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.

1. 2016 The Year of
Machine Learning:
Why Bid Algorithms Will Always
Outperform Humans

2. Our Speaker
Bryan Minor, Ph.D.
Chief Scientist
at Acquisio

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. Poll Question
Are you currently using a bid optimization solution?
a) Yes
b) No
c) I’m looking for one

5. Agenda
• What is Machine Learning?
• Machine Learning at Acquisio
• Bid & Budget Management
• Gamification
• Predictions for 2016 driven by Machine Learning
• Conclusions

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. 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. 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. 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. 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. 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. Algorithm model
• Cruise missile model
• Dynamic Non-linear optimization
• Small steps more often

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. 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. Experimental ABC data #1

16. Results: X-graph #1
Start Clicks Start CPC End Clicks End CPC
1,066 $0.51 1,848 $0.27

17. Results: X-graph #2
Start Clicks Start CPC End Clicks End CPC
29 $1.24 56 $0.63

18. Results: X-graph #3

19. Results: ABC-graph #3

20. Results: X-graph #4

21. Results: ABC-graph #4

22. Virtual Auctions

23. BBM Spend - Nov 2015

24. BBM Constraint Obedience - Nov 2015
 Constraint
Constraint
day
CPC
CPC CPC
CPC

  
  
  

25. Proving Machine Learning works:
• 20,000 Campaigns in AdWords
• 12,000 on BBM
• 8,000 not on BBM
• June 2015

26. BBM Search - Daily Budget spend
Case (Spent Daily Budget %) times (Not BBM %)
BBM 3.6
BBM(3.7+ grade) 4.0

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. 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. Daily Budget Spend (%)

30. Distributions of Daily Budget spent (%) - Human

31. Distributions of Daily Budget spent (%) – BBM no
Skynet

32. Distributions of Daily Budget spent (%) – BBM with
Skynet

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. 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. 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. 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. 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. Faster. Smarter. Better.
Questions?