Be a Big Data Voodoo Daddy or Mama)

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A few chapters on how to weave the the current big data / marketing technology hairball into a tapestry of customers, each as unique as a thumbprint

Transcript of Be a Big Data Voodoo Daddy or Mama)

Page 1: Be a Big Data Voodoo Daddy or Mama)

1Agenda / Menu

Be a

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Big

Data Voodoo

Daddy

2# BDVDAgenda / Menu

# FutureM

Be a

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3

Big

Data Voodoo

Daddy

(or mama)

3# BDVDAgenda / Menu

# FutureM

Be a

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Ed Alexaner

@fanfoundry

Ed Alexander, Managing Consultant

4

# BDVD

# BDVDAgenda / Menu

# FutureM

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What is it? News and Views Cultural and consumer trendsCorporate Trends Technology Landscape (the Cool Tool Pool) Demo Time A Test Methodology (BADIR)Use Cases Ways to test your own dataGet Better Data (7 Quiz Questions)Public & Private Sector Data Mashups Get Real (time) Summary, Future Events, Resources

Agenda / Menu

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( = link button )

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Variety

types and

sources

Volume How much is enough?

Velocity In/out

speed

Defining “big data” – the four V’s:

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Veracity accuracy reliability

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If

Data

Could

Talk…

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If

Data

Could

Talk…

( )

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Challenges – tooling up to:

• Capture, combine and curate • Store, search and share • Analyze and visualize

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Cross-channel marketing challenges

35% - Managing campaign execution across multiple channels

33% - Understanding customer interactions across channels

25% - Controlling marketing budgets that depend on IT collaboration

Source: “ The Key to Successful Cross-channel Marketing”, an Oct. 2012 Forrester / ExactTarget survey of 211 US marketers

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Opportunities

• Internet search • Business informatics• Medical research • Genomics • Astronomy • Aviation • Meteorology • Finance

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Sources – 2 new quintillion bytes / day

• Sensors • Mobile devices • Cameras • Microphones • Social graph – UGC

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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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Big Data in the news…

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for children

Big Data and

What next? (kidding!)

Hey, kids!

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What next? (kidding!)

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Special Big Data

Issue

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What next? Not kidding!

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Analysts & Techs Quoted: • Kantar Retail • Symphony IRI Group • Catalina Marketing Modiv Media’s “Scanit!” device• 89 Degrees

Sunday magazine article - upshot: • It’s about big data, not Wal-mart • The customer has all the power

Example: Kroger (coupon response)• 70% of targeted • 3.4% of mass mailed

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The corporate view: big data in marketing

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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

# FutureM

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The corporate view: big data in marketing

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Bloomberg Business Week Research Services

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The corporate view: big data in marketing

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Bloomberg Business Week Research Services

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The corporate view: big data in marketing1. CXOs now paying attention. Why?• Compete – lead up, catch up, patch up PR • Add Predictive Intelligence – detect, adapt, seize opportunity • Optimize - avoid leaving money on the table

2. Elusive answers are suddenly more attainable everywhereOperations, Sales, Marketing, Customer Care, R&D, etc.

3. Transformation can now be justified with data + judgment• Managers are now analysts who produce & consume data• Managers leverage business savvy 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

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Cultural trend:

Data-driven, custom communication

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Cultural trend:

Data-driven, custom communication

1992: sad :(PointCastIntrusive In your face Off-target Poor quality

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Cultural trend:

Data-driven, custom communication

1992: sad :(PointCastIntrusive In your face Off-target Poor quality

2002: mad ):“Push sux”SubversiveIntrusive Spooky Invasive

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1992: sad :(PointCastIntrusive In your face Off-target Poor quality

2012: rad! :)I want my MDV Welcome Expected Preferred …but secured?

