This document discusses creating and considering user generated reviews. It notes that reviews disrupt the traditional path to purchase and are influential for consumers. There are two main types of reviews: professional reviews from a single entity that can be tracked, and user/consumer reviews from a crowd that may be biased. User reviews can build relevance and momentum through their numbers and natural language. They provide a full picture for consumers but come with responsibilities around moderation, fake reviews, and ensuring objectivity. In the end, both professional and user reviews have benefits and challenges to consider.

1. @dawnieando
CREATING COMMERCE REVIEWS
& CONSIDERING USER GENERATED
REVIEWS

2. @dawnieando
Dawn Anderson
Managing Director - Move It Marketing
Associate Lecturer – Digital marketing
& Search – Manchester Metropolitan
University
UK, US, EU, MENA Search Awards
Judge
International & Technical SEO
Consultant – 10+ Yrs in SEO
Googebot Hunter (Practice &
Academia)

3. @dawnieando
THE POWER OF REVIEWS
“81% of UK consumers read
customer reviews/ratings and 47%
read blogs” (Deloitte,2014)

4. @dawnieando
“Reviews Disrupt The
Traditional Path To
Purchase” (Deloitte, 2014)
REVIEWS DISRUPT

5. @dawnieando
Source: Google / Ipsos, 2016
REVIEWS CONVERT

6. @dawnieando
EMERGING REVIEWS MARKETS
Beaty & Fitness
reviews continue
to pick up pace
Year on Year

7. @dawnieando
GOOGLE TRENDS

8. @dawnieando
2 TYPES OF REVIEWS
PROFESSIONAL
CONSUMER /
UGC
SINGLE
ENTITY
‘THE
CROWD’
TRACKABLE,
TRACEABLE,
TRUSTABLE
‘MAY BE
BIASED’
‘A NOISY
MASS’
‘THE GENERAL
OPINION’

9. @dawnieando
ARE REVIEWS WORTH IT?

10. @dawnieando
213 KEYWORDS RANKING FOR
ONE PAGE
http://www.trustedreviews.com/news/ps4-­‐
pro-­‐vs-­‐ps4

11. @dawnieando
RANKING FOR LONGER PHRASES

12. @dawnieando
REVIEWS DEFINED

13. @dawnieando
REVIEW
DEFINED
CRITICAL

14. @dawnieando
CRITICAL THINKING
TAKING AN OBJECTIVELY
INFORMED VIEW AND
CONCLUDING BASED ON ALL OF
THE EVIDENCE AVAILABLE

15. @dawnieando
REVIEW DEFINED

16. @dawnieando
REVIEW DEFINED

17. @dawnieando
OBJECTIVITY WITHOUT BIAS

18. @dawnieando
OBJECTIVITY

19. @dawnieando
TRUST

20. @dawnieando
Guha, R., Google Inc.,
2009. Search result ranking based
on trust. U.S. Patent 7,603,350.
NOTE: THIS IS ONLY
A PATENT SO WE
CANNOT BE SURE IT
IS IN USE AT ALL
TRUST

21. @dawnieando
AUTHOR CREATED
STAR RATING

22. @dawnieando
PIGEONS

23. @dawnieando
JOIN THE CONVERSATION

24. @dawnieando
WHAT ARE PEOPLE
ASKING?
WHO
WHAT
FEATURES
PROS

25. @dawnieando
WHAT DO USERS
WANT TO KNOW
AND LEARN?

26. @dawnieando
QUESTION ANSWERING

27. @dawnieando
PEOPLE ALSO ASK QUESTIONS
IN SERPS

28. @dawnieando
RELATED SEARCHES INSPIRATION

29. @dawnieando
FEATURES & SPECS ARE KEY

30. @dawnieando
COMPARE, BEST,
RECOMMENDED, REVIEW,
REVIEWS
SPEC, FEATURES, PRICE,
COST, SPEED,
SPECIFICATION, WHEN,
RELEASE

31. @dawnieando
REVIEW PAGE STRUCTURE

32. @dawnieando
CONTENT LENGTH
Top Ten Reviews for ‘Playstation
4 Reviews’ were well over 1500
words long
Long Form Content

33. @dawnieando
9 seconds
“I can concentrate
for longer than
you… so there”
SHORT ATTENTION SPAN

34. @dawnieando
CATER FOR IMPATIENCE - BE
FAST & INCLUDE TL:DR

35. @dawnieando
KEY TAKEAWAY SOUNDBITES

36. @dawnieando
THE URL

37. @dawnieando
GLOBAL NAVIGATION STRENGTH

38. @dawnieando
LOCAL NAVIGATION RELEVANCE
TABLE OF CONTENTS STYLE IN
PAGE NAVIGATIONAL HEURISTIC
FOR SEARCH ENGINE AND
HUMAN
PAGINATED TAB THROUGH ON
SECTIONS OF REVIEW

39. @dawnieando
BREADCRUMBS

40. @dawnieando
H1s – What IS This Page About?

