Crafting Data Driven Buyer Personas

Presented by Justin Gray, Founder and CEO of LeadMD
Crafting Data Driven
Buyer Personas

Today’s
Promise
 Understand principals of data science
 Make it not sound so incredibly nebulous
 Make it actionable

About LeadMD
 Digital Marketing
consultancy specializing
in making strategy
actionable
 Focused on the Marketo
platform
 7 Years and 2600+
engagements

Workshop objectives
 To improve your knowledge of how data, analytics and
predictive marketing can help you better target and engage
customers and prospects at all stages
 To give you a set of tools that will help you design, implement
and succeed with applying buyer intelligence and predictive
data modeling to build intelligent buyer personas

At the end of the day,
we know one thing:
Our best customers are hard to predict at the
onset & flat data points don’t tell the story

The Wave of “Data Modeling &
Analytics”
Introduction

B2B Predictive Trends
 B2B predictive analytics is an emerging market with less than a
$100M in aggregate vendor revenue.
 36.8% of high growth companies investing in predictive
analytics over the next 12 months. (TOPO)
 As the market accelerates, buyers need a framework to reduce
adoption risk and demonstrate ROI.

The Machine Learning Evolution
Vs.

Danny Sullivan, MarketingLand on the topic of
Machine Learning and Google
‘‘To greatly simplify, it’s like
teaching the search engine to
paint by numbers, rather
than teaching it how to be a
great artist on its own.

So, [data] science you say?
September 1994 BusinessWeek publishes a cover story on “Database Marketing”
“Companies are collecting mountains of information about you, crunching it
to predict how likely you are to buy a product, and using that knowledge to
craft a marketing message precisely calibrated to get you to do so…”
(Source Forbes Media 2013)
Can you say you’re currently doing this?

Data Science Principals
Big data
Data sets so large and complex, that
traditional data processing applications
are inadequate.
Data modeling
The Formalization and documentation of
existing processes and events that occur
during application software design and
development.
Machine learning
A science of getting computers to act
without being explicitly programmed to
do so, studying user pattern recognition
and technological learning theory
Regression testing
The process of testing changes to
programs to ensure that the older
programming still works with the new
changes.

What is a Data
Model?  A data model organizes data
elements and standardizes how
the data elements relate to one
another.
 Data elements document real
life people, places and things and
the events between them, the
data model represents reality, for
example a house has many
windows or a cat has two eyes

Where are you at now?
But first…

Let’s take a quick poll:
No scalable lead
score model:
Our reps do a cursory
review of the lead’s data to
determine quality
Scoring via
FIRMOGRAPHIC
data points
Scoring via MA platform on
demographic and behavior
activity
Scalable Predictive
Presence
Using a data model to align
new prospects to known
buying traits and doing that
at scale
1 2 3
Poll #1: Where do you stand?

Where are
your peers at?
 Lead Scoring Benchmark
 (Source: EverString benchmark survey results)

But just because someone clicked a button
doesn’t mean they’re ready to buy
What marketing thinks sales wants: What sales actually wants:

The
traditional
funnel is just
garbage

For every 400
inquiries, only 1
becomes a closed
opportunity.
That is a
conversion rate
of .25 percent

The state of today
As we know, lead scoring is a combination of:
Behavioral
Click-throughs
Form submission
User activity
Firmographic
(inclusive of business behaviors)
Job title
Industry
Company revenue
These are all traits that make up marketer-driven models

What is the
future of
marketing?

The Future Role of ”Predictive”

What we mean by “model”
When we use the word “model” in predictive analytics,
we are referring to a representation of the world, a
rendering or description of reality, an attempt to relate
one set of variables to another.

A purely behavioral
model (Lead Scores)
predicts only 2% of the
variance in amount
purchased by buyers
(mildly predicts buyer
commitment, but not
spending).
Adding
demographic &
psychological data
bump lead scoring
up to 85%.
This is HUGE.

 Targeting your marketing to who you think your buyers
are won’t give you the concrete results that targeting
with data would.
 Data helps you know who they are, vs who you think
they are.

