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Predictive Lead 
Scoring 
A guide for vendor evaluation and 
selection 
1
Demystifying Predictive Lead Scoring Vendors 
What We’ll Cover Today: 
• What differentiates predictive from traditional l...
Traditional Lead Scoring 
What’s the problem?
Traditional Lead Scoring Fosters This View: 
Implicit Explicit 
© 2014 SiriusDecisions. All Rights Reserved 4
Reality Looks Much More Like This: 
Implicit Explicit 
Behavior 
- Hiring 
- Expansion 
- New products 
- Social media 
- ...
Beware of The Next Big Thing 
1. Conceptualize and prioritize use 
© 2014 SiriusDecisions. All Rights Reserved 6 
cases 
2...
Prioritizing Your Need 
What are the typical use cases that allow you to compare 
vendors?
Predictive Scoring Has Uses Throughout Waterfall 
For most, there’s a substantial 
drop-off between TQL/TGL 
and SQL quali...
Top Use Cases 
SiriusPerspective: 
There are several different needs or use cases that predictive scoring 
can help with. ...
Model 
Use Case 
Starting 
Point 
Entity 
Predicted 
© 2014 SiriusDecisions. All Rights Reserved 10 
Source of 
Predictors...
What Questions Should You Ask? 
Understand the differences between vendors and avoid some 
common pitfalls.
Question 1: Model Design and Development 
• How is the model designed and 
refined? 
• Current Data 
• Feedback incorporat...
Question 2: Data Sources 
• What are the primary sources 
of external data? 
• How do external data sources 
align with yo...
Question 3: Integration 
• How will the vendor’s predictions 
be shown or integrated? 
• Real-time or batch Integrations 
...
Question 4: Experience and Partnership 
• What is the vendor’s experience 
with similar clients? 
• Experience level 
• Se...
The Most Common Pitfalls of Predictions 
The promise of “big data” 
Lack of useful insights 
Deals like snowflakes 
© 2014...
Know the Lingo 
Certain terminology is used by predictive lead scoring vendors 
17
Terminology 
•BIG DATA
Terminology 
•“Training the 
Model”
Terminology 
•Machine 
learning
Terminology 
•Propensity 
Modeling
Key Take-aways 
• Beware of the next big thing! Be clear about the need 
for predictive scoring before pursuing the soluti...
Rishi Kumar 
Head of Customer Success 
@rishimkumar
Where to Begin?
What is predictive lead scoring? 
How predictive models are 
built 
Getting buy-in on predictive 
Predictive playbooks
Home Run Initiative 
Risk 
Go-Live 
Model Customer Value 
Build 
Instant Adoption 
Day 30 
+100% increase in 
win rates an...
Look for the Success Stories
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...
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 Promote...
Rishi Kumar 
Head of Customer Success 
@rishimkumar 
Questions 
Ashley Paris 
Research Analyst 
@ashesvv
SiriusDecisions Webinar: How to Evaluate Predictive Lead Scoring Vendors
SiriusDecisions Webinar: How to Evaluate Predictive Lead Scoring Vendors
SiriusDecisions Webinar: How to Evaluate Predictive Lead Scoring Vendors
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SiriusDecisions Webinar: How to Evaluate Predictive Lead Scoring Vendors

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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.

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SiriusDecisions Webinar: How to Evaluate Predictive Lead Scoring Vendors

  1. 1. Predictive Lead Scoring A guide for vendor evaluation and selection 1
  2. 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. 3. Traditional Lead Scoring What’s the problem?
  4. 4. Traditional Lead Scoring Fosters This View: Implicit Explicit © 2014 SiriusDecisions. All Rights Reserved 4
  5. 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. 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. 7. Prioritizing Your Need What are the typical use cases that allow you to compare vendors?
  8. 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. 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. 10. Model Use Case Starting Point Entity Predicted © 2014 SiriusDecisions. All Rights Reserved 10 Source of Predictors Data Building a Model
  11. 11. What Questions Should You Ask? Understand the differences between vendors and avoid some common pitfalls.
  12. 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. 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. 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. 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. 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. 17. Know the Lingo Certain terminology is used by predictive lead scoring vendors 17
  18. 18. Terminology •BIG DATA
  19. 19. Terminology •“Training the Model”
  20. 20. Terminology •Machine learning
  21. 21. Terminology •Propensity Modeling
  22. 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. 23. Rishi Kumar Head of Customer Success @rishimkumar
  24. 24. Where to Begin?
  25. 25. What is predictive lead scoring? How predictive models are built Getting buy-in on predictive Predictive playbooks
  26. 26. Home Run Initiative Risk Go-Live Model Customer Value Build Instant Adoption Day 30 +100% increase in win rates and conversion
  27. 27. Look for the Success Stories
  28. 28. 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 “ ”
  29. 29. 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 “ ”
  30. 30. Rishi Kumar Head of Customer Success @rishimkumar Questions Ashley Paris Research Analyst @ashesvv

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