Produced for Brands and Agencies, this presentation addresses the challenges and risks associated with Programmatic buying.

1. © 2015 Encore Media Metrics.
Confidential and Proprietary. May not be shared without permission.
Investing Confidently in Programmatic Media
March 2015

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Information is confidential and may not be shared. © Encore Media Metrics 2014
Executive Summary
q The promise of self-optimizing buying platforms is intriguing…"
…but it doesn’t replace the need for objective, transparent analysis
q Programmatic introduces new threats and challenges to optimizing
§ Relying on antiquated metrics will do more harm than good
q Algorithmic attribution is critical for measuring and optimizing media
§ Fractional, statistical attribution is needed for cross-channel, full-funnel insights
q As brands increase reliance programmatic, the need for objective, attribution-
based insights analysis will become even more critical

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Context: The Rise of Programmatic Media
Digital media is growing market share
q $47bn spend, growing >10% annually
q #2 media channel (and gaining on TV)
Digital is experiencing yet another significant shift
q 2000: Ad networks emerged to fill gaps in publisher-
direct buying
q 2008: Exchange traded (programmatic) media
began taking share from direct and network buys
§ Low-funnel tactic relies heavily on Retargeting via cookie
matching
§ Often has lowest CPA vs. other strategies
Programmatic is growing rapidly
q Accounting for 10% of digital spend in 2014, now
growing 25-30% annually
q Projected to double by 2017 to $9bn

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Programmatic Measurement Challenges
Despite the advantages of Programmatic buying, drawbacks exist:
q Most programmatic impressions are Retargeting (vs. Prospecting)
§ Cookie matching is the most prevalent strategy
q 30-70% of programmatic impressions are wasted (see http://bit.ly/RTBwaste)
§ Non-viewable and Fraudulent ads comprise 20-40%
§ Overlapping reach and inefficient frequency comprise additional 20-40%
q Trading desks optimize on the wrong metrics
§ Clicks and post-impression (view-through) conversions are predominant KPIs
§ Limited metrics are easily gamed (via cookie-bombing)
q Lack of transparency
§ Few brands know which strategies actually work (and how they work)

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Lesser Known Programmatic Challenges
‘Spray and Pray’ prospecting (common strategy) relies on Retargeting:
q Serve 1 ad to large group of users
q Wait to see which exposed users visit advertiser’s site
q Retarget visitors (across exchanges) to win the last touch before conversion
q Claim success for the Prospecting campaign
Lookalike modeling relies heavily on Retargeting:
q Trading desks use retargeting to develop profiles of likely converters
q Often continue to Retarget within Lookalike campaigns to keep CPA low
q Advertisers have no visibility into what is really going on within each buy
Our Views
q ‘Spray and pray’ is not prospecting (it’s more like giving coupons to people in your
parking lot)
q If using Lookalike modeling, Retargeting is largely redundant
q Lack of transparency and perverse incentives give way to significant waste
Investing in programmatic media increases the need for advanced analytics

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Programmatic Frequency Challenges
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Visitors)as)%)of)Exposed)Users)by)Frequency))
Frequency of 1 is
least effective
56% of users
served 1 ad
5% served more
than 36 ads
As shown below, Programmatic buying may include significant waste
q Frequency of 1 has lowest probability of success (yet accounts for most Imps)
q Excessing frequency (due to retargeting) represents another significant cost

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Programmatic’s Attribution Problem
DSPs still optimize largely on "
‘Last-touch’ attribution models
q Last view or click gets credit "
(the indicator of success)
‘Last-touch’ is a flawed approach
q Rewards retargets and low-funnel "
placements
§ Overstates results by 20-100%
q Penalizes brand-building, upper-"
funnel placements
§ Understates results by 25-200%
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Vendor information obfuscated
Optimizing via last-touch can do more harm than good.

