[webinar] how to be a data-driven marketing powerhouse with predictive analytics & big data

© 2015 Mintigo. All Rights Reserved. www.mintigo.com

How To Be A Data-Driven Marketing Powerhouse With Predictive Analytics & Big Data

Tony Yang Webinar Host

Russell Glass Head of Products

Megan Heuer VP & Group Director,

Data-Driven Marketing

John Bara President & CMO


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Mintigo Enterprise Predictive Marketing Platform

Our mission is to master data science to revolutionize the way

people market and sell.


Russ Glass!Head of Products!

@glassruss!

@LinkedIn!

The Age of DATA

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“The number of transistors on a computer chip will double approximately every two years.” Gordon Moore, founder of Intel

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BIG DATA

“Big data is the most disruptive business force there is. Big data is the stuff that is really moving economic power from one group to another.” Geoffrey Moore, Crossing the Chasm and Inside the Tornado

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“Half the money I spend on advertising is wasted; the trouble is I don't know which half.” John Wanamaker, Pioneering 19th Century Retailer

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Don Draper IT’S TOASTED

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IBM 360

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THE BIG QUESTION ABOUT BIG DATA: How do I implement big data principles in my own business?

PRINCIPLE NO. 1

Determine what you know (and want to know) about your customer.

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PRINCIPLE NO. 2

Start small by thinking, ‘Big data, little triggers.’


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PRINCIPLE NO. 3

Be prudent but not shy about investing in technology: CRM systems are a must, marketing automation is becoming so, and analytics tools are a no-brainer.


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PRINCIPLE NO. 4

Hire the right people. Marketers must hire data-oriented people, math majors, and left-brained thinkers.


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PRINCIPLE NO. 5

Test, test, test, measure, measure, measure. Ideally, measure your contribution to revenue: It is the way to prove marketing’s value.


John Bara!President & CMO!

@John_Bara!

@Mintigo!


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~80% of marketing budget is wasted…

This is a scientific fact!

THE MARKETING BLACK HOLE


WHY IS THIS?

MQL

SAL 66%

SQO 32%

Won 7%

“ T h e C l i f f “ There is no leverage in today’s demand gen process

2.89 wins per 1,000 inquiries*

93% Loss Rate

AN INEFFICIENT PROCESS

60% to 80% Data is bad or has no chance of closing

40% to 50% Wrong nurture track / inability to see stage

4x to 10x Top line growth leU on

the table

Process relies on data shared by the customer with liWle augmentaXon

MARKETING AUTOMATION

Campaigns List buys

Whitepapers Webinars Tradeshows

DEMAND ACTIVITY MARKETING FUNNEL

Nurture Score Analyze

MQL SAL SQL

DEMAND GEN PROCESS

Cost per Win Outstrips ROI in Most Cases

AN EXPENSIVE PROCESS

2.89 wins per 1,000 inquiries @ average $43 per inquiry

Leverage vast amounts of data and science to predict which markeXng acXons have a high probability to succeed and which ones will probably fail.

DEFINITION: PREDICTIVE MARKETING


•  Leverage Data Science in every step of your workflow

•  Put Data Science in your hands

FIRMOGRAPHIC DATA: Company name Domain Industry Revenue CONTACT INFORMATION: Name Title Email BEHAVIORAL DATA: Downloaded whitepaper

Data Is The Key – Current Data Points

Technologies •  API Provider •  Saas product •  Databases:

MySQL User, MS SQL Server, Oracle DB •  Mobile Developers: iOS Developers •  VMWare User & VirtualizaXon Experts •  Oracle User •  Cloud Compu6ng Tech: AWS •  Cloud Compu6ng Tech: Azure •  Data Center User

Apps & Tools •  Email Service: MS Exchange Online •  MS Office 365 User •  MS SharePoint User •  CollaboraXon Tools User: Jive, Yammer, ChaWer •  Atlassian & Jira Users •  Hiring Enterprise Content Mgmt Expert

Web Technologies •  DNS: Neustar, GoDaddy, Dyn, MadeEasy, Amazon •  CDN Technology: Akamai, Amazon •  CMS Technologies:

