Marketing Festival 2016

1. Will Robots Take Over PPC?

2. About Me
@SiliconVallaeys
frederick@optmyzr.com

3. Agenda: Trends That Will Change PPC
Automation
The On-Demand Economy
Artificial Intelligence

4. Trend #1
Automation

5. Making a Case For Automation...

6. The Case Against Automation...

7. 5
Levels of automation for PPC

8. Level 1 - Monitor and Alert
Alert a human when
something may need
attention.
Try this AW Script: bit.ly/GranularAnomalyDetector

9. Level 2 - Manage Individual Components
Run automated tasks
for each entity,
keywords, bids, ads,
etc.
→ Automated Rules

10. Level 3 - Manage Multiple Systems To
Achieve a Simple Goal
Set a CPA target and
automation finds
keywords, manages
bids and budgets.
Try custom AdWords Scripts

11. The current state of
Bid Management

12. CPC Bid
A Tale of Two Kinds of Bids
Demographics /
RLSA
Geographic
Dayparting
Device
Modifiers

13. Matrix Bid Management
Locations
Devices
Dayparts
Audiences
1 keyword may have tens of thousands of permutations
1000 locations * 3 devices * 6 dayparts * 2 genders * 5 age ranges = 180.000 bids
CPC Bid

14. How to Do Matrix Bidding
Requires a very fine grained account with lots of duplication
Single Keyword Campaigns (Skamps)
AdWords has a 10.000 campaign limit per account
You can’t do this at scale without automation
Alternatively use Enhanced CPC to get the benefit of real-time auction data

15. Average Data
A Common Bid Management Mistake Since
Enhanced Campaigns Started
Mobile
Data
Desktop
Data
Tablet
Data
vs

16. How to Manage Bids Correctly With Multiple Devices
Desktop Bid (Your CPC bid)
Mobile Bid
Modifier

17. Anchored Bids Introduce Management Complexity and Dependencies
Desktop Bid
When the bid
you are
anchored to
changes,
modifiers must
also be
adjusted.

18. Anchored Bids Introduce Management Complexity and Dependencies
Desktop Bid
Mobile Bid
Modifiers

19. Generic Static Bid OR a bid based on all device types
Mobile Bid
Modifier
Tablet Bid
Modifier
Desktop Bid
Modifier

20. Bidding By Weather

21. Discovering a Relevant Signal

22. Predicting Conversion Rates For Shopping Queries
PREDICTIONS
1. Prediction for data: ["big and tall undershirt",0,43,1,"Style Exact","custom0=style exact and product_type_l1=t-shirts and
product_type_l2=short sleeve and brand=gildan"] is 0.025902
2. Prediction for data: ["hanes sleevles tees",0,1,1,"Style Color","custom0=style color and product_type_l1=t-shirts"] is -0.000386
3. Prediction for data: ["bulk baseball sleeve shirts",0,1,1,"Style","custom0=style and product_type_l1=t-shirts and brand=anvil"] is -
0.023049
4. Prediction for data: ["big & tall t shirt green",0,1,1,"Style Exact","custom0=style exact and product_type_l1=t-shirts and
product_type_l2=short sleeve and brand=gildan"] is 0.066616
5. Prediction for data: ["navy and gray raglan",0,4,1,"Style Color","custom0=style color and product_type_l1=t-shirts"] is -0.015015
6. Prediction for data: ["cuffed long sleeve shirt",0,1,1,"Style Color","custom0=style color and product_type_l1=t-shirts"] is -
0.049287
7. Prediction for data: ["big and tall mens shirts sale",1,41,2,"Style Exact","custom0=style exact and product_type_l1=t-shirts and
product_type_l2=short sleeve and brand=gildan"] is 0.242856
8. Prediction for data: ["plain tees",0,19,1,"Style Exact","custom0=style exact and product_type_l1=t-shirts and
product_type_l2=short sleeve and brand=gildan"] is 0.045151
9. Prediction for data: ["mens fleece quarter zip pullover",0,5,1,"Style Exact","custom0=style exact and product_type_l1=jackets"] is
0.099721http://bit.ly/PredictShoppingQueries

23. Level 4 - Intelligent Systems Manage Entire
Well Defined Accounts
We set parameters for several things and the
system manages the account.
→ locations, products (i.e. business parameters)
→ goals (e.g. ROAS)
→ restrictions (e.g. only run on Google Search)

24. Level 5 - Fully Automated
Connect to your
business financials,
write a blank check to
Google and let their
robots handle it.

