Hummingbird Unleashed discusses Google's Hummingbird update and how it has changed search. It suggests a new approach to SEO that focuses on identifying related entities for a topic, matching them to audience interests, and creating content around those entities. This new approach could help businesses like a small pizza chain in NYC optimize their SEO under Hummingbird by focusing content on relevant entities like types of pizza crust instead of specific keywords.

1. Hummingbird Unleashed
Sugges&ng
a
new
SEO
methodology
Gianluca Fiorelli - @gfiorelli1
Global
Associate

2. Hummingbird is a
complete rewrite of our
search system

3. It’s not me saying it, it’s this guy

4. Before talking about

5. we should talk about

6. and

7. Caffeine
Panda
Penguin
Because a logic in the Google
Updates sequence exists

8. The mobile revolution
forced us to change how
we think

9. Mobile may overtake
desktop for Google
searches within a
year
(SMXW 2014)
It’s not me saying it, it’s this guy

11. Do you remember when
Google what showing us
“searches similar to…” in
the SERPs?

12. Usually it was for queries
like:
“The best way of cooking a
pizza with antichokes in a
electric oven”

13. (Long) Long Tails
Verbose Queries

14. (Long) Long Tails
Verbose Queries

15. Hummingbird
=/
Humminguin

17. How Hummingbird
(possibly) works

18. To take synonyms and
Knowledge Graph and
other things

19. It’s not me saying it, it’s (again) this
guy

20. SYNONYMS

21. Google is dealing
with them since a
long time

22. Read
this
post
by
Vanessa
Fox
on
Search
Engine
Land:
hDp://itseo.org/OEJCDG
Keyphrases don’t
need to be in their
original form. We
do a lot of synonym
work, so we can
find good pages
that don’t happen to
use the same
words a the user
typed (Matt Cutts)

23. The issue is that a word can be a
synonym or not depending on the
Context
Coche
Automóvil
Carro

24. That Contest was the problem was clear since the
beginning, as we can understand reading this patent
by Amit Singhal: http://itseo.org/1gPt32l
The issue is that a word can be a
synonym or not depending on the
Context

25. Hummingbird is how Google
solves the Contest issue,
thanks to Search Entities and
Semantics
Search Entities: http://itseo.org/1fwbfoL

26. Search Entities
• A query a searcher submits;
• The documents responsive to the query;
• The search session during which the searcher submits
the query;
• The time at which the query is submitted;
• Advertisements presented in response to the query;
• Anchor text in a link in a document;
• The domain associated with a document.
Suggested read: this post by Bill Slawski

27. Words =/ Things

29. Words = Verbal Representation of Things

30. Google transforms Words into Concepts
thanks to Search Entities

31. And Concepts can be disambiguated over
the base of their Context

32. This is what Google already started to do
with Knowledge Graph

33. BEWARE!
SEMANTIC SEO =/ SCHEMA.ORG
Schema.org is instrumental to Semantic, it’s
not SEMANTIC

34. OTHERS THINGS…
I suspect that the third factor are co-
occurrences

35. Consequences

36. Google understands better
the queries and their
intentions

37. Google may expand the
number of documents that
respond significantly to a
query

38. If verbose query A = simplier query C
and
If verbose query B = simplier query C and
similar to verbose query A
then I’ll show just the SERPs answering to
query C

39. Tl;dr:
Simpyfing the queries,
Google also reduces the
number of unnecessary
SERPs

40. And that’s good also in
term of Adwords…

41. What about links?
Won’t they be an
essential factor anymore?

42. Links are clearly an
important signal about the
importance of your
content. They’re still very
valuable

43. It’s not me saying it, it’s (again!) this
guy

44. From theory to practice.
For a new model of SEO

45. Mario has a small pizzerias’ chain in NYC

46. He has only a very small difficulty

47. The old way (not considering Local Search)
1) Pizzeria Tribeca;
2) Best pizzeria in NoLiTa;
3) Calzone Theater District;
4) Pizza Special Chelsea;
5) Where to eat the best pizza in
Manhattan
6) etc etc

48. #FAIL
(not provided)

49. #FAIL
Penguin (monster)

50. #FAIL
Bounce Rate (tons of it)

51. #FAIL
Hummingbird (killing the long tail)

52. Il Nuovo Metodo
We identify the Entities related to our niche and how
they are connected
We match them with our Audience interests
We creat Content Architecture based on Content Hub
using Ontology
We conducts a Keywords Research and Mapping
with Entities in mind

53. Entities identification
Freebase APIs: http://itseo.org/1h3RgOS

54. Entities identification
Yahoo Glimmer: http://glimmer.research.yahoo.com/

55. Entities identification
Bottlenose: http://bottlenose.com/

56. Entities identification
RelFinder: http://www.visualdataweb.org/relfinder.php

57. Audience Matching
Read this: http://itseo.org/1gYLi5q

58. Audience Matching (audience personas)
Followerwonk first

59. Audience Matching (audience personas)
And after we use Tribalytics: http://tribalytics.com/

60. Ontology & Taxonomy
Read twice this deck by Abby Covert:
http://itseo.org/1oHVWjt

61. Ontology & Taxonomy (based on Entities Search
and Audience Matching
Pizza
Thin
Thick
Regular crust
Organic
Rossa Bianca
Napoletana Romana

62. Content Hub Creation
Home
Tribeca
Our
Stories
From the
oven
People of
Tribeca
Why we love
Tribeca
SOCIAL

63. From Content Strategy to Content Marketing
Recipes > Schema.org > Rich Snippets
Recipes > Video Marketing > VideoObject > Rich
Snippets
Infografics – Data Visualization – Charts (passive link
building opportunities)
Guides > Long Form > Authorship > In-Depth Articles
UGC
Q&A

64. Protip – Newsjacking & Unconventional
Marketing as a Plus

65. Keyword Research based on Entities
Much more ideas in this post by Dan Shure:
http://itseo.org/1h479od

66. And if someone tells you that SEO is dead…