Predictive Lead Scoring - What's All The Buzz About? [SF Marketo User Group Presentation]

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Mark your calendars and plan to attend the San Francisco Marketo User Group meeting. Take the opportunity to share with, and learn from, Marketo users from various industries and experience levels. Reserve your seat today!

Topic: Predictive Scoring

We are pleased to have Tony Yang Director, Demand Generation with Mintigo as our speaker. Learn how they launched this program in their organization and how you can apply predictive scoring in your current lead generation program.

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  • Intro to my exp, 2 years using Marketo
    1 sentence on Mintigo
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  • Predictive Lead Scoring - What's All The Buzz About? [SF Marketo User Group Presentation]

    1. 1. © 2014 Mintigo. All Rights Reserved. www.mintigo.com Marketo User Group – San Francisco 8/7/2014 Predictive Lead Scoring: What’s All The Buzz About? @tones810 Connect with me at: Tony Yang Director of Demand Gen at Mintigo tony@mintigo.com
    2. 2. © 2014 Mintigo. All Rights Reserved. www.mintigo.com Why Is Lead Scoring So Hard To Implement? Aarggh! Advanced filters!
    3. 3. © 2014 Mintigo. All Rights Reserved. www.mintigo.com Reason #1: I Don’t Know If The Data I’m Using To Score Are The Right Ones - OR - RULES
    4. 4. © 2014 Mintigo. All Rights Reserved. www.mintigo.com Reason #2: It’s Not Accurate Because It’s Based On False Correlations
    5. 5. © 2014 Mintigo. All Rights Reserved. www.mintigo.com Reason #3: It Takes A Long Time To Gather Data & A Lot Of Work To Get It Right Utilizing Progressive Profiling To Collect Firmo/Demographic Data Fostering Engagement To Gather Behavioral Data For Implicit Scores
    6. 6. © 2014 Mintigo. All Rights Reserved. www.mintigo.com Reason #4: It Becomes Super Complex If You Sell Many Products Or To Multiple Personas A B A+12 +20+35 +10
    7. 7. © 2014 Mintigo. All Rights Reserved. www.mintigo.com Source: Poll taken during the Marketo LaunchPoint webinar on “Predictive Lead Scoring: How To Turn Data Into Revenue” You’re Not Alone – Other Marketers Think It’s Hard Too
    8. 8. © 2014 Mintigo. All Rights Reserved. www.mintigo.com The Problem With Current Lead Scoring Implicit Explicit Current lead scoring fosters this view of the world…
    9. 9. © 2014 Mintigo. All Rights Reserved. www.mintigo.com The Problem With Current Lead Scoring Implicit Explicit Behavior - Hiring - Expansion - New products - Social media - Communities Fit - C-level attitudes - Tech Ecosystem - Financial Health - Competition - Positioning When reality looks a lot more like this…
    10. 10. © 2014 Mintigo. All Rights Reserved. www.mintigo.com Basing Our Lead Scoring On A Limited View Of Our Customers Is Like This
    11. 11. © 2014 Mintigo. All Rights Reserved. www.mintigo.com The Best-in-Class B-to-B Scenario Source: SiriusDecisions Conversion % AQL  TQL 66.6% TQL  SQL 48.8% Conversion from AQL  SQL : 32.6%
    12. 12. © 2014 Mintigo. All Rights Reserved. www.mintigo.com How Can We Do Deep Qualification Faster & At Scale
    13. 13. © 2014 Mintigo. All Rights Reserved. www.mintigo.com What Is Lead Scoring? A methodology for ranking leads in order to determine their sales- readiness by combining the power of predictive modeling and big data to discover the most accurate and relevant data points for which to score. Predictive
    14. 14. © 2014 Mintigo. All Rights Reserved. www.mintigo.com How Predictive Lead Scoring Works Customer Data From Your Data Sources Thousands of Online Data From Web Machine Learning & Predictive Model + Ex: - Tech Industry - Sales roles - Has lots of outside sales - Hiring CRM admin - Has call center Ideal Target Profile (aka CustomerDNATM) Predictive Score Shows How Closely Matched Unknown Lead Is To Ideal Profile
    15. 15. © 2014 Mintigo. All Rights Reserved. www.mintigo.com Replace Traditional Scoring with Predictive Scoring?
    16. 16. © 2014 Mintigo. All Rights Reserved. www.mintigo.com It depends….
    17. 17. © 2014 Mintigo. All Rights Reserved. www.mintigo.com USE CASE #1 PRIORITIZING YOUR HOUSE LIST
    18. 18. © 2014 Mintigo. All Rights Reserved. www.mintigo.com • B2B SaaS Core Product: VisitorTrack • Global clientele across various industries such as tech, manufacturing, HR, & retail • Lots of leads, no scoring system previously
