A few chapters on some cool tools, analytic techniques, case examples and sales/marketing partnership opportunites.

1. Agenda / Menu
1

2. Big
Data Voodoo
Daddy
Agenda / Menu
# BDVD # FutureM 2

3. Big
Data Voodoo
Daddy
(or mama)
Agenda / Menu
# FutureM 3
# BDVD 3

4. # BDVD
Ed Alexaner
Ed Alexander, Managing Consultant
@fanfoundry
Agenda / Menu
# BDVD # FutureM 4

5. Agenda / Menu
What is it? News and Views
Cultural and consumer trends
Corporate Trends
Technology Landscape (the Cool Tool Pool)
Demo Time
A Test Methodology (BADIR)
Use Cases
Ways to test your own data
Get Better Data (7 Quiz Questions)
5 Public Sector Mashups
Get Real (time)
Future Events, Resources
# BDVD # FutureM 5

6. Defining “big data” – the four V’s:
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7. If
Data
Could
Talk…
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8. If
( )
Data
Could
Talk…
# BDVD # FutureM 8

9. Challenges – tooling up to:
• Capture, combine and curate
• Store, search and share
• Analyze and visualize
# FutureM 9
# BDVD 9

10. Opportunities
• Internet search
• Business informatics
• Medical research
• Genomics
• Astronomy
• Aviation
• Meteorology
• Finance
# FutureM 10
# BDVD 10

11. Sources – 2 new quintillion bytes / day
• Sensors
• Mobile devices
• Cameras
• Microphones
• Social graph – UGC
# FutureM 11
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12. The news, in general…
The worst economic crash in 75 years
A world economy with no place to hide
“Always on” connectivity
Widespread distrust of business
Activist shareholders and special interest groups
How does it impact your marketing agenda?
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13. Big Data in the news…
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14. Big Data in the news…
Article upshot:
Don’t blame Wal-mart
The customer has all the power
Example: Kroger (coupon response)
• 70% of targeted
• 3.4% of mass mailed
Analysts & Techs Quoted:
• Kantar Retail
• Symphony IRI Group
• Catalina Marketing
Modiv Media’s “Scanit!” device
• 89 Degrees
# FutureM 14
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15. What next?
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16. What next?
Special
Big Data
Issue
# FutureM 16
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17. The corporate view: big data in marketing
Emerging stages – some business sectors have gone
mainstream; Marketing is tooling catching up
Mainly departmental - not much data integration or sharing
Intuition based on business experience is still a driver; data
analytics plays a supporting role
Data challenges persist: accuracy, consistency, access, realtime
Talent shortage - challenges business to apply results
Culture’s role: orgs with a “culture of measurement “ succeed
# BDVD # FutureM 17

18. The corporate view: big data in marketing
Bloomberg Business Week Research Services
# FutureM 18
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19. The corporate view: big data in marketing
Bloomberg Business Week Research Services
# FutureM 19
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20. The corporate view: big data in marketing
1. CXOs now paying attention. Why?
• Competition – lead, catch up, patch up PR
• Predictive Intelligence – detect, adapt, seize opportunity
• Optimization - don’t want to leave money on the table
2. Elusive answers are suddenly more attainable
• Operations, Sales, Marketing, Customer Care, R&D, etc.
3. Transformation can now be justified with data
• Train managers as analysts so they can produce and consume data
• Rely on their business knowledge to interpret and act on data
4. Priorities can be tuned
• Identify top few “needle mover” opportunities and focus on them
• Decision support can gain visibility based on proven results 20
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21. Cultural trend:
Data-driven, custom communication
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22. Cultural trend:
Data-driven, custom communication
1992: sad :(
PointCast
Intrusive
In your face
Off-target
Poor quality
# BDVD # FutureM 22

23. Cultural trend:
Data-driven, custom communication
1992: sad :( 2002: mad ):
PointCast “Push sux”
Intrusive Subversive
In your face Intrusive
Off-target Spooky
Poor quality Invasive
# BDVD # FutureM 23

