big data : daat day 2016 - breakout media room

© 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


Jen | Gym Instructor

Lululemon Athletica is fantastic.Felix | College Professor

VERIZON LTE 4GSamsung Galaxy S4

Fred Leger Age 30

Pier 83

Fort

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Pier 83

Forth

Ave

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10th

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T-Mobile 4Nokia Lumia 520

Sue Child

Chris ManchesterElisa Sanchez

Age 40

Jerry | Stock Broker

AT&TIphone 4s

@AdobeMktgCloudAlbert

Sushi

3,343, 4,453

Welcome to the Era of Digital

© 2016 Adobe Systems Incorporated. All Rights Reserved. Adobe Confidential. 3

2009We are a digital marketing company!


20122013

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

20112010

2009Core Services

Access & Usability

Capability


Marketing Cloud Leadership

Magic Quadrant for Digital Marketing Hubs Q4 ‘15

Forrester Wave: Digital ExperiencePlatforms, 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.© 2016 Adobe Systems Incorporated. All Rights Reserved. Adobe Confidential. 7

Adobe Marketing Cloud

Solutions

Core services

Places

PlatformPeople

AssetsMobile

ActivationData science

Data Content

MediaOptimizer

Social

AudienceManager

Target PrimetimeExperienceManager

Campaign

Analytics

© 2016 Adobe Systems Incorporated. All Rights Reserved. Adobe Confidential.© 2016 Adobe Systems Incorporated. All Rights Reserved. Adobe Confidential. 8

Adobe Marketing Cloud

Core services

Places

PlatformPeople

AssetsMobile

ActivationData science

Data Content

MediaOptimizer

Social

AudienceManager

Target PrimetimeExperienceManager

Campaign

Analytics

Solutions

© 2016 Adobe Systems Incorporated. All Rights Reserved. Adobe Confidential.© 2016 Adobe Systems Incorporated. All Rights Reserved. Adobe Confidential.

CONSUMER

AWARENESS

CONSIDERATION

PURCHASE

SERVICE

LOYALTY

Consumer LookingOnline

InterestedIn Product

Buys Product,BecomesCustomer

Likes

Digital Billboards

Viral Emails

Online Ads

Word of Mouth

PR

Radio, TV, Print, Outdoor

Evolution of Alternatives

Trial/Get Advice

Validate Advice

Download

DeliveryMobile

Website

In Store

Subscribe to…

Blog

Email

Newsletter

Promotion on Invoice

Mailing List of Ads and Promotions

GETTING FROM CUSTOMER JOURNEY TOCYCLE 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

AdobeMedia Optimizer

AdobeAnalytics

AdobeTarget

AdobeCampaign

AdobeMarketing Cloud

Audience Manager

DISPLAY

SOCIAL

SEARCH

Engagement& Conversion

FIRST PARTY

CRMFIRST PARTY

SOLUTION LEVELSEGMENTATION

PartnersSECOND PARTY

AudienceMarketplace

THIRD PARTY

Audience Valuation

Look-alike

Modeling

Creative Response

Offer Optimization

ALGORITHMS

Paid MediaDISPLAY, SOCIAL,

SEARCH

ExperiencesADS & PAGES

Cross-ChannelEMAIL, SOCIAL,

MESSAGING

DELIVERYEXPERIENCE

Asset SharingMarketing Cloud core servicesDIGITAL ASSET MANAGER

AD TIED TOOPTIMIZED

LANDING PAGE

SearchAds

DisplayAds

SocialAds


Media Optimizer & Analytics


Media Optimizer& Analytics


Adobe Analytics Data in Media Optimizer

Conversion metricsfrom Adobe Analytics

Engagement metricsfrom 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

CRMAdobe 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

Rea

ch

Accuracy

Trait A A

Influ

ence

Noi

se

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:



2ND PARTY





DISPLAY TARGETING



AUDIENCE DATA INGESTION IDENTIFY & SEGMENT


Visitors§ CRM, POS



SYNDICATE TO:



2ND PARTY




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 visitors2. Personalizing returning WeRetail visitors

3. Personalizing WeRetail for known/loyal customers

4. Targeting digital signage for WeRetail customers

5. Targeting advertising to high value prospects6. 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

ReportsAnalytics

WeRetailData Sources

Target

Experience ManagerAssets

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 WeRetailand 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)


data)

Data Workbench


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


ReportsAnalytics

3

5

1

2

24

Use Case 2: Personalizing returning WeRetail visitors based on previous behavior



Assets


MariaUse Case 3: Personalizing WeRetail visitors based on known customer data

Sample Narrative: Maria is a loyal WeRetailin-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)


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.

ReportsAnalytics


5

12



Marketing Cloud Audiences4

3

Assets


MariaUse Case 4: Targeting digital signage based on shopper location and behavioral data

Sample Narrative: Maria has recently been browsing women's tops on WeRetailand is also an active WeRetailmobile app user.

Maria walks into the WeRetailstore 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)

Data Workbench

Target

Experience Manager

Campaign

Assets


ReportsAnalytics


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




Use Case 5: Targeting advertising to high value prospects

Example Narrative: WeRetailunderstands that active WeRetailLoyalty members have a high life time value.

A segment of known active WeRetailLoyalty 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.



Data Workbench

TargetSteps: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



ReportsAnalytics

Media OptimizerData

Provider(second

party data)


data)

Audience Manager


3

2


1 Campaign

14

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 WeRetailStore 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)

Audience Manager


Media Optimizer Target


Steps:1. Propensity Model Assigns Each

Visitor a Score based on their Propensity to Apply for an WeRetailCredit 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



Data Workbench

ReportsAnalytics


1

2 Campaign2

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

© 2016 Adobe Systems Incorporated. All Rights Reserved. Adobe Confidential.© 2016 Adobe Systems Incorporated. All Rights Reserved. Adobe Confidential.

Thank youSupree Thongpetch

[email protected]+66816140341

big data : daat day 2016 - breakout media room

Data & Analytics