1. Brought to you by In association with
A Marketer’s Guide to Data Analytics:
Gaining Real Insight from Big Data
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2. Today’s Speakers
A Marketer’s Guide to Data Analytics
Colin Linsky
Worldwide Predictive Analytics Leader
IBM SPSS
James Lawson
Consultant Editor
marketingfinder.co.uk

3. Interact with us
A Marketer’s Guide to Data Analytics
Follow the conversation on twitter #AnalyseData

4. © 2012 IBM Corporation
Agenda
 Analytics – Time to Rethink
 Predictive Analytics – The Competitive Advantage
 Using Data Analysis to Increase Sophistication
 Turning Insight into Action – The Path to Personalization
4#AnalyseData

5. © 2013 IBM Corporation
1. Analytics – Time to Re-think?
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6. © 2013 IBM Corporation
Thinking big about data presents huge opportunities for marketers
Billions of consumer
preference and
satisfaction indicators
available across call centers,
web sites, transaction logs,
face-to-face interactions and
social media
1 in 5 minutes
online is spent on social
networks
only 6.8%
of marketers believe social
media is integrated into
strategy
5 billion+
mobile phones globally
49% of consumers use two or more technologies to shop,
and 53% of active adult social networkers FOLLOW A BRAND
Big Data
Data in
many forms
Data in doubt
Terabytes and
zettabytes of data
Variety
Batch and
streaming
Velocity
Veracity Volume
400 million
tweets are sent daily
7.6% of budget
in marketing is allocated to
social media
55%
use their mobile phone for
price comparison
34%
scan QR codes
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7. © 2013 IBM Corporation
Outperformers are twice as good at deriving value from data – key
to engaging customers as individuals
Outperformers strongly differentiate their organizations in three key areas
Outperformers
Underperformers
Draw insights from data
54%
Translate insight into action
57%
54%
26%
26%
31%
108%
more
108%
more
84%
more
Access to data
Source: Q22 ―How good is your organization at driving value from
data?[Today]‖ (Global n=631 to 636) (Retail n=103 to 104)
#AnalyseData

8. © 2013 IBM Corporation
Analytics-driven organizations are distinguished by their ability to
leverage:
All perspectives
Past (historical, aggregated)
Present (real-time, scenarios)
Future (predictive,
prescriptive)
At the point
of impact
All decisions
Major and minor
Strategic and tactical
Routine and exceptions
Manual and automated
All information
Transaction/POS data
Social data
Click streams
Surveys
Enterprise content
External data (competitive,
environmental, etc.)
All people
All departments
Front line, back office
Executives, managers
Employees
Suppliers, customers and
consumers
Partners
#AnalyseData

9. © 2013 IBM Corporation
Customer Analytics – an increasing requirement
Organisations are trying to determine how to keep the best customers, increase loyalty and
demonstrate customer commitment while reducing costs AND increasing margin.
Intensifying Competition
Soaring Customer
Expectations
Channel Proliferation
and Complexity
Social Networking
Shrinking Wallet Share
Decreasing Loyalty
#AnalyseData

10. © 2013 IBM Corporation
Increasing sophistication of Customer Analytics means:
 Improvements in consumer relationships, brand loyalty, increased customer
satisfaction, and development of a continuous dialog with top consumers
 Analyzing consumption data to understand product affinity, infer behavior and
deliver the right action/offer at the right time
 Maximize marketing spend and improve effectiveness
 Improve financial performance, and drive increased profit and margins
 Up-sell and cross-sell
Attract, retain and grow consumers with Customer Analytics
 Purchase history
 Browsing habits
 Loyalty program details
 Social Media
 Historic campaign responses
 Price sensitivity
 Current interaction
 Customer demographics
 Call centre records
 Communication preferences
 Advanced customer segmentation
 Product affinity
 Propensity to purchase
 Market basket analysis
 Social media analytics
 Targeted promotions
 Next best action
 Deeper customer engagement
 Campaign insight & optimization
 Life time profitability analytics
 Price management
 Channel performance
 Reporting and analysis
 Advanced analysis and
predictions
 Scorecarding & dashboarding
 Planning, budgeting and
forecasting
 Business rules and
optimization
 Real-time decisions
 Scenario and what-if modeling
Customer
Data
Analytics
Capabilities
Insight into
Action
#AnalyseData

