Mapping Startups & Services Filtering For Relevance In A Matrix

On September 2, 2010, in Social Web, by Mahendra Palsule

After looking at the different approaches to filtering for Relevance, I have been seeking a way to map them visually. There are many different startups competing in this space along with the giants, and a way to map them in a matrix would help us see the big picture of how the battle for relevance is evolving on the social web.

What are the fundamental ways in which these approaches and startups differ? These could form the axis around which we can then proceed to map them.

The Popular – Personalized Axis

Filtering either works by showing us the most popular stuff being shared online, or by understanding our individual preferences and surfacing personalized content. Thus, we have the following axis:

PopularPersonalized

The Serendipity – Search Axis

You either search for content or you see it serendipitously without seeking anything specific. Search is actively initiated by the user and is goal-driven, while serendipitous discovery is gifted with the user being passive at the receiving end. This gives us our second axis:

SerendipitySearch

The Filtering for Relevance Matrix (FORMAT)

We combine these two axes to form the backbone of our visualization. We then place different services within our matrix as per their core filtering approach. The result is the Filtering FOR Relevance Matrix (FORMAT) as seen below:

 

Format

Let us now look at each quadrant closely.

Popular – Search Quadrant

This is the simplest and oldest of all. Search powered by algorithms to surface most popular content online. This also includes other Twitter search services like Topsy. These services are powered by algorithms such as PageRank, PersonRank, Resonance, etc. to surface the most popular result relevant to a query.

This approach dominated the Web 1.0 era before the advent of the social web.

Popular – Serendipity Quadrant

Services in this category help you find the most popular content being shared online across different social networks. These were the next to evolve in the Web 2.0 era, beginning with social bookmarking services like Reddit, StumbleUpon, etc.

There is an element of personalization provided by many of these, in that you “follow” some users, but the motive behind such following is less to seek personalized content, more to seek trending, viral content.

Note how Digg is attempting to move from this quadrant to the personalized quadrant, and facing hurdles along the way.

Search – Personalized Quadrant

A breed of services has evolved around delivering personalized recommendations and content tailored for your needs. Hunch learns about you and acts as a “taste engine”, while Blekko allows you to personalize your searches with slashtags. Google is making forays in this space with its Social Search service, which tries to personalize search results based on your social graph.

Personalized Serendipity Quadrant

This is the hottest space where most of the competition is today.

Twitter Lists are personalized (created by you) and deliver fresh, serendipitous content relevant to your interests. Facebook Likes give you serendipitous discovery from your personal friends. Flipboard provides a social magazine based on your personal social circle on Facebook and Twitter. My6sense delivers new content using ‘Digital Intuition’. Vertical networks like Last.fm deliver music recommendations based on your individual taste. Personalized Twitter newspapers give you fresh content filtered by your social graph on Twitter.

Note how Datasift lies at the center of the matrix. This is because Datasift is a platform providing different filtering services and approaches. Developers may use the platform to develop different services and apps that can lie in any of these quadrants.

How does FORMAT help?

So what is the point of this exercise? Using FORMAT:

  • We see the big picture of how services providing relevance and filtering are evolving.
  • We see how personalized serendipity is the holy grail of the social web right now.
  • We see how different services relate to each other and who is competing with whom and how.
  • We see how identifying the target quadrant is important for any new startup in this space.
  • We see how users provide friction when a service tries to change quadrants (Digg).

If you are involved in a startup aiming to provide filtered, relevant content to users, which quadrant would you target? See how FORMAT helps?

Tagged with: •
 
View Comments Comments
Leave A Response

DataSift Curation Engine Aims for Relevance in Real-time

On August 27, 2010, in Social Web, by Mahendra Palsule

As I have said many times previously, if 2009 was all about the hype of Real-time, the future is all about capturing Relevance in real-time. Datasift has partnered with Twitter to get the full Twitter firehose and is building a platform to enable curation and filtering in real-time.Datasift

An introductory video about Datasift was posted in their first blog post, which didn’t reveal much about how the platform works. Now, uber-geek Robert Scoble has posted a video of an extensive discussion with Datasift’s founder, Nick Halstead.