24. Cultural trend:
Data-driven, custom communication
1992: sad :( 2002: mad ): 2012: rad! :)
PointCast “Push sux” I want my MDV
Intrusive Subversive Welcome
In your face Intrusive Expected
Off-target Spooky Preferred
Poor quality Invasive …but secured?
*MDV: Massive Data Visualization
# BDVD # FutureM 24

25. The new consumer demand:
“I want my MDV”:
We’re always on, and doing it now -
• Showrooming
• Facebooking
• GPS navving
• Socializing – Foursquare, Twitter, Instagram, etc.
• Shopping & Banking
• Customer care
Cool tool • Audience & Community building
• World blending (ex: QR, text, POS, Call Center
Retail, ecommerce, mobile
# BDVD # FutureM 25

26. The new consumer demand:
“I want my MDV”:
Millenials are Digital Natives – mobile, social and always on
They blur the lines between the digital and physical world
They are less concerned about what’s going on with their data *
By 2020, they will account for 50% + of retail spending
Post-millenials are growing up digital *
They seek trust, transparency and
authenticity
# FutureM 26
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27. Corporate Trends
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28. Big Data's Shifting Focus: Transaction > Engagement
Personal
Systems Analog Transaction Engagement Experiential
Fulfillment
Circa Pre-1950's 1950+ 2000+ 2005+ 2010+
Reliability & Continuous Sense and Agility and Intention
Design Point
stability improvement response flexibility driven
Challenge Human Computing Social Contextual Individual
Comm. Style Analog Systems Dictatorial Conversational Role tailored Personalized
Multi-channel, Bionic,
Social-led,
UX Physical Machine based
real time portable
omni-media
Time / space
Speed Governed Just in time Real time Right time
continuum
Corporate & Personal,
Reach Physical Corporate Value chains
Internet one to one
Information & structured Immersive Self-aware,
Word of mouth Knowledge flows
Knowledge records & data information embedded
Social Tangentially Fundamentally Pervasively Ubiquitously
Water cooler
orientation social social social social
Intelligence Human based Hard coded Business rules Predictive Pattern based
Loyalty, Social
Community &
Examples assembly line Payroll, ERP, CRM reward, games, relationship
social business
context management
Source: R Wang & Insider Associates, LLC.
# FutureM 28
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29. # BDVD # FutureM 29

30. # FutureM 30
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31. Gartner: 72% have a “CMTO” today
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32. http://www.emarketer.com/Article.aspx?R=1008909
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33. ( What, no real time? )
72%
http://www.emarketer.com/Article.aspx?R=1008909
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34. Technology Landscape (Cool Tool Pool)
DAM SEO
Email Testing & Search & PPC ads
Marketing Optimization
VIdeo
Landing Site add-ins
Web sites
Pages
Marketing E-commerce SM Ads
Automation Webinars
Targeting Display ads
CRM Community
Personalization
SM marketing Call center
B2B Data Multi-channel
Gamification
Analytics Mobile
Databases Design Creative
Chat
Big Data Events Video ads
Datasets PR
APIs Surveys
Collaboration
Cloud
Business Customer Loyalty
Intelligence Experience Location Agile
# BDVD # FutureM 34

35. Technology Landscape (Cool Tool Pool)
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36. Stretch Goals for Cool Tools
1. Rapid time to value - always on, omni-channel, user chummy
for staff and customers
2. Point and click customization - user-driven, brain dead simple
3. 360 degree customer view – every salient data source linked,
integrated and secure
4. Real time visibility - instant refresh for all customer-facing and
decision making (tactical) occasions
5. Clean data - easy for all users to maintain, inspect and fix
6. High adoption - self-training, guided navigation, less clutter
7. Extended success – new & extended capability, new advantage
8. Broad community - best / better practice sharing – each one
teach one
# FutureM 36
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37. The payoff: central data + cool tools
Strategic Goals
1. Boost productivity and efficiency
• Centrally accessible, multichannel marketing data
• Serves across addressable marketing channels
• Easier to find and act on than data trapped in silos.
2. Reduce costs, improve marketing productivity
Centralized multi-channel marketing data:
• Improves ability to target and glean subscriber intelligence
• Improves efficiency of data intelligence tasks
• Improves organizational alignment
3. Enhance customer segmentation and personalization
• Consistent view into multichannel customer data
• Improve segmentation, 1:1 personalization, relevance
# FutureM 37
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38. The payoff: central data + cool tools
Tactical goals
• Campaign analytics and testing
• Optimization, Acquisition, Lead Generation
• Predictive Modeling – what is your killer niche?
• Segmentation / Personae – who acts how?
• Attribution precision – across channels, online and offline
• Valuation of social media
• Design testing (multivariate testing)
• Websites
• Emails
• Offers
• Messages
# BDVD # FutureM 38

