The document discusses advanced advertising analytics using Adobe Analytics and Media Optimizer. It covers topics like fostering curiosity through the right people, processes and technology; going beyond basic monitoring; balancing work between humans and machines; mitigating risks of advanced analytics; and expanding perceptions of attribution and allocation.

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Advanced Advertising Analytics
Marketing Insights through Adobe Analytics & Media Optimizer

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3
Dan Cardamone
Manager - Search
dancard@cdw.com
Chris Haleua
Product Marketing Manager
chaleua@adobe.com

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4
1 | Foster curiosity with people, process, & technology
2 | Break the habits of basic monitoring
3 | Balance the load between man & machine
4 | Mitigate the risk of advanced analytics
5 | Expand your perception of attribution & allocation
Summary

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MyAnalyticsScore.com

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myanalyticsscore.com
adobemarketingpro.com
adobemediaoptimizerpro.com
Surveys

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Takeaways
8
People
Align structure to
break down silos
Process
Foster curiosity &
reduce chores
Tech
Build trust in single
source of truth

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People
9
Advertising-analyst hybrids
automate basic reporting to
focus on advanced insights

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Process
Targeting
Analysis
Testing
BiddingCoverage
Structure
Life
IDEAL COMMON
Meetings
Training
Email
Bugs
Planning
Calls

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Technology
11
Media Optimizer
Conversion Metrics
Campaign Classifications
Engine Data
Adobe Analytics
Site Traffic Metrics

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 2 eVars
 EFID = serialize clicks
 SKWCID = unique ad/keyword combination
 5 events
 Impressions
 Clicks
 Cost
 Position
 Quality Score
Nuts and Bolts of the Integration

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Raw Tracking Codes
13
http://www.carnival.com/
?CID=Psearch_A_B_HONEY_1241236
&SE=Bing&KW=all_inclusive_honeymoon_cruise
&CM=General_Non_Brand Life_Style_Cruise
&AG=Honeymoon_Cruise
&_vsrefdom=nonbrand
&gclid=CICe6NDYusMCFYuafgodxC0AOw
&gclsrc=ds

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SAINT Classifications
14

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SAINT Classifications
15

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1 | Foster curiosity with the right people, process, & technology
2 | Break the habits of basic monitoring
3 | Balance the load between man & machine
4 | Mitigate the risk of advanced analytics
5 | Expand your perception of attribution & allocation

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What does basic
really mean?

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Getting Started

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Volume vs. Efficiency

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Pathing & Fallout Bottlenecks

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Funnel Averages

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FILTERS
Volume
Leaders
Efficiency
Threats
SORTS
Conversion
Success
Non-Converting
Spend
Conversions > 0
Conversions = 0
Cost > 0
Revenue ROAS Pos.
Pos.CPCCost
Performance Segmentation
22
FILTERS

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FILTERS
Volume
Leaders
Efficiency
Threats
SORTS
Non-Converting
Spend
Ignored
Impressions
Conversions = 0
Cost > 0
Cost = 0
Impressions > 0
Pos.
Pos.
CPCCost
Impressions Quality Score
Performance Segmentation
23
FILTERS

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 You might know your total conversions
 ...but do you know the minority driving the majority?
 You might know your efficiency threats...
 ...but do you know your deadends in the step before?
 You might know your loss leaders...
 ...but do you know the segments & pivots that caused them?
Performance Segmentation

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29
1 | Foster curiosity with the right people, process, & technology
2 | Break the habits of basic monitoring
3 | Balance the load between man & machine
4 | Mitigate the risk of advanced analytics
5 | Expand your perception of attribution & allocation

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Advertising Insights

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Day of Week
Spend Distributions at weekly spend of 17500
Monday Tuesday Wednesday Thursday Friday Saturday Sunday
Flat 2500 2500 2500 2500 2500 2500 2500
Current 2500 2500 2500 2500 2500 2500 2500
Optimal 2564 2408 2564 2720 3032 2096 2252
Uplift from current
to
optimal allocation
+ 0.4%

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Budget Caps
Relevant portfolio settings
Spend Multiple: Predicted
campaign spend variance allowed
to give head room for cost
Multiple 1.5
Status too low

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Quality Score Counts
Quality Score
Tiers
Keyword
Count
Good 731
Average 1489
Poor 61
In the last 30 days,
32.0% of keywords
had a Quality score in
the 7 to 10 range
adobe_systems | ACRO : NA : GG : PURCH : UNITS | 2016-02-20 to 2016-03-20
Impression weighted Quality Scores rounded up to nearest integer

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Portfolio Objective Weights
Sale Closed
1
Lead Completed
0.6
Lead Started
0.4
Whitepaper Download
0.2

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Prediction Accuracy

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Correlation between Predictors
Properties considered 25
Properties removed 21
No early signal 0
Do conversion data 11
Redundant data 10
Low correlation 0
Included in final model 4

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amosummit.gamification.ws

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Audit Summary
2 of 30
number of days with at
least one bid change
6.8%
impressions forfeited due to
inadequate campaign budgets
2%
of bids were changed in
increments of 5%
65%
of all keywords have
the same Five bids
99%
of all adgroups have the same
mobile bid adjustment
35%
of total investment is
flowing through mobile

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1 | Foster curiosity with the right people, process, & technology
2 | Break the habits of basic monitoring
3 | Balance the load between man & machine
4 | Mitigate the risk of advanced analytics
5 | Expand your perception of attribution & allocation

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How does advanced
really differ from basic?