Robert Scoble with Datasift founder Nick Halstead

This post is a summary of Datasift as discussed above concluding with my own thoughts.

The Basics

Twitter’s firehose at present has around 800 tweets/sec, or 70 million tweets/day. Datasift can filter this firehose using over 20 variables. Examples of these variables include:

  • Profile information like name, location, bio, number of follows, followers, lists, etc.
  • Text and language of tweets
  • Geo-location of tweets
  • Verified users
  • Source of tweets – web, Seesmic, TweetDeck, etc.
  • Number of Retweets
  • Whether tweet contains a hyperlink

Datasift is a rules-based engine that can filter this firehose using thousands of complex rules and provide a filtered stream in real-time within milliseconds. It is built using a Service Oriented Architecture and has an API.

The Rules

Rules can comprise of any combination of filters using the above variables. Rules can be combined and merged, or added and subtracted, into a single new rule. Stream outputs from Datasift using such rules can become columns in Twitter clients like TweetDeck.

Here are a few examples of how rules can be used:

  • Show me tweets containing “google” from users who don’t have “social media” in their bio, and who have more than 500 followers.
  • Show me tweets from my curated Twitter list of tech brands that have more than 100 Retweets.
  • Show me tweets originating from within a radius of 5 miles from the location of XYZ Conference that don’t have swear words, irrespective of whether their tweets contain the hashtag for the conference.
  • Show me tweets originating from Starbucks shops around the world, of users who are “Verified Accounts”, irrespective of what they’re about.

Datasift’s website is intended as a community website for curators and developers to collaboratively work on developing these rules. You can leverage rules created by others to avoid duplication of effort. Rules are classified with tags, and Datasift provides search, ranking and trending for easier discoverability of rules.

Partnerships for Influence Tracking and Sentiment Analysis

Datasift has partnered with PeerIndex and Klout to enable filtering using their influence and authority scores. It has also partnered with a firm for real-time sentiment analysis.

Thus, any of the above rules can be filtered further using such scores, and a stream of tweets with negative sentiment about a brand or product, combined with any other rules, can be monitored in real-time.

Alerts and Analytics

For esoteric rules that may provide a result infrequently, alerts can be set up. The example discussed is of any politicians from a Twitter list tweeting the word “scandal”. Developers can send these alerts as email, SMS, or notifications on smartphones.

The resulting streams from all rules applied by the engine are stored by Datasift. This data can be extracted, segmented, and analyzed later. For example, this can be used to track the performance of social media campaigns.

Relevance Filtering of Links

Datasift can use TweetMeme and other databases to check the links in tweets, and determine whether they are relevant to a specific topic. Not much details on how this is achieved, but apparently, Nick says that all sites are already classified into different subjects by Tweetmeme and other such databases.

Blekko-style Twitter Search

Datasift has developed a prototype of Twitter search along the lines of Blekko’s slashtags. Thus, along with your query text, you can use filters such as “/nolinks” to get tweets without links, or “/California” to get tweets originating from CA.

RSS Feeds

Compared to the massive volume of the Twitter firehose, the volume of RSS is minimal. Datasift plans to have their own PubSubHubbub server. Developers and third-parties can plugin any RSS feeds and use Datasift’s filtering rules to get an output feed.

Revenue Model

One option is free access to the stream with in-stream ads. Ads will be tailored and designed for the target form factor – desktop/mobile/tablet/etc.

Second option is selling data B2B for developers and brand companies, charged by volume of data consumed.

Prospective Partners

Datasift is seeking to work with startups like Flipboard, who are creating new ways for curated content consumption. This can also include any of the startups focusing on Relevance, such as TwitterTimes or Paperli.

My Thoughts

When I compared approaches to filtering information for relevance, I had suggested that the service most likely to succeed would be the one that supports multiple approaches and platforms. We can easily see that Datasift supports all platforms and several approaches like crowdsourced filtering, influence filtering, location filtering, etc. It is easily the most powerful relevance filtering engine I have seen yet.

