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A few chapters on some cool tools, analytic techniques, case examples and sales/marketing partnership opportunites.
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1Agenda / Menu
Ed Alexaner
@fanfoundry
Ed Alexander, Managing Consultant
4
# BDVD
# BDVDAgenda / Menu
# FutureM
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)5 Public Sector Mashups Get Real (time) Future Events, Resources
Agenda / Menu
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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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Challenges – tooling up to:
• Capture, combine and curate • Store, search and share • Analyze and visualize
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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
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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Analysts & Techs Quoted: • Kantar Retail • Symphony IRI Group • Catalina Marketing Modiv Media’s “Scanit!” device• 89 Degrees
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
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
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The corporate view: big data in marketing
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Bloomberg Business Week Research Services
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?• 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
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
# FutureM
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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Cool tool
Retail, ecommerce, mobile
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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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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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http://www.emarketer.com/Article.aspx?R=1008909
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72%
# FutureM
( What, no real time? )
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
Technology Landscape (Cool Tool Pool)
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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 & extended capability, new advantage8. 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
# FutureM
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
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
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?
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?
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?
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
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?. . . . . . . . . . . . . .
535353
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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54545454
Case Study #1:
Next up: Multichannel attribution
Behavioral Scoring
Social Sharing impact
Geo/Pop/Wealth
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Business Question
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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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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7373
Public 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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Quake-Catcher Network Stanford, 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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Centers for Disease ControlAtlanta, GA
Source: • Google & Twitter search trends
Use: • Speed disease detection• Enable response precision• Prevent & contain outbreaks• Eliminate SARS-like recurrence • 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 SecurityWashington, DC F.A.S.T Module
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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The world is your mashup
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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
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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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Marketing Automation
Persona triggers
Lead Lists
CustomerAnalytics
BI / Prospect Intelligence
Real Time Direct Marketing Tools
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But now who owns it?
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Marketing Sales
Persona triggers
Lead Lists
CustomerAnalytics
BI / Prospect Intelligence
# BDVD # FutureM
Example:
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So, now who owns it?
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Marketing
Sales IT
CommunitiesChannelsCRM Support Service
Call centerCatalogEvent Mobile POS Print Social Web
Storage, Integration,
Access, Privacy, Security
WWDDD ?
# FutureM
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Discuss, discuss
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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?
Future Events and Resources
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A DMA / NCDM Dec. 2012 Event # FutureM
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)
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