Predictive Lead Scoring: How to Turn Data into Revenue

© 2013 Marketo, Inc. Marketo Proprietary and Confidential
Predictive Lead Scoring:
How To Turn Data Into Revenue
#LaunchPoint
Gary DeAsi, SmartBear Software
Tony Yang, Mintigo
Patrick Chen, Marketo

Your Speakers
Gary DeAsi
Sr. Marketing Manager
& Marketo Champion,
SmartBear Software
Tony Yang
Director of Demand
Generation,
Mintigo
Patrick Chen
Sr. Manager,
Marketing Operations,
Marketo

Quick Housekeeping
• Chat box is available if you have any
questions
• There will be time for a Q&A at the end
• We will be recording the webinar for
future viewing
• All attendees will receive a copy of the
slides and the recording of today’s
webinar
• Twitter hashtag: #LaunchPoint

Source: David Raab. http://customerexperiencematrix.blogspot.co.il/2013/10/marketing-automation-user-satisfaction.html
http://customerexperiencematrix.blogspot.co.il/2013/10/which-b2b-marketing-automation-features.html

Why Is Lead Scoring So Hard To Implement?

Reason #1: I Don’t Know If The Data I’m
Using To Score Are The Right Ones
- OR -

Reason #2: It’s Not Accurate Because It’s
Based On False Correlations

Reason #3: It Takes A Long Time To Gather
Data & A Lot Of Work To Get It Right
Utilizing Progressive Profiling To
Collect Firmo/Demographic Data
Fostering Engagement To Gather
Behavioral Data For Implicit Scores

Reason #4: It Becomes Super Complex If You
Sell Many Products Or To Multiple Personas
A B A+12 +20+35 +10

Poll
What are your biggest challenges in lead scoring?
• It’s not very accurate
• I don’t know how to set it up
• I don’t know what data to score on
• I have many personas or products
• It requires too much work
• No challenges. We’re completely happy with our scoring.
• Other (please type them into the chat box)

What If Your Lead Scoring…
• Is based on the true profile of your ideal customers?
• Can target the prospects most likely to become buyers?
For each of your products?
• Can determine the best
nurture path for
each lead, instead
of using nurturing
to gather behavioral
data?

Predictive Lead Scoring FTW!

What Is Predictive Lead Scoring?
A methodology for ranking leads in order to
determine their sales-readiness by using predictive
modeling to discover the most accurate and relevant
data points for which to score.

How Predictive Lead Scoring Works
Thousands of Online
Data From Web
Ex: - Tech Industry
- Sales roles
- Has lots of outside
sales
- Hiring CRM admin
- Dreamforce mention
- Has call center
Customer Data From
Your Data Sources
Machine Learning
& Predictive Model
Predictive Score Shows How Closely
Matched Unknown Lead Is To Ideal Profile
+
Ideal Customer Profile (aka CustomerDNATM)

12+ products

User Personas

Test
Ops
Dev
Audience Overlap
Developer Tester
Operations

Audience Overlap
Test
Ops
Dev
Developer Tester
Operations

Marketing Challenges
“I got 99 problems…
and they are all very different depending on product and
market segment.”

Very Different Challenges
• Too many leads VS too few leads
• Non-challenged industry leader VS highly competitive
market VS entering new market
• Net New VS Land n Expand
• 3 week sales cycle VS 3 month sales cycle
• Cross-sell, up-sell
• Segmentation, cross-personas, departments & markets
• Marketing Operations
• Feeding many hungry mouths, all different taste buds
• Dev, Test, and Ops people hate marketers

Solution:
Lead Scoring

$ Qualified Lead
Explicit
Demo/Firmo
BANTImplicit
Behavioral
• Web visits
• Email engagement
• Content downloads
• Webinar reg/attendance
• Trial downloads/activations
• Product usage
• Form completions
• etc
• Contact data
• Job title/role
• Industry
• Custom qualification
fields

Multi-Product Lead Scoring

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 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
• Limited to track-able implicit behavior and
explicit form completions
• Scoring data = time to collect, build, maintain
• We are only human 

$$$ Qualified Lead
Explicit
Demo/Firmo
BANTImplicit
Behavioral
Mintigo
Big data,
predictive,
Custom MIs

Tool-Assisted Code Review
Develope
r
Use Case #1: Custom
MIs

Develop
er
Custom
MIs
o Proportion of Software
Engineer Titles
o Programming
Language: PHP, Java,
.Net, etc
o Indications/Hiring:
Agile, CMMI,
SCRUM…
Code Review

Enterprise &
Verticals
Develop
ero Proportion of Software
Engineer Titles
o Programming
.Net, etc
Agile, CMMI,
SCRUM…
Code Review
o Forbes Global 2000
o Fortune 1000
o Regulated industry
compliances &
Certifications
Custom
MIs

Enterprise &
Verticals
Develop
er
Multi-
Locations
o Fortune 1000
compliances &
Certifications
o International
locations/positions
o Has multiple
locations
o Has development
teams in multiple
locations
Custom
MIs
Engineer Titles
o Programming
.Net, etc
Agile, CMMI,
SCRUM…
Code Review

Enterprise &
Verticals
Develop
er
Multi-
Locations
o Fortune 1000
compliances &
Certifications
o International
locations/positions
o Has multiple
locations
o Has development
teams in multiple
locations
Custom
MIs
*Developer at existing Collaborato
customer organization
Engineer Titles
o Programming
.Net, etc
Agile, CMMI,
SCRUM…
Code Review

Use Case #2: Cross-Sell
Web/App
Performance, API
Monitoring, UXM
API Testing
Develope
r
Tester
Operation
s
Marketer

CustomerDNATM
Cross-Sell

CustomerDNATM
SoapUI Pro
Customer INC
Dev Test
Cross-Sell

CustomerDNATM
SoapUI Pro
Customer INC
Dev Test
Ops
Cross-Sell

Cross-
Sell

API Testing
Develope
r
Use Case #3:
Predictive Scoring
Tester

Mintigo
Challenge
Positive Sample: All SoapUI leads generated in 2013
converted to Closed Won Opportunities in 2013
Negative Sample: All SoapUI Leads Generated in
2013 not converted to opportunities
Mystery Sample: All SoapUI leads generated in 2013-
2014
Mintigo Challenge: Which leads generated in 2013-
2014 closed in Q1 2014?
?

