Managing Big Data and Reaping Benefits

© 2016 Adobe Systems Incorporated. All Rights Reserved. Adobe Confidential.
Big Data Management & challenge to manage big data
and reap the benefits?
Supree Thongpetch, Adobe Marketing Cloud Business Partner : Thailand

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Welcome to the Era of Digital

3
2009
We are a digital
marketing
company!

4
2012
2013
2014
Adobe Marketing Cloud // Journey
• Adobe Marketing
Cloud
• Social
• Analytics
• Experience Manager
• Core Services
• Platform
• Mobile• Campaign
• Video
• Target
• Media
Optimizer
• Visualization
• Automation
• Integration
2015
2011
2010
2009
Core Services
Access & Usability
Capability

Marketing Cloud Leadership
Magic Quadrant for Digital Marketing
Hubs Q4 ‘15
Forrester Wave: Digital Experience
Platforms, Q4 2015
IDC MarketScape World Wide
Marketing Cloud Platform Report 2016

Adobe Current Leadership in Digital Marketing
Web Content Mgmt. Systems
Online Testing
Platforms
Data Mgmt. Platforms Social Media Mgmt.
As of 11/13/15
Mobile App Dev PlatformsMultichannel Campaign Mgmt.
Cross-Channel Campaign Mgmt.
Web AnalyticsDigital Experience Platforms
Web Content Mgmt.
Enterprise Marketing Software Suites
Digital Marketing HubsDigital Marketing Analytics

© 2016 Adobe Systems Incorporated. All Rights Reserved. Adobe Confidential. 7
Adobe Marketing Cloud
Solutions
Core services
Places
PlatformPeople
Assets
Mobile
Activation
Data science
Data Content
Media
Optimizer
Social
Audience
Manager
Target Primetime
Experience
Manager
Campaign
Analytics

Adobe Marketing Cloud
Core services
Places
PlatformPeople
Assets
Mobile
Activation
Data science
Data Content
Media
Optimizer
Social
Audience
Manager
Target Primetime
Experience
Manager
Campaign
Analytics
Solutions

CONSUMER
AWARENESS
CONSIDERATION
PURCHASE
SERVICE
LOYALTY
Consumer
Looking
Online
Interested
In Product
Buys Product,
Becomes
Customer
Likes
Digital Billboards
Viral Emails
Online Ads
Word of Mouth
PR
Radio, TV, Print,
Outdoor
Evolution of
Alternatives
Trial/Get Advice
Validate Advice
Download
Delivery
Mobile
Website
In Store
Subscribe to…
Blog
Email
Newsletter
Promotion on
Invoice
Mailing List of Ads
and Promotions
GETTING FROM CUSTOMER JOURNEY TO
CYCLE OF CUSTOMER CONVERSION

Proportion of
companies using
Proportion of companies
saying impact on ROI is high
Personalization data types and perceived ROI effect

Analytics Driven Segmentation

Simple Example: Analytics Is Gathering All Digital Interaction Behavior
12

Simple Example: Analytics Is Gathering All Digital Interaction Behavior
13
Digital Interactions Collected Within Analytics

Leverage All Digital
Interaction Data To Create
Valuable Audience
Segments
Activate This Audience
Within the Adobe Marketing
Cloud Solutions

Audience Becomes
Available to Marketing
Cloud Platform

Leverage Predictive
Algorithms To Decision The
Experience to Deliver to This
Audience
Test Relevant Offers,
Experiences and Messaging
to This Audience

Leverage Demographic Data
to Find Similar Individuals
(Look Alike Modeling)
Share This Audience to
Additional Destinations (e.g.
Advertising Platforms)
Enhance This Audience with
2nd and 3rd Party Data And
Demographics

Leverage Audiences Within
Your Journey Planning
Workflows
Select from Any of the
Marketing Cloud Shared
Audiences
Leverage Journey Planning
Capabilities to Orchestrate
Communications with this
Audience

Share Audience Updates
Back to the Marketing Cloud

Key Components of Personalization Data
Activated Audience Profile
Experience Decisioning
Analysis, Attribution and
Predictive Profiling
Experience Creation and
Management