The market of end-users for curated real-time content is at present unknown. Startups involved in creating pleasant experiences for consuming content have yet to find a monetization strategy. The degree of Datasift’s success from an end-user perspective is largely dependent on:

  • The creativity of developers and curators to create compelling experiences, and
  • How the monetization strategies of presentation apps fare and how Datasift is able to work with them

Nevertheless, with the amount of content being created online growing exponentially, curation and filtering will eventually become necessities for any social media client. It is just a matter of time.

I also see a bright future on the B2B front. By partnering with influence and authority tracking companies, combined with sentiment analysis, Datasift may already be a compelling choice for brand monitoring and social media reputation tracking.

Lastly, thanks to Robert Scoble and Nick Halstead for the interesting interview.

Tagged with: • • • •
 
View Comments Comments
Leave A Response

Google Already Has An Invisible Like Button For Google Me, You Just Don’t Know It

On August 7, 2010, in Social Web, by Mahendra Palsule

Another day and I read another post on how Facebook’s Like button is slowly obliterating Google’s Link as the next currency of the web. The pondered question in this case is what is going to be Google’s counter-offensive against the Like.

The assumption is that Google as a search engine has worked on the principle of ranking web pages according to the number of other pages linking to it. Well, here’s the deal: when a person likes something on the web, in most cases, a link is created. Google can see this Link, and hence can understand and incorporate the Like, in its scheme of things.

This mechanism has already been publicized by Google, but I’m surprised how many folks still keep discovering it as if it were something new. For example, see this from yesterday.

Google’s Invisible Like Mechanism

Google’s Like mechanism was announced by Google in Oct 2009 in a blog post announcing Social Search, which linked to this help article that explains how it works in the background.

Google Socal Search Like Button

The battle is between Facebook’s Like and Google’s Profiles. For Facebook to capture your Like, it requires you to have an account on Facebook. For Google to capture your Likes, you need to have a Google Profile. Now, let’s compare what Facebook and Google can capture:

Facebook can capture only your Facebook Likes.

Google Profiles can capture:

  • Public content you share on Facebook
  • All tweets on Twitter
  • All shares on Google Reader
  • All shares on FriendFeed
  • All status updates on LinkedIn
  • All favorites from YouTube
  • All likes, faves, photos from Flickr and Picasa
  • All bookmarks from Delicious
  • All stories you have Digged
  • Everything you have Stumbled Upon
  • Everything you have Disqused
  • All your Blogger and WordPress blog posts
  • And dozens of existing and future sites using the XFN or FOAF standards (see FAQ)

Get the picture? From a technical standpoint, Google has all the arms and ammunition to capture Likes across a plethora of social websites. If you have a Google Profile, every action on any of your connected social websites (sort of) results in a Like being submitted to Google.

Google’s Challenge

Presentation: Currently, Google is surfacing all this behind-the-scenes information only through Social Search results. Google doesn’t have a social web site where you can see your friends’ Likes and interact with them. This is potentially the core of what Google Me is all about.

Numbers: Facebook has 500 million, very few have Google Profiles. We have been waiting for that big push for Google Profiles. It is imminent, and apparently, very close.

Tagged with: •
 
View Comments Comments
Leave A Response

Schmidt: Google Buzz is “an extension of Gmail”

On August 5, 2010, in Social Web, by Mahendra Palsule

Some interesting comments by Eric Schmidt during the Techonomy conference.

From CNET:

Schmidt said that Buzz, by contrast is doing well with tens of millions of users, basically Gmail users that also use the short-status product.

"Today Buzz is really an extension of Gmail," he said.

From TechCrunch:

“The Buzz team is doing very well,” Schmidt said. But he noted that “we tend to lump Buzz into the Gmail success.”

Compared with the initial hype, I felt these came across as pretty disparaging remarks about Buzz. I am afraid of seeing it languishing as “just an extension” of Gmail.

To my mind, this also indicates the following:

  • Google Buzz will never be separated from Gmail, what some considered the key to unlocking its true potential
  • Google Buzz never was and never will be a competitor to Facebook
  • Google Me is indeed just a social gaming network, as the WSJ had reported
  • Google Me will not be integrated with Google Buzz
  • Since Buzz is tightly integrated with Google Profiles, Google Me isn’t likely to be
  • “Tens of millions” typically means 20-30 million. Thus about 15 to 20% of the estimated 173+ million Gmail users use Google Buzz.