2002: mad ):“Push sux”SubversiveIntrusive Spooky Invasive

Cultural trend:

Data-driven, custom communication

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*MDV: Massive Data Visualization

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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 • Audience & Community building• World blending

(ex: QR, text, POS, Call Center

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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

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Corporate Trends

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Big Data's Shifting Focus: Transaction > EngagementSystems Analog Transaction Engagement Experiential Personal

Fulfillment Circa Pre-1950's 1950+ 2000+ 2005+ 2010+

Design Point Reliability & stability

Continuous improvement

Sense and response

Agility and flexibility

Intention driven

Challenge Human Computing Social Contextual Individual

Comm. Style Analog Systems Dictatorial Conversational Role tailored Personalized

UX Physical Machine based Multi-channel, real time

Bionic, portable

Social-led, omni-media

Speed Governed Just in time Real time Right time Time / space continuum

Reach Physical Corporate Corporate & Internet Value chains Personal,

one to one

Information & Knowledge Word of mouth structured

records & data Knowledge flows Immersive information

Self-aware, embedded

Social orientation Water cooler Tangentially

socialFundamentally

socialPervasively

socialUbiquitously

social

Intelligence Human based Hard coded Business rules Predictive Pattern based

Examples assembly line Payroll, ERP, CRM Community & social business

Loyalty, reward, games,

context

Social relationship

managementSource: R Wang & Insider Associates, LLC.

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Gartner: 72% have a “CMTO” today

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http://www.emarketer.com/Article.aspx?R=1008909

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http://www.emarketer.com/Article.aspx?R=1008909

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72%

# FutureM

( What, no real time? )

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Gamification

Email Marketing

DAM Testing & Optimization

SEO

VIdeoLanding Pages

Marketing Automation

CRM

Webinars

Web sites

E-commerce

Site add-ins

SM Ads

Community

SM marketing Call centerPersonalization

Targeting Display ads

Multi-channelMobileAnalytics

Search & PPC ads

B2B Data

DatabasesChat

Events

Design Creative

Video ads

PR

Big Data

DatasetsCloud

APIs Surveys

LoyaltyLocation

Collaboration

Agile

Technology Landscape (Cool Tool Pool)

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Business Intelligence

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CustomerExperience

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Stretch Goals for Cool Tools

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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) occasions5. Clean data - easy for all users to maintain, inspect and fix 6. High adoption - self-training, guided navigation, less clutter 7. Extended success – new capabilities & advantages8. Broad community - best / better practice sharing – each one

teach one

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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

The payoff: central data + cool tools

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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

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The payoff: central data + cool tools

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It’s

Demo

Time!

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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, staff3. Run a pilot project – involve those end-users 4. Invest in ways that will help meet your challenges.

Framing the Discussion (Surprise!)

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Business Question

Analysis Plan

Data Collection

InsightsRecommend

Solutions

A Test Methodology: BADIR

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Business Question

Analysis Plan

Data Collection

InsightsRecommend

Solutions

A Test Methodology: BADIR

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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

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Vague: How should I improve my marketing spend?

Specific: How can I identify underserved customers?

Hypothesis: What business beliefs will we test, and how?

Specific: Only collect the data you need

Choices: The right methodologies and techniques

How do your findings answer the business question?

Business Question

Analysis Plan

Data Collection

InsightsRecommend

Solutions

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A Test Methodology: BADIR

# FutureM

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Case Study #1:

Vague: How should I improve my marketing spend?

Specific: How can I identify underserved customers?

Business Question

Analysis Plan

Data Collection

InsightsRecommend

Solutions

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Hypothesis: What business beliefs will we test, and how?

Specific: Only collect the data you need

Choices: The right methodologies and techniques

How do your findings answer the business question?

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Vague: How should I improve my ticket sales?

Specific: How can I identify productive ticket sales initiatives?

Case Study #1:

Business Question

Analysis Plan

Data Collection

InsightsRecommend

Solutions

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Hypothesis: What business beliefs will we test, and how?

Specific: Only collect the data you need

Choices: The right methodologies and techniques

How do your findings answer the business question?

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Vague: How should I improve my ticket sales?

Specific: How can I identify productive ticket sales initiatives?

Case Study #1:

Hypotheses: 1. Will an early bird discount sell tickets?2. Will a promo code help sell tickets? 3. Will a promo code stimulate referrals who buy? 4. Will people still buy at full price?

Let’s analyze current data

Business Question

Analysis Plan

Data Collection

InsightsRecommend

Solutions

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Hypothesis: What business beliefs will we test, and how?

