44 Steps to Cloning Your Best CustomersCreating Data Driven Buyer Personas

All statements presented are supported by 5 years of Marketing, Sales and Enablement data. The relationship metaphor is speculative, but it fits so well that it ceases to be a metaphor (since business is relationships and biology governs the world).Eric Smith, PhD Firstsight

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After 2400 marketing automation engagements we have

never encountered a true buyer persona as established by an organization

Buyers Self-Prescribe & Leave Way Too Early

THE FUNDAMENTAL ISSUE

THE HYPOTHESIS + Positive interactions are often

defined by perception + Revenue + Deal size + Length of engagement

+ Overcome the self-prescription bias + Marketers & clients are both culprits + Hand raisers don’t usually determine

the best buyer

The signals you can’t see, are perhaps more important than signals you can.

+ Stereotype + Develop a repeatable model + Personalize at scale

1. Data gathering is limited and biased2. Personas don’t translate well into

actions that engage

THE PROBLEM WITH PERSONAS IS TWO FOLD

RESEARCH (OR LACK THERE OF) IS WHERE EVERYONE FAILS5 CORE CONCEPTS

BUYER INSIGHT

RESEARCH

BUYER ARCHETYPE

DESCRIPTION

BUYER PERSONA

MENTAL MODELSTM

BUYER STRATEGY

BUYER PERSONA

SCENARIOSTM

Perform insight generation & inform buyer strategies

Create buyer persona descriptions based on concept archetypes

Interpret attitudes, perceptions, beliefs, & goals into mental models

Perform insight generation & inform buyer strategies

Identify the multiple buyer stories, activities, & buying scenarios

1STEP ONE Establish Definitions of “Good” & “Bad”1

SO, LETS JUST TAKE A STEP BACK & DEFINE...

... the customers we want

... the leads that will never become customers

... what differentiates the BEST customers from just “OK”

THE SIGNALS

+ Develop definitions of “Positives” + Qualified leads + Won opportunities

+ Develop definitions of “Negatives” + Unqualified leads + Lost ‘at-bats’ + Time wasters

ISSUES WE KNOW

Firmagraphics Marketers base models on data they know is crap (Salesforce)

BehavioralData that simply indicates good content (Marketo)

Deconstructed Data is subjective

+ “In Head” Data + Subject to Prejudice + Subjective / Biased

+ Interactions + Engagement + Content Fallacy

+ Field Based Data + Latency Issues + Quality Issues

+ We simply don’t know + We need to solve for this plus:

— Mining data out of someone’s head — Find negative conversion data points

+ It’s not about causality – its about correlation.

+ EverString provides a data hosting layer that’s not feasible for humans to comprehend

+ It’s about ACTIONS!

HOW DOES BIAS’ PLAY INTO OUR INTERACTIONS?

GOOD QUESTION!

THE PARTNERS

ERIC SMITH, PHD, FIRST SIGHT

+ Data Collectors + Data Analysis + Model Analysis

+ Data Modeling + Scoring Software

+ Research Psychologist

2 STEP TWO DEFINE THE PROBLEM2

APPLY PREDICTIVE LEAD SCORING WHENEVER NECESSARY.

Build a strong foundation – not just basics around revenue, titles, and roles.

ITS ABOUT MINDSET

+ Closed won/lost is not enough. + Activity MUST be in the system. + Accumulate knowledge about

your best sales reps and marketing campaigns.

+ Focus on messages and content.

CAPTURE MORE DATA

THE PROBLEM

Clone not only your best customers, but also clone your best sales interactions tailored to those personas.

3 STEP THREE GATHER THE DATA3

PsychologicalIntent

EngagementCompany Fit Score

BehavioralDemographic

THREE CORE DATA SETS

THE DATA DISTRIBUTIONA represents 10% of your prospects. 10% of our data has the potential to become our BEST customers.