This clarifies a lot of ambiguity over Buzz and Google Me. But that’s reading a lot into Schmidt’s comments, so we still have to wait and see.

Tagged with: •
 
View Comments Comments
Leave A Response

Challenges for PeerIndex, Lessons for Klout

On August 4, 2010, in Social Web, by Mahendra Palsule

Continuing the discussion in my earlier post, PeerIndex: Rating Authority and Relevancy, let’s look in detail at some of the challenges for PeerIndex and what lessons Klout can learn.

Challenges for PeerIndex

1. Get More Accurate Scores of the Big Guys

I saw several folks yesterday seeing their PeerIndex score as Zero. Apart from that, look at just these examples:

Scoble PeerIndex

JayRosen PeerIndex

Something is clearly not right. These are two of the most influential people in tech and media. These are folks who can make or break a startup, and you better get their scores right if you’re to gain any credibility and leverage their influence.

2. Adapt to Different Content Curation Approaches

Different people use Twitter in different ways. For example, Louis Gray’s sharing is primarily through Google Reader, which is tweeted by @lgstream. Robert Scoble’s content curation is through his Twitter Favorites.

These are influential early-adopters, who consume and filter from a massive information stream, and have hence tweaked their Twitter usage habits to suit their needs. The use of their primary Twitter account is for conversation, while curated content gets a separate, dedicated feed.

PeerIndex probably needs to find a way to incorporate multiple Twitter accounts and Twitter favorites into its ranking.

3. Offline Influence Tracking

This is a tough nut to crack and I’m only reiterating it here for the sake of completeness.

Lessons for Klout

1. Diversify Beyond Twitter

If you’re not leveraging Facebook, you’re yet to capitalize on the social web. The Twitterverse is a significant, but small part of the social web.

2. Remember Your Promises

In January 2010, Klout announced that they will be releasing lists of the top influencers for a new country every week. By August 2010, how many country lists have been published? Three – Brazil, UK, and Germany.

3. Leverage Twitter Lists

For a startup aiming to build definitive influence ranking on Twitter, you would think you can readily follow top influencers by region, topic, etc. from their Twitter account. Here are the only lists Klout has created on Twitter:

Klout Lists

This is a failure to capitalize on and leverage a core Twitter feature.

4. Don’t Sacrifice Functionality For UI

I have said it before and I’ll say it again: If You’re Removing Features, Please Tell Your Users!

In May, Klout launched a revamped site with a new classification system and UI. What was not announced was that you no longer had the ability to view the top influencers in a topic, or see the Klout scores of users in a Twitter List.

It is critical for users to be able to use Klout not just to check scores of people they know, but to aid discoverability, easily create Twitter lists using Klout and so on.

Both these startups are very innovative and doing some great work. These are my thoughts on some of the challenges they face and lessons they can learn.

Tagged with: • •
 
View Comments Comments
Leave A Response

PeerIndex: Rating Authority and Relevancy

On August 3, 2010, in Social Web, by Mahendra Palsule

Since Analyzing Twitter Lists-Follower Ratio As An Indicator Of Influence, I have been occasionally checking out Klout as the least followers-driven and interesting influence tracker on Twitter. Now, there’s a new kid in town in the influence measurement space and more – PeerIndex.PeerIndex Logo

Coverage of PeerIndex on Guardian, VentureBeat, and TechCrunch Europe focused only on the influence measurement aspect. Azeem Azhar, founder of PeerIndex, is a former Reuters Innovation Head, who’s also worked with The Economist, Guardian, and BBC. This rich media background drives the topic-based approach of PeerIndex and distinguishes its vision from Klout.

Comparing Ranking Methodology with Klout

Klout Score is a normalized ranking based on:

  • True Reach: The people who regularly pay attention to what you say.
  • Amplification Probability: How far (and often) your content spreads.
  • Network Influence: The influence of your engaged network.

PeerIndex Score has a fundamentally different approach. Rather than calculating a global score first, it defines topics and then calculates an Authority Score in that topic. The rationale behind this is sound: that experts in one topic are not necessarily experts in another.

