Increasing the odds: The conditions and correlates for predictive lead scoring success

Kerry CunninghamResearch Director, SiriusDecisions

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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