11. © 2013 IBM Corporation
1/30/13
―Our experience is that within broad consumer
movements, small groups of users (often
overlooked in cursory analyses) actually drive the
economics. Achieving a more refined
understanding of who is doing what requires a
thoughtful segmentation—incorporating data about
consumers’ demographics, household
characteristics, usage patterns, spending,
attitudes, and needs—supported by ―big data‖
analytics."
―iConsumer: Digital Consumers Altering the Value Chain.‖ W. Duncan, E. Hazan and K. Roche. McKinsey & Co. 2013

12. © 2013 IBM Corporation
2. Predictive Analytics – The Competitive Advantage
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13. © 2013 IBM Corporation
Business Analytics
13
What
happened?
Why?
What to do
next?
BI PA
From Sense and Respond to Predict and Act
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14. © 2013 IBM Corporation
Predictive Analytics – What is it?
• A true analytics process is the one that transforms raw data into actionable insights, the true
transformation from "So What?" to "Now What?".
• Business Analytics is the process that transforms raw data into actionable strategic
knowledge to guide decisions aiming to increase market share, revenue and profit.
• Drive your business by making informed decisions based insights derived from analyzing
one of you most valuable company assets, data.
• Analytics takes data and translates it into meaningful, value-added options for leadership
decisions.
• Actionable, statistically supported insights from data that help drive competitive advantage.
• ―By 2014, 30% of analytic applications will use proactive, predictive and
forecasting capabilities‖ Gartner Forecast, 2011
http://www.readwriteweb.com/enterprise/2011/01/business-analytics-predictions.php
#AnalyseData

15. © 2013 IBM Corporation
Key Moments of Truth
 Research and Browse
 Browsing and cart use
 Pre-purchase
 Checkout and payment
 Delivery
 Multi-Channel use
 Sign-up to a Loyalty Program
 Response to a campaign or promotion
 Credit application
 Complaint
 Claim
 Customer Service Request
 Warranty registration
 Blog/Twitter
 Social Media
 Product out-of-stock
 Destruction of perishables
 Low velocity product sales
 Demand forecast
Attract
Grow
Retain
Fraud
Risk
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16. © 2013 IBM Corporation
Consolidated Data Sources
16
Single
Customer
View
Loyalty
Scheme
eCommerce
Direct
Marketing
Catalogue
Ordering
Customer
Service
Social
Networking
Home
Delivery
3rd Party
Data
POS /
Transactions
Browsing
History
Purchase /
Return
History
Product
Catalogue
Store /
Channel
Landscape
#AnalyseData

17. © 2013 IBM Corporation
Driving Smarter Business Outcomes
Capture
DataCollection
Enabling a complete view of
the customer combining
enterprise and social media
based data
Act
Deployment
Technologies
Deploy predictive analytics
within business processes,
across access platforms,
maximizing operational
impact
…
…
Predict
Platform
Pre-built Content
StatisticsText
Mining
Data
Mining
Understand customers micro-behavior
across channels, predict their next
move and make the next best offer
RetainUp-sellAttract
#AnalyseData

18. © 2013 IBM Corporation
Variety
Data in
Many Forms
Structured,
unstructured, text,
multimedia
Velocity
Data in Motion
Streaming data,
milliseconds to
seconds to respond
Volume
Data at Rest
Terabytes to
Exabytes of existing
data to process
Veracity
Data in Doubt
Uncertainty due to
data inconsistency
& incompleteness,
ambiguities, latency,
deception, model
approximations
Worried about Big Data?
#AnalyseData

19. #AnalyseData
A Marketer’s Guide to Data Analytics:
Gaining Real Insight from Big Data
Quick Poll

20. © 2013 IBM Corporation
3. Filling the Tank with Fuel
#AnalyseData

21. © 2013 IBM Corporation
Advanced Affinity Analysis - Propensity to Purchase
Transactions from
all customers
Market basket insights
• If A then B
• If C then D
• If E and F then G
• If H, then H then I
Special Offer – This Week Only
10% off on any of these
combinations: A + B…G + H….
Promotional Display
Buy X get Z for only $1.49!
1 Brand Razors
Brand Shampoo
House brand shampoo
House brand hair color
Brand Toothpaste
Brand Skin care
Men’s fragrance
Woman’s fragrance
House brand sun care
Optician
Feminine hygiene
Online photo service
Personal Electrical
2-4-1 Discount
5% Extra Reward Points
2
4
5
6
7
8
9
10
11
12
13
14
15
3
Offers
Transactions from this
customer
• Cardholder since YYYYMM
• Average transaction value
• Monthly transaction value
• Categories purchased
• Brands purchased
Descriptive
• Age
• Gender
• Family situation
• Zip code
Interactions
• Web registration
• Web visits
• Customer service contacts
• Channel preference
Attitudes
• Satisfaction scores
• Shopper type
• Eco score
Offer
inserts
? ?
? ?
1512
311
Offer
inserts
3 13
6 12
+
+
+
#AnalyseData