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Averages LIE!

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PIVOTS

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Cart vs Partner
47

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Cart vs Partner
48
Adobe revenue
driven by Adobe
Adobe revenue
driven by Other

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Portfoli
o
Time Product
Pivots

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PREDICTIVE

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Anomaly deep-dive in Analytics Workspace
53
unusually high revenue
for the contributing
products as a whole
Customer A
Customer B
Customer C
Customer D
Customer E
Customer F

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Anomaly
Custome
r
Product
Predictive

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Customer Segments to RLSA
55

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Visitors of
Page
Advanced
Definition
Aligned
targeting &
analysis
RLSA

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SEGMENTS

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Solving for the (not provided) conundrum
58

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Solving for the (not provided) conundrum
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Entry Page Site Section % New
Segments

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Customer loyalty by Marketing Channel
61

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Using visit numbers to create new/returning visit %
new visits segment
return visits segment
new visits % metric
return visits % metric

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Using visit numbers to create new/returning visit % (cont.)
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Loyalty Channel % Return
Loyalty

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OFFLINE

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Engagement Metrics

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Call Tracking in AMO
67

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Call Tracking in Analytics
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page views = calls
create a segment based on those
page views
and then a calculated metric that
counts those page views
then bring more
dimensions in
through an eVar

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Page Views
= Calls
Segment =
Page Views
Dimension
= Caller ID
Call Tracking

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Customer Attributes in the People Core Service
Solutions
Core services
Places
PlatformPeople
Assets
Mobile
Activation
Data science
Data Content
Media
Optimizer
Social
Audience
Manager
Target Primetime
Experience
Manager
Campaign
Analytics

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Customer Attributes
Age & Gender Job Function PropensityMember Level Proximity
Adobe Marketing Cloud:
People Core Service
Customer
records
ID
aliases
User login
Marketing
Cloud Visitor
ID Service
Customer ID
= A383278
Customer ID
= A383278
Age=30
Gender=Female
Membership=Gold

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Customer Attributes
VALIDATE schema and subscribe to 3 attributes in Analytics
2UPLOAD area of the Marketing Cloud
1

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Customer Attributes
CREATE custom reports, segments, and metrics
3 ANALYZE behavioral patterns and share
segments4

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Similarities
 Applies forward & back
Differences
 Attached to the visitor
 Entire life of the cookie
 uniques
 Available to all solutions
Are These Classifications?

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Export
from CRM
Upload to
People
Core
Service
Segment by
Attributes
Customer Attributes

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ALLOCATION

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1 | Foster curiosity with the right people, process, & technology
2 | Break the habits of basic monitoring
3 | Balance the load between man & machine
4 | Mitigate the risk of advanced analytics
5 | Expand your perception of attribution & allocation

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What do these pictures have in common?
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Comparisons of Different Models
79
Model Pros Cons
Participation Fair to all Doesn t distinguish touch roles
First Focuses on triggers Overvalues awareness
Last Focuses on closure Overvalues low funnel & brand
Linear Fair to all Doesn t distinguish values
SPC / U Shaped Weights both ends Requires expertise and can run into user bias
Adjacency / Decaying Weights the latest Less specific in how values are assigned
Latency Weights for recency Time may not be a factor based on business
Algorithmic Statistics eliminate bias No control over assignment of values

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Reporting Flexibility
80

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Going Beyond Attribution with Adobe Analytics
Attribution
Rules, Fractional,
Marginal, Algorithmic
Anomalies
Detection &
Contribution
Correlations
Causes & Statistics
Clustering
Sequential Segments
& Propensity Scoring
Allocation
Budget, Time, Focus
MaturityLevel

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Prioritize
perspectives
Double-check
Deltas
Expand
beyond
budgeting
Attribution

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Key Takeaways
People
Trade easy for
important
Keep learning to
become a hybrid
Process
Drag & drop
workspaces
Pivot advertising
by analytics
Tech
Machines essential
for AI audits
Humans essential
for exploration

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 4 minutes
 Winner: $1000
 3 Runners Up: $100
 Passing Grade gets a
Adobe Summit Programmatic Quiz
#AdobeAdGenius
Test your knowledge
here

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Q&A
87
Dan Cardamone
dancard@cdw.com
Chris Haleua
chaleua@adobe.com

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