The document outlines how businesses can effectively use predictive analytics and big data to enhance marketing strategies. It emphasizes principles for implementing big data, including understanding customer needs, starting small, investing in technology, hiring data-oriented personnel, and continuous testing and measurement. The content also discusses common pitfalls and the importance of selecting the right partners to improve performance and decision-making in marketing.

1. © 2015 Mintigo. All Rights Reserved.
www.mintigo.com
How To Be A Data-Driven Marketing Powerhouse
With Predictive Analytics & Big Data
Tony Yang
Webinar Host
Russell Glass
Head of Products
Megan Heuer
VP & Group Director,
Data-Driven Marketing
John Bara
President & CMO

2. © 2015 Mintigo. All Rights Reserved.
www.mintigo.com
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3. © 2015 Mintigo. All Rights Reserved.
www.mintigo.com
Mintigo
Enterprise Predictive Marketing Platform
Our mission is to master data
science to revolutionize the way
people market and sell.

4. © 2015 Mintigo. All Rights Reserved.
www.mintigo.com
Russ Glass!
Head of Products!
@glassruss!
@LinkedIn!

5. The Age of
DATA
5

6. “The number of transistors
on a computer chip will
double approximately
every two years.”
Gordon Moore, founder of Intel
6

7. 7

8. 8
BIG
DATA

9. 9

10. “Big data is the most
disruptive business force
there is. Big data is the stuff
that is really moving
economic power from one
group to another.”
Geoffrey Moore, Crossing the Chasm and Inside the Tornado
10

11. 11

12. 12

13. “Half the money I spend
on advertising is
wasted; the trouble is I
don't know which half.”
John Wanamaker, Pioneering 19th Century Retailer
13

14. Don Draper
IT’S TOASTED
14

15. IBM
360
15

16. THE BIG QUESTION
ABOUT BIG DATA:
How do I implement big
data principles in my
own business?
PRINCIPLE NO. 1
Determine what you know
(and want to know) about
your customer.
16

17. THE BIG QUESTION
ABOUT BIG DATA:
How do I implement big
data principles in my
own business?
17
PRINCIPLE NO. 2
Start small by thinking,
‘Big data, little triggers.’

18. THE BIG QUESTION
ABOUT BIG DATA:
How do I implement big
data principles in my
own business?
18
PRINCIPLE NO. 3
Be prudent but not shy about
investing in technology: CRM
systems are a must, marketing
automation is becoming so, and
analytics tools are a no-brainer.

19. THE BIG QUESTION
ABOUT BIG DATA:
How do I implement big
data principles in my
own business?
19
PRINCIPLE NO. 4
Hire the right people. Marketers
must hire data-oriented people,
math majors, and left-brained
thinkers.

20. THE BIG QUESTION
ABOUT BIG DATA:
How do I implement big
data principles in my
own business?
20
PRINCIPLE NO. 5
Test, test, test, measure,
measure, measure. Ideally,
measure your contribution to
revenue: It is the way to prove
marketing’s value.

21. © 2015 Mintigo. All Rights Reserved.
www.mintigo.com
John Bara!
President & CMO!
@John_Bara!
@Mintigo!

22. © 2015 Mintigo. All Rights Reserved.
www.mintigo.com
22
~80%
of marketing budget is wasted…
This is a scientific fact!
THE MARKETING BLACK HOLE

23. © 2015 Mintigo. All Rights Reserved.
www.mintigo.com
WHY IS THIS?

24. MQL
SAL
66%
SQO
32%
Won
7%
“
T
h
e
C
l
i
f
f
“
There
is
no
leverage
in
today’s
demand
gen
process
2.89
wins
per
1,000
inquiries*
93%
Loss Rate
AN INEFFICIENT PROCESS

25. 60%
to
80%
Data
is
bad
or
has
no
chance
of
closing
40%
to
50%
Wrong
nurture
track
/
inability
to
see
stage
4x
to
10x
Top
line
growth
leU
on
the
table
Process
relies
on
data
shared
by
the
customer
with
liWle
augmentaXon
MARKETING
AUTOMATION
Campaigns
List
buys
Whitepapers
Webinars
Tradeshows
DEMAND
ACTIVITY
MARKETING
FUNNEL
Nurture
Score
Analyze
MQL
SAL
SQL
DEMAND GEN PROCESS

26.
Cost
per
Win
Outstrips
ROI
in
Most
Cases
AN EXPENSIVE PROCESS
2.89
wins
per
1,000
inquiries
@
average
$43
per
inquiry

27. Leverage
vast
amounts
of
data
and
science
to
predict
which
markeXng
acXons
have
a
high
probability
to
succeed
and
which
ones
will
probably
fail.
DEFINITION: PREDICTIVE MARKETING

28. © 2015 Mintigo. All Rights Reserved.
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• Leverage Data Science in every step
of your workflow
• Put Data Science in your hands

29. FIRMOGRAPHIC DATA:
Company name
Domain
Industry
Revenue
CONTACT INFORMATION:
Name
Title
Email
BEHAVIORAL DATA:
Downloaded whitepaper
Data Is The Key – Current Data Points

