To watch the entire webinar replay, please visit: http://www.mintigo.com/demystifying-predictive-lead-scoring/ Description: As B2B marketers, we know that lead scoring is a valuable method to identify the leads that are most valuable to our organization. Yet, most of us do not know whether, or to what extent, lead scores correspond to lead conversion through the demand waterfall or sales funnel. This is because most of the lead scoring models that we set up in our marketing automation platforms are based on gut instinct, intuition, and guesswork. Enter predictive lead scoring. You’ve heard it mentioned amongst your peers, talked about at marketing conferences, and perhaps even read some blog posts about it. But what exactly is predictive lead scoring, how does it differ from “traditional” lead scoring, and why should you care? We’ve invited the experts at SiriusDecisions to join us in this webinar as we unravel the mystery behind predictive lead scoring. In this webinar, you will learn: - What predictive (anything) really means - Why current lead scoring models may not be enough - How statistics enable insight and prediction - How predictive lead scoring improves lead quality About The Guest Speaker: Kerry Cunningham, Research Director at SiriusDecisions As vice president of operations for a leading b-to-b teleservices organization for more than 15 years, Kerry has been a thought leader in the design and implementation of inside sales, tele-prospecting, telemarketing and processes and teams for a wide array of b-to-b products, solutions and services. From more than a decade spent straddling the fence between marketing and sales, Kerry has also developed a wealth of experience expertise in the alignment of marketing and sales organizations. Along the way, Kerry has developed implemented lead management processes for many of the world’s most prominent b-to-b brands, amassing substantial real-world expertise in lead acquisition, governance and propensity modeling.

1. © 2014 Mintigo. All Rights Reserved.
www.mintigo.com
Demystifying Predictive
Lead Scoring
Host:
Tony Yang
Director of Demand Gen
Mintigo
@tones810
Guest Presenter:
Kerry Cunningham
Research Director
SiriusDecisions

2. © 2014 Mintigo. All Rights Reserved.
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@SiriusDecisions
#PredictiveLeadScoring
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4. © 2014 Mintigo. All Rights Reserved.
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Mintigo
Enterprise Predictive Marketing

5. © 2014 Mintigo. All Rights Reserved.
www.mintigo.com
Our Guest Presenter Today
Kerry Cunningham
Research Director at SiriusDecisions
• More than 20 years in B2B
• Expertise in inside sales, telemarketing &
marketing-sales alignment
• BA, Indiana University &
MS, San Francisco State University

6. Propensity Modeling
6
Kerry Cunningham
Demand Creation Services

7. © 2014 SiriusDecisions. All Rights Reserved 7
SiriusDecisions, Kerry Cunningham
• Research Director, SiriusDecisions
• Lead Development & Management
• 15+ years in b-to-b demand generation and lead
management
• VP Operations for b-to-b teleservices organization
• Research methods and analytics
• 5 years social science research
• Organizational behavior
• Employee selection science
• Propensity modeling people
• Behavioral economics
• Personality correlates of well-being, Book Chapter,
Summer 2014

8. © 2014 SiriusDecisions. All Rights Reserved 88
Peering behind the curtain…

9. What We’ll
Cover
Demystifying
Predictive…
• Where lead scoring has come from and is going
• What predictive (anything) really means
• 4 key considerations for planning a predictive lead
scoring program
• 4 factors for making good predictions
© 2013 SiriusDecisions. All Rights Reserved 9

10. © 2014 SiriusDecisions. All Rights Reserved 10
Predictive Lead Scoring Assumptions

11. © 2014 SiriusDecisions. All Rights Reserved 11
Predictive Lead Scoring Assumptions
• Not all leads convert

12. © 2014 SiriusDecisions. All Rights Reserved 12
Predictive Lead Scoring Assumptions
• Lead Scoring is an attempt to predict which will
• Not all leads convert

13. © 2014 SiriusDecisions. All Rights Reserved 13
Predictive Lead Scoring Assumptions
• Lead Scoring is an attempt to predict which will
• Somewhere in the world are clues as to which leads are most likely to
convert (big data)
• Not all leads convert