The PeerIndex Score is a normalized ranking based on:

  • Authority: Quality of the links you share and content you recommend.
  • Activity: How active you are in a topic based on relevance.
  • Audience: Number of people you can reach after discounting spam/gamed/inactive accounts

The key difference between the two approaches is:

PeerIndex also analyzes quality of content you share, rather than just monitoring Twitter activity.

MyPeerIndex

Influence Tracking vs. Relevancy Rating

While Klout is focused solely on Twitter mechanics, PeerIndex also focuses on relevancy of content shared in the context of a topic. Azeem tells me that they are presently tracking over 100 topics, and more topics will be made public in phases.

LinkedIn and Facebook Integration

PeerIndex integrates your LinkedIn and Facebook profiles in scoring, while Klout only works with Twitter. I think this is a huge difference that will affect the usefulness of such services. If you have a Facebook fan page with lots of fans, and are connected to other influencers on LinkedIn, Klout won’t take that into account, but PeerIndex can.

At present, it only considers raw number of connections, but may use more engagement metrics from these services in the future.

Blog Integration

Another interesting factor is the ability to add your blog or website to your profile. At present, there is little effect of adding a blog on one’s PeerIndex score, but it is a step in the right direction. It will be interesting if PeerIndex can assess the authority of your blog and factor it in its ranking.

PeerIndex Add Blog

People Focus: No Brands

The team has made a conscious decision to keep brands and organizational accounts out of its site. The focus is on finding people, exclusively. So, @TechCrunch and @Mashable may have high Klout scores, but they don’t have a PeerIndex.

[Update: As Azeem clarifies in the comments, brand scores are kept internal to the system, just not made public.]

Valuing Curation: Oversharers Penalized

I had written in March about oversharing in social media and how curation increases your reputation. Now, PeerIndex puts this principle in action: there is a cost for oversharing. Noise in your feed reduces the relevance of your shares, hence your ranking goes down.

Challenges: Real World Influence

As with any web service, the challenges for PeerIndex are that there is no standard way all influencers use the social web. For example, authors of real-world books, who may in fact be really influential, may not be active users of the social web. Some may not use Twitter at all. In the end, these services are really useful only for people discovery on the social web.

Future: Authoritative News Aggregation

PeerIndex plans to sell a premium service to brand marketing and PR, to help them identify which influencers their clients should target. More interestingly, Azeem also shared with me the idea of collating the opinions of different authorities to create an aggregated newspaper.

The possibilities are fascinating. Imagine sections from Flipboard – like FlipFinance or FlipTech being powered by topic-based authorities from PeerIndex. According to Azeem, their topic model is not constrained and can be extended to any number of topics. What we have here is an Open-ended Authority-cum-Relevance Ranking Engine.

API? Coming soon.

Azeem’s Interview With Scoble

Do check out this really insightful interview by Robert Scoble with Azeem Azhar.

Tagged with: • •
 
View Comments Comments
Leave A Response

Why We’re Moving From Status Updates to Q&A

On July 29, 2010, in Social Web, by Mahendra Palsule

Within the past few weeks, Quora went public in June, Ask.com reverted to its Q&A roots, and Facebook Questions were formally introduced.ask-logo

Why this sudden trend and momentum towards Questions and Answers?

First, Status Updates are passé. The social web needs to move beyond.

Second, Check-ins were an evolution of the status update, but they lack mainstream appeal, have privacy related issues, and are limited in scope (you have to move to check-in to a different location).

Third, Q&As are more attractive to both users and businesses:

  • Q&As vastly increase the actual usefulness of a social site by several orders of magnitude. This is the obvious, perceived benefit for users.
  • Meaningful questions reveal more about a person than mindless status updates. This leads to better profile information than what people may or may not reveal in their profiles.
  • Questions reveal Intent. Advertisers are more likely to target “Which are the best places to travel to Goa?” (Question) than “Wish I could spend the New Year in Goa!” (Status Update).
  • Questions have a much greater possibility of eliciting responses, leading to greater interaction, translated as more time spent using the site/service.
  • Answers reveal more information about a person’s expertise and interest than what people may or may not reveal in their profile.

