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Digital Media
Foundation Discussion
July 29, 2015
Purpose
 Demystify the world of digital media through an explanation of its
foundational elements
 Provide an overview of how TD Bank capitalizes on the impact of
digital marketing, primarily Search & Display
– How digital marketing is purchased for TD Bank
– How we monitor/track performance
– What data sources we use and how they are used
2
The Language of Digital Media
3
Terms by Category
4
Channels/Buying
• Search Engine Marketing
(SEM)
• AdWords
• Bid Strategies
• Match Types
• Digital Display
• Programmatic
• DSP/SSP
• Networks
• Direct-to-Site
• CPM/CPC
• Retargeting/Remarketing
• Frequency Caps
• Viewability
Tracking
• Impressions
• Click-through-Rate (CTR)
• Share of Voice (SOV)
• Cost per Application
(CPA)
• Floodlight Tags
• Omniture Coding
• UTM Codes
• Conversion Tracking
• Attribution
• Multi Touch Attribution
(MTA)
Data Sources
• DoubleClick / DART
• Omniture
• Google Analytics (GA)
• comScore
• Programmatic Platform
• Data Management
Platform (DMP)
• Social Media Partners
(e.g., Facebook)
CHANNELS/BUYING
5
TD Bank Digital Spend
 Consumption and opportunity has driven significant growth
9%
6%
85%
2010
Display Search Other Media
19%
14%
67%
2015
Display Search Other Media
+133%
6
2015 TD Bank Digital Spend vs.
Financial Services Industry
 TD Bank is projected to over-index on
Display and Video % allocation as
compared to US Financial Services
average
– In part, driven by Bank Human 3.0
launch, as well as Fiscally Fit
promotional campaign
 TD is projecting to under-index on
programmatic % allocation vs. US
Financial Services average
– However, when looking at spend
excluding Bank Human 3.0 launch and
Fiscally Fit campaign
7Source: eMarketer “Industry Ad Spending and Forecast” Report, May 2015
Tactic
2015 US Fin.
Services 2015 TD Bank*** Index
Search 47.3% 30.1% 63.6
Display* 42.0% 69.9% 166.4
Video 10.5% 13.8% 131.4
Other** 10.7% NA NA
Total 100% 100%
*Includes social
**Email, Directories, Lead Gen
***Core LOB = Customer Acq., Checking, HELOC, Mortgage, Small Business
Tactic
2015 US Fin.
Services 2015 TD Bank* Index
Non-Programmatic 45.0% 60.8% 135.1
Programmatic 55.0% 39.2% 71.3
Total 100% 100%
*Core LOB = Customer Acq., Checking, HELOC, Mortgage, Small Business
Tactic
2015 US Fin.
Services 2015 TD Bank** Index
Non-Programmatic 45.0% 40.6% 90.2
Programmatic 55.0% 59.4% 108.0
Total 100% 100%
**Core LOB, excluding BH3.0 and Fiscally Fit
The Power of SEM and SEO
 Paid Search Engine Marketing (SEM) and Search Engine
Optimization (SEO) are both critical marketing / media tactics that
can connect TD Bank with true consumer intent leading to a valuable
web site visit and possible account conversion
8
SEO SEM
Instant Results
C
Branding
C C
Connection With
Clients C C
Product
Understanding C C
Viral Impact
C
Targeted
Marketing C C
Immediate ROI
Analysis C
ROI Analysis
Within 6+ Months C
SEO vs. SEM
 Our analysis shows the differential between SEO traffic and SEM
traffic to the TDBank.com website is substantial
– This shows in market media messaging is having a lasting impact on
consumers consideration
– Note that some of SEO traffic is purely consumer navigation to
TDBank.com
9
12,134
2.4MM
How To Identify Keywords: 3 Options
10
SERP
Review
1 2
3
Negative Keywords: Hyper Relevance
 Negative keywords are
words or phrases that
filter out irrelevant
searches. They are an
essential aspect of any
SEM campaign as they
ensure that every dollar
allocated to SEM is
focused on an exact
customer need with zero
waste
 Sample negative TD
Bank terms used for
HELOC are shown to the
right
11
Identifying Keyword Drivers
 When examining specific keywords for overall performance, our team can
examine front end media metrics and /or back end, TDBank.com web
metrics
 Currently the Tierney team does not have access to Omniture data,
therefore our current optimizations are focused on CTR, Impressions, CPC,
as well as any back end data gathered from Floodlight Tags
12
Example: Web site data pulled from Omniture for
performance optimizations Is needed for enhanced
optimizations
Example: Front end CTR% is our current way of
predicting keyword performance drivers
SEM Bid Strategies: Match Types
Defined
Allows your ad to show on similar phrases and
relevant variations (searches can include
misspellings, synonyms and related searches)
Used to refine broad match; searches contain the
modified term (or close variations, but not
synonyms), in any order
Allows your ad to show for searches that match the
phrase
Allows your ad to show for searches that match the