39. It’s
Demo
Time!
# BDVD # FutureM 39

40. Framing the Discussion (Surprise!)
It’s not about data & dashboards, it’s about culture & context.
Ask: how can data help solve problems and guide decisions?
1. Decide which challenges you’d like to address. Examples:
reducing customer churn ● improving sales
reducing inventory cost ● improving upsell / cross sell
improving service ● improving user experience
2. Develop a use case – customers, partners, departments, staff
3. Run a pilot project – involve those end-users
4. Invest in ways that will help meet your challenges.
# BDVD # FutureM 40

41. A Test Methodology: BADIR
Business Analysis Data Insights Recommend
Question Plan Collection Solutions
# BDVD # FutureM 41

42. A Test Methodology: BADIR
Business Analysis Data Insights Recommend
Question Plan Collection Solutions
Sidebar:
Use BADIR not only to test and report on data, but to vet those Cool Tools.
Ask:
Does that “cool tool” help break down silos?
Does it support integration of processes and data?
Okay, moving on…
# FutureM 42
# BDVD 42

43. A Test Methodology: BADIR
Business Analysis Data Insights Recommend
Question Plan Collection Solutions
Vague: Hypothesis: Specific: Choices: How do your
How should I What business Only collect The right findings answer
improve my beliefs will we the data you methodologies the business
marketing test, and how? need and techniques question?
spend?
Specific:
How can I
identify
underserved
customers?
# BDVD # FutureM 43

44. Case Study #1:
Business Analysis Data Insights Recommend
Question Plan Collection Solutions
Vague: Hypothesis: Specific: Choices: How do your
How should I What business Only collect The right findings answer
improve my beliefs will we the data you methodologies the business
marketing test, and how? need and techniques question?
spend?
Specific:
How can I
identify
underserved
customers?
# BDVD # FutureM 44

45. Case Study #1:
Business Analysis Data Insights Recommend
Question Plan Collection Solutions
Vague: Hypothesis: Specific: Choices: How do your
How should I What business Only collect The right findings answer
improve my beliefs will we the data you methodologies the business
ticket sales? test, and how? need and techniques question?
Specific:
How can I
identify
productive
ticket sales
initiatives?
# BDVD # FutureM 45

46. Case Study #1:
Business Analysis Data Insights Recommend
Question Plan Collection Solutions
Vague: Hypothesis: Specific: Choices: How do your
How should I What business Only collect The right findings answer
improve my beliefs will we the data you methodologies the business
ticket sales? test, and how? need and techniques question?
Specific: Hypotheses:
How can I 1. Will an early bird discount sell tickets?
identify 2. Will a promo code help sell tickets?
productive 3. Will a promo code stimulate referrals who buy?
ticket sales 4. Will people still buy at full price?
initiatives? Let’s analyze current data
# BDVD # FutureM 46

47. Case Study #1:
Business Analysis Data Insights Recommend
Question Plan Collection Solutions
Vague: Hypothesis: Specific: Choices: How do your
How should I What business Only collect The right findings answer
improve my beliefs will we the data you methodologies the business
ticket sales? test, and how? need and techniques question?
Specific: Hypotheses: QTY PCT
How can I 1. Will an early bird discount sell tickets? . . . . . . . . . 231 28%
identify 2. Will a promo code help sell tickets? . . . . . . . . . . . 149 19%
productive 3. Will a promo code stimulate referrals who buy? 262 32%
ticket sales 4. Will people still buy at full price?. . . . . . . . . . . . . . 168 21%
initiatives? 810
# BDVD # FutureM 47