14. © 2014 SiriusDecisions. All Rights Reserved 14
Predictive Lead Scoring Assumptions
• Lead Scoring is an attempt to predict which will
• Somewhere in the world are clues as to which leads are most likely to
convert (big data)
• Theoretically, it is possible to know and account for all of those
clues
• Not all leads convert

15. © 2014 SiriusDecisions. All Rights Reserved 15
Predictive Lead Scoring Assumptions
• Lead Scoring is an attempt to predict which will
• Somewhere in the world are clues as to which leads are most likely to
convert (big data)
• Theoretically, it is possible to know and account for all of those
clues
• Practically, it is not possible to account for 100% of the
clues
• Not all leads convert

16. © 2014 SiriusDecisions. All Rights Reserved 16
Predictive Lead Scoring Assumptions
• Lead Scoring is an attempt to predict which will
• Somewhere in the world are clues as to which leads are most likely to
convert (big data)
• Theoretically, it is possible to know and account for all of those
clues
• Practically, it is not possible to account for 100% of the
clues
• The more we can account for, the better we can
predict whether any given lead will convert
• Not all leads convert

17. © 2014 SiriusDecisions. All Rights Reserved 17
Predictive Lead Scoring Assumptions
• Lead Scoring is an attempt to predict which will
• Somewhere in the world are clues as to which leads are most likely to
convert (big data)
• Theoretically, it is possible to know and account for all of those
clues
• Practically, it is not possible to account for 100% of the
clues
• The more we can account for, the better we can
predict whether any given lead will convert
• Current lead scoring probably doesn’t account
for as much as we might hope
• Not all leads convert

18. Traditional Lead Scoring
What’s the problem?

19. © 2014 SiriusDecisions. All Rights Reserved 19
The Problem With Current Lead Scoring
Implicit Explicit
Current lead scoring fosters this view of the world…

20. © 2014 SiriusDecisions. All Rights Reserved 20
The Problem With Current Lead Scoring
Implicit Explicit
Behavior
- Hiring
- Expansion
- New products
- Social media
- Communities
Fit
- C-level attitudes
- Tech Ecosystem
- Financial Health
- Competition
- Positioning
When
reality
looks a lot
more like
this…

21. © 2014 SiriusDecisions. All Rights Reserved 21
What We Are Trying To Do When We Predict
To better understand that,
just look a little further down
the waterfall from where
current lead scoring occurs

22. © 2014 SiriusDecisions. All Rights Reserved 22
The Best-in-Class B-to-B Scenario
For most, there’s a substantial
drop-off between TQL/TGL and
SQL qualification…
Conversion %
AQL > TQL 66.6%

23. © 2014 SiriusDecisions. All Rights Reserved 23
The Best-in-Class B-to-B Scenario
For most, there’s a substantial
drop-off between TQL/TGL and
SQL qualification…
Conversion %
AQL > TQL 66.6%
TQL > SQL 48.8%

24. © 2014 SiriusDecisions. All Rights Reserved 24
The Best-in-Class B-to-B Scenario
For most, there’s a substantial
drop-off between TQL/TGL and
SQL qualification…
Conversion %
AQL > TQL 66.6%
TQL > SQL 48.8%
Conversion from AQL to SQL =
32.6%

25. © 2014 SiriusDecisions. All Rights Reserved 25
Downstream People and Processes
Today, most of that
qualification
involves
teleprospecting and
sales calls

26. © 2014 SiriusDecisions. All Rights Reserved 26
Downstream People and Processes
§ Call decision makers
§ Ask key qualifying
questions

27. © 2014 SiriusDecisions. All Rights Reserved 27
Downstream People and Processes
• Expensive
• Slow
• Limited to stock on
hand
• Very high propensity

28. © 2014 SiriusDecisions. All Rights Reserved 28
The Future of B-toB Lead Development
Find clues that exist out in
the world, which reliably
point to qualifying criteria
you would ask the decision-
maker if you could get him/
her on the phone?
The Role of Data Science

29. SiriusPerspective:
© 2014 SiriusDecisions. All Rights Reserved 29
The Promise of Big Data/ Predictive Lead Scoring
Cheap/ Fast
Shallow Qualification
Deep Qualification
Slow/
Expensive
Before
1. List purchase and
selection
unsophisticated
The promise of predictive technology is to perform deep
qualification of accounts and opportunities through data science and automation.