exact phrase exclusively
Broad
Broad Modified
Phrase
Exact
SEM Bid Strategies: Match Types
Defined
Match Type
Example (home equity)
Advertiser Keyword
Ad Will Be Shown for
These Searches
Ad Will Not Be Shown for
These Searches
Broad home equity • home equity
• find home equity
• what is equity
• home loan
• hme loan
NA
Broad
Modified
+home + equity • home equity
• find home equity
• equity in your home
• what is equity
• home loan
Phrase “home equity” • home equity
• find home equity
• home equity line of credit
• equity in your home
• equity line of credit
Exact [home equity] • home equity • home equity line of credit
In addition to this, we can add negative terms to the campaign to ensure our ad is not triggered when certain
term is typed in by the user
SEM Bid Strategies: KPI Driven
Adjustments
15
SEM SOV Analysis
16
Display targeting evolution
1990s
Early-Mid
2000s
Mid-Late
2000s
Current
Awareness-Focused
Performance/Data-Driven
• Broad
placements;
Limited targeting
opportunities
• Sponsorship
placements
• Performance-
based media
• Behavioral
targeting
• Re-targeting/
marketing
• Look-alike
targeting
• Programmatic
buying
• Paid Social
Requires Floodlight Tags / Pixeling
Targeting Evolution
FY Sponsorships
(Direct to Site)
Network + Portal
(Behavioral /
Contextual)
Programmatic
(Audience Buying,
Retargeting, FBx Right
Rail Ads)
Paid Social
(Promoted Content,
Newsfeed Ads)
2012 55% 34% - 11%
2013 42% (-24% YOY) 33% (-3% YOY) 20% (NA) 4% (- 64% YOY)
2014 26% (-32% YOY) 15% (-55% YOY) 52% (+160% YOY) 7% (+75% YOY)
2015* 34% (+31% YOY) 9% (-40% YOY) 39% (-25% YOY) 18% (+157% YOY)
Digital Display Spend Evolution
19
*Projected. %s heavily skewed by Bank Human 3.0 Campaign which is primarily Direct to Site Video and Social.
Represents core LOB only (Customer Acquisition, Checking, HELOC, Mortgage, Small Business)
What Is Programmatic?
Auction-Based
Ad Buying
Automated
Ad Buying
Audience-Based
Ad Buying
Data-Driven
Ad Buying
“Programmatic ad buying typically refers to the
use of software to purchase digital advertising.”
Improves Efficiency of:
Media, Staff, Targeting & Typically Response
What’s Next: The Choice Is Yours
2015 Programmatic Roadmap
21
Delivering a Comprehensive Plan
 Goal: Drive new account
openings across several
channels
 Targeting: Conquesting
and new banker
acquisition for checking
and financial products
 Geo: Targeted to the TD
Bank location footprint
22
Primary Strategies
• Predictive Audience Buying
• Site Retargeting
Display
• Predictive Audience Buying
Mobile
• Predictive Audience Buying
• Site Retargeting
Social (FBx)
Geographic Site Relevance
 We activate your campaign
differently in each of the states
within your geographic footprint
 This allows us to find the right
price and content for your
brand based on the context and
location of customers
 By examining data within
geography, we’re able to do an
analysis by the main
geographic regions you service,
and help to make sure that we
are properly balancing scale
(availability) and performance
(conversion rate) in each of
those areas throughout the
year
23
Sequencing Digital Ads –
Current Customer Journey
24
S1: Prospecting
Marketing Landing Page
S2: Remarketing (Research)
Product Page
S3: Remarketing (Consideration)
OAO-App Start
Sequencing Digital Ads
25
1 2
3 4
Ad Viewability: Defined
 The industry standard as developed under the leadership of the
Media Rating Council (MRC) calls for desktop display ads to be
considered viewable if:
– 50% of their pixels are in view for a minimum of one second
– for desktop video that standard is 50% for 2 seconds
– for larger desktop ad units, 30% of pixels in view for 1 second
26Source: Viewable Impressions
Ad Viewability: Historical
Implementation
 Direct To Publisher Placement Guarantees
 Buying Above The Fold Higher % In View
 Performance Based Buying Paying Only For Success
27Source: Viewable Impressions
Ad Viewability: Deeper Placement
Analysis
28
Ad Viewability: The Trade Off
29
37%Increase in CTR %
2.5%Increase in Engagement
30%Increase in CPM %
Ad Viewability
30
54%of Online Ads are Never in View
Source: Viewable Impressions
DATA SOURCES
31
Data Sources
32
Source Description
Use
Access
Plan Implement Analyze
Research tool that provides site, audience and competitive insights and
analytics.  Tierney
3rd party web analytics tool used to examine client and competitive web
properties in terms of web site traffic, referring web sites, analysis of
SEO engagement and more
 Tierney
3rd party SEO ad tech platform used for analysis of SERP activities for
brands and competitive web properties. Technology also offers SOV
analysis for SEM campaigns.