41. @dawnieando
H2’s & H3’S
(H4-H6 even)

42. @dawnieando
COMPARE HEADINGS

43. @dawnieando
THE MONETIZATION CTA

44. @dawnieando
TOPICAL MODELLING
SEMANTICS & LINGUISTICS
STEMMING & LEMMATIZATION
NATURAL LANGUAGE

45. @dawnieando
BUT MOSTLY…
JUST GIVE A WELL
STRUCTURED, UNBIASED,
CRITICAL REVIEW IN A
WELL ORGANISED
MANNER, LABELLED WITH
HEADINGS &
CONSIDERING THE
CONVERSATION POINTS

46. @dawnieando
BRAND VARIANTS
ON PRODUCT
UNDER REVIEW

47. @dawnieando
PRESENTING THE
ARGUMENTS

48. @dawnieando
BOTH SIDES OF
THE COIN

49. @dawnieando
STRUCTURED & UNSTRUCTURED
DATA
STRUCTURED UNSTRUCTURED
• ’Data’ marked up for
search engines to
understand ‘things’
• Data for data
• The ‘Web of Data’
• Schema, JSON-­‐LD
• Text without special
markup
• HOWEVER:
• Ordered lists
• Unordered lists
• Tabular Data
THERE IS STILL
AN ‘ORDER’ TO
THIS TYPE OF
DATA FOR
SEARCH
ENGINES TO
UNDERSTAND
MORE EASILY

50. @dawnieando
PRIMARY RESEARCH
POLLS
SURVEYS
TESTING
INTERVIEWS
STRESS-­‐
TESTING
TRIALSDIARIES
WALK-­‐
THROUGHS

51. @dawnieando
REVIEW DEFINED

52. @dawnieando
TABULAR DATA

53. @dawnieando
LISTS

54. @dawnieando
RICH MEDIA FORMATS
IMAGES
TECHNICAL
DRAWINGS
VIDEO
DON’T FORGET
THE ALT AND
TITLE TAGS!!!

55. @dawnieando
THE VERDICT

56. @dawnieando
SUPPLEMENTARY CONTENT

57. @dawnieando
BOTTOM
BREADCRUMBS
FOR THE WIN

58. @dawnieando
IN CONTEXT LINKS WITH
SURROUNDING RELEVANCE

59. @dawnieando
BUILD STRONG TOPICAL HUB PAGES
Soure: Broder: Bowtie of the web:
Wikimedia
BOWTIE OF THE WEB
STRONGLY
CONNECTED
COMPONENT

60. @dawnieando
COMMON CHARACTERISTICS
OF A WINNING COMMERCIAL
REVIEW

61. @dawnieando
EXAMPLE
COMMERCIAL
REVIEW PAGE
STRUCTURE
1. URL (probably contains /review (if single professional review or
reviews at directory level + product name)
2. Well Crafted Page Title
3. Call to action Meta Description
4. Navigational Help
5. H1 – What is this page’s primary purpose?
6. TLDR: ANSWER THE QUESTION / VERDICT
7. QUICK COMPARISON TABLE
8. H2 – Secondary tag line
9. Feature summary list (ordered or unordered)
10. H3 – Features Explored / Review Sections
11. Arguments For Each Feature (H3)
12. Arguments Against Each Feature (H3)
13. Comparison with previous model (H2)
14. Primary research results (H2)
15. Pros & Cons on balance of evidence (H2)
16. Rich media (video, spec drawings & images throughout)
17. The Verdict
18. Review stars & rating
MONETIZATION

62. @dawnieando
THE CASE FOR CONSUMER
GENERATED REVIEWS

63. @dawnieando
ARE USER GENERATED REVIEWS
WORTH IT? Ranking
positions
1,2,3 for
“best
pushchair”

64. @dawnieando
THEY BUILD NATURAL
‘RELEVANCE’
Rich in natural
language

65. @dawnieando
THERE IS STRENGTH IN NUMBERS
WITH MOMENTUM
MUM’S
reviews

66. @dawnieando
DATA MINING TO
UNDERSTAND
CONVERSATION

67. @dawnieando
TEN YEARS OF CHANGING TOPICS
IN WEB SEARCH & DATA MINING
TEN YEARS OF WSDM (WEB SEARCH
AND DATA MINING) – RICARDO
BAEZA-­‐YATES, WSDM 2017
CONFERENCE -­‐CAMBRIDGEWSDM 2017 Keynote by Ricardo Baeza-Yates

