Presentation on Marketing analysis to the Graduate Business program at University of California Irvine

1. © 2015 IBM
Presentation to
University of California
Irvine
Dr. Arvind Sathi
February 25, 2015

2. © 2015 IBM2
The Dance Vacation Product Idea
A vacation for
dance enthusiasts
Using the
DWTS format
Complete with
Disney costumes
On Disney Cruise
Line
Concept

3. © 2015 IBM3
What this scenario demonstrates
A high value, high margin business opportunity
A micro-segment of customers which can not be reached via
broad marketing campaigns
A combination of Disney and external data, correlated to
formulate the product, and the campaign
A custom defined ecosystem which gets access to this product
and related campaigns
A set of interactions geared towards specific micro-segments.

4. © 2015 IBM4
Overview
Changing Winds
Proposition 1: From “Sample recalls” to “Observing the Population”
Proposition 2: Marketing through Collaborative Influence
Proposition 3: From silo’ed to Orchestrated Marketing
Technological Enablers
Changes to Marketing Ecosystem and Organization

5. © 2015 IBM5
Changing Winds
Rise of Digital Society
Ubiquitous use of Mobile Platform
Savvy customers discover Social Computing
Crowd-sourced analytics tools
Monetization
Private and public clouds
Customer preferences and privacy concerns

6. © 2015 IBM6
How was your first marketing exposure to the Social Media?

7. © 2015 IBM7
Internet of Things – Ecosystem Map from Beecham Research
Source: M2M/IoT Sector Map by Beecham Research

8. © 2015 IBM8
Monetization of data – emergence of a market place
www.lumapartners.com, reprinted with permission

9. © 2015 IBM9
Proposition 1: From “Sample recalls” to “Observing the
Population”
Census data
Social media data
Location data
Product usage data
Shopping data
Conversation data
Purchase data

10. © 2015 IBM10
Data
Cell tower locations
Wi-fi locations
Device locations
Device usage data – apps, web
sites
Customer data – demographics
Refined locations
Mobility Patterns
Hang outs
Hang outs correlated with
business locations
Mode of transportation
Traveling buddies
Analytics
Location Data

11. © 2015 IBM11
Discovery from location data
• A typical discovery uses statistical tools to identify pattern in data.
• Discovery may contribute new derived attributes for further analysis or reporting.
Night Owls at Night
Delivery People
During the Day
Quiet Weekday people
go for dinner on weekends
Almost no Homebodies any time

12. © 2015 IBM12
Buddies, Hangouts, Sofa Surfers
Three areas of analysis:
n Subscriber level Lifestyle and Mobility profiles
n Popular Locations with specific profiles
n Subscriber Pairings or Buddies
Who Are You?
Homebody
Daily Grinder
Delivering the Goods
Globetrotter
Sofa Surfer
10 Top Hangouts
Best BuddiesID Rank Night Morning Lunch Dinner Breakfast Afternoon Total Result
54796109xxx 1 34 7 11 15 9 12 88
54809186xxx 2 33 7 11 15 9 12 87
30931430xxx 3 32 7 11 15 9 12 86
54802704xxx 4 31 7 11 15 9 12 85
54796392xxx 5 29 5 11 15 6 11 77

13. © 2015 IBM13
Competitive Locations Have Different
Profiles of Traffic Throughout the Day
Location of Latte
Land is very close to
Starbucks, but has
more evening traffic
Time of
Day Store
Visits per
interval

14. © 2015 IBM14
Subscriber URL Activity
Mined to Create Interest
Profile
- Use Social Media (Twitter) data to
create profiles
§ Soccer: User interest in soccer,
favorite teams
§ Telco: Services provided by Telco
§ Others: Users viewing experience,
Users comments on Apps including
what they like/dislike
- Research URL Analytics asset
and Tag Cloud asset
§ Identify categories user will be
interested in based on URL
analytics
§ Identify word clouds based on
pages associated with category
Interest Profile

15. © 2015 IBM15
System U / Deriving Personality Profile
Psycho-linguistic Profile

16. © 2015 IBM16
Group with no leader
Social Network using Voice Call Data

17. © 2015 IBM17
Slice and Dice of my purchase data
www.slice.com, reprinted with permission