30. Technologies
• API
Provider
• Saas
product
• Databases:
MySQL
User,
MS
SQL
Server,
Oracle
DB
• Mobile
Developers:
iOS
Developers
• VMWare
User
&
VirtualizaXon
Experts
• Oracle
User
• Cloud
Compu6ng
Tech:
AWS
• Cloud
Compu6ng
Tech:
Azure
• Data
Center
User
Apps
&
Tools
• Email
Service:
MS
Exchange
Online
• MS
Office
365
User
• MS
SharePoint
User
• CollaboraXon
Tools
User:
Jive,
Yammer,
ChaWer
• Atlassian
&
Jira
Users
• Hiring
Enterprise
Content
Mgmt
Expert
Web
Technologies
• DNS:
Neustar,
GoDaddy,
Dyn,
MadeEasy,
Amazon
• CDN
Technology:
Akamai,
Amazon
• CMS
Technologies:
SiteCore,
Joomla,
WordPress,
Drupal
• Web
Analy6cs
Technologies:
WebTrends,
OpXmizely,
CoreMetrics,
Adobe
Omniture,
Website
Technology:
Ad
Services,
Live
Chat
Company
• Growing
Company:
Hiring
>250
Employees
• Has
MulXple
LocaXons
• Company
Employs
Field
WorkForce
• Mobile:
BYOD
IniXaXve
• Mobile:
MDM/MAM
Technology
• Alexa
Ranking
• PPC
Budget
Spend
• Company
has
Call
Center
• Compliance:
SOX,
HIPAA,
FINRA/FISMA
• AdverXsing
Technologies:
Atlas,
Google
Adroll,
Google
Adwords,
DoubleClick
for
AdverXsers
DATA: Mintigo’s Marketing Indicators

31. IDENTIFY THE CUSTOMERDNA™
Ideal
Prospect
150-­‐250
MIs
Customers
Prospects
Fit
score
&
appended
Leads
2,500+
MI’s
10
MM
Companies
150
MM
Contacts
Data
as
is
.
.
.
Enriched
Validated
Appended

32. AUTO POPULATE CUSTOMERDNA™
2,500+
MI’s
10
MM
Companies
150
MM
Contacts

33. • Who are my ideal prospects ? Discover your CustomerDNATM
• How should I communicate to them ? Use Marketing Indicators to create
micro-segmentations
• What should I say to them ? Predict best content to segment fit
• Where do I put my resources & focus ? Create scoring models
Decisions: Making the Right Marketing Decision

34. LEAD PRIORITIZATION
• IdenXfy
leads
most
like
to
convert
• Pass
high
scores
directly
to
sales
• Nurture
B
leads
• Scale
for
capacity
• Leverage
MI’s
to
assign
nurture
Lead
Enrichment
&
PrioriXzaXon
Telesales
ProducXon

35. AUTO LEAD ROUTING / NURTURE ASSIGNMENT
Audiences
Actions
Decisions

36. CASE STUDY: RED HAT

38. Loosen
Status
Quo
Commit
to
Change
Exploring
SoluXons
Commit
to
SoluXon
JusXfy
Decision
Make
Decision
COVERING THE BUYER’S JOURNEY
Discovery Consideration Decision
Fit
Analysis
-­‐
Journey
Relevance
Inside the Funnel Outside the Funnel
Mintigo Data Customer Data
SiriusDecisions, Buyers Journey Model
Intent
Analysis
-­‐
Journey
Relevance
Behavior
Analysis
-­‐
Journey
Relevance

39. PREDICTIVE IS MORE THAN A SCORE
Target
Accounts
Air Traffic
Control
Customer
Lifetime Value
Campaign
Design
Segmentation
Cross
Sell
Lead Enrichment
& Prioritization
Telesales production
Nurture
Design
Customer focused
Contentfocused
ValueFocused
Higher
ASP
Data Validation
& Enrichment
Insights Up
Sell
Customer
Retention
Partner
cDNA
Net New Focused
List
Buys
Incentivized
Content
Syndication
ABC
Real-Time
New
Accounts

40. Marketing Data > Marketing Decisions

41. © 2015 Mintigo. All Rights Reserved.
www.mintigo.com
Megan Heuer!
VP & Group Director!
Data-Driven Marketing!
@megheuer!
@SiriusDecisions!

42. © 2015 SiriusDecisions. All Rights Reserved 42
Getting Started With Predictive Analytics & Big Data
• How can you tell it's time to bring in analytics to improve performance?
• Where are the most valuable places to apply data-driven approaches
right now?
• What are the biggest pitfalls to avoid with introducing data-driven
approaches?
• What is important to consider when choosing an outside partner?

43. © 2015 SiriusDecisions. All Rights Reserved 43
Big Data’s Value Comes Down To Doing The Basics Better
Data-driven marketing
is analyzing and applying what we know to the
choices we make about who and how to engage
and to measuring how much
those actions contribute to growth.

44. © 2015 SiriusDecisions. All Rights Reserved 44
Data-Driven Marketing Delivers On The Ideal Combination of
Math Problem and Personality Test
Marketing must align its efforts to the
accounts and actions most likely to
deliver growth.
Marketing must execute
in a way that respects and engages
individual customers based on their
needs, preferences and timing.