48. Case Study #1:
Data Insights
Collection
QTY PCT
231 28%
149 19%
262 32%
168 21%
810
# FutureM 48
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49. Case Study #1:
Data Insights
Collection
QTY PCT
231 28%
149 19%
262 32%
168 21%
810
# BDVD # FutureM 49

50. Case Study #1:
Data Insights
Collection
QTY PCT
231 28%
149 19%
262 32%
168 21%
Community 810
# FutureM 50
# BDVD 50

51. Case Study #1:
Business Analysis Data Insights Recommend
Question Plan Collection Solutions
Vague: How do your
How should I findings answer
improve my the business
ticket sales? question?
Specific: QTY PCT
How can I 231 28%
identify 149 19%
productive 262 32%
ticket sales 168 21%
initiatives? Community 810
# FutureM 51
# BDVD 51

52. Case Study #1:
Business Analysis Data Insights Recommend
Question Plan Collection Solutions
Next up:
Multichannel
attribution
Behavioral
Scoring
Hypotheses: QTY PCT
Social Sharing 1. Will an early bird discount sell tickets? . . . . . . . . . 231 28%
impact 2. Will a promo code help sell tickets? . . . . . . . . . . . 149 19%
3. Will a promo code stimulate referrals who buy? 262 32%
Geo/Pop/Wealth 4. Will people still buy at full price?. . . . . . . . . . . . . . 168 21%
810
# FutureM 52
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53. Case Study #1:
Business Analysis Data Insights Recommend
Question Plan Collection Solutions
Next up:
Multichannel
attribution
Behavioral
Scoring
Social Sharing
impact
Geo/Pop/Wealth
# FutureM 53
# BDVD 53

54. Case Study #1:
Business
Question
Next up:
Multichannel
attribution
Behavioral
Scoring
Social Sharing
impact
Geo/Pop/Wealth
# FutureM 54
# BDVD 54

55. Case Study #2: Catalog Retailers
(national brands)
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56. A Marketing Optimization Map
PLANNING OPTIMIZATION WEB SERVICES ENGAGEMENT
MORE
M Analytics Optimization Response
Internal C
A Dashboards Management O
R External N
K S
Reporting Request
E Chat U
Management
T M
Web
E E
Offer Consumer
R Offer Portal Catalog Data R
Messaging +
Data Catalogs
Adapters + Demos & Lifestyle
+ Life-Stage
CUSTOMER ECOMMERCE + Purchase Behaviors
DW SYSTEMS
+ Security & Preferences
AND POS
Enhancement
Client Systems Data
# BDVD # FutureM 56

57. Testing your data
# BDVD # FutureM 57

58. Ways to test your own data
Multivariate Testing - testing more than one element of an
offer, website, email etc. in a live environment. Multiple A/B
tests.
Grail quest: optimize content across channels and contacts
Content
Contacts Channels
Limits:
• Time – to obtain statistically valid samples
• Complexity – although tooling helps greatly
• Computing power – although Cloud apps / hosting helps
# BDVD # FutureM 58

59. Where to test?
Online is easiest (but offline can be tested, too)
Email:
• Open, click & convert rates
Website:
• Landing page conversions
• User registration pages
• E-commerce checkout processes
Offline:
POS, Call
Center, Catalog, Brochure, Signage, Layout
# BDVD # FutureM 59

60. What to test?
Effect or response to changes in Physical Appearance Elements
• Copy
• Layout
• Images
• Colors (backgrounds, etc.)
Effect or response to changes in Content Elements
• Price points
• Purchase incentives
• Premiums
• Trial periods
# BDVD # FutureM 60

61. Testing’s biggest challenge:
Complexity – it happens quickly!
Example: To test 3 different images in 3 different locations,
you need to test how many possible combinations?
a) 9
b) 18
c) 27
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62. Testing’s biggest challenge:
Complexity – it happens quickly!
Example: To test 3 different images in 3 different locations,
you need to test how many possible combinations?
a) 9
b) 18
c) 27
# BDVD # FutureM 62

