Predictive scoring is all the rage amongst data-driven marketers, but how do you know which vendor is best? If you want to hit a home run for your B2B company, it’s time to educate yourself on what exactly predictive lead scoring is, and how you can choose the right partner to help you hit it out of the park. Infer invites you to join one of the authors of SiriusDecisions’ new Field Guide to Predictive Lead Scoring for a free educational webinar.

1. Predictive Lead
Scoring
A guide for vendor evaluation and
selection
1

2. Demystifying Predictive Lead Scoring Vendors
What We’ll Cover Today:
• What differentiates predictive from traditional lead scoring?
• What are important questions to ask when evaluating vendors?
• What are the potential use cases to consider when evaluating vendors?
• What are some pitfalls to avoid when considering predictive lead
scoring?
© 2013 SiriusDecisions. All Rights Reserved 2

3. Traditional Lead Scoring
What’s the problem?

4. Traditional Lead Scoring Fosters This View:
Implicit Explicit
© 2014 SiriusDecisions. All Rights Reserved 4

5. Reality Looks Much More Like This:
Implicit Explicit
Behavior
- Hiring
- Expansion
- New products
- Social media
- Communities
© 2014 SiriusDecisions. All Rights Reserved 5
Fit
- C-level attitudes
- Tech Ecosystem
- Financial Health
- Competition
- Positioning

6. Beware of The Next Big Thing
1. Conceptualize and prioritize use
© 2014 SiriusDecisions. All Rights Reserved 6
cases
2. Understand vendor differences
3. Be honest about support and
maintenance needs
4. Understand your technology current
stack

7. Prioritizing Your Need
What are the typical use cases that allow you to compare
vendors?

8. Predictive Scoring Has Uses Throughout Waterfall
For most, there’s a substantial
drop-off between TQL/TGL
and SQL qualification…
Traditional Lead Scoring
Predictive
Predictive
© 2014 SiriusDecisions. All Rights Reserved 8
Predictive
Predictive
Source net-new
inquiry based on
ideal buyers
Uncover upsell and
cross-sell
opportunities during
the active sales
cycle
Upsell and
renewals
Enhance accuracy
of traditional
model

9. Top Use Cases
SiriusPerspective:
There are several different needs or use cases that predictive scoring
can help with.
Find Net New Find Existing Find Other
Opportunities
© 2014 SiriusDecisions. All Rights Reserved 9
Improve
Accuracy of
Existing
Scoring
Gain insights

10. Model
Use Case
Starting
Point
Entity
Predicted
© 2014 SiriusDecisions. All Rights Reserved 10
Source of
Predictors
Data
Building a Model

11. What Questions Should You Ask?
Understand the differences between vendors and avoid some
common pitfalls.

12. Question 1: Model Design and Development
• How is the model designed and
refined?
• Current Data
• Feedback incorporation
• Black Box vs. White Box?
• Explicit and Implicit?
• Number of Models
• Change protocol
• Entity
• Time to market
© 2014 SiriusDecisions. All Rights Reserved 12

13. Question 2: Data Sources
• What are the primary sources
of external data?
• How do external data sources
align with your ideal prospects?
• Job postings, business transactions
• Social listening and semantic
analysis
• Publisher sites
• Understand data storage/
security implications
© 2014 SiriusDecisions. All Rights Reserved 13

14. Question 3: Integration
• How will the vendor’s predictions
be shown or integrated?
• Real-time or batch Integrations
• Field types
• Interface
• Reporting
© 2014 SiriusDecisions. All Rights Reserved 14

15. Question 4: Experience and Partnership
• What is the vendor’s experience
with similar clients?
• Experience level
• Service model
• Support structure
© 2014 SiriusDecisions. All Rights Reserved 15

16. The Most Common Pitfalls of Predictions
The promise of “big data”
Lack of useful insights
Deals like snowflakes
© 2014 SiriusDecisions. All Rights Reserved 16
Data Set = Dirty & Small
Unpredictable future
Time and length
Accuracy testing

17. Know the Lingo
Certain terminology is used by predictive lead scoring vendors
17

18. Terminology
•BIG DATA

19. Terminology
•“Training the
Model”

20. Terminology
•Machine
learning

21. Terminology
•Propensity
Modeling

22. Key Take-aways
• Beware of the next big thing! Be clear about the need
for predictive scoring before pursuing the solution.
• Get your (data) house in order to give your vendor the
best chance to create an accurate model.
• Develop a plan for socializing a new scoring
approach to peripheral stakeholders, ESPECIALLY if
you’ve had adoption problems in the past.
© 2014 SiriusDecisions. All Rights Reserved 22

23. Rishi Kumar
Head of Customer Success
@rishimkumar

24. Where to Begin?

25. What is predictive lead scoring?
How predictive models are
built
Getting buy-in on predictive
Predictive playbooks

28. Home Run Initiative
Risk
Go-Live
Model Customer Value
Build
Instant Adoption
Day 30
+100% increase in
win rates and
conversion

29. Look for the Success Stories

31. It took us two weeks to get stared
and less than a month for Infer to
pay for itself.
Kevin Gaither, VP of Inside Sales
ZipRecruiter
“
”

32. Net Promoter Score
How likely would you be to recommend this product?
0 1 2 3 4 5 6 7 8 9 10
Detractors Neutral Promoters
90% of our promoters come from
our Infer A-Leads
Randhir Vieira, VP of Product and Marketing
Mindflash
“
”

33. Rishi Kumar
Head of Customer Success
@rishimkumar
Questions
Ashley Paris
Research Analyst
@ashesvv