45. © 2015 SiriusDecisions. All Rights Reserved 45
“Data-Driven” Really Means “Customer Driven” at Scale
Our organization and
products
The tactics marketing loves
to use
The accounts and buying
centers sales wants to target
The tactics that should be
used
The target audience within
accounts our business needs to
address
Who or what is most
influential on its decisions
FROM:
TO:

46. © 2015 SiriusDecisions. All Rights Reserved 46
How To Tell When You Can No Longer Live Without
Data-Driven Demand Creation
Inquiry
OutboundInbound
Marketing Qualification
Teleprospecting Qualified
Leads (TQLs)
Teleprospecting Accepted Leads (TALs)
Automation Qualified Leads (AQLs)
Teleprospecting
Generated Leads (TGLs)
Sales Qualification
Sales Accepted
Leads (SALs)
Sales Generated
Leads (SGLs)
Sales Qualified Leads (SQLs)
Close
Won Business
Response Rate Low(er)?
Harder to Get Right Contacts
on the Phone?
Sales Acceptance Lower Than
It Used to Be?
Too Much In “Dreaded Stage 0?”
Takes Longer to Close Deals?
Marketing Not Sourcing
As Much Pipeline?

47. © 2015 SiriusDecisions. All Rights Reserved 47
Identify Areas That Require Insights Then Prioritize Efforts
Based on Impact, Data Access and Skills
2. Personalization 5. Outbound
Outreach
1. Market
Intelligence
Who do I want to
engage, where do
they want to
engage and what
will they care
about?
How do I maximize
conversion to revenue
by only spending time
on leads we can close?
How do I make sure
leads get to the right
person fast?
How can I segment
customers and prospects
using meaningful insights
and behavior triggers?
How do I maximize
engagement at all stages
of buying and post-sale?
What and who do I
need to know in my
target market(s) and
accounts and what
do I have already?
6. Reporting
7. Analytics
How do I prove the
value of marketing?
How do I use past
performance to
improve future
results?
How do I make
smarter predictions?
Seven Data-Intensive Marketing Focus Areas
3. Lead Scoring
4. Lead Routing

48. © 2015 SiriusDecisions. All Rights Reserved 48
Don’t Try This At Home: Pitfalls on The Road To Becoming a
Data-Driven Marketing Powerhouse
Pitfall Group One:
Things We Don’t Have or Don’t Do
Lack of communication
Lack of training/skill development
Lack of outside tools or services
Not tracking results or impact

49. © 2015 SiriusDecisions. All Rights Reserved 49
Don’t Try This At Home: Pitfalls on The Road To Becoming a
Data-Driven Marketing Powerhouse
Pitfall Group Two:
Marketers Who Do Too Much
Taking on too many projects
Making projects too complicated
Attempting full roll-out too soon
Covering up data issues

50. © 2015 SiriusDecisions. All Rights Reserved 50
The Scariest Pitfall Of All Is The Monster We Create Ourselves
Internal politics
and culture

51. © 2015 SiriusDecisions. All Rights Reserved 51
Choosing the Right Partner:
Experience Is The Key Driver of B-to-B Decisions
+
B-to-B Buying Decision Drivers From 2015 SiriusDecisions Study
Question: What was most significant driver of decision to select vendor of choice?
Direct
34% Previous experience with the company
8% Implementation of customer support services were the best
7% Relationship with salesperson
10% Influence of references sourced independently
8% Influence of references provided by vendor
4% Perception of brand with no previous experience
Indirect
71% of b-to-b decision drivers are based on
direct or indirect customer experience
vs. 18% of decision based on promise of offering to meet needs
vs. 9% of decision based on the price was the best
=

52. © 2015 SiriusDecisions. All Rights Reserved 52
Choose Partners Wisely: Three-Part Checklist
2. Technology Fit 3. Experience Fit1. Solution Fit
• Do we have goals for what we
want the solution to deliver?
• Do we know how we’ll measure
whether we achieved those goals?
• Do we have resources on our
team prepared to make the
solution work (i.e., skills,
bandwidth, project management)?
• Will this tool work within existing
infrastructure?
• Will the tool/service address most of
what we need?
• Is it redundant with anything we
already have?
• What integrations are required for it
to be valuable?
• How much of the solution will we be
able to use right away based on our
current state of data and skills?
• What do customers with similar goals
say about their experience?
• Will we get help we need to deploy?
• Will post-sale support and account
management be high quality?
• Can we learn from this vendor?
• Are we comfortable with them in
meetings with our team?
• Would we be comfortable if they had
to present to our boss?
• Would we feel comfortable if one of
their executives met our CEO?

53. © 2015 Mintigo. All Rights Reserved.
www.mintigo.com
Q&A
Tony Yang
Webinar Host
Russell Glass
Head of Products
Megan Heuer
VP & Group Director,
Data-Driven Marketing
John Bara
President & CMO

54. © 2015 Mintigo. All Rights Reserved.
www.mintigo.com
THANK YOU!