22. © 2013 IBM Corporation
Let’s start with some simple transaction data…
#AnalyseData

23. © 2013 IBM Corporation
• Create
− Time of Day
− Day of Week
− Week/Month/Season
− Time hierarchies
− Channel
− Discount/Promotion
− Returns
− Browsing/Researching/Purchasing
• Aggregate: Transactions
− Value
− Quantity
• Model
− Product Mix (Associations)
#AnalyseData

24. © 2013 IBM Corporation
Transaction Data
#AnalyseData

25. © 2013 IBM Corporation
• Create
− Product Catalogue Hierarchy
 Attributes
 Quantity
 Value
− Basket Margin
• Aggregate: Baskets
− Quality Diversity
− Brand Diversity
− Hierarchy Diversity
• Model
− Basket Mix (Associations)
 Across hierarchies
 Across attributes
Transaction Data
Product Data
+
#AnalyseData

26. © 2013 IBM Corporation
• Create
− Sequences
− First Transaction
− Last Transaction
− Time horizons
− Lapses
− Addition of 3rd Party data enrichment
(e.g. geospatial, demographic and
lifestyle data)
− Target attribute flagging
• Aggregate: Customers
− Temporal summaries
− Shopping mission statistics
− Longitudinal spend
− Cross category/line statistics
• Model
− Recency Frequency Monetary Value
(RFM)
− Lifetime Value (LTV)
− Category/Line/Product
Recommendations
− Propensity to Lapse
− Propensity to develop
Transaction Data
Product Data
Customer Data
+
#AnalyseData

27. © 2013 IBM Corporation
• Create
− Location demographics
− Channel operations
− Channel/Store format attributes
• Aggregate: Channel
− Location Assortment Mix
− Staffing and Service provision
− Contact statistics
− Usage (e.g. visitors, footfall, traffic)
− Customer profile analysis
− Operations statistics
• Model
− Anomalous Activities
− Revenue Protection Indicators
Transaction Data
Product Data
Customer Data
Channel Data
+
#AnalyseData

28. © 2013 IBM Corporation
• Create
− Macro-economic indicators
− Environmental metrics
− Identify significant events
• Aggregate: Environment
− Build useful historic time structures
Transaction Data
Product Data
Customer Data
Channel Data
Context Data
+
#AnalyseData

29. © 2013 IBM Corporation
• Create
− Summarizing interaction history
 Offers/recommendations made
• Aggregate: Environment
− Frequency of responses
− Frequency of activity
• Model
− Campaign Responsiveness
− Offer Sensitivity
Transaction Data
Product Data
Customer Data
Channel Data
Context Data
Interaction Data
+
#AnalyseData

30. © 2013 IBM Corporation
• Create
− Solidifying into the Single Customer
View from all sources
− Track changes in metrics, measures
and attributes over time
• Model
− Profiling
− Segmentation
− Clustering
− Anomalous patterns
− Affinity Analysis
− Forecasting
Single
Customer
View
#AnalyseData

31. #AnalyseData
A Marketer’s Guide to Data Analytics:
Gaining Real Insight from Big Data
Quick Poll

32. © 2013 IBM Corporation
4. The Path to Personalization
#AnalyseData

33. © 2013 IBM Corporation
Turning customer insight into action with analytics
360 View
Of
Customer
Customer
Segmentation
Predictive
Modeling
CUSTOMER
• Detect patterns
• Find preferences
• Identify drivers
• Predict demand
• Score results
• Model scenarios
• Connect all touch points
• Create 360 view
• Analyze historic activity
• Explore all data
Gain a deep understanding of customer segments, micro-segments, individuals
#AnalyseData

34. © 2013 IBM Corporation
Historic data
Present data
Suggested future
behavior
360 View
Of
Customer
Customer
Segmentation
Predictive
Modeling
Reporting
and KPIs
Real-time
Decisions
CUSTOMER CONTEXT
• Combine history with current activities
• Add other relevant external data (weather, demographics)
• Add other relevant internal data (stock position, call center)
• Identify behavioral drivers
• Determine propensity to respond
• Scoring – individuals not interested in everything equally!
Turning customer insight into action with analytics
Add context with historical sales data to understand product affinity, propensity to
purchase and market basket analysis
#AnalyseData