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SWOT Analysis: Investing in Programmatic
Strengths
Weaknesses
Opportunities
Threats
• More efficient
• Lower cost
• Programmatic
• Scalable
• Waste from redundant reach
• Wasted frequency
• Optimizing on wrong KPIs
• Improve efficiency
• Improve Return On Spend
• Suspect Quality / Fraud
• Reliance on Retargeting
• Lack of transparency
• Perverse incentives
• Strengths propel you forward
• Weaknesses inhibit progress
• Opportunities create upward lift
• Threats inhibit lift
Programmatic Efficiencies may be offset by Weaknesses and Threats

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Solution: Algorithmic Insights
Objective, expert analysis of digital media
q Cross-channel, full-funnel conversion path
modeling
q Fractional (multi-touch) algorithmic attribution
q Clear recommendations for optimizing spend
q Delivered by an objective, independent partner
Compelling benefits
q Mitigate risk via independent analysis
q Reduce waste
q Optimizing on the right KPIs
q Forecast performance with accuracy
Display
Imp: #1
Paid
Visit
#1
Display
Imp: #4
Display
Imp: #3
Display
Imp: #2
Paid
Visit
#2
Non-
Paid
Visit
Display
Imp: #5
Conversion
The need for independent insights are Illustrated in the following SWOT Analysis

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SWOT: Investing Confidently in Programmatic
Strengths
Weaknesses
Opportunities
Threats
• More efficient
• Lower cost
• Programmatic
• Scalable
• Redundant reach
• Insufficient frequency
• Optimize on the wrong metrics
• Decrease Return On Spend
• Improve efficiency
• Improve Return On Spend
• Validated Insights
• Suspect Quality / Fraud
• Reliance on Retargeting
• Perverse incentives
• Lack of transparency
Mitigate threats and maximize opportunities via algorithmic analysis
• Strengths propel you forward
• Weaknesses inhibit progress
• Opportunities create upward lift
• Threats inhibit lift

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How To Fill the Gaps in Programmatic Buying
Standard Approach
The Gap
Recommended Solution
Optimize based on:
1. Last touch or static
attribution model
2. Black-box model
1. Rewards the wrong ads
2. Susceptible to fraud
3. Lack of validation
4. Relying on bad KPIs may do more
harm than good
1. Algorithmic attribution (better
signals for optimizing)
2. Greater transparency
3. Enables better optimization
Measure average
frequency
1. Average frequency is a bad KPI
(masks waste)
2. Redundant reach and excess
frequency are common
1. Analyze distribution of impressions
served
2. Identify wasted spend from over-
serving or under-serving
Measures Programmatic
buying in a silo
1. Doesn’t incorporate direct and
network buys
2. Silo-based approach over-allocates
to Programmatic while under-
allocating other buys
1. Integrated reporting
2. Comprehensive (unified) view of
each converter
3. More accurate reports and
recommendations for optimizing

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Information is confidential and may not be shared. © Encore Media Metrics 2014
The Case for Trusted Validation
There is a growing need for independent, data-driven analysis:
q Objective auditing and validation of media (no conflicts of interest)
q Alternative models for measuring performance (one-size doesn’t fit all)
§ Determining the right KPIs for optimizing performance
§ Fractional algorithmic attribution (vs. static models, clicks or view-thrus)
§ Cross-channel analysis that incorporates paid, owned and earned media
q Expert analysis and interpretation of strategies, reach and frequency
As brands increase reliance on programmatic media, the need for
advanced, independent insights is becoming even more critical

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Information is confidential and may not be shared. © Encore Media Metrics 2014
Contact Info
encoremetrics.com
888.848.4571
@EncoreMetrics
attribution101.com
Encore Media Metrics
401 Park Ave. South, 10th floor
New York, NY 10016

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Information is confidential and may not be shared. © Encore Media Metrics 2014
Contact Info
Encore Media Metrics
401 Park Ave. South, 10th floor
New York, NY 10016
(888) 848-4571
http://EncoreMetrics.com
Info@EncoreMetrics.com
@EncoreMetrics
http://Attribution101.com