Greg Sterling
Opus Research
July 15, 2013
Social, Search and the
Future of Local
•  Lawyereditorstartupsanalyst/blogger"
•  Search + local + mobile + social media + SMB marketing"
•  Impact of digital...
The convergence of three major “online” trends: "
•  Social: word of mouth, user-generated content"
•  Local: online resea...
What Is ‘Social Search’?
Social search uses social data (likes, check-ins,
social graph) to influence or determine ranking...
Categories of ‘Social Data’
1.  Ratings/reviews (Online word of mouth)
2.  Social activities/actions (check-ins, likes,
co...
Local Word of Mouth
(social search 1.0)
Word of Mouth
Sherman, my boy, traditional
businesses have always relied on
word of mouth and personal
recommendations for...
Primary Source of Leads
Source: AMEX/Network Solutions 2/11 (n=400 US small businesses who did some form of online marketi...
Source: Nielsen Q4 2011; n=28,000 Internet respondents in 56 countries.
Consumers Trust Each Other
Do you trust online customer reviews as much as personal recommendations?
Source: BrightLocal, 3/12 (n=2,862 respondents f...
Source: Opus Research, 2012, n=1,001 US adults (multiple answers permitted)
Local: Reviews Most Important
“When searching ...
A local business needs at least 6 to 10 reviews to be credible and trusted
Credibility Threshold
Source: BrightLocal, 3/12...
Source: BrightLocal Local Consumer Review Survey 2013
In the last 12 months have you recommended a local business to peopl...
Rise of Social Directories
(and the culture of participation)
Cityguides: WoM Online
•  In roughly 1994 – 1995 multiple
cityguide sites launched
•  Restaurant, events &
entertainment d...
Social Directories
•  Yellow Pages 2.0 (directories + reviews)
-  YP publishers initially resisted reviews;
perceived conf...
More Reviews, More Categories
•  “Social directories” had similar
ambition as earlier generation of
cityguides
•  But soug...
Parallel Rise of Social Nets
•  The Well – 1985
•  Craigslist – 1995
•  Friendster – 2002
•  MySpace – 2003
•  LinkedIn – ...
Social Evolution
Search Gets Social
(and vice versa)
Search + Social Social + Search
“You got peanut butter in my chocolate. You got chocolate in my peanut butter.”
You Complete Me
•  Wants the social data to improve
search (and compete with Facebook)
•  Wants to implement search to del...
Crowdsourcing Search
Crowdsourcing and social content have been at the heart of
the search experience from beginning:
•  Y...
Original Google Algo ‘Social’
Source: searcheverywhere.net (2012)
Social Evolution
•  Social an “organic” development for search
- From html docsdocs, offline places, people
- Real-world ...
Eurekster: 2004
Eurekster saw “social graph” as a filter/personalization tool (see Blekko)
Problem: not enough social/comm...
Q&A: ChaCha & Vark
Real-time Q&A has always held promise but no one has made it work
•  Aardvark (2008) was effort at real...
Facebook Connect = Social Filtering
•  Facebook Connect (2008) enabled users to see or filter content their
friends had “L...
Bing’s Social Sidebar
•  Bing introduced (2010)
“social sidebar”
•  Relevant content from
multiple networks
•  Ability to ...
Makes Sense on Paper
You Made Us Do It
•  Facebook wont allow Google to crawl site
•  Bing-Facebook alliance
•  Google wants social-graph data ...
Search  Social Feedback Loop
Search
Social
•  Beyond question that social activity improves ranking on Google
•  Specific...
Facebook Ranking Factors
Here’s a non-exhaustive list of probable Graph Search
ranking variables:
•  Social graph/network
...
Social Search & Local
(back where we started – sort of)
‘Local Is Social’
•  Social search + local a natural fit (see WoM)
•  Social content often local (e.g., Yelp, TripAdvisor,...
Places in Graph Search
•  Graph Search (now wide) and Nearby Places Search
(app) – use same underlying platform
•  Key fea...
Limited but Promising Results
. . . Or Go Old School
MINUTES
MINUTES
66 MINUTES
74 MINUTES
81
MINUTES
64
MINUTES
43
Sources: comScore Q1 2013
User Behavior Developing
Can’t Do That on G
Google’s Local Carousel
Google+ Local
New Google Maps w/Recs
Foursquare Social Rankings
Yelp Recommendations
Personal + contextual +
social variables:
•  Location
•  Yelp check-ins and
reviews
•  Yelp friends
•...
Pinterest: ‘Social Discovery’ Offline
Summary: Uses of Social Data
•  To improve algorithm/results
-  Real-world feedback: comments, likes, follows, check-ins
-...
Future: Personal Assistant
(the return of SoLoMo)
Siri & Google Now
Siri brought “assistant” concept into focus Google Now: “predictive
search” (with multiple data inputs)
...
‘Conversational Search’
•  At Google I/O company demonstrated
“conversational search”:
-  Phone understands context
-  Sea...
The Star Trek Computer
Google has
repeatedly
talked about
building the
“Star Trek
computer”
Questions
Given the devastating
completeness of the information
presented, Sherman, I should think
not….
Do you think ther...
Greg	
  Sterling	
  	
