Creating Commerce Reviews and Considering The Case For User Generated Reviews
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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.
Creating Commerce Reviews and Considering The Case For User Generated Reviews
- 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
- 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’
- 10. @dawnieando 213 KEYWORDS RANKING FOR ONE PAGE http://www.trustedreviews.com/news/ps4-‐ pro-‐vs-‐ps4
- 11. @dawnieando RANKING FOR LONGER PHRASES
- 14. @dawnieando CRITICAL THINKING TAKING AN OBJECTIVELY INFORMED VIEW AND CONCLUDING BASED ON ALL OF THE EVIDENCE AVAILABLE
- 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
- 25. @dawnieando WHAT DO USERS WANT TO KNOW AND LEARN?
- 27. @dawnieando PEOPLE ALSO ASK QUESTIONS IN SERPS
- 29. @dawnieando FEATURES & SPECS ARE KEY
- 30. @dawnieando COMPARE, BEST, RECOMMENDED, REVIEW, REVIEWS SPEC, FEATURES, PRICE, COST, SPEED, SPECIFICATION, WHEN, RELEASE
- 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
- 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
- 40. @dawnieando H1s – What IS This Page About?
- 41. @dawnieando H2’s & H3’S (H4-H6 even)
- 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
- 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
- 54. @dawnieando RICH MEDIA FORMATS IMAGES TECHNICAL DRAWINGS VIDEO DON’T FORGET THE ALT AND TITLE TAGS!!!
- 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
- 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
- 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