CAN WE USE BIG DATA TO
CLONE GEORGE CLOONEY
IN OCEANS 11?
Abner Germanow
@AbnerG
Wrongless.tumblr.com
Director, WW Marketi...
JUNIPER AT A GLANCE
2012 Revenue: $4.5 Billion
124 Offices In 45 Countries: 16 24x7 Support Centers: 5 R&D Centers
9000 Em...
TRIBAL KNOWLEDGE
THE CHALLENGE

5
4
3
2
1
0
A TALE OF TWO REPS

1 Account

1,200 Accounts

Knows all the key people

Knows some people

Knows the organization

Knows ...
GEORGE KNOWS:
HUMAN BEHAVIORS

Product or training certifications
Social media activity & engagement
Competitor product or...
GEORGE KNOWS:
ORGANIZATIONAL BEHAVIORS & ATTRIBUTES
Prior purchase history
Deployed technologies
Industry
Financial health...
A TALE OF TWO LEADS
Lead 1

Lead 2

Attended a Data Center Webinar

Attended a Data Center Webinar

Clicked through to whi...
ANALYTICS BY ENGAGEMENT STAGE

Data Source: SFDC Deal Flow
Current Data Visibility: XXXXX
Current Data Quality: XXXXX
Anal...
HOW I SPEND MY TIME

Navigating Legacy
Data, Systems, and
People
Training / Culture

Play Design

Customers
3 CULTURAL
BARRIERS
HOW DOES SALES SELL?

VS.
HIPPO VS. OBSERVED BEHAVIOR

5
4
3
2
1
0
MODEL VS MODEL

VS.

Culture eats models for breakfast.
The capacity and tools to consume a
model beat model quality by 10...
RESULTS
It gives us the
information we need so
we don’t look dumb.
- Inside rep
RESEARCH IN SALESPRISM IS CORRELATED TO
LARGER OPPORTUNITIES
Research use case

Targeting use case

All accounts
Pipeline,...
@AbnerG
WrongLess.tumblr.com
Data Driven Marketing: Can We Clone George Clooney in Oceans 11?
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Data Driven Marketing: Can We Clone George Clooney in Oceans 11?

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We now have enough data to move beyond linear lead scoring and account targeting based on tribal knowledge. In this deck, I share some of what we have found on the journey to data driven B2B marketing.

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  • We have cool customers
  • Tribal Knowledge is valuable. To what extent can you rely on it?
  • How do you augment or challenge tribal knowledge with data?
  • We also have or can buy much of this data.
  • Again, we have or can buy much of this data. No one buys a network because they want to, they buy because of a forcing or group of trigger functions.
  • If you don’t look at the problem holistically, you risk “random acts of analytics” – point your money at the biggest problems, not just the sexy stuff.
  • I spend time with the data, but also with customers. Why? Because data doesn’t tell the whole story.
  • The spectrum is broad. Calling down a list isn’t productive, but it’s what some people do. Ocean’s 11 style selling can win big, but takes a large investment in time and money.
  • There is value to the Highest Paid Person’s Opinion, but (most of the time) not at the expense of observed behavior.
  • Don’t spend time on “this model is slightly better than your model” If you have made it to the point where that conversation actually matters, you are already winning. Otherwise focus on culture and adoption.
  • It’s a low bar, but darn it, it is a good one.
  • Data Driven Marketing: Can We Clone George Clooney in Oceans 11?

    1. 1. CAN WE USE BIG DATA TO CLONE GEORGE CLOONEY IN OCEANS 11? Abner Germanow @AbnerG Wrongless.tumblr.com Director, WW Marketing Juniper Networks
    2. 2. JUNIPER AT A GLANCE 2012 Revenue: $4.5 Billion 124 Offices In 45 Countries: 16 24x7 Support Centers: 5 R&D Centers 9000 Employees 1997: Service Provider Core #2 In Core Routing, SP Routing, Network Security 2004: Secure Enterprise Edge #1 In High-end Firewall & Mobile VPN 2008-13: Switch, WLAN, Mobile Security #3 In Edge Routing, Ethernet Switching 20,000+ Customers, Including 96 Of Fortune 100 Powering 6 Of The World’s 7 Largest Stock Exchanges Juniper Is Deployed In More Than 380 Federal Government Agencies
    3. 3. TRIBAL KNOWLEDGE
    4. 4. THE CHALLENGE 5 4 3 2 1 0
    5. 5. A TALE OF TWO REPS 1 Account 1,200 Accounts Knows all the key people Knows some people Knows the organization Knows some organizations
    6. 6. GEORGE KNOWS: HUMAN BEHAVIORS Product or training certifications Social media activity & engagement Competitor product or training certifications Work history Event attendance and interaction Role and responsibilities Browsing history Content consumption / engagement Title / job responsibility Incentives / bonus structure
    7. 7. GEORGE KNOWS: ORGANIZATIONAL BEHAVIORS & ATTRIBUTES Prior purchase history Deployed technologies Industry Financial health Employee population changes Real estate moves, adds, and changes Social media buzz Credit scores Technical support events Geographic footprint Executive changes Merger & Acquisition activity Private equity investment Information intensity of business Technology investment persona Customer segmentation attributes Competitor Investments
    8. 8. A TALE OF TWO LEADS Lead 1 Lead 2 Attended a Data Center Webinar Attended a Data Center Webinar Clicked through to white paper Clicked through to white paper Financial Services $200k security installed base Real Estate Move
    9. 9. ANALYTICS BY ENGAGEMENT STAGE Data Source: SFDC Deal Flow Current Data Visibility: XXXXX Current Data Quality: XXXXX Analytics Needed: XXXXX Predictive Opportunity: Optimize current Q close Build one of these for each stage MQL SAL SQO Install Base CrossUpsell WON Are these the right attributes to assess? Sales Data Source: salesPrism Current Data Visibility: XXXX Current Data Quality: XXXXX Analytics Needed: XXXXX Predictive Opportunity: Optimize sales sourced opps Responder Inquiries Marketing Data Source: Eloqua Activity Current Data Visibility: XXXX Current Data Quality: XXXXX Analytics Needed: XXXXX Predictive Opportunity: Optimize lead scoring
    10. 10. HOW I SPEND MY TIME Navigating Legacy Data, Systems, and People Training / Culture Play Design Customers
    11. 11. 3 CULTURAL BARRIERS
    12. 12. HOW DOES SALES SELL? VS.
    13. 13. HIPPO VS. OBSERVED BEHAVIOR 5 4 3 2 1 0
    14. 14. MODEL VS MODEL VS. Culture eats models for breakfast. The capacity and tools to consume a model beat model quality by 100x
    15. 15. RESULTS
    16. 16. It gives us the information we need so we don’t look dumb. - Inside rep
    17. 17. RESEARCH IN SALESPRISM IS CORRELATED TO LARGER OPPORTUNITIES Research use case Targeting use case All accounts Pipeline, $ Thousands Accounts with plays Pipeline, $ Thousands +34% +82% When reps researched an account in salesPrism, they created larger opportunities. $152 When reps engaged accounts with recommendations, t hey created larger opportunities. $172 $129 $83 Not engaged Engaged Not engaged Engaged: rep views account details page and/or logs activity against the account in PRISM. Engaged
    18. 18. @AbnerG WrongLess.tumblr.com

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