 Tierney
Google’s integrated ad-technology platform that enables marketers to
create, manage and measure digital campaigns; includes DoubleClick
Campaign Manager (DCM) – Planning, Trafficking, Reporting and
Attribution, and DoubleClick Search (DS).
   Tierney
Google’s native search management tool, used to create, manage and
analyze paid search campaigns; includes planning tools. Integration
with DS.
   Tierney
Microsoft’s native search management tool, used to create, manage and
analyze paid search campaigns (Bing and Yahoo!); includes planning
tools. Integration with DS.
   Tierney
Facebook’s page and ad management tool, used to create, manage and
measure campaigns, as well as monitor page metrics; includes Ads
Manager and Power Editor.
   Tierney
Twitter’s ad management tool, used to create, manage and measure
campaigns.    Tierney
Adobe’s comprehensive site side analytics and marketing tool; includes
Omniture suite of products, Adobe Audience Manager.  TD Bank
Online Audience & Behavior
 Learning based on Exelate data:
– Captures data through consumers' self reporting and online behavior
– Across multiple publisher sites throughout the web
– Profiles created by tying attributes of Exelate users to TD users based
on an ID match
– Includes both Paid and Organic conversions
 Largest online converters segment is:
– Male
– Adults 25-34
– Suburban
33
Demographics
 Through our programmatic lens powered by Exelate*, we have
insight into TD Bank converters – both paid & organic
34
*Exelate captures data in two main ways: through consumers' self reporting and then through their online traffic behavior. Both instances involve Exelate
partnering with thousands of publishers, and capturing consumers' traffic from site to site across all of those publishers' sites.
AGEGENDER
LOCATIONBANKING
109
92
100
103
98
110
0.0% 5.0% 10.0% 15.0% 20.0% 25.0% 30.0%
18-24
25-34
35-44
45-54
55-64
65+
97
103
0.0% 10.0% 20.0% 30.0% 40.0% 50.0% 60.0%
Male
Female
119
115
106
108
79
0.0% 5.0% 10.0% 15.0% 20.0% 25.0% 30.0%
Urban Core
Urban
Rural
Suburban City
Suburban
98
90
100
95
117
108
110
102
103
89
0.0% 2.0% 4.0% 6.0% 8.0% 10.0%12.0%
U.S. Bank
Bank of America
Wells Fargo
Chase
No household bank
Fifth Third Bank
Internet bank (EtradeBank, etc.)
PNC Bank
Citibank
Any credit union
CAMPAIGN TRACKING
35
KPIs by LOB Overview
36
LOB/
Product
Desired On-Site User Action Optimization Metrics
Money In:
Checking
• Application Start
• Application Complete
• Back End (Heavy Focus) – CPA, as well as
App Start & Complete Rates
• Front End (Consideration) – CPC, CTR
Money Out:
HELOC
• Application Start
• Application Complete
• Back End (Heavy Focus) – CPA, as well as
App Start & Complete Rates
• Front End (Moderate Focus) – CPC, CTR
Money Out:
Mortgage
• Application Start
• Application Complete
• Rate Quote Page Visit
• Balanced mix of Front End and Back End
metrics (due to limited volume of App
Starts/Completes)
− Front End – CPC, CTR
− Back End – CPA, CPV
Small
Business:
Checking
• No hard conversion metric
• Measuring:
− Lead Form Submission
(Set-Up Appointment)
− Checking Selector Tool Visits
• Balanced mix of Front End and Back End
metrics
− Front End – CPC, CTR
− Back End – Target page/s visits, CPV
Floodlight Tags
 A Floodlight Tag is an HTML tag that you place on your web site to
track conversions, such as a consumer making a purchase or
completing an online form
37
DS: DoubleClick Search
DDM: DoubleClick Digital Marketing
DCM: DoubleClick Campaign Manager
Implemented by
Tierney Communications
in 2013
Floodlight Tags on tdbank.com*
38
Landing
Page
Category
Money In Money Out Small Business
Customer Acq.