    19. 19. © 2014 Mintigo. All Rights Reserved. www.mintigo.com A: Great fit! Both company & prospects match netFactor’s CustomerDNATM B: Company fit, but prospect doesn’t match buyer profile C: Company does not match CustomerDNA D: Low quality data (i.e., bad emails) No Scoring To Predictive Scoring For Fit
    20. 20. © 2014 Mintigo. All Rights Reserved. www.mintigo.com USE CASE #2 MULTIPLE TARGET AUDIENCE & PRODUCTS CROSS-SELL & UPSELL
    21. 21. © 2014 Mintigo. All Rights Reserved. www.mintigo.com
    22. 22. © 2014 Mintigo. All Rights Reserved. www.mintigo.com Explicit-Demo/Firmographic • Contact data • Job title • Industry • Custom fields Implicit-Behavioral • Web visits • Email engagement • Content downloads • Webinar reg/attendance • Trial downloads/activations • Product usage • Form completions Already Have A Multi-Product Lead Scoring
    23. 23. © 2014 Mintigo. All Rights Reserved. www.mintigo.com 0.05 % 0.14 % 0.81 % 2.15 % 0.00% 0.50% 1.00% 1.50% 2.00% 2.50% Sales Promo CR by Lead Score Great conversion rates, but: • Limited to track-able implicit behavior and explicit form completions • Scoring data = time to collect, build, maintain • We are only human! Great Rates, but wait…
    24. 24. © 2014 Mintigo. All Rights Reserved. www.mintigo.com Predictive Score Identifies Target & Cross-Sell Opportunities In Real Time Test OpsDev 42 82 19 24 11 95 77 79 35 6
    25. 25. © 2014 Mintigo. All Rights Reserved. www.mintigo.com USE CASE #3 FREEMIUM CONVERSION BEHAVIORAL SCORING “A Leading File Sharing/Cloud Storage Provider”
    26. 26. © 2014 Mintigo. All Rights Reserved. www.mintigo.com 1st Goal – Identify Needles In The Haystacks Challenges: • Freemium & Free Trial SaaS Provider • Large database (millions of contacts) • Large amount inbound (free user, inquiries) • Very few sales reps • No current lead scoring system What They Need • Better way to look for potential buyers of premium subscriptions within free user base & inbound
    27. 27. © 2014 Mintigo. All Rights Reserved. www.mintigo.com Predictive Model Found Common Traits Of Converted Freemium Users 2.5X Lift Privacy/Security related attributes (Truste, SSL, Hiring security compliance positions) 2.2X Lift Manufacturing related attributes (CAD and CAM usage, supply chain) 1.6X Lift Tools That Integrate With Their Product (Salesforce.com, MS Exchange or SharePoint) 1.4X Lift Remote Workforce attributes (BYOD, field workforce)
    28. 28. © 2014 Mintigo. All Rights Reserved. www.mintigo.com Potential Next Steps? • Export data from their product on user behaviors, usage data and features accessed by converted free users • Run predictive model on this data set to find common behaviors that correlate to the most lift • Create a rules-based behavior scoring in MAP to identify activities of free users that perform these activities • *Bonus – create nurture programs that drive users to access these features in the product
    29. 29. © 2014 Mintigo. All Rights Reserved. www.mintigo.com USE CASE #4 NEW MARKET EXPANSION
    30. 30. © 2014 Mintigo. All Rights Reserved. www.mintigo.com Traditional Demo/Firmographic Scoring Mintigo’s Sweet Spot: – Job Titles: • Demand Gen, Marketing Operations • General Marketing Management – Company size over 250 employees – House List Size Over 300K Contacts – Users of Eloqua, Marketo and/or Salesforce.com – High Tech vertical, companies such as:
    31. 31. © 2014 Mintigo. All Rights Reserved. www.mintigo.com Expanding Into Financial Services
    32. 32. © 2014 Mintigo. All Rights Reserved. www.mintigo.com Traditional Scoring based on: – Job Titles: • Demand Gen, Marketing Operations • General Marketing Management – Company size over 250 employees – House List Size Over 300K Contacts – Users of Eloqua, Marketo and/or Salesforce.com – Industry = Financial Services Predictive Scoring based on: Traditional firmo/demographic score to determine fit for new market, Predictive score to determine propensity to buy
    33. 33. © 2014 Mintigo. All Rights Reserved. www.mintigo.com Predictive Score identifies propensity to buy Traditional Score shows fit based on demo/firmographic data
    34. 34. © 2014 Mintigo. All Rights Reserved. www.mintigo.com Webinar Replay: “Demystifying Predictive Lead Scoring” Guest Presenter from SiriusDecisions Want To Learn More? Go to www.mintigo.com/resources Webinar Replay: “Predictive Marketing: The Science Behind Marketing” Presented by Mintigo Chief Data Scientist *New eBook Coming Soon: “Applying Predictive Marketing to B2B” By David Raab
    35. 35. © 2014 Mintigo. All Rights Reserved. www.mintigo.com Thank You! @tones810 Connect with me at: Tony Yang Director of Demand Gen at Mintigo tony@mintigo.com

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