35. © 2013 IBM Corporation
Historic data
Present data
Suggested future
behavior
360 View
Of
Customer
Customer
Segmentation
Predictive
Modeling
Reporting
and KPIs
Real-time
Decisions
CUSTOMER CONTEXT
COMMUNITY
• Social communities
• Staff
• Influencers
• Known / unknown
Turning customer insight into action with analytics
Integrate social media data to understand purchase influencers and behavior
#AnalyseData

36. © 2013 IBM Corporation
Historic data
Present data
Suggested future
behavior
360 View
Of
Customer
Customer
Segmentation
Predictive
Modeling
Personal
Interactions:
Actions
Offers
CONTENT
Reporting
and KPIs
Real-time
Decisions
CUSTOMER CONTEXT
COMMUNITY
• Product assortment
• Inventory levels
• Relevant promotions
• Continuous,
meaningful dialogue
Personalize customer experiences with analytics
Customize experience and interactions by understanding what drives, enhances,
and shapes purchase decisions
#AnalyseData

37. © 2013 IBM Corporation
Historic data
Present data
Suggested future
behavior
360 View
Of
Customer
Customer
Segmentation
Predictive
Modeling
Personal
Interactions:
Actions
Offers
CONTENT
Reporting
and KPIs
Real-time
Decisions
CUSTOMER CONTEXT
COMMUNITY
COMMERCE
• Conduct transaction
• Exchange money for
goods or services
Personalize customer experiences with analytics
Simplify purchase process by enabling anytime, anywhere exchange of
money for goods
#AnalyseData

38. © 2013 IBM Corporation
Historic data
Present data
Suggested future
behavior
360 View
Of
Customer
Customer
Segmentation
Predictive
Modeling
Personal
Interactions:
Actions
Offers
CONTENT
Reporting
and KPIs
Real-time
Decisions
CUSTOMER CONTEXT
COMMUNITY
COMMERCE
Measure and analyze all touches, interactions, responses to refine approach
Personalize customer experiences with analytics
Measure key metrics at every step and use to continuously refine experience
#AnalyseData

39. © 2013 IBM Corporation
Turning insight into action: Analytic components and processes
Path to personalization means leveraging all forms of data, interactions and time horizons
BATCH
PROCESSING
Predictive
Modeling
Customer
Online Browsing
and Transactions
In-Store
Transactions
Products
Social Media
Demographics
Reporting
and KPIs
• Historic data
• Present data
• Suggested
future
behavior
Business
Rules
Domain
Expertise
Predictive Model
Scoring
Analytical
Decision
Management
Personal Interactions
with Consumers
REAL-TIME PROCESSING –
ONLINE, IN STORE, MOBILE
Association
Classification
Segmentation
Propensity
Inventory
Supply Chain
Path to personalization means
leveraging all forms of data,
interactions and time horizons
#AnalyseData

40. © 2013 IBM Corporation
5. Summary
#AnalyseData

41. © 2013 IBM Corporation
Summary
 Today, filtering and selection is not enough – it’s all about finding useful patterns that are
in your data
 Access, create, aggregate and model
 Measures over time, context and changes in circumstances are almost always worth the
effort finding
 You can never have too much data but start small and grow
Data sources and types
Data management activities
Data analysis techniques
 Make analytics a habit and integrate into regular processes
 Regularly monitor metrics and effectiveness – it is a dynamic world and you should
refresh regularly to spot changes in your customer’s behaviours and attitudes
 Always aim to turn insight into action
#AnalyseData

42. © 2013 IBM Corporation
1/30/13
For further information please email:
spssmktg@uk.ibm.com
For demonstrations and event information:
http://www-01.ibm.com/software/uk/analytics/spss/events/

43. Your Questions
A Marketer’s Guide to Data Analytics
James Lawson
Consultant Editor
marketingfinder.co.uk
Colin Linsky
Worldwide Predictive Analytics Leader
IBM SPSS

44. A Marketer’s Guide to Data Analytics:
Gaining Real Insight from Big Data
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1. You can request more information on data
analytics
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45. #AnalyseData
Thank You
A Marketer’s Guide to Data Analytics:
Gaining Real Insight from Big Data
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