(Brand)
Checking HELOC Mortgage Credit Cards Checking
Marketing/
Campaign
LPs
 Pages
tagged as
needed
 Pages
tagged as
needed
 Pages
tagged as
needed
 Pages
tagged as
needed
 Have not
had
external
media
campaigns
 Pages
tagged as
needed
Product
LPs
 tdbank.com
homepage
 Several
pages
tagged
 Several
pages
tagged
 Several
pages
tagged
 Some old
pages
tagged but
will need to
review /
retag
 Several
pages
tagged
Tools NA  Checking
Selector
Tool
 (3)
Calculators
 Rate
Request
Page
NA  Checking
Selector
Tool
Application NA 

All OAO
Steps
MAO
Pages
 App Start/
Complete
 App Start/
Complete
 Apps
pages not
tagged
(new)
 Lead Form
Complete
Mobile page tagging has been a challenge
*As of June 2015.
DoubleClick Conversions
 A conversion takes place when a user sees or interacts with an ad, then
performs another action, such as visiting the advertiser's webpage or
completing a purchase
 Conversions are divided into view-through and click-through conversions,
depending on the user action that led to the conversion
– Floodlight considers clicks to be more significant than impressions, therefore, to
avoid double-counting conversions, Floodlight counts a conversion as click-through if
the user has clicked on one of your ads within the lookback window for clicks, even
though the activity is also view-through
39
Conversion Definition
Lookback
Window*
Click-Through
(Post Click)
For a Click-Through conversion to be counted the following conditions must be met:
30 days
 User click within lookback window
View-Through
(Post View,
Post Impression)
For a View-Through conversion to be counted the following conditions must be met:
14 days
 User click within lookback window
 User has DoubleClick cookie
 User impression within lookback window
*A lookback window is a period of days for which an impression or click is considered relevant for Floodlight reporting
(Adobe) Omniture Coding
40
Attribution: The Importance of
Tagging (DoubleClick)
41
Attribution Ideology
42
Digital Attribution Case Study
 Digital Attribution Modeling shows a
positive correlation between different
digital media tactics
– In this case, there is a proven positive
correlation between digital display and
paid search marketing
43
Note: DBCK Attribution Shown
TD Checking Digital Tactic Attribution
44
Note: DBCK Attribution Shown
Tierney Attribution Methodology
Recommendation: Position Based or
Time Decay
45
0.00%
5.00%
10.00%
15.00%
20.00%
25.00%
30.00%
35.00%
1 2 3 4 5 6 7 8 9 10 11 12
Conversion Path Analysis
11/1/13 - 10/31/14 11/1/14 - 3/31/15
Source: Dart Tracking TD Bank 11/1/13 – 10/31/14 / Compared to 11/1/14 – 3/31/15
Diving Deeper: The MTA Landscape
46
DIGITAL ONLY
ATTRIBUTION
MTA ONLINE
CONVERSION
MTA IN-
STORE
Path To Data Visualization
Implementation Roadmap
Dashboard Demo: The Power of MTA
The MTA Scorecard
50
Company Convertro Visual IQ ADOBE Adometry Google
Analytics
1st party on View & Click Tags YES NO YES NO NO
ROI Snapshot frequency DAILY MONTHLY YES Weekly MANUAL
Automated Cost Data Import from all
Sources
YES NO YES NO NO
Cross Device Tracking YES SOME YES SOME NO
Cross Browser Tracking YES SOME YES (SOME) SOME NO
Granular Recommendations
Offline/Online Channels
YES NO YES (SOME) NO N/A
Mrketing Optimization Reports YES NO YES YES NO
Predictive Modeling (Spend Allocations
At Macro – Channel Level)
YES YES NO NO NO
Predictive Modeling (Spend Allocations
At Micro – e.g. Keyword Level)
YES NO NO NO NO
Implementation Time to Dashboard with
Recommendations
180 Days 180 Days 3-6 Months 120 Days N/A
Contract Minimum Length 12 Months with 6
MONTH OUT
18 Month 12 Months 12 Months N/A
Cost MEDIUM High High Medium Lowest
MTA: Adobe vs. DoubleClick
 Here are a couple of reasons why there are
discrepancies
– Ad Servers report clicks that result in a redirect to a
web page. There is no guarantee that the visitor makes
it to the webpage or isn’t further redirected.
– The statistics are affected by a user who closes a
browser after clicking an ad, hijacking (toolbars that
redirect traffic), bots, and in some cases an ad server
that times out. Ad servers accurately measure ad
displays and clicks. They are not so accurate at telling
you how many people visited a website.