18. © 2015 IBM18
How can this be utilized by Marketers
Amazon
Apple
iTunes
PayPal
eBay
Target
Groupon
Living Social
Netflix
Google Play
Best Buy
Newegg
Walmart
Zappos
Woot
Monoprice.com
www.slice.com, reprinted with permission

19. © 2015 IBM19
Building Context and Intent from Location data
Deriving location: location information may be derived using multi-
modal information
• CDR data, tower data, device data, Wi-fi etc.
• Accuracy of location information depends on data fidelity etc.
Building context: making sense of the location information
• Correlate location information with business data
• Various other correlation rules may be used to build a rich context
Inferring intent: infer consumer level intents by leveraging location and
mobility patterns
Deriving Location Inferring IntentBuilding Context

20. © 2015 IBM20
Proposition 2: Marketing through Collaborative Influence
Personalized customer / product research
Online advertising
Multi-channel shopping
Intelligent campaigns
Big ticket items and auction / negotiation markets
Games, videos, smart phones and tablets
Influence through crowd-sourced reviews
Endorsements and viral “buzz”

21. © 2015 IBM21
Customer Needs and Usage Mapped to Products
Customers Needs Usage Offerings Components
Micro
Segment

22. © 2015 IBM22
Customer Needs and Usage Mapped to Products
Customers Needs Usage Offerings Components
Day time
Work at
Home
Work day
High Usage
Off time
Low Usage
Home Office
Bandwidth
Network
Policy

23. © 2015 IBM23
A not so intelligent campaign

24. © 2015 IBM24
Drive
Interact
with
the
customer
to
seek
permission
to
use
loca3on
informa3on
and
send
campaign,
record
interac3on
and
results.
Discover
Collect
historical
behavioral
data,
past
acts,
and
success
rates.
Analyze
historical
data
to
formulate
pa?erns
and
changes
required
to
detect,
and
inves3gate
steps
Decide
Use
background
informa3on,
past
campaigns,
privacy
preferences,
customer
reac3on
to
past
campaigns,
purchase
intent,
preferences
expressed
in
social
media
to
design
campaign.
Detect
Detect
in
real
3me
if
a
transac3on
relates
to
targeted
subscribers.
Iden3fy,
align,
score,
and
send
for
further
processing
(e.g.,
a
targeted
customer
driving
towards
mall)
Smarter Campaigns using D4
Detect
observa,ons
about
a
target
Take
ac,on
in
real
,me
–
when
it
ma8ers
Find
new
targets
by
analyzing
historical
data
Iden,fy
pa8erns
over
,me
and
ac,ons
required
Drive
Detect
Discover
Decide
Target
Subscriber
24

25. © 2015 IBM25
Digital Advertising Marketplace
Publisher
Advertisers
Supply Side Platform
(SSP)
Demand Side Platform
(DSP)
Data Management Platform (DMP)
Represents publishers, and
runs auctions for inventory in
real-time, finding the highest
bidder
Represents brands, and bids
on auctions for inventory in
real-time, finding the best
price / consumer propensity
match
Sources data wherever it can to help DSPs in
particular to make better predictions about inventory
so that they can be more certain about the likely
customer intent, and therefore bid higher and
secure more conversions.

26. © 2015 IBM26
Google India advertisement goes viral
https://www.youtube.com/watch?v=gHGDN9-oFJE
Published on Nov 13, 2013
Partitions divide countries, friendships find a way
(Use captions to translate the film in 9 languages including French, Malayalam and Urdu)
The India-Pakistan partition in 1947 separated many friends and families overnight. A
granddaughter in India decides to surprise her grandfather on his birthday by reuniting him
with his childhood friend (who is now in Pakistan) after over 6 decades of separation, with a
little help from Google Search.
Views
It is a 3 minute, 32 second advertisement that would be considered too long for a conventional
advertisement. It shows the Google products being used in a “use case,” and it attracted
more than 3 million viewers in the first three days it was posted.

