Tag: Social Network

Know Thy Neighbor Nextdoor

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Every conceivable kind of content that we consume on the internet today comes with explicit social networking markers.

Today with over 1000 Social Networking sites known to exist globally and 16 virtual communities with more than 100 million active users to date, Social Networks/ Virtual Communities have truly changed us and as also our definition of SELF(!). For most of the activities that we partake in our daily life like work, sports, hobbies, shopping, travel, dine, drive, and the list goes on, there is a virtual community / social network for each if it.

Now a social network built exclusively for local neighborhoods – called NextDoor is in the news for stepping into the elite territory of start ups.  On NextDoor.Com, each neighborhood is a closed social network where users have to verify their real name and address to gain membership. Once we are in, the idea is to be able to connect with our neighbors, strike conversations, while finding out everything from local deals, finding nearby help or even be alerted of neighborhood crime.

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(Image Source)

Developed on the insight that The Neighborhood has always been one of the “original social networks”, it has recently raised $60m fundraising led by John Doerr, the Kleiner Perkins Caufield Byers partner who led the IPOs of the likes of Amazon and Netscape and Tiger Global Management – prominent VC firms. (source)

This latest round of fund raising values the company at nearly $ 500 m as NextDoor.Com is slated to expand to more than the currently listed US neighborhoods on its site. Nirav Tolia, co-founder and CEO of Nextdoor, says there was already “incredible demand” abroad and goes on to say..

 “We see this whole notion of building safer and stronger communities is not an American thing at all, it is something all people share”.  

What I really like about the idea of a Neighborhood focused Social Network is the degree of relevance that can finally be attributed to the potential ads that can be placed in the network and the ways in which it can benefit all the players in the equation:

  • The Advertiser: Businesses in the locality can micro target the ‘captive’ user base in the neighborhood via their ads while also nurturing a local community of customers on an ongoing basis. The biggest opportunity here is for the SMBs.
  • The User: A user can find the most relevant offers from around her apartment rather than get bombarded by offers from across the country if not the world!
  • The Publisher: The publisher can facilitate the precious need of SMBs in the locality to micro target and reach out to their customer base in a very cost effective way, while also being able to track and optimize listings on a real time basis.

It’s almost like combining the best parts of Yelp, Craigslist, Foursquare, Path and Facebook with a liberal dash of a local flavor.  Like any other virtual community while it does come with its own privacy related nuances that need to be managed if it were to thrive across localities for the long term, one thing is nearly certain ..

We can finally get to know our neighbor. Albeit at least through the window of our screen.

PS: Two interesting things that you might want to check out:

  1.  To celebrate the ‘most neighborly holiday of the year’, Nextdoor launched just in time for this Halloween, a Treat Map to give you an insider’s guide to the best streets for treats in our neighborhood (relevant mostly to the US as of now)
  2. Funnily, Nextdoor has a page on Facebook 🙂

The Job Hunt – Part 3/3

Check out my previous two posts for the part 1 and 2 of this series.

For many job openings, getting the foot into the door – getting recruited – tends to be the most tricky part. Obviously different companies have different ways of going about this. 3 latest trends that I see playing out in the job hunt marketplace:

  • Recruitment by Resonance.
  • Recruitment by Challenge.
  • Recruitment by Algorithm.

This post shall be on the third trend.

Recruitment By Algorithm

In a recent interview with the renowned business psychologist, Dr Tomas Chamorro-Premuzic, he shares an interesting insight on the evolution of the internet. From a ‘consumer/user’ perspective, he says, there are three significant stages in the internet era. Loosely put they can be called as:

  1. Knowledge Era: This was around 1998 when Google became mainstream. We searched for something on Google and the first hit magically turned out to be the answer to what we were looking for – this was a breakthrough from past search engines and a breakthrough in machine learning systems.
  2. Social Era: This was around 2004 with the introduction of Facebook, when the focus shifted from retrieving information to ‘retrieving people’. Whereas Google connected consumers to information, Facebook (and other such Social media portals) connect consumers to each other and make ‘products’ out of consumers.
  3. Social Knowledge Era: The third era, which has only just about begun, combines the two previous ones: it is the era where people can instantly capture and aggregate all the information on a given topic. And vice versa –  all information about people out there can be aggregated and captured. 
Welcome to the era where ego surfing – self googling — is now more important than updating your CV.
As a consequence of spending so much time online, we now leave traces of our personality everywhere. This social media foot print that we leave each single day (think of all the info that we leave on Facebook, Twitter, LinkedIn, Google+, Pinterest, Path, Renren, Orkut,  Amazon, Gmail, Tripadvisor, or any multitude of other sites each passing day and you get the idea) manifests itself as a folio of Digital Reputation that we build over time.
Given this, and as aptly noted in the HBR blogpost, we are stepping into a time when employers are likely to find their future leaders in cyberspace owing to 3 reasons:
  1. The web makes recruiting easier for employers and would-be employees
  2. The web makes recruiting less biased and less clubby
  3. Web analytics (speak of Big Data) can help recruiters become more efficient

Result: we will soon witness the proliferation of machine learning systems that automatically match candidates to specific jobs and organizations – not just based on our preferences/qualifications and experience (like the current day job portals) but on the basis of our digital footprint that is much bigger and more complicated that we can ever imagine.

For starters, how many times did you google your new boss or colleague? And that begets the question, how many times did you ‘investigate’ yourself and how many times did you reverse engineer your Digital Reputation…..in the recent past?