Specific: Only collect the data you need

Choices: The right methodologies and techniques

How do your findings answer the business question?

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Vague: How should I improve my ticket sales?

Specific: How can I identify productive ticket sales initiatives?

Case Study #1:

QTY PCT 231 28% 149 19%262 32%168 21% 810

Hypotheses: 1. Will an early bird discount sell tickets? . . . . . . . . .2. Will a promo code help sell tickets? . . . . . . . . . . .3. Will a promo code stimulate referrals who buy? 4. Will people still buy at full price?. . . . . . . . . . . . . .

Business Question

Analysis Plan

Data Collection

InsightsRecommend

Solutions

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Hypothesis: What business beliefs will we test, and how?

Specific: Only collect the data you need

Choices: The right methodologies and techniques

How do your findings answer the business question?

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Case Study #1:

Data Collection

Insights

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QTY PCT 231 28% 149 19%262 32%168 21% 810

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Case Study #1:

Data Collection

Insights

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QTY PCT 231 28% 149 19%262 32%168 21% 810

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Case Study #1:

Data Collection

Insights

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Community

# FutureM

QTY PCT 231 28% 149 19%262 32%168 21% 810

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Business Question

Analysis Plan

Data Collection

InsightsRecommend

Solutions

Case Study #1:

How do your findings answer the business question?

Vague: How should I improve my ticket sales?

Specific: How can I identify productive ticket sales initiatives?

QTY PCT 231 28% 149 19%262 32%168 21% 810

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Community

# FutureM

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Business Question

Analysis Plan

Data Collection

InsightsRecommend

Solutions

Case Study #1:

Next up: Multichannel attribution

Behavioral Scoring

Social Sharing impact

Geo/Pop/Wealth

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QTY PCT 231 28% 149 19%262 32%168 21% 810

Hypotheses: 1. Will an early bird discount sell tickets? . . . . . . . . .2. Will a promo code help sell tickets? . . . . . . . . . . .3. Will a promo code stimulate referrals who buy? 4. Will people still buy at full price?. . . . . . . . . . . . . .

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545454

Business Question

Analysis Plan

Data Collection

InsightsRecommend

Solutions

Case Study #1:

Next up: Multichannel attribution

Behavioral Scoring

Social Sharing impact

Geo/Pop/Wealth

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55555555

Case Study #1:

Next up: Multichannel attribution

Behavioral Scoring

Social Sharing impact

Geo/Pop/Wealth

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Business Question

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Case Study #2: Catalog Retailers(national brands)

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CONSUMER

MORE

DashboardsMARKETER

Reporting

Offer Portal

Client Systems

CUSTOMER DW

ECOMMERCESYSTEMS AND POS

+ Demos & Lifestyle+ Life-Stage+ Purchase Behaviors+ Security & Preferences

Enhancement Data

Data Adapters

Offer Catalog

ConsumerData

Analytics OptimizationInternal

External

Chat

Web

Messaging +Catalogs

Response Management

Request Management

WEB SERVICESOPTIMIZATION ENGAGEMENTPLANNING

A Marketing Optimization Map

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Testing your data

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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

Limits: • Time – to obtain statistically valid samples • Complexity – although tooling helps greatly • Computing power – although Cloud apps / hosting helps

Ways to test your own data

Contacts

Content

Channels

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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

Where to test?

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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

What to test?

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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

Testing’s biggest challenge:

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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

Testing’s biggest challenge:

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Browser side (page tagging) Examples (visit www.whichmvt.com for more) :

Server Side (DNS proxy, or hosted in your data center)Examples:

Test tools

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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

Test methods

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Get better data

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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?

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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.

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7 Quiz Questions for Better Data

3.

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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.

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7 Quiz Questions for Better Data

4.

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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.

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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.

7 Quiz Questions for Better Data

5.

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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.

7 Quiz Questions for Better Data

6.

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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.

7 Quiz Questions for Better Data

7.

