A guide for vendor evaluation and selection

Predictive Lead Scoring

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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?

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Traditional Lead Scoring Fosters This View:

Implicit Explicit

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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

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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?

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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:

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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

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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.

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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

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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

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Question 4: Experience and Partnership• What is the vendor’s experience

with similar clients?• Experience level• Service model• Support structure

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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

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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