Why LeadMD uses predictive
The customers we talk to
are vastly different. Our
customers don’t necessarily
align to an industry or size.
Targeting shouldn’t be
based on hunches
1 2

Exercise 1: Let’s go
ahead and define the
“Who”
 Who are the customers we want?
 Who are the leads that will never
become customers
 An What differentiates the BEST
customers from just “OK”

Exercise 1: Define the Who
 What describes your best
buyers?
- Characteristics
 Firmographic/Demographic
 Behavioral
 What differentiates your BEST
from just ‘OK’?
 What describes your worst
buyers?
- Characteristics
 Firmographic/Demographic
 Behavioral

Exercise: Building
the foundation of your
predictive model
• What’s your positive and negative signals?
• What’s your unstructured data?
• How does this compare to what LeadMD
did?

Exercise 2: The role of signals
 Develop definitions of “Positives”
- Qualified leads
- Won opportunities
 Develop definitions of “Negatives”
- Unqualified leads
 Ensuring everyone gets the feedback on why they are such
 Use that status, they aren’t ready to buy now, so lets

Psychological Data
’Intent’ Data: The buyers mindset
& maturity allow us to win
 The Largest Predictor!!

We have to zero in on two
main descriptive signals
Personality/past experience
Position in the organization
What LeadMD Found…

This is Difficult!
 What blockers do you foresee?

The role of bias
 Where are your biases? For example, if you’re only looking at
opportunity creation, the predictive model you build has a natural
assumption that only the customers you’re working with now are who you
want to work with.
Good indicators:
 MQL – Do these people belong in your TAM?
 SQL – Are these people truly part of your ICP?

Sample Intent Survey
https://leadmd.getfeedback.com/r/7SxOWfyd

Let’s talk about data structure under this model

What is an Total
Addressable Market?
 Total addressable
market (TAM) is a term that is
typically used to reference the
revenue opportunity available
for a product or service.

Example: The LeadMD T.A.M.
 All marketers
- ICP all Marketo users/consider purchase
 With a layer of data nuances
- IDP 4/5 persona
- It’s truly based on interest

What is an ideal
customer profile?
 A description of a customer or
set of customers that includes:
- Demographic
- Geographic
- Psychographic characteristics
- As well as buying patterns,
- Creditworthiness
- Purchase history

What is an ideal
buyer persona?
A buyer persona is a detailed profile
of your ideal buyers based on
market research and real data about
your actual clientèle.
The more detailed your personas
are, the more results they’ll yield.

No lead left behind
The worst thing you can do, not assigning a lead
 Make sure statuses are always up to date
 It’s important to close off the bad behaviors
 Bad leads, stuck in bunk status = Time wasters
 Feedback loop, never going to happen.

Develop a process that works for your sales
org. You can write the process that the rep
retains the opp for 6 months.
That’s how marketing should be
enabling sales

Firmagraphics
Who are they?
What is it?
Field Based Data
Latency Issues
Quality Issues
Behavioral
What are they doing?
What is it?
Interactions
Engagement
Content Fallacy
Deconstructed
Experience driven data
What is it?
“In Head” Data
Subject to Prejudice
Subjective / Biased

THE RULES
Qualitative  Quantitative  Qualitative

Looking beyond score
Chances are, your data is incomplete.

Surveys as a game changer
 Our valuable data points
 Evolves in real time
 Quantifies what’s not known to the model

Meet Our Buyers
Extremely knowledgeable
who’s personality differs
based on her organization
 60% of buyers
 Guards her “island” and is
most cautious.
 Doesn't want a long term
engagement.
 Most purchasing authority
 Always looking for “gotchas”
so be on your game
Rising RitaEntrenched Edward Startup Sue
Young up and comer
in a rising institution
 15% of buyers
 Least time at position
 Replacing the old
guard's contractual
relationships.
 Aspiring to be the best
of the best
 A bit arrogant, but
smart, ultimately an
influencer you want on
your side
Tenured Exec with the
same lead manager
doing the same thing
and is bored to death
 20% of buyers
 Most time at position
 They want a fling and
they want it now
 High budget control, can
be a third party
consultant
Young, aggressive &
looking for love
 5% of buyers
 Most tech literate
 Lowest revenue,
smallest firm,
influencer level
 A marketing unicorn
who does a little bit of
everything
 A great partner for a
long lasting business
relationship
Poly Pam