THE DATA INFLUENCE ON CONVERSATIONA converts at 4.3x the baseline and 14.3x the rate of D prospects. A & B leads account for the vast majority of leads who convert to our positives.

THE DATA DISTRIBUTION + CONVERSATIONMost of our positive outcomes are weighted strongly to that A bucket.

80% of leads are D leads = how do you know?

What is the impact to your brand by not understanding the buyer?

What’s the impact to the buyer if your timing is off?

THE RULES

QUALITATIVE QUALITATIVEQUANTITATIVE

THE PROOF

Behavioral or Demographic data points are worthless if you can’t interpret what motivates the behavior, or how the demographic points relate. Unlock the hidden dimension of your leads.

INFORMATION IS NOT INTELLIGENCE

THE RESULTS

Opportunity lift from better understanding of buyers

5X5X

Adding demographic & psychological data bump lead scoring up to 82%. THIS IS HUGE.

KEY FINDINGSA purely behavioral model (Lead Scores) predicts only 2% of the variance in amount purchased by buyers (mildly predicts buyer commitment, but not spending).

COMPANY DATAAccount level data allows us to define the universe

BEHAVIORAL MODEL DATAEngagement allows us to zero in on timing

+ The fact is, activity on assets you own are already restrictive.

+ By changing this to encompass the WORLD and not just your own assets, we finally break the numbers game.

+ We can blow up the funnel concept!

PSYCHOLOGICAL DATAThe buyer’s mindset & maturity allows us to win

+ Historically the largest predictor could only be compiled by talking to the buyer.

+ We are closer than ever to causality – and that’s only after 60 days

4 STEP FOUR EXTRAPOLATE THE DATA4

THE SOLUTION

Nature finds balance by doing both extremes, not by doing neither or diluting both.

Your clients are like cuckolding birds... + They want the long term mate that,

although boring, provides them with a sense of security (in biology, to take care of the kids).

+ But, sometimes they just want to hook up with a rockstar (in biology, to give the kids better genes).

MEET THE BUYERS

Rising Rita. Young up & comer in a rising institution

+ 15% of buyers + Least time at position + Replacing the old guard's

contractual relationships + Team player - new wave

of management style

Entrenched Edward. Tenured Exec with the same lead manager doing the same thing & is bored to death

+ 20% of buyers + Most time at

position (bored) + Wants a fling - Now

(timing) + High budget control

Startup Sue. Young, aggressive & looking for love

+ 5% of buyers + Most tech literate + Least sales background + Lowest revenue + Smallest firm

The Small Crew. Only looking for a fling

+ 60% of buyers + Most cautious -

No interest in a long-term relationship

+ Most purchasing authority

+ Most sales background

Over 20,000 Signals Per Record

YOUR CUSTOMER PROSPECTS

Technology

API’s usedWeb technologies used SaaS applications used

IT skills on LinkedInHosting provider

Web traffic

Organizational

Prevalence of titlesComposition by department

ProductsPartner ecosystemFederal filling dataJob requirements

Event based

Funding activityM&A activityPatent fillings

Event attendanceGeographic expansion

Key hires

Social

FacebookLinkedInTwitter

Google+YouTube

Blogs

KEY FINDINGS

KEY TAKEAWAYS

+ 20% of the database contains 80% of the potential. + This is nothing new + Data builds trust/validity in the belief

+ Behaviors alone bear no indication of potential. + The key to unlocking the buyer is the Psych Data.

BUILD YOUR OWN MODEL + Randomly shuffle the output (target variable)

on the training data to “break the relationship” between it and the input variables.

+ Search for combinations of variables having a high concentration of interesting outputs.

+ Save the “most interesting” result, and repeat the process many, MANY times.

WHAT ARE WE TESTING? + Product alignment to personas + Sales team assignment via personas + Consultant assignment + Tailored sales plays + Persona based nurture through Marketo persona assignment + Post opportunity survey

LET’S START BLOWING STUFF UP

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