engaging customers using big data - presentation to berlin school of creative leadership
TRANSCRIPT
© 2015 IBM
Presentation to Berlin School of Creative Leadership Dr. Arvind Sathi January 29, 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.
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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 Consumer vs. Corporate Marketing – Convergence or Divergence
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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
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How was your first marketing exposure to the Social Media?
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Internet of Things – Ecosystem Map from Beecham Research
Source: M2M/IoT Sector Map by Beecham Research
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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
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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
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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
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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
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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
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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
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System U / Deriving Personality Profile
Psycholinguistic Profile
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Leaders in a communications network
Group with no leader
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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
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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”
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Customer Needs and Usage Mapped to Products
Customers Needs Usage Offerings Components
Micro Segment
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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
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A not so intelligent campaign
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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
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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.
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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.
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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
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Dance Vacation product requires a single customer profile connecting diverse interests.
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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
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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
Integrated Profile
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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
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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
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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
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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
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What is Big Data?
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A Wordle diagram of the text used in this book
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Time plot of customer blog key words in Indian market
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Identity Resolution • Identity resolution provides a way to connect various facts about an entity and resolve
differences.
Job Applicant
Identity Thief
Top 200 Customer
Criminal Investigation
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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
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Direct Negotiations in the Broadcast Era
Business Model
Media Formats
Audiences Advertiser
Radio
TV
Billboard
Direct Negotiations
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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 41
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 Consumer vs. Corporate Marketing – Convergence or Divergence