SiteCore, Joomla, WordPress, Drupal •  Web Analy6cs Technologies:

WebTrends, OpXmizely, CoreMetrics, Adobe Omniture, Website Technology: Ad Services, Live Chat

Company •  Growing Company: Hiring >250 Employees •  Has MulXple LocaXons •  Company Employs Field WorkForce •  Mobile: BYOD IniXaXve •  Mobile: MDM/MAM Technology •  Alexa Ranking •  PPC Budget Spend •  Company has Call Center •  Compliance: SOX, HIPAA, FINRA/FISMA •  AdverXsing Technologies: Atlas, Google Adroll,

Google Adwords, DoubleClick for AdverXsers

DATA: Mintigo’s Marketing Indicators

IDENTIFY THE CUSTOMERDNA™

Ideal Prospect 150-‐250 MIs

Customers

Prospects

Fit score & appended Leads

2,500+ MI’s 10 MM Companies 150 MM Contacts

Data as is . . . Enriched Validated Appended

AUTO POPULATE CUSTOMERDNA™

2,500+ MI’s 10 MM Companies 150 MM Contacts

•  Who are my ideal prospects ? Discover your CustomerDNATM

•  How should I communicate to them ? Use Marketing Indicators to create micro-segmentations

•  What should I say to them ? Predict best content to segment fit

•  Where do I put my resources & focus ? Create scoring models

Decisions: Making the Right Marketing Decision

LEAD PRIORITIZATION

•  IdenXfy leads most like to convert •  Pass high scores directly to sales •  Nurture B leads •  Scale for capacity •  Leverage MI’s to assign nurture

Lead Enrichment & PrioriXzaXon Telesales ProducXon

AUTO LEAD ROUTING / NURTURE ASSIGNMENT

Audiences

Actions

Decisions

CASE STUDY: RED HAT

Loosen Status Quo

Commit to Change

Exploring SoluXons

Commit to SoluXon

JusXfy Decision

Make Decision

COVERING THE BUYER’S JOURNEY

Discovery Consideration Decision

Fit Analysis -‐ Journey Relevance

Inside the Funnel Outside the Funnel

Mintigo Data Customer Data SiriusDecisions, Buyers Journey Model

Intent Analysis -‐ Journey Relevance

Behavior Analysis -‐ Journey Relevance

PREDICTIVE IS MORE THAN A SCORE

Target Accounts

Air Traffic Control

Customer Lifetime Value

Campaign Design

Segmentation

Cross Sell

Lead Enrichment & Prioritization Telesales production

Nurture Design

Customer focused

Content focused

Valu

e Fo

cuse

d

Higher ASP

Data Validation & Enrichment

Insights Up Sell

Customer Retention

Partner cDNA

Net New Focused

List Buys

Incentivized Content

Syndication

A B C

Real-Time Optimization

New Accounts

Marketing Data > Marketing Decisions


Megan Heuer!VP & Group Director!Data-Driven Marketing!

@megheuer!

@SiriusDecisions!

© 2015 SiriusDecisions. All Rights Reserved

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Getting Started With Predictive Analytics & Big Data

• How can you tell it's time to bring in analytics to improve performance?

• Where are the most valuable places to apply data-driven approaches right now?

• What are the biggest pitfalls to avoid with introducing data-driven approaches?

• What is important to consider when choosing an outside partner?


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Big Data’s Value Comes Down To Doing The Basics Better

Data-driven marketing is analyzing and applying what we know to the choices we make about who and how to engage and to measuring how much those actions contribute to growth.


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Data-Driven Marketing Delivers On The Ideal Combination of Math Problem and Personality Test

Marketing must align its efforts to the accounts and actions most likely to deliver growth. Marketing must execute in a way that respects and engages individual customers based on their needs, preferences and timing.