Update: To illustrate the last point further, let’s say you answer the question “What’s the best telescope to buy at home?” and it gets voted up. Bingo! Now, even if you don’t have Astronomy listed as a hobby or interest, Facebook knows you’re an Astronomy enthusiast. Also remember, all this information is public and search engines would be glad to get their hands on it. Imagine Blekko with a slashtag search of all Q&A sites – it would be a gold mine for marketers.

What’s next? I wouldn’t be surprised if check-ins and Q&A were tied together. Answers from a person geographically closer may be of higher relevance for certain type of questions. You can add special Badges for the most answered questions about a location, and you get the next version of “Mayor” in FourSquare.

Tagged with:
 
View Comments Comments
Leave A Response

Comparing Approaches to Information Filtering for Relevance

On July 15, 2010, in Social Web, by Mahendra Palsule

In a previous post, we looked at the big shift From Numbers To Relevance. There are dozens of apps/sites that are focusing on filtering information today, but which of them will succeed?

To attempt to answer that question, let’s first look at the different approaches employed by such apps/sites today in the search for Relevance. This is a topic that is usually the subject of scholarly research papers in academia; this is only a layman’s overview.

The different approaches I observe are:

  • Algorithmic Filtering
  • Filtering Based on Social Graph
  • Human Filtering
  • Crowdsourced Filtering
  • Shared Sources Filtering (Meta)
  • Influence Filtering
  • Social Search
  • Location Filtering

Algorithmic Filtering

If you tell us what you want or like, our software can show you what you will like.

Google Suggest

The predominant use of algorithmic filtering is in web search, where Google has dominated and driven the web economy for the past two decades. You search for something and Google’s search algorithm filters billions of web pages to find the most relevant results.

Google also uses algorithmic filtering to suggest items in Google Reader’s “Sort by Magic” feature.

Pros: Highest relevance when searching for information.
Cons: No serendipity. Only useful for goal-oriented task of search. No personalization (search engines typically unaware of demographic information).

Filtering Based on Social Graph

If your friends like it, you’ll probably like it too.

This is the dominant approach being used today by various apps and websites. For example, Facebook uses the EdgeRank formula to determine what to display in your news feed:

edgerankform2

The key driving factor is the affinity score between you and the source.

Google also uses this approach when recommending posts in Google Buzz.

Most of the apps listed in my previous post, as well as the new Digg, use this or a similar approach that employs your Twitter or Facebook friends to recommend items.

Pros: High serendipity. Helps being “in the know”, a socially cool factor. Higher personalization.
Cons: Relevance depends on social graph, which often is not optimized for relevance, as Kevin Anderson noted.

Human Filtering

I trust a specific person to share all of the good stuff I like to know.

Some people make it a habit to go through news items every day and share what they deem to be the most significant ones. Others begin relying on them as trusted news sources.

Pros: High serendipity. Easy to use. Quickly become part of social circle of an influencer.
Cons: Unreliable. Susceptible to preferences and agendas of other people.

Crowdsourced Filtering

Quickly see what’s most important to know.

TweetMeme, OneRiot, Digg, and many other social bookmarking services aggregate the actions of millions of people to surface the most popular services. Techmeme and MediaGazer add human curation to the aggregation of thousands of websites to surface the most important tech and media stories.

Pros: Be up-to-date with the most important/popular need-to-know information.
Cons: No personalization. Popular doesn’t always equate to relevant.

Shared Sources Filtering (Meta)

If you read from sources similar to someone else, you’ll probably like their other sources too.

Facebook uses this approach to suggest new Fan Pages that you may like because your friends like them. Google Reader also uses this to recommend new RSS feeds. Toluu also compares your subscribed RSS feeds with other users to help you discover new feeds.

GR Recommendations

Pros: Useful for discovering new sources in social networks.
Cons: Filters sources, not actual news items, hence limited in scope.

Influence Filtering

Only read what influential people are saying/sharing.

This approach uses influence scores of sources to filter the news feed. An example of this is HootSuite, which uses Klout to let you filter tweets according to their Klout scores.

