Published on March 12, 2014
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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