  
greg.sterling@gmail.com	
  
	
  
Twi4er.com/gsterling	
  
Questions?
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Graph Search, Facebook Nearby & Beyond: How Social Search Impacts the Future of Local by Greg Sterling - SIMposium

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Social recommendations have been a primary driver of real-world commerce for generations. But the rise of online reviews and social networking have made them the rival of traditional word of mouth in local. Social connections and online comments now form the raw material of a new generation of discovery tools and search algorithms. After years of discussion and speculation, social search is finally real with Graph Search, Foursquare and Google+. What is the current state of social search and how will it impact the future of local?

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Graph Search, Facebook Nearby & Beyond: How Social Search Impacts the Future of Local by Greg Sterling - SIMposium

  1. 1. Greg Sterling Opus Research July 15, 2013 Social, Search and the Future of Local
  2. 2. •  Lawyereditorstartupsanalyst/blogger" •  Search + local + mobile + social media + SMB marketing" •  Impact of digital media on real-world consumer behavior" •  Twitter: @gsterling! " About-Me Slide
  3. 3. The convergence of three major “online” trends: " •  Social: word of mouth, user-generated content" •  Local: online research  offline buying " •  Mobile: the internet “in context” (time/space)" Last Year: SoLoMo
  4. 4. What Is ‘Social Search’? Social search uses social data (likes, check-ins, social graph) to influence or determine ranking and relevance compared with conventional algorithmic search, which uses text and/or link analysis
  5. 5. Categories of ‘Social Data’ 1.  Ratings/reviews (Online word of mouth) 2.  Social activities/actions (check-ins, likes, comments) 3.  The “social graph” (connections, followers)
  6. 6. Local Word of Mouth (social search 1.0)
  7. 7. Word of Mouth Sherman, my boy, traditional businesses have always relied on word of mouth and personal recommendations for new leads Gosh
  8. 8. Primary Source of Leads Source: AMEX/Network Solutions 2/11 (n=400 US small businesses who did some form of online marketing) Businesses historically viewed word of mouth as the primary driver of business
  9. 9. Source: Nielsen Q4 2011; n=28,000 Internet respondents in 56 countries. Consumers Trust Each Other
  10. 10. Do you trust online customer reviews as much as personal recommendations? Source: BrightLocal, 3/12 (n=2,862 respondents from the US, UK and Canada) 26% 21% 20% 33% 28% 24% 20% 28% Yes, if there are multiple reviews Yes, if the reviews are authentic Yes, for some types of businesses no for others No 2010 2012 Reviews Trusted Like WoM
  11. 11. Source: Opus Research, 2012, n=1,001 US adults (multiple answers permitted) Local: Reviews Most Important “When searching for a local businesses online, what types of information are most important?” 33.1% 30.3% 24.5% 20.3% 11.5% Reviews of the business Business name, address & phone Pricing information Maps & directions Images of business
  12. 12. A local business needs at least 6 to 10 reviews to be credible and trusted Credibility Threshold Source: BrightLocal, 3/12 (n=2,862 respondents from the US, UK and Canada)
  13. 13. Source: BrightLocal Local Consumer Review Survey 2013 In the last 12 months have you recommended a local business to people you know by any of the following methods? WoM Still Dominant
  14. 14. Rise of Social Directories (and the culture of participation)
