After focusing in previous years on the future of search technologies, this presentation focuses instead on the future of marketing for search. What do we need to change about our marketing to continue to appeal to a changing Google?

1. -- President of Western Union, 1876
(to Alexander Graham Bell)
This ‘telephone’ has too many
shortcomings to be seriously
considered as a means of
communication

2. -- New York Times, 1936
A rocket will never be able to
leave the Earth’s atmosphere

3. -- Microsoft CEO, Steve Balmer, 2007
There’s no chance that the
iPhone is going to get any
significant market share.

4. -- Nobel Prize-winning physicist Niels Bohr
It's hard to make predictions,
especially about the future

5. In 2013
I talked about:

6. This query really shouldn’t work

7. But it does

8. In 2014
I talked about:

9. VISION: STRONG AI

10. REALITY: WEAK AI

11. 2015:
what we need to change in
our campaigns

12. brought to you by...
WHAT DOES GOOGLE
WANT?
WILL CRITCHLOW, FODM 2015

13. Results are heavily
personalised and contextual
(best shown via voice search - see this blog post)
1

20. Different
“algorithms” for
different queries
See Dr. Pete’s presentation
2

21. Even Google engineers
don’t know why one page
ranks above another
(Complex systems)
3

22. The more techniques mimic human thought, the less explainable they are
Image source: The Analytics Store

23. Tricks
Rules (set
by Google)
Best
practices
ML /
Panda

24. Tricks
Rules (set
by Google)
Best
practices
ML /
Panda
Towards AI
We are
here

25. See Kelvin’s excellent presentation for more on this

27. All this means you need to
be TESTING

28. So. Two kinds of testing
Google is aiming for
satisfied users

29. We can test against the users, or
against Google
GOOGLEBOT

30. For users: “CRO”
1

34. There are some pitfalls

35. BUT: Google’s proxies for “quality” aren’t perfect
e.g. duplicated content can be a great user experience

36. It’s hard to measure user “happiness”
Satisfied
Delighted
Worn down

37. For googlebot: “SEO testing”
2

39. Say with me:
Correlation does not imply
…?

40. ...causation. Wait. What?
Further reading for those interested:
● Predicting the present with Bayesian
structural time series [PDF]
● Inferring causal impact using
Bayesian structural time series [PDF]
● CausalImpact R package
● Finding the ROI of title tag changes

41. Consider typical traffic data

42. Read Ben Este’s blog post for more
We can extract:

43. Read Ben Estes' blog post for more
Seasonality...
We can extract:

44. Read Ben Este’s blog post for more
...and trend
We can extract:

45. Then we can make forecasts
(Dotted line = forecast, blue area = confidence interval)

46. We can detect anomalies
We use this on our client-only ops platform to spot unexpected changes

47. Forecast is
correct Beats the
forecast

48. It’s not all roses

49. It’s hard to make these kinds of changes:

50. Check out this post from Pinterest’s
engineering blog

51. Haven’t worked out how to test
internal linking
Where the effect propagates throughout the site

52. But it is powerful

53. Optimise for actual Google
preferences
(rather than guessing how Google will interpret user preferences)
1

54. Avoid wide scale losses
from unsuccessful tests
2

55. In short:

57. THANK YOU
@willcritchlow

58. One more thing...

59. One more thing...
We have an early private beta of a product
to do SEO split testing. Come see me
afterwards if you are interested

60. Speaker
photo
goes
here
WILL CRITCHLOW
CEO and Co-Founder, Distilled
will.critchlow@distilled.net
@willcritchlow