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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
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