  15. 15. Cityguides: WoM Online •  In roughly 1994 – 1995 multiple cityguide sites launched •  Restaurant, events & entertainment directories: -  Seen as profoundly threatening to newspapers; less so to YP (at the time) •  In 1999 Citysearch (IAC) acquired Sidewalk from MSFT
  16. 16. Social Directories •  Yellow Pages 2.0 (directories + reviews) -  YP publishers initially resisted reviews; perceived conflicts between advertiser and consumer interests •  Sometimes called “social search” sites •  Angie’s List (est. 1995; online 1999) •  2003 – 2006: -  Tribe.net -  Judy’s Book -  Insiderpages (acq’d by IAC ‘07) -  Yelp (2004; same year as FB) -  Kudzu -  Others
  17. 17. More Reviews, More Categories •  “Social directories” had similar ambition as earlier generation of cityguides •  But sought to bring more consumer reviews to more categories •  Improve process of selecting a local business online •  Yelp: “real people, real reviews”
  18. 18. Parallel Rise of Social Nets •  The Well – 1985 •  Craigslist – 1995 •  Friendster – 2002 •  MySpace – 2003 •  LinkedIn – 2003 •  Facebook – 2004 •  Twitter – 2006 •  Google+ – 2011 •  Tumblr, Instagram, Snapchat, etc., etc. Most social nets are not “utilitarian” initially
  19. 19. Social Evolution
  20. 20. Search Gets Social (and vice versa)
  21. 21. Search + Social Social + Search “You got peanut butter in my chocolate. You got chocolate in my peanut butter.”
  22. 22. You Complete Me •  Wants the social data to improve search (and compete with Facebook) •  Wants to implement search to deliver more utility and realize the financial opportunity
  23. 23. Crowdsourcing Search Crowdsourcing and social content have been at the heart of the search experience from beginning: •  Yahoo Directory and DMOZ (‘98) used human editors to organize the web •  Larry Page envisioned “back links” as “democratic voting” by the entire web re topic authority (1996) – superior to keyword density •  Vertical sites (e.g., travel, shopping) enjoyed high rankings b/c of social content/reviews
  24. 24. Original Google Algo ‘Social’ Source: searcheverywhere.net (2012)
  25. 25. Social Evolution •  Social an “organic” development for search - From html docsdocs, offline places, people - Real-world input from people (WoM) - More holistic treatment of query •  Humans offer more direct and relevant “answers” vs. machine algorithm - Every search query a question
  26. 26. Eurekster: 2004 Eurekster saw “social graph” as a filter/personalization tool (see Blekko) Problem: not enough social/community
  27. 27. Q&A: ChaCha & Vark Real-time Q&A has always held promise but no one has made it work •  Aardvark (2008) was effort at real-time Q&A/ social search •  Problem: not enough “critical mass” •  Acquired by Google for $50 million •  Began as human-aided search (expert guides); now mostly machine generated •  Problem: humans too expensive
  28. 28. Facebook Connect = Social Filtering •  Facebook Connect (2008) enabled users to see or filter content their friends had “Liked” on Facebook •  Integrated into Blekko and Bing in 2010, to differentiate from Google
  29. 29. Bing’s Social Sidebar •  Bing introduced (2010) “social sidebar” •  Relevant content from multiple networks •  Ability to ask Facebook friends query •  Sidebar changed, redesigned multiple times Showing “asynchronous social recommendations” addresses the real-time critical mass problem
  30. 30. Makes Sense on Paper
