is IT’s
Problem
#DataMyths

23. Bad Data is IT’s Problem
• He who reporteth upon it...
• Treat it like a project
• Choose Data Quality applications that
don’t require a PhD in Computer
Physiology
#DataMyths

24. MYTH
The company’s name
is more important than
the website address
#DataMyths

25. Company Based
Changes
Decade
Multi year
Year
Contact based Event & Activity Based
Quarter
Month
Day
Static
Data types
Acquisition
method
URL
Database
merging +
algorithm
Update
strategy
#DataMyths
Hour
Dynamic
Corp Name
City
State
Address
Zip
Phone
Competitors
Editorial &
Aggregation
Revenue
Employees
Products
Services
Financials
Editorial + SEC
spidering
Static, compiled and
online databases
Names
Titles
Emails address
Phone
Biographies
Social Network Links
Real time
content
spidering
News
Email content
Blogs
Net links
social networks
newsgroups
Tweet
Check-In’s
Proximity
Website visits
Email reads
Semantic
monitoring
services
Real time
API’s
Real time

26. MYTH
I know how to search
for duplicates
#DataMyths

27. I know how to search for duplicates
• It gets messy
• Users may not have access
• Even if you do, is that a good use of your
(user’s) time?
#DataMyths

28. MYTH
My vendor’s data is
better than mine
(they are the specialists right?)
#DataMyths

29. Data industry processes
•Buy data from multiple sources
•Refresh top companies with editors
•6 month cycle (top 10K companies)
•6-12 month (next 40K companies)
•24 month cycle on the next 2 million
•Nothing past the top 2 million
•Add social data (good for top 10%)
•Add news feeds (good for top 5%)
•Mob source
#DataMyths

30. Buying data...why, how and gotchas
How recent is the list as whole? How quickly was the list produced?
Different from record freshness. Contact data degrades 3% per month
(5% in a stressed economy). A list of 1000 records can be built over 60
days. In the case below, the first 500 records are 8 weeks old (5.68%
inaccurate) upon list delivery.
96.8%
#DataMyths

31. Buying data...why, how and gotchas
86.5%
#DataMyths
59.5%

32. Your data vs. your vendor’s
• Your data is less complete
• Your data has a better competitive
advantage
• Use their data to fill in your data
#DataMyths

33. MYTH
My data is awesome!
#DataMyths

34. CRM Data Quality
Points
4
3
2
1
Fresh
<30 days
<60 days
<90 days
<180 days
Accurate
95.00%
80% +
70% +
60% +
Multi-venue
All
available
Basic + 2 social
Basic + 1 social
Basic
(email+phone)
Built fast
<14 days
< 60 days
< 90 days
< 180 days
Normalized
Enforced
Plan + culture
Has plan
no
Scored
Custom
rules
Accessible
rules
white box
scoring
black box
scoring
Factors
Total data quality score:
#DataMyths
Your
score

35. CRM Competitive Advantage
Points
4
3
2
1
target by self
description
hand built
keywords
SIC code
built on-demand
mashed from
many sources
pulled from
larger sample
Complete
95%+
80.0%
60.0%
40.0%
Exclusive
no competitors
limited access
anyone can buy
access
free
Sources
transparent
sources known
sources
available
By a person
Marketing
automation
email
Factors
Targeted
Custom
Transparent
Verified
Total competitive advantage score:
#DataMyths
Your
score

36. Where is your CRM data?
Competitive
Advantage
24
12
0
12
Data Quality
#DataMyths
24

37. What is your data potential?
Qualitative /
Event-Driven
Qualitative
Cyclic
Quantitative /
Cyclic
Quantitative/
commodity
Competitive Advantage
Quadrant Key
24
Influence
Relationship
m
o
a
12
ng
ti
ke
ar
ut
a
on
ti
CRM+
90 days
CRM+
180
m
CRM+ days
360
days
Cold Call
0
new
CRM
lead
Warm call
12
Data Quality
#DataMyths
24

38. MYTH
Preventing Duplicate
Records Based on
Email is Sufficient
#DataMyths

39. Preventing Duplicate Records
Based on Email is Sufficient
● No. Not even for sending emails.
● Email addresses are not social security
numbers
● True story: I had four email addresses at one
company
#DataMyths

40. MYTH
My sales team lets me know
what they need
#DataMyths

41. The Evolution of Sales Desire
I want...More data (lists)
I want... Better selection (databases)
I want... More contacts per company (zoom)
I want... Fresher contacts(Jigsaw)
I want... More information (LinkedIn)
I want... More knowledge
(many sources)
I want... More process (crm)
I want... Sustainable process
Data
Information
#DataMyths
Knowledge
Process

42. Questions?
If you have questions or would like to begin a free trial, please contact us!
Join the RingLead Usergroup
+1-888-240-8088
sales@ringlead.com
www.RingLead.com
RingLead.com/LinkedIn
Follow us on Twitter
@ringlead
@iDonato
@michaelforce
Become a Fan on Facebook
RingLead.com/Facebook
Subscribe to our YouTube Channel
RingLead.com/YouTube
#DataMyths