Barry ParshallVice President, Product Strategy+1 (503) 553-2741Barry.parshall@webtrends.com

PPC Optimization – An Introduction to WebTrends Ad Director

Agenda

• Automated systems – a brief history lesson• Demystify algorithmic solutions

– The problems with bid management– The mathematics of WebTrends Ad Director

• The WebTrends Ad Director solution• Case studies• Digital marketing maturity

– Multi-touch attribution– Leveraging web analytics data

• Questions and discussion

Historical Examples of Automation

• Credit card fraud costs the credit industry billions of dollars every year– Since the introduction of automated fraud detection

in 1992, fraud has been reduced by 70%

• Human-powered switchboards replaced by automated routing systems– Today, billions of communication

connections are made possible by mathematically-based systems

Historical Examples of Automation

• In 1997, an IBM chess program and computer beat the reigning chess master in a 6-game match– Today, no one can beat a properly

designed chess program running on modern computing technology

• In 2008, $4.5B in paid search advertising was lost to manual processes and bid management tools– Over $5B will be wasted in 2009

Barriers to PPC Optimization

• Failure to leverage data and statistical models• Failure to leverage computing power• Failure to apply human insight towards strategic functions• Failure to optimize SEM holistically

• Bid management tools contribute to all of these problems

Problems with Bid Management

1. Bid rules do not optimize all campaign elements– Is the right ad being used?– Is the right landing page being used?– Is the right match type being used?– Are the right geo-targets being used?

Billions of attribute combinations in large-scale PPC campaigns

20 ad creativesx 5 landing pagesx 3 match typesx 5 positionsx 24 hours in a dayx 3 search enginesx 10,000+ geo-targets= 1,000,000,000+ combinations

… for just 1 keyword

Testing and Optimizing Campaign Elements

Testing and Optimization Techniques

• A/B/n split testing– Ideal for a small number of values for a single variable– Many trials can be executed for each value

• Multi-variable testing– Operates the same as split testing, but for multiple

variables

• Multivariate optimization– Required when number of variations is large– Uses statistical analysis techniques to determine optimal

combination of elements with minimal trial data– Taguchi methods are used in digital marketing

applicationsGenichi Taguchi

Problems with Bid Management

1. Bid rules do not optimize all campaign elements– Is the right ad being used?– Is the right landing page being used?– Is the right match type being used?– Are the right geo-targets being used?

2. Bid rules do not properly optimize keyword portfolios– NOTE: “portfolio-based bid management“ ≠ portfolio-based optimization

Modern Portfolio Theory• When applied to investment portfolios, uses asset diversification to

achieve optimal returns within a risk tolerance

• When applied to keyword portfolios, uses keyword diversification to achieve optimal results with a minimal and predictable ad spend– Depends on accurately inferring returns for each keyword

Harry Markowitz

Problems with Bid Management

1. Bid rules do not optimize all campaign elements– Is the right ad being used?– Is the right landing page being used?– Is the right match type being used?– Are the right geo-targets being used?

2. Bid rules do not optimize keyword portfolios– NOTE: “portfolio bid management“ ≠ portfolio-based optimization

3. Rules-based approaches do not properly estimate expected results for a given keyword

Inferring Results• Example of bid rule approach (David Rodnitzky):

– Bid = RPC (1-MG), where• RPC is revenue per click calculated from rolling 7, 14 or 28 day average• MG is margin goal

• WebTrends Ad Director approach:– Uses a hierarchical Bayesian inference model to

statistically infer results from existing data– Ideal for sparse data (tail terms)– WebTrends has pending patents of the application

of statistical inference in PPC campaigns– Designed by Ph.D mathematicians

• Leo Chang, Ph.D Mathematics, MIT• Peter Kassakian, Ph.D Statistical Mathematics, U of C Berkeley• John Rodkin, Ph.D Mathematics and Computer Sciences, MIT

Thomas Bayes

Inferring Results – Tail Term Example• Bid rule vs. statistical inference approaches

– Bid rule: simplistic math and heavy reliance on recent data cause bids to fluctuate dramatically

– WebTrends Ad Director: statistical modeling quickly finds the optimal bid rates and does not “react” to short-term statistical anomalies

1 5 9 13 17 21 25 29 33 37 41 45 49 53 57 61 65 69 73 77 81 85 89 93 970

20

40

60

80

BID RULE - ROAS = $3,735 WEBTRENDS AD DIRECTOR - ROAS = $7,745

David Rodnitzky’s Bid Strategy (7-day)

WebTrends Ad Director

Bottom Line

• A properly designed program will always out-perform humans at optimizing large-scale PPC campaigns, while reducing human costs

• SEM manager efforts better spent on strategic functions• Some vendors are intentionally misrepresenting their solutions