  31. 31. You Made Us Do It •  Facebook wont allow Google to crawl site •  Bing-Facebook alliance •  Google wants social-graph data (hence privacy policy change for 360 view) •  2009: Google introduces “social search” (small “s”) - Public content from friends/contacts at bottom of search results; also a social filter at one point •  2011: Google launches Google+ (also +1 buttons) •  2012: “Search Plus Your World;” focus on personalization but social content instrumental
  32. 32. Search  Social Feedback Loop Search Social •  Beyond question that social activity improves ranking on Google •  Specific variables open to debate "
  33. 33. Facebook Ranking Factors Here’s a non-exhaustive list of probable Graph Search ranking variables: •  Social graph/network •  Completeness of business data on profile/Page •  Ratings •  Likes •  Check-ins •  Business location vis-à-vis user query Pages that are more engaging/active (feature more content and interaction) are also going to rank higher
  34. 34. Social Search & Local (back where we started – sort of)
  35. 35. ‘Local Is Social’ •  Social search + local a natural fit (see WoM) •  Social content often local (e.g., Yelp, TripAdvisor, OpenTable) •  Friend recommendations vs. “10 blue links” -  Mobile factor: efficiency (“answers not links”) •  “Local is social” (Marissa Mayer, June 2012)
  36. 36. Places in Graph Search •  Graph Search (now wide) and Nearby Places Search (app) – use same underlying platform •  Key feature of Graph Search is Places •  Q: How committed is Facebook? - Probably: statements + $$ oppty - Experience is uneven (even crude) but shows promise
  37. 37. Limited but Promising Results
  38. 38. . . . Or Go Old School
  39. 39. MINUTES MINUTES 66 MINUTES 74 MINUTES 81 MINUTES 64 MINUTES 43 Sources: comScore Q1 2013 User Behavior Developing
  40. 40. Can’t Do That on G
  41. 41. Google’s Local Carousel
  42. 42. Google+ Local
  43. 43. New Google Maps w/Recs
  44. 44. Foursquare Social Rankings
  45. 45. Yelp Recommendations Personal + contextual + social variables: •  Location •  Yelp check-ins and reviews •  Yelp friends •  Time of day •  Weather
  46. 46. Pinterest: ‘Social Discovery’ Offline
  47. 47. Summary: Uses of Social Data •  To improve algorithm/results -  Real-world feedback: comments, likes, follows, check-ins -  Objective rankings (social actions = community voting) •  Social graph: explicit filter (asynchronous WoM) •  To enable “discovery” (implicit) -  Way back: Amazon collaborative filtering -  Personalization (along with history, etc.) -  Search w/o searching (persistent/ambient) •  Together w/mobile (“context”) social data enable next generation of services: PVAs
  48. 48. Future: Personal Assistant (the return of SoLoMo)
  49. 49. Siri & Google Now Siri brought “assistant” concept into focus Google Now: “predictive search” (with multiple data inputs) Other apps/entities use metaphor of virtual assistant (e.g., Nina, Tempo)
  50. 50. ‘Conversational Search’ •  At Google I/O company demonstrated “conversational search”: -  Phone understands context -  Search can build on previous queries •  Coming Motorola MotoX to have “always on” listening capability, ready to respond to voice commands •  Wake-up phrase: “OK Google Now”
  51. 51. The Star Trek Computer Google has repeatedly talked about building the “Star Trek computer”
  52. 52. Questions Given the devastating completeness of the information presented, Sherman, I should think not…. Do you think there will be any questions Mr. Peabody?
  53. 53. Greg  Sterling     greg.sterling@gmail.com     Twi4er.com/gsterling   Questions?

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