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2016 the year of machine learning 12.16.2015

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Think you know everything about PPC? Don’t miss out on this exclusive webinar driving you to think about PPC like you never have before. Bryan Minor, Acquisio’s chief scientist will dive into the power of machine learning and game changing statistics on our industry.

Veröffentlicht in: Marketing
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2016 the year of machine learning 12.16.2015

  1. 1. 2016 The Year of Machine Learning: Why Bid Algorithms Will Always Outperform Humans
  2. 2. Our Speaker Bryan Minor, Ph.D. Chief Scientist at Acquisio
  3. 3. Housekeeping • The webinar is recorded and will be made available by email A • The slides will also be available by email • Q&A session at the end of the webinar • Use the Chat box to submit your questions at any time For those that would like a trial or demo in Portuguese or Spanish, and are from a Latam country, please contact: Ghislain Nadeau, gnadeau@ibrainholding.com • For anywhere else please contact: acquisio-marketing@acquisio.com
  4. 4. Poll Question Are you currently using a bid optimization solution? a) Yes b) No c) I’m looking for one
  5. 5. Agenda • What is Machine Learning? • Machine Learning at Acquisio • Bid & Budget Management • Gamification • Predictions for 2016 driven by Machine Learning • Conclusions
  6. 6. What is Machine Learning? Machine learning is a subfield of computer science that evolved from the study of pattern recognition and computational learning theory in artificial intelligence. Machine learning explores the construction and study of algorithms that can learn from and make predictions on data. – Wikipedia (https://en.wikipedia.org/wiki/Machine_learning)
  7. 7. Bid and CPC comparison Campaign Type SubType (Bid/CPC) Number Campaigns Search Others 2.33 11,087 Search Brand 7.13 1,919 Search Dynamic Search 1.40 294 Search RLSA 3.71 826 Display Others 1.89 1,118 Display Remarketing 1.86 762
  8. 8. Mobile Optimization Problem • No pure Mobile campaigns • Can only set Bid at the device level  Mobile  Other (Computer and Tablet) • Mobile bid modifier  -100% to +300% • Budget shared across all devices in Campaign  Controlling mobile spend
  9. 9. Using Machine Learning in solving BBM problem • Setting Daily Budget • Setting Bid every 30 minutes • Managing Mobile bidding • Anomaly detection (ensuring success) • Allocation of Budget across Publishers (AdWords, Bing, Yahoo!Japan,…) • Day of week % of spend allocation
  10. 10. BBM Problem: For a fixed Budget for budget period (month) With a group of Campaigns (Budget Group) Make Daily Budget last whole Day Maximum Average CPC per day limit Fairly compete Campaigns based on value of Clicks (conversions) Maximize Clicks (conversions)
  11. 11. Continuous SEM Optimization Features: 1. Examination and adjustment of Bids in regular intervals many times per day 2. Examination of Budget spend precision many times per day with hyper accurate control 3. Updating of modeling parameters in algorithms on a longer characteristic time scales 4. Auto detection and dealing with anomalies
  12. 12. Algorithm model • Cruise missile model • Dynamic Non-linear optimization • Small steps more often
  13. 13. BBM Theory C B A A A minCPC 0 2 4 6 8 10 0 5 10 15 20 25 30 35 CPC Clicksday
  14. 14. Continuous SEM Optimization • B graph – Daily Budget spent • C graph – Daily Budget not spent • A – location of maximum number of Clicks for a fixed Daily Budget obeying constraints • minCPC – Lowest value of CPC produces Clicks