Getting Formal:
Ask your sales & customer service reps
 You’ll get different answers based on:
- Spend
- Length of engagement
- Relationship (scale)
 1:3 additional
 NPS
 In-head data

Consumer-level data:
a new look at demographics
We talk about buyers being more than businesses,
but we don’t make that actionable

We’re not tapping into the
best practices of B2C that
we can leverage in B2B

Anyone
seen this
email
lately?

Opportunity &
Account Management
Part IV

Exercise 3:
Creating intelligent
buyer conversations
Right time, right place, right message
– a primer to intelligent lead routing
 Who handles ICP Qualified Buyers/Accounts?
 Who follows up with potential ICP additions?
 Where do non-ICP/IBP Buyers Route?
- Is there any value here?

A = Goes to Sales
B = BDR
C = Off to Marketing
Align the relevant resource
D = Off to Marketing

Exercise 3 (cont): Content Mapping Exercise
 Buyer/Account Persona
 Buying Stage
 Tailored Content that Converts
 Marketing & Sales Messaging is more than ’Air Cover’
- It is central to ABM Strategy & Execution

Scale to a sales playbook
 Personality of sales & service based on buyer
 Linguistics & Style based on Reps
MessageChannelBuyer Timing

Lead and Contact Routing @ LeadMD
69© 2014 LeadMDLeadMD Sales Playbook
SFDC Type Lead Contact
Record
Type
Master Business Account
Individual
Account
Recruiting
Prospect
Lead
Status or
Account
Type
New Lead
Warm
Lead
Hot Lead
AQL
Hot Lead
MQL
White-
label
Customer
Customer,
Inactive
Graveyard Prospect
Partner,
Reseller,
Vendor,
Press,
Competito
r
Customer Prospect
Owner
Lead
Queue
BDR
Queue
BDR Rep
Round
Robin To
SC
Round
Robin To
SC
90 Day
Business
Logic **
Initial
Owner
Transferre
d From
Lead
Owner or
Round
Robin'd to
SC
Justin Gray
Round
Robin To
SC
HR
Director

Marketing & Sales Alignment
 Key is routing not only AQL v SQL but also surrounding
campaigns
- Persona based nurture (engagement program)
- Show how marketing & sales work together on a “lead”

Look at interactions
It’s important to align your internal personas with your external
Big 5 Personality Traits Political Compass
Name Openness Concientiousness Extraversion Agreeableness Neuroticism Economic Social
Josh Wagner 4.3 (59%) 2.9 (24%) 4.7 (96%) 3.4 (22%) 1.2 (1%) 2.88 -3.33
Kurt Vesecky 3 (5%) 4 (76%) 3.8 (75%) 3.8 (39%) 2.1 (16%) 2.00 -1.28
Andrea Lecher-Becker 4.7 (82%) 3.8 (66%) 2.9 (41%) 3.2 (16%) 2.3 (22%) -4.63 -3.28
Caleb Trecek 3.3 (12%) 3.6(57%) 2.5(27%) 3.9 (45%) 2.3 (22%) -1.63 -0.15
Shauna Bradley 4.3 (59%) 3.8 (66%) 4.7 (96%) 4.4 (74%) 1.4 (3%) -8.25 -3.33

The Role
of Content
 Show how persona’s drive:
- Ideation
- Alignment
- Creation
- Execution
- Analytics

The
outcome
 Creating a home for
your content, driven by
best practices based on
what your buyers are
looking for

Part V
Where do we go from here?

Takeaways you can use tomorrow
 What are you going to do to clone your best customers?
 How are you going to use in-head data?
Resources to Use:
 Today’s Preso
 LeadMD & Everstring Case Study
 TOPO Predictive Report on LeadMD

Crafting Data Driven Buyer Personas

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