For all of the resources I mention during this presentation, head over to: http://www.etumos.com/summit Presentation Video can be found here: https://youtu.be/0pyK_q7T2Kw Presentation Overview: This talk goes over the maturity stages for Lead Scoring in Marketo. We focus briefly on Stage 1 for baseline, then dive deep into Stage 2 and Stage 3 of scoring maturity. We discuss how to use Data Science teams in Marketing Operations to create incredibly-accurate scoring models based on real data instead of intuition. We give you a list of key Score components, what they do, and how to improve them. Finally, we talk about an iterative model of how to extract the most value from Stage 5 Maturity, and how to jump up a level in Maturity. ~~~ For more Etumos-specific resources, check out our other videos or head over to our website. Feel free to send me an email if you have any questions! ~Edward and the Etumotes

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Let’s face it, Lead Scoring needs to be reinvented.
Presented by Edward Unthank of Etumos

2. © 2019 Adobe. All Rights Reserved. Adobe Confidential.
Scoring
Philosophically
Implementation
and Testing
Scoring
Maturity
Stages and
Deepdives
Agenda

3. © 2019 Adobe. All Rights Reserved. Adobe Confidential.
What is Scoring?
A number representing
whatever you care about –
like a score!
y = b0 + b1x1 + b2x2 + … + bnxn + E
All of the factors that
affect (in some way)
the result
ECONOMETRICS

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Types of Scoring y = b1 + b2 + b3 + b4 + … + bn
BEHAVIORAL
DEMOGRAPHIC
PSYCHOGRAPHIC
FIRMOGRAPHIC
ACCOUNT-BASED
ENGAGEMENT MINUTES
FIT SCORE
INTENT SCORE
PROFILE SCORES
Meaning triggered based on measured activity
Database person required, like Title, Department or Role
Probably less for B2B (religion, etc.)
Company demographics (employee #, revenue, etc.)
Employee (contact) scores added together
Measured (ish) minutes of company engagement
How similar you look to an ideal prospect
How many relative “likely to buy” signals
How similar you look to A PROFILE

5. © 2019 Adobe. All Rights Reserved. Adobe Confidential.
ECONOMETRICS
A measure
of how much
a person
interacts with your
properties
Behavior Score = amount of high value web page visits
+
+
+
+
+
x
x
x
x
x
amount of low value value web page visits
amount of email clicks
amount of webinar attendees
amount of form fill-outs
n
score # per that event
score # per that event
score # per that event
score # per that event
score # per that event
y = b1 + b2 + b3 + b4 + + bn

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Maturity StagesMarketing Intelligence Operations
STAGE 1:
Directional
Math
a. Lead Score
b. Behavior Score + Demographic Score
c. Fit Score + Intent Score
1
3
STAGE 2:
Predictive Tools
a. Fit Score
b. Intent Score
c. Lookalike Score
STAGE 3:
Data Science Owned
a. Expected LTV (real-time)
b. Expected ACV (real-time)
c. Custom ”Best Message” profiling
d. On-demand reporting and intelligence
2

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Scoring is an additive measure
✓ Stack-ranking value
✓ Fast and dirty
✓ Better than nothing
PROS
☓ Usually inaccurate
(so inaccurate that Sales ignores them)
☓ “What does that score mean?”
CONS
STAGE 1: Finger in the Wind Scoring
It’s a bottom-up measurement.
Does not correlate with,
How much does this person want to be
contacted by sales?”

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CONS
✓ Way better than before
✓ If you have enough data
(~1,000 opportunities
per year minimum )
Requires large Sales throughput
to be valuable
Slow speed to changes, mostly
unchangeable from
a few scores (Fit, Intent)
This will literally
make you
money if your
Sales team
adapts.
PROS
Scoring is “infinitely” multifactorial
STAGE 2: Predictive Analytics Vendor

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CONS “Bring your own Infrastructure”
Tight talent market
Needs organization buy-in
PROS
✓ Enormous competitive
advantage
✓ Regressions, neural networks,
or any other tool
SCIENTIFIC
MARKETING!
Peer-reviewable!
Withstands
extreme scrutiny!
Scoring needs close human supervision
STAGE 3: Data Science-Run (MIOPs)

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Percent
of MOPs
Organizations
STAGE 0
Nothing
STAGE 1
Finger in Wind
STAGE 2
Predictive
Vendor
STAGE 3
MOPs Data
Science
60%
15%
20% 5%
50%
100%
0%
* Source = Edward’s Anecdotes, not actual data
Maturity Distribution Among Companies
with MOPs

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How to Math Your Score Weights
Failure Group
Success Group
BEHAVIORAL DEMOGRAPHIC
20 2 0.25 0.6
5 2 0.01 0.6
STAGE 1: Simple Cohort Comparison
etumos.com/summit
We have all our resources here,
including this Gsheet!

