Spring Boot vs Quarkus the ultimate battle - DevoxxUK
An Introduction to Webtrends Ad Director
1. PPC Optimization – An Introduction to
WebTrends Ad Director
Barry Parshall
Vice President, Product Strategy
+1 (503) 553-2741
Barry.parshall@webtrends.com
2. 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
3. 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
4. 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
5. 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
6. 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?
–
7. Testing and Optimizing Campaign Elements
20 ad creatives
x 5 landing pages
x 3 match types
x 5 positions
x 24 hours in a day
x 3 search engines
x 10,000+ geo-targets
= 1,000,000,000+ combinations
… for just 1 keyword
Billions of attribute combinations in
large-scale PPC campaigns
8. 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
Genichi Taguchi
– Taguchi methods are used in digital marketing
applications
9. 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
10. Modern Portfolio Theory
• When applied to investment portfolios, uses asset diversification to
achieve optimal returns within a risk tolerance
Harry Markowitz
• 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
11. 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
12. 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
Thomas Bayes
• 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
13. 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
100
80
60
40
20
0
1 5 9 13 17 21 25 29 33 37 41 45 49 53 57 61 65 69 73 77 81 85 89 93 97 101
BID RULE - ROAS = $3,735 WEBTRENDS AD DIRECTOR - ROAS = $7,745
16. Bottom Line
A properly designed program will always out-perform humans at
•
optimizing large-scale PPC campaigns, while reducing human costs
Best Possible ROI
Result gap increases
Automated Optimization
Result
with campaign size
ROI
Gap
and complexity
Manual / Bid Management
Human Effort
SEM manager efforts better spent on strategic functions
•
Some vendors are intentionally misrepresenting their solutions
•
17. 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
18. 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
23. CUSTOMER SUCCESS
Lead Generation: Safelite Auto Glass
Weekly Conversions
Business Objective:
• Maximize conversions while
maintaining CPA and budget targets
Results:
• 21% decrease in CPO
• 42% increase in daily sales within the
first two months
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17
• 80% increase in daily average click
volume to site
Before WebTrends
24. CUSTOMER SUCCESS
eCommerce: Orion Telescopes & Binoculars
Search Engine Revenues
Business Objective:
150%
• Maximize revenue while maintaining
140%
ROAS and budget goals
130%
Results:
120%
110%
• 35% increase in search advertising
100%
revenues Y/Y
90%
• 25% increase in CTR
80%
• Reduced number of hours spent on
Before WebTrends
campaign management
25. “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
26. Digital Marketing Maturity
Automated campaign optimization
and statistical attribution modeling
Visitor-centric business intelligence
Affinity scoring and targeted cross-
CUSTOMER-LEVEL INSIGHT
channel communications
Bid management and last-click attribution
Aggregate online marketing reporting
Triggered e-mails
Acquire
Convert
Manual campaign management
Retain
Site activity reporting
E-mail blasts
MA RK E TI NG O P TI MI ZATI O N
27. Maturity Model
Centralization
Cross Media Optimization
Channel Optimization
Channel Specific Process
Fragmentation
29. Campaign Attribution Models
• Last click-through
– Same visit Traditional approaches that provide
– Across visits little insight into performance of
multi-channel campaign strategies
– Configurable timeout
• First click-through
• Equal distribution Emerging models positioned
as providing greater campaign
• Configurable attribution rules
mix insight
– E.g. 50% to last (N), 30% to N-1, 20% to N-2
True insight requires
• Statistical variance modeling
statistical modeling to
– E.g. Cov(x,y;w) = ∑i wi(xi - m(x;w))(yi - m(y;w)) / ∑i wi measure causality
33. 3rd Generation Solutions
business
analysts
data warehouses
and business
intelligence
Visitor-level
Detail Data
merchandising
acquisition
marketing
customer
marketing
34. 3rd Generation Solutions
Best Possible ROAS
Automated Optimization
ROAS
Visitor-level
Manual / Bid Management
Detail Data
Human Effort
attribution
acquisition
modeling
marketing
35. To receive a copy of this presentation, text i1 and your email address to 88769.
Leave a space between the keyword and your email address.
EX: i1 sally@webtrends.com
To rate this presentation, text PPC and your rating on a scale of 1 to 5, 1 being
fair and 5 being excellent, to 88769. Leave a space between the keyword and
your rating.
EX: PPC 5
Barry Parshall
Engage Text powered by