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© 2015 IBM
Presentation to University of California Irvine Dr. Arvind Sathi February 25, 2015
© 2015 IBM 2
The Dance Vacation Product Idea
A vacation for dance enthusiasts
Using the DWTS format
Complete with Disney costumes
On Disney Cruise Line
Con
cept
© 2015 IBM 3
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.
© 2015 IBM 4
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
© 2015 IBM 5
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
© 2015 IBM 6
How was your first marketing exposure to the Social Media?
© 2015 IBM 7
Internet of Things – Ecosystem Map from Beecham Research
Source: M2M/IoT Sector Map by Beecham Research
© 2015 IBM 8
Monetization of data – emergence of a market place
www.lumapartners.com, reprinted with permission
© 2015 IBM 9
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
© 2015 IBM 10
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
© 2015 IBM 11
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 peoplego for dinner on weekends
Almost no Homebodies any time
© 2015 IBM 12
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 Buddies ID 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
© 2015 IBM 13
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
© 2015 IBM 14
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
© 2015 IBM 15
System U / Deriving Personality Profile
Psycho-linguistic Profile
© 2015 IBM 16
Group with no leader
Social Network using Voice Call Data
© 2015 IBM 17
Slice and Dice of my purchase data
www.slice.com, reprinted with permission
© 2015 IBM 18
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
© 2015 IBM 19
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 Intent Building Context
© 2015 IBM 20
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”
© 2015 IBM 21
Customer Needs and Usage Mapped to Products
Customers Needs Usage Offerings Components
Micro Segment
© 2015 IBM 22
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
© 2015 IBM 23
A not so intelligent campaign
© 2015 IBM 24
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
© 2015 IBM 25
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.
© 2015 IBM 26
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.
© 2015 IBM 27
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
© 2015 IBM 28
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
Con
cept
D
ata
Ser
vice
s
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
© 2015 IBM 29
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
Sentiments Customer Experience Profile
Permissions & Data Privacy
QoS Scores
Behavioral Data
OTT Favorites Mobility Profiles Usage 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
© 2015 IBM 30
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
© 2015 IBM 31
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
© 2015 IBM 32
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
© 2015 IBM 33
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
© 2015 IBM 34
What is Big Data?
© 2015 IBM 35
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.
© 2015 IBM 36
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.
© 2015 IBM 37
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.
© 2015 IBM 38
A Wordle diagram of the text used in this book
© 2015 IBM 39
Time plot of customer blog key words in Indian market
© 2015 IBM 40
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
© 2015 IBM 41
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
© 2015 IBM 42
Direct Negotiations in the Broadcast Era
Business Model
Media Formats
Audiences Advertiser
Radio
TV
Billboard
Direct Negotiations
© 2015 IBM 43
Massive Audience Fragmentation and Auction Markets
Business Model
Social
Search
Radio
Video
Media Formats
Auction Markets
Smartphones
Devices
Digital Billboards
Connected TVs
Computers
Tablets
Audiences Advertiser
Display
Apps
© 2015 IBM 44
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
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