My BrightonSEO deck September 2016. Why and how to run SEO split tests, and some lessons we have learned from running our own tests via our DistilledODN platform.
Will CritchlowBusiness owner / founder and stats geek. à SearchPilot
4. Both of them died,
along with a bystander
Via: reddit
5. Before germ theory, 25-50% of
patients died from infections
(Speed also used to be a prized
surgical skill pre-anaesthetic)
It wasn’t always confidence-inspiring
9. The “Liston” of site migrations
Step 1: fail to put redirects in place
10. The “Liston” of site migrations
Step 2: rel=canonical every page to the homepage
11. Good for the patientBad for the patient
Accidental
Deliberate
12. Good for the patientBad for the patient
Accidental
Deliberate
Mercury for syphilis
13. Good for the patientBad for the patient
Accidental
Deliberate
Mercury for syphilis
Not washing hands
14. Good for the patientBad for the patient
Accidental
Deliberate
Mercury for syphilis
Not washing hands Garlic + Onion
15. Of course a lot of deliberate things were
neither harmful nor beneficial
16. Cargo cult:
During WW2, Pacific islanders who had never seen
manufactured equipment saw modern military
planes bring cargo to their remote islands.
Read Richard Feynman’s speech
17. Cargo cult:
After the war, cults developed that tried to recreate
the conditions that “brought” the planes
(runways, control towers, military uniforms)
without understanding what had really happened.
Read Richard Feynman’s speech
22. In medicine, old wives tales are a great place to start. Looking at things that appear to work, but we have
no idea why is a good source of hypotheses.
A great example is this 1,000-year-old “spell” that included garlic, onion and cow’s stomach, and turned
out to kill MRSA.
I guess the SEO equivalent is to ask the old timers.
23. In all areas of science, you are at an advantage if you can figure from first principles. Richard Feynman
famously used to draw what became known as “Feynman diagrams” to understand sub-atomic
interactions through thought experiments alone.
The SEO equivalent is to stay abreast of information retrieval and ML papers and formulate hypotheses
based on an understanding of how the algorithm likely works.
24. Finally, you can go mining the data.
The obvious SEO equivalent is the various correlation studies into ranking factors.
In both medicine and SEO, you obviously have to be wary of spurious correlations. Blindly mining data
can get arbitrarily high correlations (the example above has a correlation of 0.993!).
25. The scientific method
Step 2:
Try things in the lab
Problem: results may not hold, or may come with new side effects
26. In the SEO space, this is work like that done by
IMEC labs.
It involves attempting to run controlled
experiments on test domains and / or with
volunteer participants. The outcomes are
normally not improved rankings or traffic that
participants care about.
28. What works in tests may not work in the real world
Source: National Institutes of Health
29. Do you recommend http → https migrations?
All else being equal, secure is better. All else is never equal.
Side effects may include ranking fluctuations, traffic drops, difficult conversations with your boss.
Side effects
may include headache, nausea, vomiting, death, dizziness, dysentery, cardiac
arrhythmia, mild heart explosions, varicose veins, darkened stool, darkened
soul, lycanthropy, trucanthropy, more vomiting, arteriosclerosis,
hemorrhoids, mild discomfort, vampirism, spontaneous dental hydroplosion,
sugar high, even more vomiting, and mild rash.
31. TL;DR scurvy bad, science hard
You should read the story of one of the first controlled scientific experiments that proved lemons could
cure scurvy (in 1747!). The incredible story of how the discovery supported British naval supremacy, and
then how compounding errors involving the colonial supply-chain, faster steam-powered ships, and polar
bear offal led to the loss of the knowledge, the death of polar explorers, and the eventual rediscovery of
vitamin C.
Source: idlewords
39. Instead of comparing the performance of the control pages directly with the variant pages, we build a
forecast of what’s called the counterfactual which is an estimate of what would have happened if we hadn’t
made the change. We use the control group to make a counterfactual forecast that takes into account
seasonality and site-wide changes.
