webinar: the science of predictive lead scoring

The Science of

Predictive Lead Scoring

2#predictiveleadscoring

Housekeeping

If you can see the slides and hear me please raise your hand in the GoToWebinarDashboard

Your speakers

Dan Chiao Jessica Cross


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


Benefits of Lead Scoring

• Increased sales efficiency and

effectiveness

• Increased marketing effectiveness

• Tighter marketing and sales alignment


Lead Scoring

6

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

Target persona

VP of Sales

7

• Job Title: +20



• Visit careers pages: -10

• Possible Score = 16

Lead Score


Target persona

Social Media Manager

8

• Early stage content: +3



• Watch demos: +5

• Mid-stage content: +8

• Late-stage content: +12

• Possible Score = 34

Lead Score


Flaws with conventional lead scoring

94% of all MQLs

will never convert


Flaws with conventional lead scoring

10

52% of sales reps

will not make their quota


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


What is predictive lead scoring

12

Lead Scoring

Predictive

Lead Scoring


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?


What is predictive lead scoring?

14

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


• The application of statistical methodology to historical sales

results to determine the likelihood a new lead will close.

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


Why do I need 230 data points?

16

HiringNeed / New

budget

Previous Purchase

Willingness to buy

Social ProfilesSeniority / Size Stage / Industry


Why do I need machine learning?


The classic process


Visit website: +5 points

The SpendScore process

19

Historical Sales

3,000+ Signals / 40+ Data Sources

Machine Learning

Model Tournament

Scored Lead


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


Norman HappVice President of Sales

Want to learn more?

Contact us for your own

predictive lead scoring model

21

Q&A – 10 Minutes

Thank you.

Contact us for your own predictive lead scoring model

22

Dan Chiao

VP of Engineering

Jessica Cross

Dir. Marketing