– A log analyzers reports on pages served by a web
server, it doesn’t see pages served from caching
proxies used by ISPs and doesn’t see pages served
from a browser’s cache. Log analyzers accurately
report server activity and nothing else.
– Java script based metrics (like Google Analytics):
Reports accurately if the end user has java script and
no software that blocks your tracker (7-15% of
computers have this depending on who’s metrics you
are using). Java script based metrics tell you within 7-
15% what pages have been viewed.
– Coding errors on the website – checking web analytics
tags are laborious which means it is easy to miss
something. You can use this tool to check tags
http://wasp.immeria.net/
51
DISCUSSION
52

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Digital Media Foundation

  • 2. Purpose  Demystify the world of digital media through an explanation of its foundational elements  Provide an overview of how TD Bank capitalizes on the impact of digital marketing, primarily Search & Display – How digital marketing is purchased for TD Bank – How we monitor/track performance – What data sources we use and how they are used 2
  • 3. The Language of Digital Media 3
  • 4. Terms by Category 4 Channels/Buying • Search Engine Marketing (SEM) • AdWords • Bid Strategies • Match Types • Digital Display • Programmatic • DSP/SSP • Networks • Direct-to-Site • CPM/CPC • Retargeting/Remarketing • Frequency Caps • Viewability Tracking • Impressions • Click-through-Rate (CTR) • Share of Voice (SOV) • Cost per Application (CPA) • Floodlight Tags • Omniture Coding • UTM Codes • Conversion Tracking • Attribution • Multi Touch Attribution (MTA) Data Sources • DoubleClick / DART • Omniture • Google Analytics (GA) • comScore • Programmatic Platform • Data Management Platform (DMP) • Social Media Partners (e.g., Facebook)
  • 6. TD Bank Digital Spend  Consumption and opportunity has driven significant growth 9% 6% 85% 2010 Display Search Other Media 19% 14% 67% 2015 Display Search Other Media +133% 6
  • 7. 2015 TD Bank Digital Spend vs. Financial Services Industry  TD Bank is projected to over-index on Display and Video % allocation as compared to US Financial Services average – In part, driven by Bank Human 3.0 launch, as well as Fiscally Fit promotional campaign  TD is projecting to under-index on programmatic % allocation vs. US Financial Services average – However, when looking at spend excluding Bank Human 3.0 launch and Fiscally Fit campaign 7Source: eMarketer “Industry Ad Spending and Forecast” Report, May 2015 Tactic 2015 US Fin. Services 2015 TD Bank*** Index Search 47.3% 30.1% 63.6 Display* 42.0% 69.9% 166.4 Video 10.5% 13.8% 131.4 Other** 10.7% NA NA Total 100% 100% *Includes social **Email, Directories, Lead Gen ***Core LOB = Customer Acq., Checking, HELOC, Mortgage, Small Business Tactic 2015 US Fin. Services 2015 TD Bank* Index Non-Programmatic 45.0% 60.8% 135.1 Programmatic 55.0% 39.2% 71.3 Total 100% 100% *Core LOB = Customer Acq., Checking, HELOC, Mortgage, Small Business Tactic 2015 US Fin. Services 2015 TD Bank** Index Non-Programmatic 45.0% 40.6% 90.2 Programmatic 55.0% 59.4% 108.0 Total 100% 100% **Core LOB, excluding BH3.0 and Fiscally Fit
  • 8. The Power of SEM and SEO  Paid Search Engine Marketing (SEM) and Search Engine Optimization (SEO) are both critical marketing / media tactics that can connect TD Bank with true consumer intent leading to a valuable web site visit and possible account conversion 8 SEO SEM Instant Results C Branding C C Connection With Clients C C Product Understanding C C Viral Impact C Targeted Marketing C C Immediate ROI Analysis C ROI Analysis Within 6+ Months C
  • 9. SEO vs. SEM  Our analysis shows the differential between SEO traffic and SEM traffic to the TDBank.com website is substantial – This shows in market media messaging is having a lasting impact on consumers consideration – Note that some of SEO traffic is purely consumer navigation to TDBank.com 9 12,134 2.4MM
  • 10. How To Identify Keywords: 3 Options 10 SERP Review 1 2 3
  • 11. Negative Keywords: Hyper Relevance  Negative keywords are words or phrases that filter out irrelevant searches. They are an essential aspect of any SEM campaign as they ensure that every dollar allocated to SEM is focused on an exact customer need with zero waste  Sample negative TD Bank terms used for HELOC are shown to the right 11
  • 12. Identifying Keyword Drivers  When examining specific keywords for overall performance, our team can examine front end media metrics and /or back end, TDBank.com web metrics  Currently the Tierney team does not have access to Omniture data, therefore our current optimizations are focused on CTR, Impressions, CPC, as well as any back end data gathered from Floodlight Tags 12 Example: Web site data pulled from Omniture for performance optimizations Is needed for enhanced optimizations Example: Front end CTR% is our current way of predicting keyword performance drivers