30. SiriusPerspective:
© 2014 SiriusDecisions. All Rights Reserved 30
The Promise of Big Data/ Predictive Lead Scoring
Cheap/ Fast
Shallow Qualification
Deep Qualification
Slow/
Expensive
Before
1. List purchase and
selection much more
sophisticated
2. Technology does
more deep
qualification
With MAP
The promise of predictive technology is to perform deep
qualification of accounts and opportunities through data science and automation.

31. SiriusPerspective:
© 2014 SiriusDecisions. All Rights Reserved 31
The Promise of Big Data/ Predictive Lead Scoring
Cheap/ Fast
Shallow Qualification
Deep Qualification
Slow/
Expensive
Before
3. And sales
becomes more
efficient
1. List purchase and
selection much more
sophisticated
2. Technology does
more deep
qualification
With MAPPredictive
The promise of predictive technology is to perform deep
qualification of accounts and opportunities through data science and automation.

32. The Nature of Predictions
Correlation and Regression Without Math

33. © 2014 SiriusDecisions. All Rights Reserved 33
The Nature of Prediction
Propensity to…
Likelihood of…
Predict-o-meter
Inquiries
Sales Qualified Leads

34. © 2014 SiriusDecisions. All Rights Reserved 34
The Nature of Prediction
Propensity to…
Likelihood of…
Predict-o-meter
Inquiries
Sales Qualified
Automation Qualified (AQLs)

35. © 2014 SiriusDecisions. All Rights Reserved 35
The Nature of Prediction
Propensity to…
Likelihood of…
Predict-o-meter
Factors Not Accounted For

36. © 2014 SiriusDecisions. All Rights Reserved 36
The Nature of Prediction
Propensity to…
Likelihood of…
Predict-o-meter
Factors Not Accounted For
Other scorable factors

37. © 2014 SiriusDecisions. All Rights Reserved 37
Making Predictions: Correlation and Regression
Visual regression model: Predicting size from clothes
Knowing a person’s
sport coat size, can
you predict the size
of the person?

38. © 2014 SiriusDecisions. All Rights Reserved 38
Making Predictions: Correlation and Regression
Visual regression model: Predicting size from clothes
Does this new
data improve
your prediction?

39. © 2014 SiriusDecisions. All Rights Reserved 39
Making Predictions: Correlation and Regression
Jacket and shoe
size are
correlated…
knowing one helps
predict the other
Visual regression model: Predicting size from clothes

40. © 2014 SiriusDecisions. All Rights Reserved 40
Making Predictions: Correlation and Regression
Visual regression model: Predicting size from clothes
+

41. © 2014 SiriusDecisions. All Rights Reserved 41
Improving predictions – Regression Modeling
Factor 1 Factor 3
Visual Regression Model
Factor 2

42. © 2014 SiriusDecisions. All Rights Reserved 42
Improving predictions – Regression Modeling
Factor 1 Factor 3
Visual Regression Model
Factor 2

43. © 2014 SiriusDecisions. All Rights Reserved 43
“Error” in Predictions/ Noise In the Data

44. © 2014 SiriusDecisions. All Rights Reserved 44
“Error” in Predictions/ Noise In the Data
Suit too smallSuit too big

45. © 2014 SiriusDecisions. All Rights Reserved 45
Predictions
Buildings Built Employees
Visual Regression Model: Predicting Construction Management Deal Size
Const.
Workers
+ +
=
Predict-o-meter
Guessing
Perfect
Prediction

46. © 2014 SiriusDecisions. All Rights Reserved 46
Predictions
Buildings Built
Employees
Visual Regression Model: Predicting Construction Management Deal Size
Const. Workers
+ +
=
Predict-o-meter
Guessing
Perfect
Prediction

47. © 2014 SiriusDecisions. All Rights Reserved 47
Predictions
=
Recent HiresSeries C HR Leader
+ +
Visual Regression Model: Predicting HR Mgt SaaS Sales
Predict-o-meter
Guessing
Perfect
Prediction

48. © 2014 SiriusDecisions. All Rights Reserved 48
Predictions
=
Recent Hires
Series C
HR Leader
+ +
Visual Regression Model: Predicting HR Mgt SaaS Sales
Predict-o-meter
Guessing
Perfect
Prediction

49. © 2014 SiriusDecisions. All Rights Reserved 49
Predictions
?