25. Some Automation is Too Conservative

26. Humans Can Learn the Robots’ Techniques

27. Trend #2
The On-Demand
Economy

28. More Freedom and Fewer Responsibilities

29. Applying Principles From the On-Demand
Economy to PPC
Break complex PPC management into small
tasks that can be done by a freelancer

30. 30
Keyword Suggestions Example
Store a set of keyword
suggestions in Google
Sheets where they can
be reviewed by a
freelancer.

31. Get Freelance Specialists to Process the Suggested Keywords

32. Expanded Text
Ads How to achieve the results Google’s
case studies show

33. Ads That Are Optimized Outperform ETAs
“When ETAs do better, they
significantly outperform legacy ads.
The problem is that advertisers need
time to test and tweak their new ads
before they will achieve these
results.”
- A Google Product Manager
Google pushed back the sunset date for legacy
ad creation to January 2017
Analysis #1 and #2

34. Boxplots and Quartiles
Analysis #1 and #2

36. Benefits of Using DKI:
In Title of Any Type of Text Ad
Does the use of dynamic keyword insertion
boost an ad’s performance?

37. Benefits of Using DKI:
In Description of Any Type of Text Ad
Does the use of dynamic keyword insertion
boost an ad’s performance?

38. Benefits of Using DKI:
In Expanded Text Ads

39. Using Path1 and Path2
Does using the URL paths boost performance of
ads?

40. Benefits of Repeating Path Text
in Text of ETA
How often are Path1 and Path2 used in
Headline1 and Headline2
Do ads perform better when the text from the
path also appears elsewhere in the ad?

41. Does Ad Text Length Matter
AdWords doesn’t always show all the text we
submitted in our ads. How does the length of our
ad impact performance?
VS.

42. Impact of Length of
Headline 1 vs Headline 2

43. Mobile Preferred Expanded Ads
Are you upset that mobile preferred ads are not available for Expanded Text Ads?

44. 95%
Of mobile preferred ads were different from the standard ad by only a single
word

45. Run Different Mobile Expanded Ads
With Ad Customizers
1. Use the Ad Customizer attribute for “Device preference”:
2. Set up your ad using this new data:

46. Run Different Mobile Ads with
Value Track
Coming Soon! ValueTrack
{ifmobile:say this} {ifnotmobile:say something else}
Already available for use with the final URL
Coming soon to visible parts of the ads

47. Trend #3
Artificial Intelligence

49. Quality Score - Finding Correlations in CTR
Obvious correlations... Less obvious ones...

50. In our analysis, 71% of keywords
with a change in QS had no
change to the First Page Bid
Estimate on the same day.
If you use a Google Bid Strategy
that depends on ‘first page bid’ or
‘top of page bid’ estimates, be
careful!
The Correlation between QS and FPB

51. • Going from QS 4 to 8 does NOT mean your ad rank
doubles
• Hence much of the published data on monetary impact is
incorrect…
The Quality Score You See Is Not Linear

52. If Quality Score Was Linear

53. Quality Score Is Not Linear

54. Decrease in CPC for 1 Point QS Gain
Going UP from QS 5
to 6 reduces First
Page Bid estimate by
37%

55. AI Assistants
“It’s clear to me that we are moving from a
mobile-first to an AI-first world.”
–Sundar Pichai, Google

56. Alexa and Optmyzr

57. Thanks
@SiliconVallaeys
frederick@optmyzr.com