An Introduction to Webtrends Ad Director

633 views
532 views

Published on

Published in: Technology, Design
0 Comments
1 Like
Statistics
Notes
  • Be the first to comment

No Downloads
Views
Total views
633
On SlideShare
0
From Embeds
0
Number of Embeds
2
Actions
Shares
0
Downloads
16
Comments
0
Likes
1
Embeds 0
No embeds

No notes for slide

An Introduction to Webtrends Ad Director

  1. 1. PPC Optimization – An Introduction to WebTrends Ad Director Barry Parshall Vice President, Product Strategy +1 (503) 553-2741 Barry.parshall@webtrends.com
  2. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 14. David Rodnitzky’s Bid Strategy (7-day)
  15. 15. WebTrends Ad Director
  16. 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. 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. 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. 19. WebTrends Ad Director – Dashboard
  20. 20. WebTrends Ad Director – Keyword Report
  21. 21. WebTrends Ad Director – Forecast Report
  22. 22. WebTrends Ad Director – Bid Overrides
  23. 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. 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. 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. 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. 27. Maturity Model Centralization Cross Media Optimization Channel Optimization Channel Specific Process Fragmentation
  28. 28. Traditional Campaign Reports
  29. 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. 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. 31. 2nd Generation Solutions Aggregated Online Data
  32. 32. 2nd Generation Solutions Aggregated Online Data reports
  33. 33. 3rd Generation Solutions business analysts data warehouses and business intelligence Visitor-level Detail Data merchandising acquisition marketing customer marketing
  34. 34. 3rd Generation Solutions Best Possible ROAS Automated Optimization ROAS Visitor-level Manual / Bid Management Detail Data Human Effort attribution acquisition modeling marketing
  35. 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

×