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How to Math Your Score Weights
BEHAVIORAL DEMOGRAPHIC
STAGE 1: Simple Cohort Comparison
Ratio
Weight
4:1 1:1 25:1 1:1
4 1 25 1
etumos.com/summit
We have all our resources here,
including this Gsheet!
Failure Group
Success Group 20 2 0.25 0.6
5 2 0.01 0.6

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Here,
just take
these values
STAGE 1:
etumos.com/summit
Or if you have no data,
just use this...

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If you have the data,
PREDICTIVE vs. “Finger in the Wind”
will make you money
Considerations
Blackbox?
Enrichment?
IDEA
Shortlist 2 vendors
Bake-off
Lots of options, it’s up to you to decide
STAGE 2: Predictive Vendors

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Data in Warehouse
Create and Monitor
Scores for Marketing
& Sales
Run Controlled Test to
Measure Efficacy
of Marketing & Sales
Amount
Accuracy
Individual Demo Score
Company Demo Score
Recency Score
Profile Score
Random Cohort Assignment
Challenger / Champion
Controlled Tests
Responsibilities
STAGE 3: Data Scientist for Marketing (MIOPs)

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Provides
knowledge of
WHAT % of
Marketing Budget
to pitch
MarTech Budget
Marketing Cost (Staff, LinkedIn)
Media Spend
ESTIMATES
SALES ANCHOR PRICE
Estimation
Best-guess Sum of
media spend categories
(SEM, Events, etc.)
Use “Clearbit” to find what
MarTech is in use, sum
estimated costs by price lookup
in “martech pricing sheet”
GSheet via FlowBoost
Count Prospect’s Marketing
Department by roles on
LinkedIn, estimate staff cost
based on average salary data
SCORES: MATH TO MARKETING/SALES
Estimating Relevant Prospect Budget
STAGE 3: Example of Custom Estimate Score

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Change “Product
Interest” to GOAT
● Title Contains “Chief, CEO, CIO, COO”
● Employee count > 500
● Industry contains “farming, agriculture, yoga”
y = b0 + b1x1 + b2x2 + ... + bpxp + E
Product Interest
Segment: GOAT
LAST
TOUCH
FIRST
TOUCH
FIRST/LAST
TOUCH
If “Product Interest”
is empty, GOAT;
else do nothing
Hidden field “Product
Interest” = MOOSE
One-off Behaviors (instant win values)
Demographics (field values)
Profiling programs,
score-driven
lookalike/behavior
comb percentage
1
2
3
1
3
Product Interest: GOAT
Product Interest: MOOSE
Product Interest: Primary
76.2%
25.9%
GOAT
Profile Scoring: The MOPs Advantage
TARGETING
2

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S
R
I
C
M
Scalable
Clear
Robust
Intelligent
Modular
etumos.com/summit
S.C.R.I.M1) CRM Doesn’t want score
changes – it wants grade
abstractions
2) Don’t forget “low grade” leads.
Monitor or have XDRs do that –
DATA PROBLEM SURFACING
3) Roll out thoughtfully
Nobody knows
what “21” means.
We know “C” though!
Minimize unhelpful
data value changesS
I
A single remote
worker can slip
through the cracks
Implementation Tips for Advanced Scoring
C

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DATA MANAGEMENT
PROFILE SCORING
WHY?
Implementation Tips for Advanced Scoring
Marketo at a megascale should off-
load simplistic data manipulation to
another system
“What happens if 150,000 leads
run through this at once?”
REDUCE THE NOISE.
“If...then” statements and tons of
data value changes are a pain to
maintain in Marketo.

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Get a Sales friend to help.
→ Train → Measure → Shout Success
Turn it on! Sales team get it and uses!
1) Recruit a Pilot with a select Sales Team to
prioritize by New Score (a test).
2) Get the loudest person you can. Build WITH them.
3) Communicate change to come from department head, enforce vision.
4) Follow training and onboarding schedule
CHAMPIONS!
How to roll out new Scores to Sales
Roll Out and Change Management Plan
Ideal World
Real World
Political World

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Political World
4) Follow training and onboarding schedule:
1-Hour Video Training
Soft Launch
Open Office Hours for Questions
Email from Head – go on Monday!
Hard Launch
Proposed Training and Onboarding Schedule

22. © 2019 Adobe. All Rights Reserved. Adobe Confidential.
Edward Unthank
Email | eunthank@etumos.com
Twitter | @EdwardUnthank
LinkedIn | edwardunthank