50. © 2014 SiriusDecisions. All Rights Reserved 50
Predictions
?

51. © 2014 SiriusDecisions. All Rights Reserved 51
Predictions
In reality, there are often numerous predictors that
go into a predictive model
+
=
+ + + +
Predict-o-meter
Guessing
Perfect
Prediction

52. © 2014 SiriusDecisions. All Rights Reserved 52
Predictions
In reality, there are often numerous predictors that
go into a predictive model
=
1.15 * 1.05 * 1.2 * 3.3 * 12.75 * 1.75 * % Lift++ + + +
Predict-o-meter
Guessing
Perfect
Prediction

53. Predictive Lead Scoring Considerations
53

54. © 2014 SiriusDecisions. All Rights Reserved 54
Building A Model
Use Case Starting Point
Entity
Predicted
Source of
Predictors
Model

55. SiriusPerspective:
© 2014 SiriusDecisions. All Rights Reserved 55
Use Cases
Find new
businesses that
have a high
propensity to buy
from me
Among many use cases for predictive lead scoring, finding new leads,
scoring known leads, and scoring existing customers are the top three.

56. SiriusPerspective:
© 2014 SiriusDecisions. All Rights Reserved 56
Use Cases
Find new
businesses that
have a high
propensity to buy
from me
Score and prioritize
businesses already in my
database on their
propensity to buy from me
Among many use cases for predictive lead scoring, finding new leads,
scoring known leads, and scoring existing customers are the top three.

57. SiriusPerspective:
© 2014 SiriusDecisions. All Rights Reserved 57
Use Cases
Find new
businesses that
have a high
propensity to buy
from me
Score and prioritize
businesses already in my
database on their
propensity to buy from me
Score and prioritize
existing customers for their
propensity to buy other
products and services we
sell
Among many use cases for predictive lead scoring, finding new leads,
scoring known leads, and scoring existing customers are the top three.

58. © 2014 SiriusDecisions. All Rights Reserved 58
Starting Point
Historical Data
Became
Customers
Didn’t
Become
Customers
Prospects that:
• bought or not
• convert or not
• respond or not
Data that clearly
distinguishes the two
groups

59. © 2014 SiriusDecisions. All Rights Reserved 59
Starting Point
No Historical Data
Fit the
profile
Don’t fit
Prospects that:
• Have a business
problem
• the motivation and
resources to solve it
Data that clearly
distinguishes the two
groups

60. SiriusPerspective:
© 2014 SiriusDecisions. All Rights Reserved 60
Entity Predicted
Need to find best
contacts within
target accounts?
Predictive lead scoring can reach much deeper into a contact’s world to
determine who is most likely to be involved in a buying cycle.
Job Role
Common Titles
Company

61. SiriusPerspective:
© 2014 SiriusDecisions. All Rights Reserved 61
Entity Predicted
Need to find best
contacts within
target accounts?
Predictive lead scoring can reach much deeper into a contact’s world to
determine who is most likely to be involved in a buying cycle.
Company Hiring
Tech Ecosystem
Prof. Communities
Job Role
Common Titles
Content Engagement
Social Media Interaction
MAP
PLS

62. SiriusPerspective:
© 2014 SiriusDecisions. All Rights Reserved 62
Entity Predicted
Modern data science can also reach deeply into online digital artifacts to
unearth evidence of business problems and buying initiatives.
Need to identify best
company targets within large
addressable universe?

63. SiriusPerspective:
© 2014 SiriusDecisions. All Rights Reserved 63
Entity Predicted
Modern data science can also reach deeply into online digital artifacts to
unearth evidence of business problems and buying initiatives.
• Corporate websites
• Press releases
• Job postings
• Application signatures

64. SiriusPerspective:
© 2014 SiriusDecisions. All Rights Reserved 64
Source of Predictors
What is likely to be most predictive may be at the contact or the account
level, and gleaning information from both is normally important.
Top
down
Bottom
up
Some PLS providers collect and
analyze data on contacts in order to
predict what businesses are doing
Some providers focus primarily
on business level indicators to
determine where the
opportunities are

65. SiriusPerspective:
© 2014 SiriusDecisions. All Rights Reserved 65
Source of Predictors
What is likely to be most predictive may be at the contact or the account
level, and gleaning information from both is normally important.
Top
down
Bottom
up
The best models
typically include both
prospect and account
level predictors

66. On Predictive…
4 Important things to know to make good predictions

67. © 2014 SiriusDecisions. All Rights Reserved 67
Conditions For Good Predictions
Past behavior >> Future performance!

