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?

1. Greg Sterling
Opus Research
July 15, 2013
Social, Search and the
Future of Local

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. 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. 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. 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. Local Word of Mouth
(social search 1.0)

7. Word of Mouth
Sherman, my boy, traditional
businesses have always relied on
word of mouth and personal
recommendations for new leads
Gosh

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. Source: Nielsen Q4 2011; n=28,000 Internet respondents in 56 countries.
Consumers Trust Each Other

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. 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. 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. 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. Rise of Social Directories
(and the culture of participation)

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. 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. 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. 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. Social Evolution

20. Search Gets Social
(and vice versa)

21. Search + Social Social + Search
“You got peanut butter in my chocolate. You got chocolate in my peanut butter.”

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. 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. Original Google Algo ‘Social’
Source: searcheverywhere.net (2012)

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. Eurekster: 2004
Eurekster saw “social graph” as a filter/personalization tool (see Blekko)
Problem: not enough social/community

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. 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. 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. Makes Sense on Paper

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. Search  Social Feedback Loop
Search
Social
• Beyond question that social activity improves ranking on Google
• Specific variables open to debate "

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. Social Search & Local
(back where we started – sort of)

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. 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. Limited but Promising Results

38. . . . Or Go Old School

39. MINUTES
MINUTES
66 MINUTES
74 MINUTES
81
MINUTES
64
MINUTES
43
Sources: comScore Q1 2013
User Behavior Developing

40. Can’t Do That on G

41. Google’s Local Carousel

42. Google+ Local

43. New Google Maps w/Recs

44. Foursquare Social Rankings

45. Yelp Recommendations
Personal + contextual +
social variables:
• Location
• Yelp check-ins and
reviews
• Yelp friends
• Time of day
• Weather

46. Pinterest: ‘Social Discovery’ Offline

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. Future: Personal Assistant
(the return of SoLoMo)

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. ‘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. The Star Trek Computer
Google has
repeatedly
talked about
building the
“Star Trek
computer”

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. Greg
Sterling
greg.sterling@gmail.com
Twi4er.com/gsterling
Questions?