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“Data-Driven” Really Means “Customer Driven” at Scale

Our organization and products

The tactics marketing loves to use

The accounts and buying centers sales wants to target

The tactics that should be used

The target audience within accounts our business needs to

address

Who or what is most influential on its decisions

FROM:

TO:


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How To Tell When You Can No Longer Live Without Data-Driven Demand Creation

Inquiry Outbound Inbound

Marketing Qualification

Teleprospecting Qualified Leads (TQLs)

Teleprospecting Accepted Leads (TALs)

Automation Qualified Leads (AQLs)

Teleprospecting Generated Leads (TGLs)

Sales Qualification Sales Accepted Leads (SALs)

Sales Generated Leads (SGLs)

Sales Qualified Leads (SQLs)

Close Won Business

Response Rate Low(er)?

Harder to Get Right Contacts on the Phone?

Sales Acceptance Lower Than It Used to Be?

Too Much In “Dreaded Stage 0?”

Takes Longer to Close Deals?

Marketing Not Sourcing As Much Pipeline?


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Identify Areas That Require Insights Then Prioritize Efforts Based on Impact, Data Access and Skills

2. Personalization 5. Outbound Outreach

1. Market Intelligence

Who do I want to engage, where do they want to engage and what will they care about?

How do I maximize conversion to revenue by only spending time on leads we can close?

How do I make sure leads get to the right person fast?

How can I segment customers and prospects using meaningful insights and behavior triggers?

How do I maximize engagement at all stages of buying and post-sale?

What and who do I need to know in my target market(s) and accounts and what do I have already?

6. Reporting

7. Analytics

How do I prove the value of marketing?

How do I use past performance to improve future results?

How do I make smarter predictions?

Seven Data-Intensive Marketing Focus Areas

3. Lead Scoring

4. Lead Routing


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Don’t Try This At Home: Pitfalls on The Road To Becoming a Data-Driven Marketing Powerhouse

Pitfall Group One: Things We Don’t Have or Don’t Do

Lack of communication Lack of training/skill development Lack of outside tools or services Not tracking results or impact


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Don’t Try This At Home: Pitfalls on The Road To Becoming a Data-Driven Marketing Powerhouse

Pitfall Group Two: Marketers Who Do Too Much

Taking on too many projects Making projects too complicated Attempting full roll-out too soon Covering up data issues


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The Scariest Pitfall Of All Is The Monster We Create Ourselves

Internal politics and culture


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Choosing the Right Partner: Experience Is The Key Driver of B-to-B Decisions

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B-to-B Buying Decision Drivers From 2015 SiriusDecisions Study Question: What was most significant driver of decision to select vendor of choice?

Dire

ct 34% Previous experience with the company

8% Implementation of customer support services were the best 7% Relationship with salesperson

10% Influence of references sourced independently 8% Influence of references provided by vendor 4% Perception of brand with no previous experience In

dire

ct

71% of b-to-b decision drivers are based on direct or indirect customer experience

vs. 18% of decision based on promise of offering to meet needs vs. 9% of decision based on the price was the best

=


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Choose Partners Wisely: Three-Part Checklist

2. Technology Fit 3. Experience Fit 1. Solution Fit

•  Do we have goals for what we want the solution to deliver?

•  Do we know how we’ll measure whether we achieved those goals?

•  Do we have resources on our team prepared to make the solution work (i.e., skills, bandwidth, project management)?

•  Will this tool work within existing infrastructure?

•  Will the tool/service address most of what we need?

•  Is it redundant with anything we already have?

•  What integrations are required for it to be valuable?

•  How much of the solution will we be able to use right away based on our current state of data and skills?

•  What do customers with similar goals say about their experience?

•  Will we get help we need to deploy?

•  Will post-sale support and account management be high quality?

•  Can we learn from this vendor?

•  Are we comfortable with them in meetings with our team?

•  Would we be comfortable if they had to present to our boss?

•  Would we feel comfortable if one of their executives met our CEO?


Q&A

Tony Yang Webinar Host

Russell Glass Head of Products

Megan Heuer VP & Group Director,

Data-Driven Marketing

John Bara President & CMO


THANK YOU!

[webinar] how to be a data-driven marketing powerhouse with predictive analytics & big data

Business

big data principles

big question

data science

age of data

dataoriented people

marketing automation

marketing budget

marketing black hole