63. Test tools
Browser side (page tagging)
Examples (visit www.whichmvt.com for more) :
Server Side (DNS proxy, or hosted in your data center)
Examples:
# BDVD # FutureM 63

64. Test methods
Discrete Choice / Choice Modeling (complex)
Vary the attributes or content elements
Quantify impact of combinations on outcomes
Discover interaction effects
Optimal Design
Iterations and waves of testing
Consider relationships, interactions, constraints across elements
Taguchi Methods
Reduce variations yet obtain statistically valid test results
# BDVD # FutureM 64

65. Get better data
# FutureM 65
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66. 7 Quiz Questions for Better Data
1. What data should I have?
Look at your core mission, values, vision, strategy
• What 5 things will impact the business in the coming year?
o Ex: Will weather patterns affect L. L. Bean’s winter sales?
• What are revenue drivers – quarterly, annually, channelwise?
o Can new big data sources yield competitive advantage?
• What are the “subjective” success criteria? Sales? CRV? Lift?
Decide what matters, and set objectives from that.
# BDVD # FutureM 66

67. 7 Quiz Questions for Better Data
2. What metrics should I have?
• Define Measurable goals - R&D, Marketing, Support, Sales,
Ops, Finance, Engineering, HR etc.
• Determine the right metrics.
• Make certain you have the tools to measure them.
# BDVD # FutureM 67

68. 7 Quiz Questions for Better Data
3. What stands in the way?
Get clarity and agreement on how to measure goal attainment.
Example: “Better customer service” is a bit too nebulous
• Metrics with inaccurate or incomplete data
• Metrics that are complex or difficult to explain
• Metrics that complicate operations or create excessive
overhead
• Metrics that cause people to act at cross purposes with the
firm.
An outsider should be able to audit if objectives were met.
# BDVD # FutureM 68

69. 7 Quiz Questions for Better Data
4. How can I get data and
measurements on demand?
SaaS apps can help you connect dataflow to analysis.
Just beware the locked spreadsheet.
• Salesforce.com: good for sales and dealflow
• HubSpot: good for web marketing
• Quickbooks, Excel: linked via xml app to data flow for
instant financial / accounting updates and reports
Departmental dashboards can enable weekly, daily, hourly or
realtime trendspotting and fast course corrections.
# BDVD # FutureM 69

70. 7 Quiz Questions for Better Data
5. How can I empower everyone with
on-demand insights?
Create a Culture of measurement.
• Maintain transparency to avoid surprises
• Celebrate wins as they occur
• Keep people properly motivated and on the same page
Link rewards to the right performance measures
All this makes it easier to work toward common, unified,
clearly understood goals.
# BDVD # FutureM 70

71. 7 Quiz Questions for Better Data
6. Where to I start?
Start at the top.
• Set a strong example for people to follow
• Publicize goals and keep your own progress visible
• Demonstrate commitment to attaining shared goals
• Pick the 5 most important goals and get the salient data
Even if your targets were “off” at the outset, demonstrate
success toward something, even if it’s just better intelligence.
Pilot projects are learning labs.
# BDVD # FutureM 71

72. 7 Quiz Questions for Better Data
7. What should I do differently today?
Continually question, re-evaluate and refine.
• External factors can affect progress toward goals at any time.
• External factors can affect goal setting at any time.
• External factors can affect goal selection at any time.
• Cultural factors can affect generation and use of data insights
Determination is good, just keep it aimed productively.
# BDVD # FutureM 72

73. Public Sector
Mashups
# FutureM 73
# BDVD 73

74. 5 Public Sector Mashups
1. Hurricane Risk Calculator
Houston, TX
Source:
• NWS + historic data
Use:
• Neighborhood-level risk prediction http://risk.rtsnets.com
• Predict flood, wind & power
outages
• Aids go/no go evacuation decisions
# BDVD # FutureM 74