68. @dawnieando
FROM SEARCH & WEB IN 2008 TO
SOCIAL, WEB & M/L & A/I
SOURCE: TEN YEARS OF WSDM (WEB
SEARCH AND DATA MINING) –
RICARDO BAEZA-­‐YATES, WSDM 2017
CONFERENCE KEYNOTE -­‐
CAMBRIDGE

69. @dawnieando
Rich conversation
passes ‘natural’
relevance and ‘pigeon’
buzz

70. @dawnieando
Source: The Consumer Barometer, 2014/2015
PEOPLE WANT THE
FULL ‘OBJECTIVE’
PICTURE -­‐ SORTING,
FILTERING ACROSS
FEATURES
(RECENCY,
FREQUENCY, AVG)

71. @dawnieando
AUDIENCES WANT TO ENGAGE IN QUESTIONS &
ANSWERS & CO-­‐CREATE

72. @dawnieando
CO-CREATION

73. @dawnieando
“Crowd-based power… A force
in the consumer market going
beyond reviews and
recommendations into co-creation”
(Deloitte)

74. @dawnieando
CO-CREATION CASE STUDIES
COMMUNITY
ENGAGEMENT –
UPVOTE IDEAS
FOR LEGO SETS

75. @dawnieando
“Consumers are becoming both
CRITICSand CREATORSwho
want to have their say and be involved
in the process of creation” (Deloitte,
2014)

76. @dawnieando
SUSPECT
PROSPECT
CUSTOMERS
CLIENTS
ADVOCATES
ULTIMATE
ADVOCACY –
THE LADDER OF
LOYALTY

77. @dawnieando
Source: The Consumer Barometer, 2014/2015
TRIBAL POWER VIA
ADVOCACY

78. @dawnieando
EXPERT & AUDIENCE
REVIEWS IN HARMONY

79. @dawnieando
BUT… WITH REVIEWS COME
RESPONSIBILITY

80. @dawnieando
THE DOWN SIDE TO ‘USER
GENERATED CONTENT’
FAKE REVIEWS &
COMMENT SPAM

81. @dawnieando
COMMENT SPAM
MODERATION
&
MANAGEMENT

82. @dawnieando
Source: ICPEN – International Consumer Protection & Enforcement Network
-­‐
http://www.icpen.org/files/icpenDownloads/ICPEN___ORE___Guide_3___G
uidelines_for_Digital_Influencers___JUN_2016_FNL.pdf
BIAS-PREVENTION

83. @dawnieando
Source: ICPEN – International Consumer Protection & Enforcement Network -­‐
http://www.icpen.org/files/icpenDownloads/ICPEN___ORE___Guide_3___Guidelines_for_Digital
_Influencers___JUN_2016_FNL.pdf
ADMINISTRATORS,
MARKETERS,
INFLUENCERS

84. @dawnieando
IS IT
WORTH
IT?
…
YOU
DECIDE
PROs CONs
• Tribal advocay
• Increased
rankings
• Text rich
content
• Natural
language
• Relevancy
• Community
• Crowd opinion
• Moderation
• Comment spam
• Need for
objectivity
• Reputation
growing task
• Buzz ‘building’
• Fake reviews
• Review vetting
• Unbiased view

85. @dawnieando
• https://twitter.com/methode/status/879387001979535360
• http://www.google.co.uk/patents/US7603350
• https://www.icpen.org/files/icpenDownloads/ICPEN___ORE___Guide_3___Guidelines_for_Digital_Influen
cers___JUN_2016_FNL.pdf
• https://www.icpen.org/files/icpenDownloads/ICPEN___ORE___Guide_1___Guidelines_for_Review_Admin
istrators___JUN_2016_FNL_.pdf
• https://www.icpen.org/files/icpenDownloads/ICPEN___ORE___Guide_2___Guidelines_for_Traders_and_
Marketers___JUN_2016_FNL.pdf
• http://www.icpen.org/files/icpenDownloads/ICPEN___ORE___Guide_3___Guidelines_for_Digital_Influenc
ers___JUN_2016_FNL.pdf
• https://www.thinkwithgoogle.com/marketing-­‐resources/experience-­‐design/chapter-­‐2-­‐product-­‐details-­‐
and-­‐reviews/
• https://webmasters.googleblog.com/2009/11/hard-­‐facts-­‐about-­‐comment-­‐spam.html
SOURCES & REFERENCES