1. © Neustar, Inc. // Proprietary and Confidential
Scott Hirsch
Head of Content
Neustar, inc.
Bridging the Gap Between Sales &
Marketing with Predictive Analytics

2. About Me
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Title: Head of Content,
Digital Marketing at Neustar
Previous: VP Product Marketing,
Get Satisfaction
Prior to that, product innovation with BT,
Google, NYTimes, Nokia, Telefonica …
Focus: Go-to-market and content strategy
with a focus on brand awareness, point of
view marketing, and demand creation.
Scott.Hirsch@Neustar.biz

3. Staring over the demand gen cliff
MQL
WON*
0.28%
*2014 Sirius Decisions waterfall
benchmark funnel research
35%
rejected
59%
untouched
94%
of
MQL’s
are
either
rejected
or
untouched
© Neustar, Inc. // Proprietary and Confidential

4. Our job is to make sales easier
1. Which company/buyer profiles deliver results
2. Which ones don’t
3. How to score leads for “quality”
To do so, we needed to understand:
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5. 1. Fundamentally, it’s a data problem:
The Challenge – Knowing Our Customer
Normalized lead dataData that prospects give us
Company profile dataObserved digital behavior
Deep correlation analysis of why we winSimple conversion analysis
2. With the right data, we can make the right
decisions
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6. © Neustar, Inc. // Proprietary and Confidential
Step 1: Model Past Performance
Gain insight from past performance –
3 Segments / 3 DNAs / 3 Unique Models

7. Step 1: Example
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Prospects who use AWS are 5x more likely to
buy our SiteProtect and UltraDNS products

8. Step 2: New Lead Scoring
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Predictive
Scoring
Internal Lead
Scoring
More precise lead scoring and flows based on a
new universe of data

9. Step 2: Scoring that Drives Lead Quality
Before Mintigo, we looked at self-
reported demographic, known
firmographic, and observed digital
behavior.
Scores based on past conversion data.
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Highly
engaged on
website
Small Biz –
may not
ready to buy
Does not
meet
technical
requirement
Look-a-like
customer
Meets
technical
requirement
Uses
competitor
After Mintigo, we looked verified
firmographics enriched with details
including company growth and
technologies used
Scores based on historical closed
won opportunity data.
With predictive scoringBefore predictive scoring

10. Step 3: Provide Sales with Insights
Value to sales teams:
– Less time researching company
details
– New data available, such as
competitive products used
– Ability to prioritize leads queue
based on new data
– Insight that can inform cross-sell
and up-sell opportunities
© Neustar, Inc. // Proprietary and Confidential
Security Services
Marketing
Services
Not Visible to Sales
Indicators are populated in salesforce to give a
new level of insight into leads/opportunities

11. The Results: Better Funnel Metrics
‣ Successful launch of predictive scoring in Q4 ‘14 improved funnel metrics:
-49% decrease in MQL +5% increase in SAL +61% increase in SQL
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53%
44%
40%
26%
27%
12%
17% 15%
29%
19%
42%
24%
29%
33%
44%
0%
10%
20%
30%
40%
50%
60%
Q1 ' 14 Q2 '14 Q3 '14 Q4 '14 Q1 ' 15
Waterfall Metrics Trends Q1 2014 – Q1 2015
Marketing Qualified Sales Accepted Sales Qualified

12. SUMMARY
Questions?
• Who are my ideal prospects ?
Discover your CustomerDNATM
• How should I communicate with them ?
Create micro-segmentations
• Where do I put my resources & focus ?
Leverage advanced scoring models
© Neustar, Inc. // Proprietary and Confidential