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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
- 14. David Rodnitzky’s Bid Strategy (7-day)
- 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
- 19. WebTrends Ad Director – Dashboard
- 20. WebTrends Ad Director – Keyword Report
- 21. WebTrends Ad Director – Forecast Report
- 22. WebTrends Ad Director – Bid Overrides
- 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
- 30. Campaign Report of the Future Channel 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
- 31. 2nd Generation Solutions Aggregated Online Data
- 32. 2nd Generation Solutions Aggregated Online Data reports
- 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