75. 5 Public Sector Mashups
2. Quake-Catcher Network
Stanford, CA
Source:
• Laptop accelerometer data
http://qcn.stanford.edu
Use:
Improve on seismographic data
• More location specific
• Vastly cheaper
• Free (laptop drop protection)
• Easy to install in desktop PCs
# BDVD # FutureM 75

76. 5 Public Sector Mashups
3. Centers for Disease Control
Atlanta, GA
Source:
• Google & Twitter search trends
http://cdc.gov
Use:
• Speed disease detection
• Enable response precision
• Prevent & contain outbreaks
• Eliminate SARS-like recurrence
• Save lives
• Support virality research
# BDVD # FutureM 76

77. 5 Public Sector Mashups
4. Predictive Policing
Mountain View, CA
Sources / mashup:
• Foreclosures, school schedules,
past crimes, bus schedules,
library visits, weather conditions
Use:
• Predict likely crime occurrences
• Focus police intervention efforts
# BDVD # FutureM 77

78. 5 Public Sector Mashups
5. Homeland Security
Washington, DC
F.A.S.T Module
Sources:
• Human suspect readings
• Pulse, speech, CV, etc.
• Bio, Interpol, other databases
Use:
• Predict malintent
• Gather suspect intelligence
# BDVD # FutureM 78

79. The world is your mashup
Device / UI – web, mobile, social, print, POS, etc.
Meta data – session info, device state, features, sensors
Connectors, apps, processors, Cool Tools “plus”
Mashup data – public, leased, licensed
Proprietary data – customers, partners, inventory, assets
# FutureM 79
# BDVD 79

80. Get real (time)
# FutureM 80
# BDVD 80

81. Real Time Direct Marketing Tools
"Sales for Service" app Lead Nurturing
customer interaction data from call ctr & POS Lead Scoring
tailors offers quickly upon purchase / conversion
improves cross / upsell programs and offer targeting
includes: offer repository, biz rules engine, contact
history DB, predictive analytics
Turns call center from a cost to a profit center (Email marketing)
API to SFDC
consolidates response in CRM
(ID web visitors by IP)
slices by: biz size, vertical, industry, geo
(crowdsourced DBs) Find people and companies
Techprospex (ID tech used by B2B company) customer analytics
Drills down by model, version improves & automates sales response
# FutureM 81
# BDVD 81

82. Real Time Direct Marketing Tools
Persona
triggers
Lead Lists
Marketing Email
Automation
Customer
Analytics
BI /
Prospect
Intelligence
# BDVD # FutureM 82

83. Example:
But now who owns it?
Persona
triggers
Lead Lists
Sales
Email
Marketing
Customer
Analytics
BI /
Prospect
Intelligence
# FutureM 83
# BDVD 83

84. So, now who owns it?
Marketing WWDDD ?
Call center
Catalog
Event Communities
Mobile Channels
POS CRM
Support Storage,
Print Integration,
Social Service
Access,
Web Privacy,
Sales Security IT
# FutureM 84
# BDVD 84

85. Discuss, discuss
Where is your data? Do you have a handle on it?
Where does the data reside in your organization?
Are there brilliant successes you can build on?
Have you benchmarked your competitive space?
Have you benchmarked a Disney-like experience?
# FutureM 85
# BDVD 85

86. Future Events and Resources
A DMA / NCDM Dec. 2012 Event
# BDVD # FutureM 86

87. References
TechAmerica Foundation
Putting Big Data and Advanced Analytics to Work (McKinsey)
The Logic behind Retailers’ Mercurial Pricing (HBR)
The Current State of Business Analytics: Where do We Go from Here?
(SAS / Bloomberg Business Week Research Services)
Top 16 Tools to Create Infographics
Tackling Multichannel Attribution (John Young, Epsilon)
Predictive Analytics World
Taming the Big Data Tidal Wave (Bill Franks, Teradata)
# BDVD # FutureM 87

88. Resources
Analysis and Data Visualization Tools
# FutureM 88
# BDVD 88

89. Thank you!
.com
+1 (781) 492-7638 USA East
@fanfoundry
89