  • 13. SEM Bid Strategies: Match Types Defined Allows your ad to show on similar phrases and relevant variations (searches can include misspellings, synonyms and related searches) Used to refine broad match; searches contain the modified term (or close variations, but not synonyms), in any order Allows your ad to show for searches that match the phrase Allows your ad to show for searches that match the exact phrase exclusively Broad Broad Modified Phrase Exact
  • 14. SEM Bid Strategies: Match Types Defined Match Type Example (home equity) Advertiser Keyword Ad Will Be Shown for These Searches Ad Will Not Be Shown for These Searches Broad home equity • home equity • find home equity • what is equity • home loan • hme loan NA Broad Modified +home + equity • home equity • find home equity • equity in your home • what is equity • home loan Phrase “home equity” • home equity • find home equity • home equity line of credit • equity in your home • equity line of credit Exact [home equity] • home equity • home equity line of credit In addition to this, we can add negative terms to the campaign to ensure our ad is not triggered when certain term is typed in by the user
  • 15. SEM Bid Strategies: KPI Driven Adjustments 15
  • 17. Display targeting evolution 1990s Early-Mid 2000s Mid-Late 2000s Current Awareness-Focused Performance/Data-Driven • Broad placements; Limited targeting opportunities • Sponsorship placements • Performance- based media • Behavioral targeting • Re-targeting/ marketing • Look-alike targeting • Programmatic buying • Paid Social Requires Floodlight Tags / Pixeling
  • 19. FY Sponsorships (Direct to Site) Network + Portal (Behavioral / Contextual) Programmatic (Audience Buying, Retargeting, FBx Right Rail Ads) Paid Social (Promoted Content, Newsfeed Ads) 2012 55% 34% - 11% 2013 42% (-24% YOY) 33% (-3% YOY) 20% (NA) 4% (- 64% YOY) 2014 26% (-32% YOY) 15% (-55% YOY) 52% (+160% YOY) 7% (+75% YOY) 2015* 34% (+31% YOY) 9% (-40% YOY) 39% (-25% YOY) 18% (+157% YOY) Digital Display Spend Evolution 19 *Projected. %s heavily skewed by Bank Human 3.0 Campaign which is primarily Direct to Site Video and Social. Represents core LOB only (Customer Acquisition, Checking, HELOC, Mortgage, Small Business)
  • 20. What Is Programmatic? Auction-Based Ad Buying Automated Ad Buying Audience-Based Ad Buying Data-Driven Ad Buying “Programmatic ad buying typically refers to the use of software to purchase digital advertising.” Improves Efficiency of: Media, Staff, Targeting & Typically Response
  • 21. What’s Next: The Choice Is Yours 2015 Programmatic Roadmap 21
  • 22. Delivering a Comprehensive Plan  Goal: Drive new account openings across several channels  Targeting: Conquesting and new banker acquisition for checking and financial products  Geo: Targeted to the TD Bank location footprint 22 Primary Strategies • Predictive Audience Buying • Site Retargeting Display • Predictive Audience Buying Mobile • Predictive Audience Buying • Site Retargeting Social (FBx)
  • 23. Geographic Site Relevance  We activate your campaign differently in each of the states within your geographic footprint  This allows us to find the right price and content for your brand based on the context and location of customers  By examining data within geography, we’re able to do an analysis by the main geographic regions you service, and help to make sure that we are properly balancing scale (availability) and performance (conversion rate) in each of those areas throughout the year 23
  • 24. Sequencing Digital Ads – Current Customer Journey 24 S1: Prospecting Marketing Landing Page S2: Remarketing (Research) Product Page S3: Remarketing (Consideration) OAO-App Start
  • 26. Ad Viewability: Defined  The industry standard as developed under the leadership of the Media Rating Council (MRC) calls for desktop display ads to be considered viewable if: – 50% of their pixels are in view for a minimum of one second – for desktop video that standard is 50% for 2 seconds – for larger desktop ad units, 30% of pixels in view for 1 second 26Source: Viewable Impressions
  • 27. Ad Viewability: Historical Implementation  Direct To Publisher Placement Guarantees  Buying Above The Fold Higher % In View  Performance Based Buying Paying Only For Success 27Source: Viewable Impressions
  • 28. Ad Viewability: Deeper Placement Analysis 28
  • 29. Ad Viewability: The Trade Off 29 37%Increase in CTR % 2.5%Increase in Engagement 30%Increase in CPM %
  • 30. Ad Viewability 30 54%of Online Ads are Never in View Source: Viewable Impressions