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Public & Private Sector Mashups

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5 Public Sector Mashups

Hurricane Risk Calculator Houston, TX

Source: • NWS + historic data

Use: • Neighborhood-level risk prediction • Predict flood, wind & power

outages• Aids go/no go evacuation decisions

1.

http://risk.rtsnets.com

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Better Earthquake Detection Quake-Catcher Network, CA

Source: • Laptop accelerometer data

Use: Improve on seismographic data• More location specific • Vastly cheaper • Free (laptop drop protection)• Easy to install in desktop PCs

http://qcn.stanford.edu

2.

5 Public Sector Mashups

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Containing Diseases CDC, Atlanta, GA

Source: • Google & Twitter search trends

Use: • Speed disease detection• Enable response precision• Prevent & contain outbreaks• Eliminate SARS-like recurrences • Save lives • Support virality research

3.

5 Public Sector Mashups

http://cdc.gov

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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

4.

5 Public Sector Mashups

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Homeland SecurityF.A.S.T Module, Washington, D.C.

Sources: • Human suspect readings• Pulse, speech, CV, etc. • Bio, Interpol, other databases

Use: • Predict malintent• Gather suspect intelligence

5.

5 Public Sector Mashups

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Vine WhisperersFruition Sciences, Napa, CA

Sources: • Sensors implanted in vines• Weather and irrigation readings

Use: • Upload sensor readings to cloud database • Conserve water and improve vineyard yields• Build expertise in irrigation and crop

management

1.

Private / Commercial Mashups

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The world is your mashup

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User experience (UX) – web, mobile, social, print, POS, etc.

Meta data – session info, device features

Connectors, apps, processors, Cool Tools “plus”

Mashup data – public, leased, licensed

Proprietary data – customers, partners, inventory, assets

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Get real (time)

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"Sales for Service" app customer interaction data from call ctr & POS 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

(ID web visitors by IP)slices by: biz size, vertical, industry, geo

(crowdsourced DBs) Techprospex (ID tech used by B2B company) Drills down by model, version

Lead Nurturing Lead Scoring

(Email marketing) API to SFDC consolidates response in CRM

Find people and companies customer analytics improves & automates sales response

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Real Time Direct Marketing Tools

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Persona triggers

Lead Lists

Email

CustomerAnalytics

BI / Prospect Intelligence

Real Time Direct Marketing Tools

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Marketing

Persona triggers

Lead Lists

Email

CustomerAnalytics

BI / Prospect Intelligence

# BDVD # FutureM

Q: Who owns it?

Sales

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Q: Who owns it?

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Sales

Persona triggers

Lead Lists

Email

CustomerAnalytics

BI / Prospect Intelligence

# BDVD # FutureM

Example:

Marketing

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Marketing

Sales IT

CommunitiesChannelsCRM Support Service

Call centerCatalogEvent Mobile POS Print Social Web

Storage, Integration,

Access, Privacy, Security

WWDDD ?

# FutureM

A: It’s jointly owned

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A: It’s jointly owned

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CMO Main input: customer

CIOMain input: technology

Storage, Integration,

Access, Privacy, Security

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A: It’s jointly owned

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• Partner with internal functions – Sales, Marketing, I.T.o Let business needs drive infrastructure decisions

• What goals do they share? o Drive change and innovation o Manage and mitigate risk and opportunity o Develop competitive advantage (customer insight)

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Admit what you don’t know

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• Convenient sample sizes are not necessarily predictive

• A small fraction of all data is digitized; most is unstructured

• Data may reduce some biases, but creates others

• Competitive advantage ideas: a) Generate data in new ways b) Gather data in new ways c) Combine data in ways nobody else has

• Permit judgment to color your data interpretation

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Summary

91# BDVD # FutureM

• Overlay outside data on your own to gain new insights

• Engage Sales, I.T., support etc. for a 360 degree business view

• Invest in “Cool Tools” and silo-busting capability

• Benchmark your competitive space

• Solve your customer’s problems, and it will solve yours

• Make data quality everyone’s easy chore

• Acknowledge what you don’t know, and let judgment in

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Future Events and Resources

92# BDVD

A DMA / NCDM Dec. 2012 Event # FutureM

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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)

93# BDVD # FutureM

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Resources

Analysis and Data Visualization Tools

94# BDVD # FutureM

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Thank you!

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