predict 2014, siriusdecisions kerry cunningham
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Increasing the odds: The conditions and correlates for predictive lead scoring successTRANSCRIPT
Increasing the odds: The conditions and correlates for predictive lead scoring success
Kerry CunninghamResearch Director, SiriusDecisions
#predict2014
SiriusDecisions, Kerry Cunningham
Research Director, SiriusDecisions – 20 years Lead Development &
Management– Research methods and analytics
5 years social science research– Propensity modeling behavior
• Spending/ Consumption• Employee performance• Personality correlates of
well-being, Book Chapter, September, 2014
Agenda
• Why is predictive necessary?• 4 Principles of Prediction• 5 Building Blocks for Predictive lead
scoring success• 4 Keys to Success with Predictive Lead
Scoring
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To better understand why prediction is called for…
Why Predictive Is Needed
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Why Predictive Is Needed
Conversion %
AQL > TQL 10%-30%
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Conversion %
AQL > TQL 10%-30%
TQL + TGL > SQL
5%-10%
Why Predictive Is Needed
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Conversion from AQL to
SQL = 0%-3%
Why Predictive Is Needed
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Today, most of that qualification involves teleprospecting and
sales calls
Why Predictive Is Needed
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Find clues that exist out in the world, which reliably point to qualifying criteria & buying signals you would ask the
decision-maker about if you could get him/ her on the
phone?
The Role of Data Science
The Future of B-to-B Lead Development
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The Promise of Predictive
MAP-based PLS0
1.75
3.5
5.25
7
Sati
sfact
ion
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4 PRINCIPLES OF PREDICTION
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Conditions For Good Predictions
Past behavior >> Future performance
But…
1
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Conditions For Good Predictions
The Muni Problem2
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Conditions For Good Predictions
Are your sales cycles snowflakes?
2
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Conditions For Good PredictionsSirius Perspective: Predictions made by the model need to make a real difference
Wins Against Replacement Player
3
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Conditions For Good Predictions
Sirius Perspective: Time is the enemy of prediction.
4
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5 BUILDING BLOCKS OF PREDICTIVE MODELS
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Model
Use CaseStarting
PointEntity
PredictedSource of Predictors
Data
Building a Model
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Sirius Perspective:
Prediction begins with data that is
related to the outcomes that
are to be predicted.
Data
Digital artifacts 1
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Sirius Perspective: Modern data science can reach deeply into online digital artifacts to unearth evidence of business problems and buying initiatives.
• Corporate websites• Press releases• Job postings• Application signatures
Data
1
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Use Cases
Use Case
Find new businesses that have a high propensity to buy from me
2
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Use Cases
Find new businesses that have a high propensity to buy from me
2
Score and prioritize businesses already in my database on their propensity to buy from me
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Use Cases
Score and prioritize existing customers for their propensity to buy other products and services we sell
Find new businesses that have a high propensity to buy from me
Score and prioritize businesses already in my database on their propensity to buy from me
2
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Starting Point
Historical Data
Became Customers
Didn’t Become Customers
Prospects that: • bought or not• converted or not• responded or not
Data that clearly distinguishes the two
groups
3
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Starting Point
No Historical Data
Fit the profile Don’t fit
Prospects that: • Have a business
problem • the motivation and
resources to solve it
Data that clearly distinguishes the two
groups
3
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Source of Predictors
Top down
Bottom up
The best models typically include
both prospect and account level
predictors
Sirius Perspective: What is likely to be most predictive may be at the contact or the account level, and gleaning information from both is normally important.
4
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Entity PredictedSirius Perspective: Data science can reach deep into a contact’s world to determine who is most likely to be involved in a buying cycle.
Company
Contacts
Job Role
Common Titles
5
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Entity Predicted
Contacts | Accounts Company Hiring
Tech Ecosystem
Prof. Communities
Job Role
Common Titles
Content Engagement
Social Media Interaction
MAP
PLS
5
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In the organization
4 KEYS TO PREDICTIVE LEAD SCORING SUCCESS
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4 Keys to Success with Predictive Lead Scoring
• Realistic Expectations
• Pilot | Champions• SLAs• Feedback | Iteration
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Realistic Expectations
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Realistic Expectations
If it actually goes to 11, great, but…
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Realistic Expectations: The Nature of PredictionSiriusPerspective: Complexity is the (other) enemy of prediction, but the reality in b-to-b selling.
B-to-b selling, like human behavior, is
complex
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Realistic Expectations
• Experience and expression of emotions
• Personality & Well-being
• Spending and money• Biological basis of
behavior• Time perspectives• Statistical modeling of
employee performance and attrition
• Propensity modeling - predictive lead scoring
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22%
23%20%
13%
9%12%
Factor 1 Factor 2 Factor 3 Factor 4Factor 5 Factor 6
Expectation
Realistic Expectations
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Factor 1 Factor 2 Factor 3 Factor 4Factor 5 Factor 6 Factor 7 Factor 8
Reality
Realistic Expectations
Not measured
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Realistic Expectations
50%Happiness
How I feel right now.
Subjective Wellbeing
How I think my life is turning out.
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Pilots/ Champions
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Pilot teams should be comprised of
lead recipients who can be expected to
perform like typical end-users
Pilot projects help develop realistic expectations, reveal process flaws, and develop in-role champions.
Pilots | Champions
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Lead recipients normally have compelling reasons to find defects in lead scoring, and they will.
Pilots | Champions
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Pilots | Champions
A strong pilot team will help you socialize the project effectively, in terms the end-users
understand and trust.
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SLAs
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SLAs
World’s Most Popular Lead Scoring System
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Timing
Effort
SiriusPerspective: SLAs need to include accountability for both the timing and the amount or degree of follow-up.
SLAs
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Feedback | Iteration
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Feedback | Iteration
Part of every SLA should include specific, actionable feedback to marketing, enabled via technology not (just) word of mouth
Key Take-aways• Current lead scoring does not account for
enough of the variance in lead conversion• Modern data science can generate proxies for
questions your best salesperson would ask prospects if he/she could reach them all• It is possible to model contacts, accounts and
even existing customers• Prediction requires good, relevant data related
to consistent processes and outcomes• Unrealistic expectations will kill even the best
modeling efforts• Pilot projects will establish realistic
expectations and develop internal project champions