27. © 2015 IBM27
Proposition 3: From silo’ed to Orchestrated Marketing
Customer profile
Entity resolution
Personal privacy preference management
Dynamic pricing
Orchestration for context-based advertising and promotion
Cross-channel co-ordination
Market tests

28. © 2015 IBM28
Dance Vacation product requires a single customer profile
connecting diverse interests.
28
A vacation for
dance enthusiasts
Using the
DWTS format
Complete with
Disney costumes
On Disney Cruise
Line
ConceptDataServices
Facebook posts
Mobility patterns
Hang outs
Social circles
Linear views
Non-linear views
Likes
Past responses
Past purchases
Likes
Shares
Past purchases
Interests
Fan advocacy
Dance Studio
partnership
Ads via non-
linear
Campaigns
across touch
points
Campaigns
across touch
points
Customer Profile

29. © 2015 IBM29
A Multi-dimensional Customer Profile
A comprehensive data model should capture a wide range of multi- dimensional and comprehensive
information, adequate to reflect the customer’s complete digital profile
Descriptive data
Interaction data
Real Time Alerts and
NBA
Privacy and Contact
Preferences
Contextual Multichannel
Profile
Partner Sectors – 3rd
Party Data
Attitudinal data
SentimentsCustomer Experience Profile
Permissions & Data
Privacy
QoS Scores
Behavioral Data
OTT FavoritesMobility ProfilesUsage and ARPU
Profile
Mobile Payments
Digital Account Portrait Digital Signatures Onboarding and
Retention
Personalizations SmartHome Subscriptions Red Flags
Financial & Billing
Profile
Customer Lifetime
Value
Top Up Wallet

30. © 2015 IBM30
Step 1: Identifying a high value target progressively
Annon.
ID
Profile
Informa;on
Source
AB1234
None
Annon
ID
Profile
Informa;on
Source
AB1234
Interested
in
certain
types
of
phones
Website
–
Phone
page
AB1234
Interest
in
a
par,cular
phone
Website
–
Search
Interested
in
4G
Phone
Website
Use of Customer Profile in Digital Advertising

31. © 2015 IBM31
Step 2: User visits their favorite News site (Increase Brand Experience)
Offer 1
Offer 2
Offer 3
SmartPhone advertisement w/
“Fashion” callout
4G benefits advertisement
Generic Offertel
advertisement $1.50
$2.50
$12.00
Profile Information Source
Offertel homepage view Website
SmartPhone product
page
Website
4G coverage eligible Website
4G Ad Creative
Impression
Turn
4G Creative Ad Click Turn
Offertel landing page view Website
SmartPhone price plans Website
Video
Ad
Use of Customer Profile in Digital Advertising

32. © 2015 IBM32
Step 3: User visits multiple sites and eventually purchases item
Offer 1
Offer 2
Offer 3
“Premium Price Plan”
4G benefits advertisement
Generic Offertel advertisement $3.50
$4.50
$22.00
Profile Information Source
Offertel homepage
view
Website
High Income Profit Data Vendor/
Telco
SmartPhone page Website
4G coverage eligible Website
4G Ad Creative Multiple websites
4G Creative Ad Click Multiple websites
Offertel landing page Website
SmartPhone price
plans
Website
Ad
Ad
Use of Customer Profile in Digital Advertising

33. © 2015 IBM33
Technological Enablers
Volume, Variety, Velocity, Veracity of data
Stream computing to address velocity
Analytics and storage on MPP platforms for large volumes
High variety data analytics
Pattern discovery
Adaptive intelligence
Customer veracity and identity resolution
Hybrid solution architectures

34. © 2015 IBM34
What is Big Data?

35. © 2015 IBM35
Gartner Definition and Trends
Gartner defines advanced analytics as, "the analysis of all kinds of data using sophisticated
quantitative methods (for example, statistics, descriptive and predictive data mining, simulation
and optimization) to produce insights that traditional approaches to business intelligence (BI) —
such as query and reporting — are unlikely to discover.”
An advanced analytics platform provides a full suite of tools for a knowledgeable user to
perform a variety of analyses on different types of data. In today's market much of this analysis
is predictive in nature, although elements of descriptive analysis are not uncommon. While
these capabilities remain important,
Key Disruptive Trends:
Growing interest in applying the results of advanced analytics to improve business performance
is rapidly expanding the number of potential applications of this technology and its audience
across the organization.
The rapid growth in available data, particularly new sources of data — such as unstructured
data from customer interactions and streaming volumes of machine-generated data —
require greater levels of sophistication from users and systems to be able to capture their
full potential.
The growing demand for these types of capabilities is outpacing the supply of expert users,
requiring significantly higher levels of productivity from skilled users as well as increasing
the demand for "non-data-scientist-friendly" tools.