Adobe Advertising Landscape
AUDIENCEDATA
Adobe
Media
Optimize
r
Adobe
Analytics
Adobe
Target
Adobe
Campaign
Adobe
Marketing
Cloud
Audience
Manager
DISPLAY
SOCIAL
SEARCH
Engagement
& Conversion
FIRST PARTY
CRM
FIRST PARTY
SOLUTION LEVEL
SEGMENTATION
Partners
SECOND PARTY
Audience
Marketplace
THIRD PARTY
Audience Valuation
Look-alike
Modeling
Creative Response
Offer Optimization
ALGORITHMS
Paid Media
DISPLAY, SOCIAL,
SEARCH
Experiences
ADS & PAGES
Cross-Channel
EMAIL, SOCIAL,
MESSAGING
DELIVERYEXPERIENCE
Asset Sharing
Marketing Cloud core services
DIGITAL ASSET MANAGER
AD TIED TO
OPTIMIZED
LANDING PAGE
Search
Ads
Display
Ads
Social
Ads

Media Optimizer & Analytics

Media Optimizer
& Analytics

Adobe Analytics Data in Media Optimizer
Conversion metrics
from Adobe Analytics
Engagement metrics
from Adobe Analytics
Traffic and cost metrics
from Search Engines

Media Optimizer & Audience Manager

AUDIENCE TARGETING & PERFORMANCE
Prospecting & New Customer Acquisition
3RD-PARTY DATA VIA AMO OR AAM
AMO: 3rd-party On-demand Segments AAM: 3rd-Party Data Providers

Segmentation DeliveryData
CRM
Adobe Analytics
Partner Data
TARGUSinfo epsilon
Datalogix Experian
Exelate V12 group
AddThis BlueKai
janrain LiveRamp
bizo Acxiom
Alliant Quantcast
Audience
Segment
Standardization
3rd Party
1st Party
2nd Party
Audience Manager
(DMP)
Media Optimizer
(DSP)

Lookalike Modeling
29
Reach
Accuracy
Trait
A A
InfluenceNoise
Baseline Trait Statistical Analysis Audience Expansion

Use algorithmic
segmentation to
automate the process of
creating high-value look-
alike segments based on
statistical modeling

Quickly syndicate your
unique audience segments to
on and off site targeting
platforms

AMO Display DSP + AAM Integration
AUDIENCE MANAGER
(DMP)
MEDIA OPTIMIZER
(DSP)
§ Audience Segments from
AAM seamlessly delivered to
AMO DSP platform via Server
to Server Integration
§ No additional Cookie Mapping
tags (with inventory sources)
needed if AAM tags exist
§ Event level performance data
(clicks, impressions, etc.) from
AMO DSP sent to Audience
Manager for reporting and insights
§ Unique Look-a-like segments
using AMO search/social data for
targeting in AMO DSP*
Event Level Data
Segment Data
*Coming soon
ADOBE AUDIENCE MANAGER & DISPLAY

AMO Display & Audience Manager
AUDIENCE DATA INGESTION IDENTIFY & SEGMENT MULTI-CHANNEL DELIVERY
§ Online and Offline Data
§ Adobe Analytics/Site
Visitors
§ CRM, POS
1ST PARTY IDENTIFY & SEGMENT
§ High-value audiences
SYNDICATE TO:
§ Adobe Marketing Cloud
§ External Targeting
Platforms
§ Partner Data
2ND PARTY
§ Data Partner
Integrations
3RD PARTY EXECUTE ON:
§ Look-alike Modeling
§ Marketing Effectiveness
DISPLAY AD WEBSITE
SEARCH MOBILE
SOCIAL VIDEO

AUDIENCE DATA INGESTION IDENTIFY & SEGMENT REACH AUDIENCES
ACROSS THE WEB
Visitors
§ CRM, POS
SYNDICATE TO:
Platforms
§ Partner Data
2ND PARTY
§ Data Partner
Integrations
DISPLAY TARGETING

AUDIENCE DATA INGESTION IDENTIFY & SEGMENT
Visitors
§ CRM, POS
SYNDICATE TO:
Platforms
§ Partner Data
2ND PARTY
§ Data Partner
Integrations
CONSISTENT EXPERIENCE:
DISPLAY AD TO WEBSITE
DISPLAY AD
WEBSITE CONTENT