68. © 2014 SiriusDecisions. All Rights Reserved 68
Conditions For Good Predictions
Past behavior >> Future performance!
High-frequency, habitual
situations and people are
more predictable than rare
ones

69. © 2014 SiriusDecisions. All Rights Reserved 69
Conditions For Good Predictions
Past behavior >> Future performance!
High-frequency, habitual
situations and people are
more predictable than rare
ones
Larger data sets
enable more
reliable predictions
Stories are
dangerous!

70. © 2014 SiriusDecisions. All Rights Reserved 70
Conditions For Good Predictions
Past behavior >> Future performance!
Larger data sets
enable more
reliable predictions
Stories are
dangerous!
Predictions work best over
short time intervals
Tomorrow’s prediction is more
accurate than the one for next
week
High-frequency, habitual
situations and people are
more predictable than rare
ones

71. © 2014 SiriusDecisions. All Rights Reserved 71
Conditions For Good Predictions
Past behavior >> Future performance!
The anticipated
situation must be
essentially the
same as the past
situation
Larger data sets
enable more
reliable predictions
Stories are
dangerous!
Predictions work best over
short time intervals
Tomorrow’s prediction is more
accurate than the one for next
week
High-frequency, habitual
situations and people are
more predictable than rare
ones

72. © 2014 SiriusDecisions. All Rights Reserved 72
Making Predictions Count
Insights
“Something I don’t know.”
Does the model prioritize
or find prospects based on
criteria a sales rep cannot
readily acquire him- or
herself?

73. © 2014 SiriusDecisions. All Rights Reserved 73
Making Predictions Count
Insights
“Something I don’t know.”
Does the model prioritize
or find prospects based on
criteria a sales rep cannot
readily acquire him- or
herself?
Compared to selecting
prospects based on current
methods, what improved
conversion (lift) does the
model provide?
W.A.R.
Wins above replacement player

74. © 2014 SiriusDecisions. All Rights Reserved 74
Making Predictions Count
Insights
“Something I don’t know.”
Does the model prioritize
or find prospects based on
criteria a sales rep cannot
readily acquire him- or
herself?
The promise of big data is
finding important clues about
which prospects will buy.
The danger is that many
variables are related but make
little difference to that prediction
Broad v Big Data
Compared to selecting
prospects based on current
methods, what improved
conversion (lift) does the
model provide?
W.A.R.
Wins above replacement player

75. © 2014 SiriusDecisions. All Rights Reserved 75
Terminology
“Big” Data?
Big in what way?
Big Data - Buzz word. Doesn’t mean anything in particular or officially.
When “big” = Volume: many measures, records, repetitions, etc.
When “big” = Breadth: lots of new and interesting things measured

76. © 2014 SiriusDecisions. All Rights Reserved 76
Terminology
Machine Learning
Machine Learning- Buzz word.
Many propensity modelers and predictive lead scoring vendors use the term
In general, it refers to the automation of the process of incorporating feedback
loops within analytic algorithms.
It does not refer to something special about the statistical procedures
themselves.

77. © 2014 SiriusDecisions. All Rights Reserved 77
Key Take-
aways
• Marketing automation provided a great step forward in
lead qualification
• Current lead scoring does not account for enough of the
variance in lead conversion
• Modern data science can generate proxies for questions
your best salesperson would ask prospects if he/she
could reach them all
• It is possible to model contacts, accounts and even
existing customers
• Marketers should understand key considerations for
making good predictions

78. © 2014 Mintigo. All Rights Reserved.
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Replace Traditional Scoring
with Predictive Scoring?

79. © 2014 Mintigo. All Rights Reserved.
www.mintigo.com
It depends….

