Predictive Lead Scoring - What's All The Buzz About? [SF Marketo User Group Presentation]

© 2014 Mintigo. All Rights Reserved. www.mintigo.com
Marketo User Group – San Francisco
8/7/2014
Predictive Lead Scoring:
What’s All The Buzz About?
@tones810
Connect with me at:
Tony Yang
Director of Demand Gen at Mintigo
tony@mintigo.com

Why Is Lead Scoring So Hard To Implement?
Aarggh!
Advanced
filters!

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

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

Source: Poll taken during the Marketo LaunchPoint webinar on
“Predictive Lead Scoring: How To Turn Data Into Revenue”
You’re Not Alone –
Other Marketers Think It’s Hard Too

The Problem With Current Lead Scoring
Implicit Explicit
Current lead scoring fosters this view of the world…

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…

Basing Our Lead Scoring On A Limited View
Of Our Customers Is Like This

The Best-in-Class B-to-B Scenario
Source: SiriusDecisions
Conversion %
AQL  TQL
66.6%
TQL  SQL
48.8%
Conversion from AQL  SQL : 32.6%

How Can We Do Deep Qualification
Faster & At Scale

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

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

Replace Traditional Scoring
with Predictive Scoring?

It depends….

USE CASE #1
PRIORITIZING YOUR HOUSE LIST

• B2B SaaS
Core Product: VisitorTrack
• Global clientele across
various industries such as
tech, manufacturing, HR,
& retail
• Lots of leads, no scoring
system previously

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)
No Scoring To Predictive Scoring For Fit

USE CASE #2
MULTIPLE TARGET AUDIENCE & PRODUCTS
CROSS-SELL & UPSELL

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
Already Have A Multi-Product Lead Scoring

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!
Great Rates, but wait…

Predictive Score Identifies Target &
Cross-Sell Opportunities In Real Time
Test
OpsDev
42
82
19
24
11
95
77
79
35
6

USE CASE #3
FREEMIUM CONVERSION
BEHAVIORAL SCORING
“A Leading File Sharing/Cloud Storage Provider”

1st Goal –
Identify Needles In The Haystacks
Challenges:
• Freemium & Free Trial SaaS Provider
• Large database (millions of contacts)
• Large amount inbound (free user,
inquiries)
• Very few sales reps
• No current lead scoring system
What They Need
• Better way to look for potential buyers of premium subscriptions
within free user base & inbound

Predictive Model Found Common Traits Of
Converted Freemium Users
2.5X
Lift
Privacy/Security related attributes
(Truste, SSL, Hiring security compliance positions)
2.2X
Lift
Manufacturing related attributes
(CAD and CAM usage, supply chain)
1.6X
Lift
Tools That Integrate With
Their Product
(Salesforce.com, MS Exchange or SharePoint)
1.4X
Lift
Remote Workforce attributes
(BYOD, field workforce)

Potential Next Steps?
• Export data from their product on user behaviors, usage
data and features accessed by converted free users
• Run predictive model on this data set to find common
behaviors that correlate to the most lift
• Create a rules-based behavior scoring in MAP to identify
activities of free users that perform these activities
• *Bonus – create nurture programs that drive users to access
these features in the product

USE CASE #4
NEW MARKET EXPANSION

Traditional Demo/Firmographic Scoring
Mintigo’s Sweet Spot:
– Job Titles:
• Demand Gen, Marketing Operations
• General Marketing Management
– Company size over 250 employees
– House List Size Over 300K Contacts
– Users of Eloqua, Marketo and/or Salesforce.com
– High Tech vertical, companies such as:

Expanding Into Financial Services

Traditional Scoring
based on:
– Job Titles:
• Demand Gen, Marketing
Operations
• General Marketing Management
– Company size over 250
employees
– House List Size Over 300K
Contacts
– 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

Predictive Score
identifies
propensity to buy
Traditional Score
shows fit based on
demo/firmographic
data

Webinar Replay:
“Demystifying Predictive Lead Scoring”
Guest Presenter from SiriusDecisions
Want To Learn More? Go to www.mintigo.com/resources
Webinar Replay:
“Predictive Marketing: The Science Behind Marketing”
Presented by Mintigo Chief Data Scientist
*New eBook Coming Soon:
“Applying Predictive Marketing to B2B”
By David Raab

Thank You!
@tones810
Connect with me at:
Tony Yang
Director of Demand Gen at Mintigo
tony@mintigo.com

Predictive Lead Scoring - What's All The Buzz About? [SF Marketo User Group Presentation]

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