0 1 2 3 4 5 6
Hiring E-commerce related positions
Hiring Web Performance related Positions
Hiring Saas related positions
Indication of Web Application Technology
Alexa Global Rank between 1-5000
Google Page Rank - 8
Daily PPC Budget >$10000
Hiring Code Review related Positions
Hiring Agile related positions
Company has Multiple Locations
Forbes Global 2000
Multiple Locations Software Development
Fortune 1000
Rest API
Hiring SOA related positions
Hiring Devops
Hiring Application Mgmt related positions
API Provider
Buying Signals - Lifts in the model
Common Traits of Closed Won..

Negative vs Positive Traits
0% 10% 20% 30% 40% 50% 60%
Mobile Device Usage (BYOD)
Hiring Mobile Developers
Hiring MDM related positions
Mobility Technologies Indication
Test Automation Indication
Hiring Chargeback related positions
Hiring VOC related positions
Has Data Center
Hiring Web2.0
Has Data Warehouse
Has Call Center
Customer Login Area
VMWare User
Java User
Hiring Customer Service
CRM User
Has Field Workforce
Oracle User
Has International Activity
Marketing Automation User
Is Hiring (Growth)
Negative - Others
Positive - Wins

1000s of Data
Points
0% 50% 100% 150%
Country=AUSTRALIA
Country=CANADA
Country=UNITED STATES
Country=UNITED…
Country=INDIA
State=MN
State=GA
State=VA
State=CA
State=MA
State=NY
Valid Title
Valid Company Name
Is Free Mail
Negative -
Others
0% 5% 10% 15% 20% 25% 30% 35%
Number of Employees= 0 - 25
Number of Employees= > 50K
Number of Employees= 1K - 10K
Number of Employees= 10K - 50K
Annual Revenue= $0 - 1M
Annual Revenue= $500M - 1B
Annual Revenue= > $1B
Negative - Others
Positive - Wins

80% of
wins
in 27%
of
leads
95% of
wins
in 40%
of
leads
Predictive
Results

Ok, so 80% of my revenue comes from
27% of my leads.95% from 40%. Now
what?o Concentrate Marketing Time/Resources
 Targeted messaging
 Dynamic content, stack your nurture paths
 “Luxury” Initiatives
 Double down on the source(s)
 Put them under a microscope
o Arm Sales with Data, Prioritization, Tools
 Provide magnifying glass for best bets
 Sales enablement: content + intelligence
 Revisit recycled leads
 BDR calling campaigns
 Cross-sell

Test
OpsDev
42
What’s next?

Test
OpsDev
42
82
19
24
11
95
77
79
35
6
What’s next?

Mintigo + Marketo
• Real-time data append
• Share key data w/ sales (“Best Bets”)
• Build into lead scoring, and/or use for
accelerators
• Granular segmentation and dynamic
content
• Marketing channel/campaign
disposition
• Nurture stream assignment and
transition
• Continuous analytics and customer
intelligence
The possibilities…

HOW IT WORKS WITH MARKETO
Mintigo Predictive Marketing Platform

Build Your Predictive Model & CustomerDNA
Step 1
Pull A CSV Of Your Customer Records (at least 100) & Upload To Mintigo
Step 2
Mintigo Analyzes Your Data with
Thousands Of Data From The Web
Step 3
Predictive Modeling Identifies The Data Points That
Have The Strongest Correlation With Customers
Step 4
Your Ideal Customer Profile, aka Your CustomerDNATM,
Is Created From These Data Points

Set Up Webhook & Custom Field Mapping
For MI’s

Create Smart Campaign To Trigger Web Hook

Any Prospect Gets Predictively Score
Immediately
Predictive Score Tells
You How Closely
Matched The Prospect Is
To Your CustomerDNA

Bonus: Mintigo Also Enriches Leads With
Firmographic & MI Data
Mintigo enriches leads
with data points that
matter to your business
directly into Marketo so
you can better target your
offers & segment lists

Predictive Lead Scoring Is Just The Beginning
Customer
DNA
Scores
Data
Enrich-
ment
Personas
Content
Offers
Channels
PREDICTIVE MARKETING
INTELLIGENCE
Predictive Modeling Marketing Indicators
DATA
CRM, MAP & Internal Data Big Data / WWW
APPLICATION
Discover
Your Ideal Customer
Profile
Target
The Prospects Most
Likely To Buy
Engage
The Right Prospects With The
Right Message & Channel

Q&A

Gary DeAsi
Sr. Marketing Manager
& Marketo Champion
SmartBear Software
Tony Yang
Director of Demand
Generation
Mintigo
@tones810
tony@mintigo.com
Patrick Chen
Sr. Manager, Marketing
Operations,
Marketo

Thank you!

Predictive Lead Scoring: How to Turn Data into Revenue

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