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Webinar: The Science of Predictive Lead Scoring

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Information about Webinar: The Science of Predictive Lead Scoring
Technology

Published on March 12, 2014

Author: Fliptop

Source: slideshare.net

Description

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?
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The Science of Predictive Lead Scoring

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

Your speakers Dan Chiao Jessica Cross 3#predictiveleadscoring

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 4#predictiveleadscoring

Benefits of Lead Scoring • Increased sales efficiency and effectiveness • Increased marketing effectiveness • Tighter marketing and sales alignment 5#predictiveleadscoring

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 • Attend webinar: +5 • Visit any webpage/blog: +1 • Visit careers pages: -10 • Possible Score = 16 Lead Score #predictiveleadscoring

Target persona Social Media Manager 8 • 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

Flaws with conventional lead scoring 94% of all MQLs will never convert 9#predictiveleadscoring

Flaws with conventional lead scoring 10 52% of sales reps will not make their quota #predictiveleadscoring

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 11#predictiveleadscoring

What is predictive lead scoring 12 Lead Scoring Predictive Lead Scoring #predictiveleadscoring

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? 13#predictiveleadscoring

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 #predictiveleadscoring • 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 15#predictiveleadscoring

Why do I need 230 data points? 16 Hiring Need / New budget Previous Purchase Willingness to buy Social Profiles Seniority / Size Stage / Industry #predictiveleadscoring

Why do I need machine learning? 17#predictiveleadscoring

The classic process 18#predictiveleadscoring Visit website: +5 points

The SpendScore process 19 Historical Sales 3,000+ Signals / 40+ Data Sources Machine Learning Model Tournament Scored Lead #predictiveleadscoring

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

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

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