Klout Filtering

Pros: Flexibility. High serendipity. Helps being “in-the-know”.
Cons: Influence metric is unreliable. Currently only available for real-time feeds like Twitter.

Social Search: Algorithms + Social Graph

Let your social circle find the most relevant results for you.

Social Search uses a combination of algorithms and social graph to find relevant results.

Social Search

Pros: High relevance. Combines goal-orientation of search with serendipity of social. Very useful for news items from recent past.
Cons: Requires searching. Lesser utility for fresh, real-time news.

Location Filtering

If we know where you are, we can help you find relevant results.

Location is a treasure trove for relevance. As the mobile web explodes, services that provide information about nearby businesses or friends are gaining increased adoption.

Pros: High relevance. Can be serendipitous with real life impact.
Cons: Privacy concerns. Limited in scope.

Conclusion: Which Approach is the Best?

None. Relevance is dependent on the requirements of an individual at a specific moment in time. These requirements change from time to time and from person to person. There is no killer approach to relevance.

Which app or service is likely to succeed? I think the following factors will make a difference:

  • Support for multiple approaches
  • Flexibility of degree of filtering
  • Number of Mobile Platforms supported
  • Next Step: What can you do with the info? (e.g. Siri lets you take actions)

What do you think? Are there other approaches that I missed? Which other factors matter?

Tagged with:
 
View Comments Comments
Leave A Response

Googler’s Take on Social Networking Reveals Chinks in Facebook’s Armor

On July 7, 2010, in Social Web, by Mahendra Palsule

Paul Adams, lead for User Research for Social at Google, shared a presentation a few days back that was picked up by Venture Beat among others. I am sharing it here along with my own thoughts as I think it deserves a closer look.

Why? Because:

  • Paul currently works on Buzz and YouTube
  • Google is rumored to be working on Google Me, a rival network to Facebook

The Presentation

The Real Life Social Network v2
View more documents from Paul Adams.

Key Points

  • A single umbrella group of “Friends” in an online network doesn’t mirror real-life and leads to problems. Support multiple independent groups of friends.
  • Focusing on technology is a wrong strategy. Focus should instead be on Motivation and Goals.
  • Design needs are different for different relationship types – strong ties, weak ties, and temporary ties. One solution doesn’t fit all.
  • Different communication channels are needed for different types of relationships.
  • Role of influencers is over-estimated. Also need to focus on network of person being influenced. Influence works most within close ties.
  • Network should support multiple facets of identity and also anonymity.
  • We think people care less about privacy because they misunderstand complicated privacy settings.
  • People underestimate the size of their audience and persistent nature of their conversations online.

My Thoughts

  • There is no mention of any geeky stuff here – Open ID, standards, protocols, etc. It is refreshing to see truly social insights coming from Google.
  • For each of the problems identified with current online social networks, Paul uses Facebook as an example. Most of them also apply to Google’s Orkut, but Paul chooses to ignore Orkut as if it doesn’t exist.
  • While its heartening to see these insights from Google, their real challenge is for the Product Managers and Head of Social to take what they’ve got and build on this vision.
  • Google needs many more Paul Adams.
  • The critical insight is how Paul (and by extension, Google) thinks that there can be no one size fits all approach to social networking. Facebook users already experience the problems Paul describes by mixing close friends, acquaintances, and online strangers together in common conversations.
  • Taking this forward, Google may well be saying that Buzz is a network designed for your acquaintances and weak ties. And if Google Me were indeed under development, looks like it will be a network designed for close ties – family and close friends – which is how Facebook initially started.

Tagged with: •
 
View Comments Comments
Leave A Response

Is Windows Live Delivering What Google Buzz Promised?

On June 28, 2010, in Social Web, by Mahendra Palsule

While everyone has been dissecting the Google Me rumor, I’ve been taking a look at Microsoft’s Windows Live Wave 4. Here are a few key features with my thoughts on how they contrast with Google Buzz.

User Base ~300 million

Google tried to leverage its Gmail user base when it launched Buzz. What ensued was a privacy nightmare. Microsoft has had no such issues when leveraging its Instant Messaging user base.

