Greg Sterling Opus Research
July 15, 2013
Social, Search and the Future of Local
• Lawyereditorstartupsanalyst/blogger"• Search + local + mobile + social media + SMB marketing"
• Impact of digital media on real-world consumer behavior"• Twitter: @gsterling!
"
About-Me Slide
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
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
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)
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 new leads
Gosh
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
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 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
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
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)
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
Rise of Social Directories (and the culture of participation)
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
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
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”
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
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 deliver
more utility and realize the financial opportunity
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
Original Google Algo ‘Social’
Source: searcheverywhere.net (2012)
Social Evolution • Social an “organic” development for search
- From html docsdocs, 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
Eurekster: 2004 Eurekster saw “social graph” as a filter/personalization tool (see Blekko)
Problem: not enough social/community
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
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
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
Makes Sense on Paper
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
Search Social Feedback Loop
Search
Social
• Beyond question that social activity improves ranking on Google
• Specific variables open to debate "
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
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, OpenTable)
• Friend recommendations vs. “10 blue links”
- Mobile factor: efficiency (“answers not links”)
• “Local is social” (Marissa Mayer, June 2012)
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
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 • Time of day • Weather
Pinterest: ‘Social Discovery’ Offline
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
Future: Personal Assistant (the return of SoLoMo)
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)
‘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”
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 there will be any questions Mr. Peabody?