36. © 2015 IBM36
Gartner Dimensions
Dimension Description
Data Access Code-free basic data integration; advanced data integration; service-oriented architecture (SOA), Web
data integration; basic extraction, transformation and loading (ETL) functionality; advanced ETL
functionality; enterprise application access; data refresh; supported (for example, multimedia) data
types; data lineage; data mashup; geospatial data and consumer data integration; geocoding;
limitations.
Visualization and
Exploration /
Discovery
Basic charts; advanced visualization chart types; export of visualizations into reports and Web-portals;
advanced visualization GUI features; univariate and bivariate statistics; statistical significance testing;
online analytical processing (OLAP), visual interaction and exploration.
Data Filtering and
Manipulation
Binning and smoothing; feature generation dimensionality reduction and feature selection; filter and
search, rotation, aggregation and set operations; transformations; signal preprocessing; custom
mappings; dataset partitioning.
Advanced
Descriptive Analytics
Clustering and self-organizing maps; affinity and graph analysis; conjoint and survey
analysis; density estimation; similarity metrics.
Predictive Analytics Regression modeling; time-series analysis; neural nets; classification and regression trees; further rule-
induction techniques; support vector machines; instance-based approaches; Bayesian modeling;
ensembles and hierarchical models; import, call and development of other predictive models; measures
of fit; testing of predictive models.
Optimization Solver approaches; heuristic approaches; design of experiments.
Simulation Discrete events, Monte Carlo simulation; agent-based modeling.
Further Advanced
Analytics
Basic text analytics; text processing; vocabulary, language and ontology management; advanced text
analytics; multimedia analytics; geospatial analysis; financial modeling and econometrics; signal
processing and control.

37. © 2015 IBM37
Gartner Dimensions (Continued)
Dimension Description
Analytical Use Cases Marketing; sales; risk management and quality management; others.
Delivery, Integration,
and Deployment
Integration; write-back; Web deployment and info graphics/dashboards; portal support; embedded
delivery.
Platform and Project
Management
Metadata management; model management; model licensing issues; decision management; scripting
and automation; objects reuse;
multiuser capabilities; debugging and unit testing; runtime optimization; audit and logs; data encryption;
client deployment; extensibility.
User Experience Ease of use; documentation; guidance; wizards and contextual aids; user community; third-party
applications.
Performance and
Scalability
Big data, in-memory, in-database techniques; data volume scalability; algorithmic efficiency; real-time
data and streams.

38. © 2015 IBM38
A Wordle diagram of the text used in this book

39. © 2015 IBM39
Time plot of customer blog key words in Indian market

40. © 2015 IBM40
Identity Resolution
• Identity resolution provides a way to connect various facts about an entity and resolve
differences.
scrila34@msn.com
Job
Applicant
Identity Thief
Top 200
Customer
Criminal
Investigation

41. © 2015 IBM41
Changes to Marketing Ecosystem and Organization
Media planning and research
Personalized marketing actions and impact on advertising and promotion organizations
Refined product management for orchestrated marketing
Data scientists – where do they belong?
Infrastructure, data, or analytics as a service
New role for marketing communications department
Evolution vs. revolution

42. © 2015 IBM42
Direct Negotiations in the Broadcast Era
Business
Model
Media Formats
AudiencesAdvertiser
Radio
TV
Print
Billboard
Direct Negotiations

43. © 2015 IBM43
Massive Audience Fragmentation and Auction Markets
Business
Model
Social
Search
Radio
Video
Media Formats
Auction Markets
Smartphones
Devices
Digital Billboards
Connected TVs
Computers
Tablets
AudiencesAdvertiser
Display
Apps

44. © 2015 IBM44
Summary
Changing Winds
Proposition 1: From “Sample recalls” to “Observing the Population”
Proposition 2: Marketing through Collaborative Influence
Proposition 3: From silo’ed to Orchestrated Marketing
Technological Enablers
Changes to Marketing Ecosystem and Organization