  15. 15. Experimental ABC data #1
  16. 16. Results: X-graph #1 Start Clicks Start CPC End Clicks End CPC 1,066 $0.51 1,848 $0.27
  17. 17. Results: X-graph #2 Start Clicks Start CPC End Clicks End CPC 29 $1.24 56 $0.63
  18. 18. Results: X-graph #3
  19. 19. Results: ABC-graph #3
  20. 20. Results: X-graph #4
  21. 21. Results: ABC-graph #4
  22. 22. Virtual Auctions
  23. 23. BBM Spend - Nov 2015
  24. 24. BBM Constraint Obedience - Nov 2015  Constraint Constraint day CPC CPC CPC CPC          
  25. 25. Proving Machine Learning works: • 20,000 Campaigns in AdWords • 12,000 on BBM • 8,000 not on BBM • June 2015
  26. 26. BBM Search - Daily Budget spend Case (Spent Daily Budget %) times (Not BBM %) BBM 3.6 BBM(3.7+ grade) 4.0
  27. 27. BBM Search performance – Daily Budget Spent case Case Imp Share IS LTB IS LTR CPC Avg. Pos. Not BBM 47.28% 32.49% 29.14% $6.46 2.306 BBM 55.42% 18.43% 26.13% $4.04 2.417 BBM(3.7+ grade) 54.99% 15.84% 20.23% $2.95 2.484 Case Imp Share % IS LTB % IS LTR % CPC % Avg. Pos. % BBM 17.2% -43.3% -10.3% -37.5% -4.8% BBM(3.7+ grade) 16.3% -51.2% -30.6% -54.4% -7.2%
  28. 28. BBM Search performance – Daily Budget not Spent case Case Imp Share IS LTR CPC Avg. Pos. Not BBM 64.51% 35.49% $4.29 2.50 BBM 77.18% 22.82% $4.05 2.22 BBM(3.7+ grade) 73.41% 26.59% $2.91 2.35 Case Imp Share % IS LTR % CPC % Avg. Pos. % BBM 19.6% -35.7% -5.7% 11.1% BBM(3.7+ grade) 13.8% -25.1% -32.3% 6.4%
  29. 29. Daily Budget Spend (%)
  30. 30. Distributions of Daily Budget spent (%) - Human
  31. 31. Distributions of Daily Budget spent (%) – BBM no Skynet
  32. 32. Distributions of Daily Budget spent (%) – BBM with Skynet
  33. 33. Gamification • Continuously coaching user to better results • Enhances user brand loyalty o Autonomy o Mastery o Connection •Currently doing Anomaly detection daily o Setup problems o Budget underspending o Warnings • Machine Learning based
  34. 34. 2016 Predictions • Year of Machine Learning in AdTech/MarTech causing: 1. Continued suppression of CPC 2. Accelerated consolidation of Platforms 3. New quality advertising volume external to Google AdWords 4. Greatly increase verticalization of technology stack available to advertisers  Exponential growth in Algorithm economy offerings via SOA (Service Orientated Architectures) with RESTful API  Lowering of skills necessary to use these ML algorithm services (IFTTT) 5. Leveling of the Playing Field for SMB advertisers
  35. 35. Acquisio and Machine Learning • Machine Learning driving innovation in AdTech/MarTech • BBM offers Machine Learning optimization of Bid & Budget within and across publishers (AdWords, Bing, Yahoo!Japan) • Machine Learning is the cornerstone of Gamification • Required for Self-Service BBM • Cross Publisher optimization will greatly increase in 2016 • Google AdWords, Bing, Facebook, Yahoo!Japan
  36. 36. References Advancements in Machine Learning: Acquisio Summit Keynote o YouTube video: http://tinyurl.com/p6s85f2 o SlideShare: http://tinyurl.com/owwn2ow Bid vs. Pay: A Case for Automated Optimization o http://www.acquisio.com/blog/ppc-marketing/bid-vs-pay-case-automated- optimization Pay vs. Bid: Optimizing for Mobile and Non-Mobile o http://www.acquisio.com/blog/mobile/pay-vs-bid-optimizing-mobile-and-non-mobile
  37. 37. Poll Question Are you interested in learning more about Acquisio’s bid optimization solution: a) Yes b) No c) I’m ok for now
  38. 38. Faster. Smarter. Better. Questions?

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