Profile Extension
2ND-PARTY
DATA
3RD-PARTY DATA
30%
40%
Website Interactions & metrics
(PC, Mobile, Video, etc.)
Offline Data: Point of Sale (POS),
Call Center, CRM data etc.
1ST-PARTY
DATA
50%
60%
70%+
§ Site Behavior
§ Engagement
§ Frequency
§ Temporal
§ Viewed Product
§ Purchased Product
§ Credit Card: $10K+
§ Starwood Preferred
§ Sr. Executive
§ Technology
§ Sales
§ Bachelors Degree
§ Off-Line Activity
§ Loyalty
§ Spend
§ Product Affiliation
§ HH Inc. >$100K
§ Female
§ Age
§ Online Shopper
Gender, Job Function, Company Size,
Industry, Location, Seniority, Education
Partner data: Page views, conversion
funnel, past purchases, credit card
usage, travel information, etc.
Partner
Household, Gender, Retail, Lifestyle,
Financials, Demographic, Politics,
Automotive, Subscriptions
Individual
User Profile
Multi-Channel
Site Analytics

Use Cases

1. Targeting first time WeRetail visitors
2. Personalizing returning WeRetail visitors
3. Personalizing WeRetail for known/loyal
customers
4. Targeting digital signage for WeRetail customers
5. Targeting advertising to high value prospects
6. Targeting mobile app experience based on
propensity to act

LoriUse Case 1: Targeting first time WeRetail visitors
Sample Narrative: Lori comes to
WeRetail for the first time. Lori is visiting
the site from Minnesota. Lori is delivered
a banner on the home page that
promotes the new Fall Collection.

Media
Optimizer
Audience Manager
Campaign
Data
Provider
(second party
data)
Data
Provider
(third party
data)
Marketing Cloud Audiences
Data
Workbench
Reports
Analytics
WeRetail
Data Sources
Target
Experience Manager
Assets
Steps:
1. Multiple
Assets/Experiences are
Created in Experience
Manager
2. Visitor comes to WeRetail
and detectable information
(Geo, Tech, Temporal) is
gathered by Target.
3. When visitor comes to
WeRetail, Target assigns
visitor to a segment and
delivers relevant
experience to the website.
**Decision in #3 can be rules based or
an Automated Decision based on an
algorithm.
1
2
3
Use Case 1: Targeting first time WeRetail visitors
WeRetail
Infrastructure
Existing Adobe
Infrastructure

LoriUse Case 2: Personalizing returning WeRetail visitors
based on previous behavior
Sample Narrative: Lori has been to
WeRetail multiple times and recently has
been exploring the Women's Jeans
collection.
The next evening, Lori returns to WeRetail
and is displayed featured content on the
Women's Jeans collection. Lori is also
shown the top 4 Most Purchased Products
from the Women's Jeans Collection.

Media
Optimizer
Audience Manager
Campaign
Data
Provider
(second party
data)
Data
Provider
(third party
data)
Data
Workbench
WeRetail
Data Sources
Steps:
1. Previous Interaction Data is
Tracked by Analytics.
2. Segments based on
Interaction data are
created in Analytics and
Shared to the Marketing
Cloud Solutions.
3. Analytics Data is
continually used to build
algorithms based on
popular products.
4. Visitor returns to WeRetail
and is recognized as
belonging to an Audience
Segment.
5. Based on the Audience
Segment, Target assigns
and delivers a relevant
experience to the website.
Target
Experience Manager
Reports
Analytics
3
5
1
2
2
4
Use Case 2: Personalizing returning WeRetail visitors based on previous behavior
WeRetail
Infrastructure
Existing Adobe
Infrastructure
Assets

MariaUse Case 3: Personalizing WeRetail visitors based on known customer data
Sample Narrative: Maria is a loyal WeRetail
in-store shopper. She is registered in the
WeRetail Standard Rewards program, and
currently has a balance of 6000 points.
Maria is also an active browser on WeRetail,
but has not ordered online in the last 45
days.
Maria comes to WeRetail and receives a
targeted message about the additional
benefits of being a WeRetail Premium
Member (eligibility for over 6500 pts),
highlighting the Free Shipping for online
purchases.