  • 32. Data Sources 32 Source Description Use Access Plan Implement Analyze Research tool that provides site, audience and competitive insights and analytics.  Tierney 3rd party web analytics tool used to examine client and competitive web properties in terms of web site traffic, referring web sites, analysis of SEO engagement and more  Tierney 3rd party SEO ad tech platform used for analysis of SERP activities for brands and competitive web properties. Technology also offers SOV analysis for SEM campaigns.  Tierney Google’s integrated ad-technology platform that enables marketers to create, manage and measure digital campaigns; includes DoubleClick Campaign Manager (DCM) – Planning, Trafficking, Reporting and Attribution, and DoubleClick Search (DS).    Tierney Google’s native search management tool, used to create, manage and analyze paid search campaigns; includes planning tools. Integration with DS.    Tierney Microsoft’s native search management tool, used to create, manage and analyze paid search campaigns (Bing and Yahoo!); includes planning tools. Integration with DS.    Tierney Facebook’s page and ad management tool, used to create, manage and measure campaigns, as well as monitor page metrics; includes Ads Manager and Power Editor.    Tierney Twitter’s ad management tool, used to create, manage and measure campaigns.    Tierney Adobe’s comprehensive site side analytics and marketing tool; includes Omniture suite of products, Adobe Audience Manager.  TD Bank
  • 33. Online Audience & Behavior  Learning based on Exelate data: – Captures data through consumers' self reporting and online behavior – Across multiple publisher sites throughout the web – Profiles created by tying attributes of Exelate users to TD users based on an ID match – Includes both Paid and Organic conversions  Largest online converters segment is: – Male – Adults 25-34 – Suburban 33
  • 34. Demographics  Through our programmatic lens powered by Exelate*, we have insight into TD Bank converters – both paid & organic 34 *Exelate captures data in two main ways: through consumers' self reporting and then through their online traffic behavior. Both instances involve Exelate partnering with thousands of publishers, and capturing consumers' traffic from site to site across all of those publishers' sites. AGEGENDER LOCATIONBANKING 109 92 100 103 98 110 0.0% 5.0% 10.0% 15.0% 20.0% 25.0% 30.0% 18-24 25-34 35-44 45-54 55-64 65+ 97 103 0.0% 10.0% 20.0% 30.0% 40.0% 50.0% 60.0% Male Female 119 115 106 108 79 0.0% 5.0% 10.0% 15.0% 20.0% 25.0% 30.0% Urban Core Urban Rural Suburban City Suburban 98 90 100 95 117 108 110 102 103 89 0.0% 2.0% 4.0% 6.0% 8.0% 10.0%12.0% U.S. Bank Bank of America Wells Fargo Chase No household bank Fifth Third Bank Internet bank (EtradeBank, etc.) PNC Bank Citibank Any credit union
  • 36. KPIs by LOB Overview 36 LOB/ Product Desired On-Site User Action Optimization Metrics Money In: Checking • Application Start • Application Complete • Back End (Heavy Focus) – CPA, as well as App Start & Complete Rates • Front End (Consideration) – CPC, CTR Money Out: HELOC • Application Start • Application Complete • Back End (Heavy Focus) – CPA, as well as App Start & Complete Rates • Front End (Moderate Focus) – CPC, CTR Money Out: Mortgage • Application Start • Application Complete • Rate Quote Page Visit • Balanced mix of Front End and Back End metrics (due to limited volume of App Starts/Completes) − Front End – CPC, CTR − Back End – CPA, CPV Small Business: Checking • No hard conversion metric • Measuring: − Lead Form Submission (Set-Up Appointment) − Checking Selector Tool Visits • Balanced mix of Front End and Back End metrics − Front End – CPC, CTR − Back End – Target page/s visits, CPV
  • 37. Floodlight Tags  A Floodlight Tag is an HTML tag that you place on your web site to track conversions, such as a consumer making a purchase or completing an online form 37 DS: DoubleClick Search DDM: DoubleClick Digital Marketing DCM: DoubleClick Campaign Manager Implemented by Tierney Communications in 2013
  • 38. Floodlight Tags on tdbank.com* 38 Landing Page Category Money In Money Out Small Business Customer Acq. (Brand) Checking HELOC Mortgage Credit Cards Checking Marketing/ Campaign LPs  Pages tagged as needed  Pages tagged as needed  Pages tagged as needed  Pages tagged as needed  Have not had external media campaigns  Pages tagged as needed Product LPs  tdbank.com homepage  Several pages tagged  Several pages tagged  Several pages tagged  Some old pages tagged but will need to review / retag  Several pages tagged Tools NA  Checking Selector Tool  (3) Calculators  Rate Request Page NA  Checking Selector Tool Application NA   All OAO Steps MAO Pages  App Start/ Complete  App Start/ Complete  Apps pages not tagged (new)  Lead Form Complete Mobile page tagging has been a challenge *As of June 2015.
