masstlc summit_amacleod_predictiveanalytics
TRANSCRIPT
MAGIC EIGHT-BALL: MAKING PREDICTIVE
ANALYTICS WORK FOR YOUR ORGANIZATION
Allison MacLeodSr. Director, Demand Generation & Marketing Operations, Rapid7
About me
2
Allison MacLeod
Sr. Director of Demand, Customer & Marketing Ops
@ Rapid7
www.rapid7.com
@allib1121
https://www.linkedin.com/in/allisonbmacleod
Agenda
3
• Rapid7’s story with (marketing) predictive analytics & scoring
• A framework/checklist to put into practice
• Q&A
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
So Many Options!
5
Solution & Technology Chosen
INFERPREDICTIVE LEAD SCORING & ANALYTICS
WWW.INFER.COM
Why Infer?
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• 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
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)
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….
Next Steps
10
• Leverage intent driven data – nurture, sales alignment
• Model revisions (geo, industry)
• Embed in ABM efforts
A FRAMEWORK FOR GETTING STARTED
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!
THANK [email protected]