Predictive lead scoring can tap into the wealth of data in CRM and marketing automation tools. Dan Chiao, VP of Engineering for Fliptop demonstrates the science behind predictive analytics and how you can leverage it for your enterprise. We will cover: What is predictive lead scoring? How does it apply to B2B companies? What you can do to start using predictive lead scoring today?

1. The Science of
Predictive Lead Scoring

2. 2#predictiveleadscoring
Housekeeping
If you can see the slides and hear me
please raise your hand in the GoToWebinar
Dashboard

3. Your speakers
Dan Chiao Jessica Cross
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4. Today’s Webinar Agenda
1. Conventional lead scoring
2. Flaws with conventional lead scoring
3. What is Predictive Analytics
4. How does it apply to B2B organizations
5. Q&A
To submit questions during the webinar, please tweet them:
#predictiveleadscoring @fliptop
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5. Benefits of Lead Scoring
• Increased sales efficiency and
effectiveness
• Increased marketing effectiveness
• Tighter marketing and sales alignment
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6. Lead Scoring
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Source: www.marketo.com
All Names
Engaged
Prospect & Recycled
Lead
Sales Lead
Opportunity
Customer
Behaviors
• Early stage content: +3
• Attend webinar: +5
• Visit any webpage/blog: +1
• Visit careers pages: -10
• Pricing pages: +10
• Watch demos: +5
• Mid-stage content: +8
• Late-stage content: +12
Demographics
• Job Title +20
• Generic email -5
• Industry +10
• Technology +5
#predictiveleadscoring

7. Target persona
VP of Sales
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• Job Title: +20
• Attend webinar: +5
• Visit any webpage/blog: +1
• Visit careers pages: -10
• Possible Score = 16
Lead Score
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8. Target persona
Social Media Manager
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• Early stage content: +3
• Attend webinar: +5
• Visit any webpage/blog: +1
• Watch demos: +5
• Mid-stage content: +8
• Late-stage content: +12
• Possible Score = 34
Lead Score
#predictiveleadscoring

9. Flaws with conventional lead scoring
94% of all MQLs
will never convert
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10. Flaws with conventional lead scoring
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52% of sales reps
will not make their quota
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11. Conventional Lead Scoring
• Based on assumptions and intuition
• Implementations take time
• Accuracy is limited
• Requires quarterly evaluations
• Assumes lead has to visit site to be qualified
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12. What is predictive lead scoring
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Lead Scoring
Predictive
Lead Scoring
#predictiveleadscoring

13. Questions we’ll answer
• What is predictive lead scoring?
• Why can’t I do it in Excel?
• Why do I need so many data points?
• Why do I need machine learning?
• How do we put it all together?
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14. What is predictive lead scoring?
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Traditional Lead Scoring Predictive Lead Scoring
Based on intuition Fitted to historical outcomes
Linear weighted sum Statistical methods
Limited by causation Identifies correlations
Unbounded numerical score Probability of close
Expected revenue amount
Expected sales cycle
#predictiveleadscoring
• The application of statistical methodology to historical sales
results to determine the likelihood a new lead will close.

15. Why can’t I do it in Excel?
• “I can do statistics with pivot tables…”
• In customer cases so far, we’ve found it takes an average of
230 raw data points to build a model that will effectively
predict sales lift.
• So, no, you can’t
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16. Why do I need 230 data points?
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Hiring
Need / New
budget
Previous
Purchase
Willingness to buy
Social Profiles
Seniority / Size
Stage / Industry
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17. Why do I need machine learning?
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18. The classic process
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Visit website: +5 points

19. The SpendScore process
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Historical Sales
3,000+ Signals / 40+ Data Sources
Machine Learning
Model Tournament
Scored Lead
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20. 20
Intuit’s Results
57% Decrease in time to close for
new business deals
Increase in new business
pipeline
Amount spent on new
headcount to achieve results.
75%
$0
#predictiveleadscoring
Norman Happ
Vice President of Sales

21. Want to learn more?
Contact us for your own
predictive lead scoring model
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22. Q&A – 10 Minutes
Thank you.
Contact us for your own predictive lead scoring model
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Dan Chiao
VP of Engineering
Jessica Cross
Dir. Marketing