The black line on the chart above is the actual organic traffic to the variant pages. The blue line is the
counterfactual.
More: Distilled blog post and free forecasting tool
40. It’s easiest to analyse the results by looking at the cumulative difference over time between the actual
organic traffic and the counterfactual.
The pale blue area is the 95% confidence interval.
We can see a (statistically) zero effect for an initial time while Google crawls and indexes the test,
followed by steady growth. A couple of weeks in, the confidence interval goes above zero and we have a
winning test.
More: Distilled blog
41. It’s easiest to analyse the results by looking at the cumulative difference over time between the actual
organic traffic and the counterfactual.
The pale blue area is the 95% confidence interval.
We can see a (statistically) zero effect for an initial time while Google crawls and indexes the test,
followed by steady growth. A couple of weeks in, the confidence interval goes above zero and we have a
winning test.
More: Distilled blog
Hashtag winning
42. 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
More: Distilled blog
46. We got one of the fastest and clearest uplifts we have seen so far with
the addition of structured data to detail pages. This chart shows the
uplift from adding location-based data to individual property pages.
48. More: Distilled blog
This is the chart I showed you earlier when I was describing the statistics.
It’s actually an uplift from improved clickthrough rate. We didn’t detect
an accompanying ranking improvement during this experiment.
49. Make your
site mobile
friendly
I’ve spent a lot of time trying to
persuade people to do this
without data to back me up.
Now I’m going to carry on with
data.
50. More: @TomAnthonySEO
This chart shows the uplift from making a bunch of category pages
mobile-friendly (with some simple responsiveness) on a holiday site.
51. Just to help prove that these are real uplifts, we ran a “null” test
designed to have no impact
52. ...and there are tons of tests where we
don’t have pretty charts we can share yet
53. Tabbed
versus flat
We know Google in particular is
paying more attention to CSS
and JS. How much difference
does it make it content is visible
initially on page load?
54. Additional
content
You might want to test both
adding and removing additional
content on category pages.
This would test the benefit of
additional text vs. increased
focus and possibly-improved
usage metrics.
55. Breadcrumbs
How much difference does it
make if you add breadcrumbs to
product pages?
Note: this introduces the
complexity of testing internal
linking. I’ll come back to this.
56. Canonicals
vs. noindex
We’ve often argued about the
best ways of keeping certain
pages and page-types out of the
index.
Argue with data.
57. We have all kinds of keyword-targeting test ideas
● Simpler messaging
○ (what happens if you have less keyword targeting?)
● Timely keywords
○ (what happens if you add "2016" in appropriate places?)
Argue with data
58. We’re running tests like these right now
Follow @distilled to hear the results first
59. If you’re going to implement split-testing,
there are some things you should know
60. You can’t assume traffic equality
between “buckets” of pages
This is why we build a counterfactual comparison using control pages.
61. Different pages can have different
seasonality
For example, “roses” pages on valentine’s day. You need to cut outliers.
62. One site I looked at had 72<html> tags on
a single page
You’ll find some of your work more sensitive to amusingly broken
HTML
63. We’re not quite sure how to model
cross-section impacts
This will be needed for testing internal linking structures, for example.
64. You may detect unexplained
phenomena
In medicine, this would be things like the placebo effect with no known
pathway.
65. We may find that things that “shouldn’t” work, in fact do drive uplifts.
We can speculate that the continuing benefit of changing 302s to 301s (despite Google’s insistence that
302s don’t lose PageRank) is to do with them losing other link signals, but we don’t really know.
I’m not sure this matters.
67. The big one:
Business cases
I wrote more about this in my better business documents post
68. But I’m also seeing more subtle impacts on my recommendations:
● You can recommend small tweaks and see the benefits compound
● You can test wild hypotheses with unknown upsides
● You can try things that might have a downside (more focused targeting, less copy, etc.)
And that’s even before you get the benefits of testing clickthrough rate, and the benefits of pretty charts
to show the boss highlighting the impact of your work!
More: blog post