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MassTLC summit_amacleod_predictiveanalytics

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MassTLC summit_amacleod_predictiveanalytics

  1. 1. MAGIC EIGHT-BALL: MAKING PREDICTIVE ANALYTICS WORK FOR YOUR ORGANIZATION Allison MacLeod Sr. Director, Demand Generation & Marketing Operations, Rapid7
  2. 2. About me 2 Allison MacLeod Sr. Director of Demand, Customer & Marketing Ops @ Rapid7 www.rapid7.com Allison_macleod@rapid7.com @allib1121 https://www.linkedin.com/in/allisonbmacleod
  3. 3. Agenda 3 •Rapid7’s story with (marketing) predictive analytics & scoring •A framework/checklist to put into practice •Q&A
  4. 4. Confidential and Proprietary 4 Rapid7’s Story | The Challenge  Traditional lead & behavioral scoring became inaccurate  Too many ‘junk’ leads passed through= too much noise!  High lead volume model = not able to automatically scale qualification process on scoring and scrubbing data alone
  5. 5. So Many Options! 5
  6. 6. Solution & Technology Chosen INFER PREDICTIVE LEAD SCORING & ANALYTICS WWW.INFER.COM
  7. 7. Why Infer? 7 • Great POC and free trial(45 days) process – able to see it in action before purchase • Fast implementation time – 2 weeks • Dedicated CSM – model updates every 90-120 days • Accuracy and better quality= scale! • Solution direction • Cost • Integration
  8. 8. Uses 8 Contact Scoring • Inbound • Threshold for becoming Marketing Qualified Lead (MQL) • Prioritization • Focus on programs • Other uses – high volume programs/events, lists, etc. Account Scoring • Prioritization of accounts for sales – especially useful with territory models • Focus for Marketing team on ABM programs – Enterprise/Named accounts *Intent- driven • Beta stage • Greenfield accounts delivered • Intent- based (by key terms)
  9. 9. Results 9 Decrease (20%) in quantity of leads passed = higher quality 5% MQLs dispositioned as Junk 10-20 20-30 30-40 40-50 50-60 60-70 70-80 20-30 30-40 40-50 50-60 60-70 70-80 80-90 90-100 20-30 30-40 40-50 50-60 60-70 70-80 80-90 90-100 Conversion to opportunity Conversion to SQO Higher opp & deal size The higher the score….
  10. 10. Next Steps 10 •Leverage intent driven data – nurture, sales alignment •Model revisions (geo, industry) •Embed in ABM efforts
  11. 11. A FRAMEWORK FOR GETTING STARTED
  12. 12. A Framework/Checklist 12 GETTING STARTED Ask yourself…  What is the challenge I’m trying to solve?  How will I use/implement the data?  Is sales aligned?  For scoring – do I have a high volume model?* CHOOSING A VENDOR Consider…  Do they offer a POC or trial?  Upfront cost? Ongoing?  Will they commit dedicated resources?  Customizable?  Integration?  Competition?  Roadmap? IMPLEMENTATION Take the following steps…  Test/pilot with small team  Gather feedback  Refine model  Test again  Launch! ALIGNMENT & ONGOING USE Make sure you…  Create a champion in sales  Prove efficacy and value – quickly!  Gather feedback frequently  Update your models (quarterly)  Expand!
  13. 13. THANK YOU! ALLISON_MACLEOD@RAPID7.COM

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