SiriusDecisions Webinar: How to Evaluate Predictive Lead Scoring Vendors
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Transcript of SiriusDecisions Webinar: How to Evaluate Predictive Lead Scoring Vendors
A guide for vendor evaluation and selection
Predictive Lead Scoring
1
© 2013 SiriusDecisions. All Rights Reserved 2
Demystifying Predictive Lead Scoring Vendors
What We’ll Cover Today:• What differentiates predictive from traditional lead scoring?• What are important questions to ask when evaluating vendors?• What are the potential use cases to consider when evaluating
vendors?• What are some pitfalls to avoid when considering predictive lead
scoring?
Traditional Lead ScoringWhat’s the problem?
© 2014 SiriusDecisions. All Rights Reserved 4
Traditional Lead Scoring Fosters This View:
Implicit Explicit
© 2014 SiriusDecisions. All Rights Reserved 5
Reality Looks Much More Like This:
Implicit Explicit
Behavior- Hiring- Expansion- New products- Social media- Communities
Fit- C-level attitudes- Tech Ecosystem- Financial Health- Competition- Positioning
© 2014 SiriusDecisions. All Rights Reserved 6
Beware of The Next Big Thing
1. Conceptualize and prioritize use cases
2. Understand vendor differences
3. Be honest about support and maintenance needs
4. Understand your technology current stack
Prioritizing Your NeedWhat are the typical use cases that allow you to compare vendors?
© 2014 SiriusDecisions. All Rights Reserved 8
Predictive Scoring Has Uses Throughout Waterfall
For most, there’s a substantial drop-off between TQL/TGL and SQL qualification…
Traditional Lead Scoring
Predictive
Predictive
Predictive
Predictive
Source net-new inquiry based
on ideal buyers
Uncover upsell and cross-sell opportunities
during the active sales cycle
Upsell and renewals
Enhance accuracy of traditional
model
SiriusPerspective:
© 2014 SiriusDecisions. All Rights Reserved 9
Top Use CasesThere are several different needs or use cases that predictive
scoring can help with.
Find Net New
Find Existing Find Other Opportunitie
s
Improve Accuracy of
Existing Scoring
Gain insights
© 2014 SiriusDecisions. All Rights Reserved 10
Model
Use Case Starting Point
Entity Predicted
Source of Predictors
Data
Building a Model
What Questions Should You Ask?Understand the differences between vendors and avoid some common pitfalls.
© 2014 SiriusDecisions. All Rights Reserved 12
Question 1: Model Design and Development• How is the model designed and refined?• Current Data• Feedback incorporation• Black Box vs. White Box?• Explicit and Implicit?• Number of Models• Change protocol• Entity• Time to market
© 2014 SiriusDecisions. All Rights Reserved 13
Question 2: Data Sources• What are the primary sources of external data?
• How do external data sources align with your ideal prospects?• Job postings, business
transactions• Social listening and semantic
analysis• Publisher sites•Understand data storage/
security implications
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Question 3: Integration• How will the vendor’s predictions be shown or integrated?
• Real-time or batch Integrations
• Field types• Interface• Reporting
© 2014 SiriusDecisions. All Rights Reserved 15
Question 4: Experience and Partnership• What is the vendor’s experience
with similar clients?• Experience level• Service model• Support structure
© 2014 SiriusDecisions. All Rights Reserved 16
The Most Common Pitfalls of Predictions
The promise of “big data”
Lack of useful insights
Deals like snowflakes
Data Set = Dirty & Small
Unpredictable future
Time and length
Accuracy testing
Know the LingoCertain terminology is used by predictive lead scoring vendors
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Terminology
•BIG DATA
Terminology
•“Training the Model”
Terminology
•Machine learning
Terminology
•Propensity Modeling
© 2014 SiriusDecisions. All Rights Reserved 22
Key Take-aways
• Beware of the next big thing! Be clear about the need for predictive scoring before pursuing the solution.
• Get your (data) house in order to give your vendor the best chance to create an accurate model.
• Develop a plan for socializing a new scoring approach to peripheral stakeholders, ESPECIALLY if you’ve had adoption problems in the past.
Rishi KumarHead of Customer Success@rishimkumar
Use Cases
Where to Begin?
Models
Scores
TestSet
Data
SignalsBehaviorFit
What is predictive lead scoring?
How predictive models are built
Getting buy-in on predictive
Predictive playbooks
Home Run Initiative
Risk
Net V
alue
Go-Live
Customer ValueModel Build
Instant Adoption
Day 30
+100% increase in win rates and conversion
Look for the Success Stories
It took us two weeks to get stared and less than a month for Infer to pay for itself. Kevin Gaither, VP of Inside SalesZipRecruiter
“”
90% of our promoters come from our Infer A-LeadsRandhir Vieira, VP of Product and MarketingMindflash
“”
Net Promoter ScoreHow likely would you be to recommend this product?
1 2 3 4 5 6 7 8 9 100Detractors Neutral Promoters
Rishi KumarHead of Customer Success@rishimkumar
Ashley ParisResearch Analyst@ashesvv
Questions