Media
Optimizer
Audience Manager
Campaign
Data
Provider
(second party
data)
Data
Provider
(third party
data)
Data
Workbench
Use Case 3: Personalizing WeRetail visitors based on known customer data
Target
Experience Manager
Steps:
1. Previous Interaction Data is
Tracked by Analytics.
2. Known Customer Information
(Gender, Loyalty, Marketing
Preferences, etc) is Shared to
the Marketing Cloud.
3. Segments based on
Interaction data and/or
Known Customer data are
created in Analytics and
Shared to the Marketing
Cloud.
4. Visitor comes to WeRetail and
is recognized as belonging to
an Audience Segment.
5. Based on the Audience
Segment, Target assigns and
delivers a relevant experience
to the website.
Reports
Analytics
WeRetail
Data Sources
5
1
2
WeRetail
Infrastructure
Existing Adobe
Infrastructure
4
3
Assets

Maria
Use Case 4: Targeting digital signage based on shopper
location and behavioral data
Sample Narrative: Maria has
recently been browsing
women's tops on WeRetail
and is also an active WeRetail
mobile app user.
Maria walks into the WeRetail
store location, as she gets
near the digital sign by the
entrance, the sign elegantly
adjusts to feature the “Buy 1,
Get 1 50% off” on women's
tops, and highlighting the top
3 selling women’s shirts.

Media
Optimizer
Audience Manager
Data
Provider
(second
party data)
Data
Provider
(third party
data)
Data
Workbench
Target
Experience Manager
Campaign
Assets
Reports
Analytics
WeRetail
Data Sources
3
4
12
Steps:
1. Experience Manager
Serves Up Experience on
In-Store Digital Signage.
2. In-Store Beacon detects
that shopper is near, or
interacting with, the
digital sign, beacon call
made to AEM
3. The shoppers
segmentation is available
based on recent and
historical data is available
in Audiences.
4. Target decisions a
relevant digital
experience that is served
by AEM on the In-Store
Digital Sign.
Use Case 4: Targeting digital signage based on shopper location and behavioral data
WeRetail
Infrastructure
Existing Adobe
Infrastructure

Use Case 5: Targeting advertising to high value prospects
Example Narrative: WeRetail
understands that active WeRetail
Loyalty members have a high life time
value.
A segment of known active WeRetail
Loyalty members is created, and using
Audience Manager look-alike modeling,
other prospects who likely resemble the
“Active WeRetail Loyalists” segment are
identified using data from third party
data sources.
When these users then visit Instyle.com,
they see an ad promoting features of
WeRetail Loyalty program.

WeRetail
Data Sources
Data
Workbench
Target
Steps:
1. First Party Behavioral and
Anonymized CRM Data
Available to Audience
Manager Profile
2. 2nd and 3rd Party Customer
Data Available to Audience
Manager Profile
3. Audience Segment created
in Audience Manager
4. Audience Expanded to
include unknown prospects
using Look-Alike Modeling
5. Segment available to Media
Optimizer for ad targeting
Use Case 5: Targeting advertising to high value prospects
WeRetail
Infrastructure
Existing Adobe
Infrastructure
Reports
Analytics
Media
OptimizerData
Provider
(second
party data)
Data
Provider
(third party
data)
Audience Manager
3
2
Experience Manager
Assets
1 Campaign
1
4
5

Use Case 6: Targeting mobile app experience based on propensity to act
Example Narrative: Scott is a cross
channel shopper with WeRetail. Based
on data gathered on Scott, the
propensity model assigns a high score
to the visitor (Scott) that indicates he is
likely to apply for a WeRetail Credit
Card.
When Scott walks into the WeRetail
Store he receives a mobile message
indicating that he can get 30% off his
purchase if he registers for a credit card
today.

Data
Provider
(second
party data)
Data
Provider
(third party
data)
Audience Manager
Experience Manager
Assets
Media
Optimizer
Target
WeRetail
Data Sources
Steps:
1. Propensity Model Assigns Each
Visitor a Score based on their
Propensity to Apply for an WeRetail
Credit Card (any action).
2. Propensity Score Available to
Marketing Cloud Audiences and
Campaign
3. In-Store Beacon detects that shopper
has entered a WeRetail store, signal
is sent to Campaign.
4. Campaign Workflow triggered based
on beacon call and visitor’s
propensity score.
5. Relevant Push Message sent to
visitors mobile phone.
Use Case 6: Targeting mobile app experience based on propensity to act
WeRetail
Infrastructure
Existing Adobe
Infrastructure
Data
Workbench
Reports
Analytics
1
2 Campaign
2
3 4 5

ü Targeting first time WeRetail visitors
ü Personalizing returning WeRetail visitors
ü Personalizing WeRetail for known/loyal
customers
ü Targeting digital signage for WeRetail customers
ü Targeting advertising to high value prospects
ü Targeting mobile app experience based on
propensity to act

Thank you
Supree Thongpetch
supree@sirimedia.co.th
+66816140341

Managing Big Data and Reaping Benefits

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