Windows Messenger has a user base of 299 million, compared to Gmail’s 173 million. I also think that a greater proportion of Windows Messenger users will actively use its social features than the proportion of Gmail users who actively use Buzz.

Open Standards Support: Activity Streams

It’s difficult to read any Buzz propaganda without encountering the mention of Open Standards.

Windows Live uses Activity Streams-compliant feeds from Facebook, MySpace, and a dozen other partners.

Superior Privacy Settings

Read this post from the Inside Windows Live blog for a comprehensive look at the privacy options. Here are a couple of screenshots:

Windows Live Privacy Options

Windows Live Advanced Privacy Options

Not only are the options very granular to a deep level, they’re presented in a very intuitive, easy-to-understand fashion. If you customize your settings to an intricate level, you can also quickly view a Friend’s profile and see exactly what he will see from your updates.

Commitment to Data Portability

Microsoft, unlike Facebook, has unequivocally made it clear that you own your data.

…if you would like to access your Windows Live data from a different third party service, or even take your data completely to another service, you should be able to do that. To enable this, we give you ways to export your data from Windows Live into common formats, so that you can import it to wherever you like…

Developers and third-party applications can use Live ID for authentication, and use Public APIs for accessing public information.

Aggregating Other Social Networks & Web Services

At last count in Nov 2009, Windows Live had partnered with 74 services from around the web to pull in updates to your feed.

Windows Live Services

These include Facebook, MySpace, YouTube, Twitter, and a plethora of services for video sharing, photo sharing, blogging, reviews, ratings, etc. Further, these are localized in 35 languages.

Ex-Friendfeeders have migrated in large numbers to Buzz. Surprisingly, Windows Live is more Friendfeeder-friendly than Buzz, at least at present.

The process for adding your profile from another service to Windows Live is extremely simple and easy-to-use, compared to the tortuous approach in Google Buzz.

Two-Way Integration with Facebook, MySpace and LinkedIn

The Salmon protocol is not here yet, and we don’t know if Facebook and others will support it. What we do know is that you can interact with your Facebook feed from within Windows Live today.

Your status updates, photo uploads, etc. can be pushed to other networks such as Facebook, MySpace, and soon, LinkedIn as well. You can comment on your friends updates in those networks from within Windows Live.

Friends vs. Acquaintances

A nice little feature lets you perform a “reluctant accept” or a “polite decline” of friend requests.

Windows Live Polite Decline

Selecting the “Limit the access…” box lets me accept the friend request as an acquaintance who only sees the updates I set for All Friends, and not all my photos or contact information.

To me, this feature shows that Microsoft is “getting social” like nobody else today.

Mark People as Favorites

In any news feed, whether Facebook, Buzz, or Twitter, I wish there was a way to choose people whose updates I don’t want to miss. Windows Live allows you to do that.

Windows Live Favorites

Filters

Note the Highlights, Recent, etc. links in the above screenshot giving me quick access to key filters for my news feed.

One of the features much wanted in Buzz has been the ability to filter out updates based on the service imported – Twitter, Flickr, etc. Windows Live lets you do that today:

Windows Live Filter By Service

You can also set many other filters for what you wish to see in your news feed:

Windows Live Filters

See this post by Dare Obasanjo for more on how Windows Live is designed to reduce noise and focus on the signal.

Windows Live on iPhone

The free Windows Live Messenger iPhone app has chat, aggregated social feed, photo upload and email. Microsoft will have something better when Windows Phone 7 comes out later this year, but it’s noteworthy that they didn’t shun the iOS platform just because they have a rival mobile OS.

Closing Thoughts

Windows Live may not support as many open standards and protocols as Google Buzz does, but do end users really care? Windows Live doesn’t present an either-Facebook-or-Buzz dilemma, and doesn’t try to reinvent the wheel by building a network from the ground up. It leverages Messenger’s large user base without compromising on privacy and data portability. These can be important lessons for any Head of Social.

Most importantly, unlike Buzz, it keeps things simple, stupid!

Tagged with: •
 
View Comments Comments
Leave A Response

« Previous Entries
Go To Top »

Switch to our mobile site