  • 39. DoubleClick Conversions  A conversion takes place when a user sees or interacts with an ad, then performs another action, such as visiting the advertiser's webpage or completing a purchase  Conversions are divided into view-through and click-through conversions, depending on the user action that led to the conversion – Floodlight considers clicks to be more significant than impressions, therefore, to avoid double-counting conversions, Floodlight counts a conversion as click-through if the user has clicked on one of your ads within the lookback window for clicks, even though the activity is also view-through 39 Conversion Definition Lookback Window* Click-Through (Post Click) For a Click-Through conversion to be counted the following conditions must be met: 30 days  User click within lookback window View-Through (Post View, Post Impression) For a View-Through conversion to be counted the following conditions must be met: 14 days  User click within lookback window  User has DoubleClick cookie  User impression within lookback window *A lookback window is a period of days for which an impression or click is considered relevant for Floodlight reporting
  • 41. Attribution: The Importance of Tagging (DoubleClick) 41
  • 43. Digital Attribution Case Study  Digital Attribution Modeling shows a positive correlation between different digital media tactics – In this case, there is a proven positive correlation between digital display and paid search marketing 43 Note: DBCK Attribution Shown
  • 44. TD Checking Digital Tactic Attribution 44 Note: DBCK Attribution Shown
  • 45. Tierney Attribution Methodology Recommendation: Position Based or Time Decay 45 0.00% 5.00% 10.00% 15.00% 20.00% 25.00% 30.00% 35.00% 1 2 3 4 5 6 7 8 9 10 11 12 Conversion Path Analysis 11/1/13 - 10/31/14 11/1/14 - 3/31/15 Source: Dart Tracking TD Bank 11/1/13 – 10/31/14 / Compared to 11/1/14 – 3/31/15
  • 46. Diving Deeper: The MTA Landscape 46 DIGITAL ONLY ATTRIBUTION MTA ONLINE CONVERSION MTA IN- STORE
  • 47. Path To Data Visualization
  • 49. Dashboard Demo: The Power of MTA
  • 50. The MTA Scorecard 50 Company Convertro Visual IQ ADOBE Adometry Google Analytics 1st party on View & Click Tags YES NO YES NO NO ROI Snapshot frequency DAILY MONTHLY YES Weekly MANUAL Automated Cost Data Import from all Sources YES NO YES NO NO Cross Device Tracking YES SOME YES SOME NO Cross Browser Tracking YES SOME YES (SOME) SOME NO Granular Recommendations Offline/Online Channels YES NO YES (SOME) NO N/A Mrketing Optimization Reports YES NO YES YES NO Predictive Modeling (Spend Allocations At Macro – Channel Level) YES YES NO NO NO Predictive Modeling (Spend Allocations At Micro – e.g. Keyword Level) YES NO NO NO NO Implementation Time to Dashboard with Recommendations 180 Days 180 Days 3-6 Months 120 Days N/A Contract Minimum Length 12 Months with 6 MONTH OUT 18 Month 12 Months 12 Months N/A Cost MEDIUM High High Medium Lowest
  • 51. MTA: Adobe vs. DoubleClick  Here are a couple of reasons why there are discrepancies – Ad Servers report clicks that result in a redirect to a web page. There is no guarantee that the visitor makes it to the webpage or isn’t further redirected. – The statistics are affected by a user who closes a browser after clicking an ad, hijacking (toolbars that redirect traffic), bots, and in some cases an ad server that times out. Ad servers accurately measure ad displays and clicks. They are not so accurate at telling you how many people visited a website. – A log analyzers reports on pages served by a web server, it doesn’t see pages served from caching proxies used by ISPs and doesn’t see pages served from a browser’s cache. Log analyzers accurately report server activity and nothing else. – Java script based metrics (like Google Analytics): Reports accurately if the end user has java script and no software that blocks your tracker (7-15% of computers have this depending on who’s metrics you are using). Java script based metrics tell you within 7- 15% what pages have been viewed. – Coding errors on the website – checking web analytics tags are laborious which means it is easy to miss something. You can use this tool to check tags http://wasp.immeria.net/ 51