80. © 2014 Mintigo. All Rights Reserved.
www.mintigo.com
CASE STUDY #1

81. © 2014 Mintigo. All Rights Reserved.
www.mintigo.com
• B2B
SaaS
Core
Product:
VisitorTrack
• Global
clientele
across
various
industries
such
as
tech,
manufacturing,
HR,
&
retail
• Lots
of
leads,
no
scoring
system
previously

82. © 2014 Mintigo. All Rights Reserved.
www.mintigo.com
No Scoring To Predictive Scoring For Fit
• A:
Great
fit!
Both
company
&
prospects
match
netFactor’s
CustomerDNATM
• B:
Company
fit,
but
prospect
doesn’t
match
buyer
profile
• C:
Company
does
not
match
CustomerDNA
• D:
Low
quality
data
(i.e.,
bad
emails)

83. © 2014 Mintigo. All Rights Reserved.
www.mintigo.com
CASE STUDY #2

84. © 2014 Mintigo. All Rights Reserved.
www.mintigo.com
12+
products

85. © 2014 Mintigo. All Rights Reserved.
www.mintigo.com
Already Have A Multi-Product Lead Scoring
Explicit-Demo/Firmographic
• Contact data
• Job title
• Industry
• Custom fields
Implicit-Behavioral
• Web visits
• Email engagement
• Content downloads
• Webinar reg/attendance
• Trial downloads/activations
• Product usage
• Form completions

86. © 2014 Mintigo. All Rights Reserved.
www.mintigo.com
Great Rates, but wait…
0.05
%
0.14
%
0.81
%
2.15
%
0.00%
0.50%
1.00%
1.50%
2.00%
2.50%
Sales
Promo
CR
by
Lead
Score
Great conversion rates, but:
• Limited to track-able implicit behavior and explicit form completions
• Scoring data = time to collect, build, maintain
• We are only human!

87. © 2014 Mintigo. All Rights Reserved.
www.mintigo.com
Predictive Score Identifies Target & Cross-
Sell Opportunities In Real Time
Test
Ops
Dev
42
82
19
24
11
95
77
79
35
6

88. © 2014 Mintigo. All Rights Reserved.
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CASE STUDY #3

89. © 2014 Mintigo. All Rights Reserved.
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Traditional Demo/Firmographic Scoring
Mintigo’s Sweet Spot:
– Job Titles:
• Demand Gen, Marketing Operations
• General Marketing Management
– Company Size of 1,000 employees and above
– Users of Eloqua, Marketo and/or Salesforce.com
– High Tech vertical, companies such as:

90. © 2014 Mintigo. All Rights Reserved.
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Expanding Into Financial Services

91. © 2014 Mintigo. All Rights Reserved.
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Traditional Scoring based on:
– Job Titles:
• Demand Gen, Marketing
Operations
• General Marketing Management
– Company Size of 1,000
employees and above
– Users of Eloqua, Marketo and/or
Salesforce.com
– Industry = Financial Services
Predictive Scoring based on:
Traditional firmo/demographic score to determine fit for new market,
Predictive score to determine propensity to buy

92. © 2014 Mintigo. All Rights Reserved.
www.mintigo.com
Predictive Score identifies
propensity to buy
Traditional Score shows fit based
on demo/firmographic data

93. © 2014 Mintigo. All Rights Reserved.
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HOW MINTIGO WORKS
Quick Overview

94. © 2014 Mintigo. All Rights Reserved.
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How Mintigo Predictive Lead Scoring Works

95. © 2014 Mintigo. All Rights Reserved.
www.mintigo.com
Getting Started with Mintigo
Marke8ng
Need
Assessment/
Data
Discovery
Build
Predic8ve
Model
Predict
/
Score
Leads
and
select
Marke8ng
Indicators
Score
Mystery
File
Validate
model
by
iden8fying
%
of
Closed
Won
Opportuni8es
in
scored
Mystery
file
Set
up
real-­‐8me
lead
scoring
and
data
append
1
2
3
4
5
~2
weeks
6

96. © 2014 Mintigo. All Rights Reserved.
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Q&A

97. © 2014 Mintigo. All Rights Reserved.
www.mintigo.com
Host:
Tony Yang
Director of Demand Gen
Mintigo
@tones810
Guest Presenter:
Kerry Cunningham
Research Director
SiriusDecisions
THANK YOU!