Human Effort

ROI Automated Optimization

Manual / Bid Management

Best Possible ROIResult Gap

Result gap increases with campaign size and complexity

WebTrends Ad Director

Self-Learning, Algorithmic Optimization• Better performance, less wasted spend, lower total costs• Works around the clock to drive profitable search programs

– Determine and execute optimal combinations and bids– Maximize results on a portfolio-basis

• Complete transparency• Complete control to override the machine

Getting Started with Ad Director

• Dedicated account manager– Establish your goals– Set up your account

• Watch the machine learn• Supplement machine learning with human insight

– Apply bid overrides– Test new ad creatives– Expand keywords– Identify negative keywords

• Review your goals with your account manager

WebTrends Ad Director – Dashboard

WebTrends Ad Director – Keyword Report

WebTrends Ad Director – Forecast Report

WebTrends Ad Director – Bid Overrides

Weekly ConversionsBusiness Objective:• Maximize conversions while

maintaining CPA and budget targets Results:• 21% decrease in CPO• 42% increase in daily sales within the

first two months• 80% increase in daily average click

volume to site1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17

Before WebTrends

CUSTOMER SUCCESS

Lead Generation: Safelite Auto Glass

CUSTOMER SUCCESS

eCommerce: Orion Telescopes & Binoculars

Business Objective:• Maximize revenue while maintaining

ROAS and budget goalsResults:• 35% increase in search advertising

revenues Y/Y• 25% increase in CTR • Reduced number of hours spent on

campaign management

Search Engine Revenues

Before WebTrends80%

90%

100%

110%

120%

130%

140%

150%

“But what about my job?”

SEM managers should focus on functions machines can’t do:• Keyword expansion

– Uncover hidden ROAS gems– Add to portfolio diversity

• Negative keyword identification• Randomized testing of new ad creatives, offers and landing pages• Manual overrides of optimization engine, as needed• Cross-channel campaign and organic search impact analysis

– Multi-touch attribution

Best results derived from combining the insights of a human with the computational power of a machine

Digital Marketing Maturity

M A R K E T I N G O P T I M I Z A T I O N

CU

ST

OM

ER

-LE

VE

L I

NS

IGH

T

E-mail blastsSite activity reportingManual campaign management

Triggered e-mailsAggregate online marketing reportingBid management and last-click attribution

Affinity scoring and targeted cross-channel communications

Visitor-centric business intelligence

Automated campaign optimization and statistical attribution modeling

RetainConvertAcquire

Fragmentation

Channel Specific Process

Channel Optimization

Centralization

Cross Media Optimization

Maturity Model

Traditional Campaign Reports

Campaign Attribution Models• Last click-through

– Same visit– Across visits– Configurable timeout

• First click-through• Equal distribution• Configurable attribution rules

– E.g. 50% to last (N), 30% to N-1, 20% to N-2

• Statistical variance modeling– E.g. Cov(x,y;w) = ∑i wi(xi - m(x;w))(yi - m(y;w)) / ∑i wi

Traditional approaches that provide little insight into performance of multi-channel campaign strategies

Emerging models positioned as providing greater campaign mix insight

True insight requires statistical modeling to measure causality

Campaign Report of the FutureChannel Campaign Contribution Index Revenue Attribution ROAS Attribution Latency (days) Mix Recommendation

Banner 0.86 $6,347 $3,327 12.6 ↓ 23.5%

Ad1 0.77 $2,344 $1,228 10.5 37.5%

Ad2 0.91 $3,119 $1,355 15.6 62.5%

Ad3 0.14 $884 $144 12.8 0.0%

Email 1.05 $18,497 $13,189 3.4 → 37.4%

Ad1 1.02 $5,839 $4,004 3.5 34.0%

Ad2 1.08 $6,492 $4,197 2.8 26.5%

Ad3 1.05 $6,166 $4,988 3.9 39.5%

Search 1.23 $21,792 $17,310 5.5 ↑ 39.1%

Keyword1 1.33 $7,549 $5,895 4.5 28.7%

Keyword2 1.21 $7,236 $5,765 5.7 36.5%

Keyword3 1.18 $7,007 $5,650 5.8 34.8%

$46,636 $33,826

Aggregated Online Data

2nd Generation Solutions

reports

Aggregated Online Data

2nd Generation Solutions

Visitor-level Detail Data

merchandising

customer marketing

data warehouses and business intelligence

business analysts

acquisition marketing

3rd Generation Solutions

Visitor-level Detail Data

acquisition marketing

3rd Generation Solutions

Human Effort

ROAS

Manual / Bid Management

Automated OptimizationBest Possible ROAS

attribution modeling

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Barry Parshall