With Social Plus Comes Social Minus

Recently I mentioned about CodeEval. I have been observing them since then and I just saw something interesting on LinkedIn. I know they have been aggressively marketing their services via multiple social networking channels. Here is an image of the conversation on LinkedIn.

So, it looks like some guy is not able to submit his problem. So, immediately he concluded the website to be “Crap”. But what about the 1567 submissions (or is it users)? How did they manage to submit their programs?

The truth of the matter is, there are usually two types of customers. Those who work with their vendors, understand issues since they are also vendors to someone else and their service also has problems at times and work towards an amicable solution. Then there are others, who at the first opportunity of running into an issue, criticize the software or the company or the people and some may even want their money back, even if it’s their fault.

So the interesting thing to note is that just the way it’s easy to market something to a large audience via social networks, it’s equally easy to spread the negative publicity. In all possibility, it could even be a competitor trying to be a jackass.

Note: I am not affiliated to CodeEval. I am a happy member of the community and think it has a good potential.

Social Network, To SAAS or To Build Inhouse?

Websites like ning.com and onesite.com provide the ability for people to build their own social networks. If the network is no different than a web 2.0 face lift to an already existing mailing list or an egroup, then this is probably fine. However, if you are considering building the next startup that needs a social network using a SAAS offering, that may not be the right thing.

Only yesterday Facebook has released Facebook Ads, a new advertising platform. I think this is going to be lot more successful than the 0.04% click-through rate with which Facebook had been criticized a few months back.

Last few years, Google has ruled the online advertising landscape with “contextual ads”. The early social networking sites including LinkedIn and Ning (which I have used), while pioneered new concepts in social networking, hardly make use of the “intrinsic knowledge” of the members within the networks to make money. Instead, they just rely on Google’s AdSense (atleast partially), which makes use of the context, but not the network! But Brand is lot more to do with the network than with the context. Hence, to differentiate and make a good revenue out of custom advertising offerings, a Social Network should make use of the data it has about it’s network. Has to use advanced data mining techniques on both structured (such as well defined profile properties) as well as unstructured (comments, discussions, widgets being used) data to provide highly targeted branding advertisements. This is possible only if the data related to the network is available inhouse so that it’s possible to build those advanced mining techniques and experiments, and ultimately serve the ad impressions using the knowledge within the website as opposed to content on the website that is available to Google or any other contextual advertising platform.

LinkedIn, PersonRank & Google’s PageRank

I just accepted a linkedin invitation. At the bottom of the invitation email, there is a fact.

“Fact: People with 20+ connections appear in LinkedIn search results 14.6x more often”

That got me thinking and I ended up writing this article. As more and more people start using LinkedIn, and many just keep “networking” without really knowing much the other people they are connecting up at level 1, the whole linked in system is going to break at sometime.

If Google hadn’t invented PageRank, the search results would have still been bad (like those of Yahoo!, MSN which is now Live etc). So, I was thinking along the lines of a PageRank scheme for LinkedIn. That is, a system in which people linked from popular people themselves inherit some of that popularity. Just like how Google’s homepage get’s a perfect 10 on 10 as it’s PageRank, say the most popular people on LinkedIn get a LinkedIn Rank of 10. Now, I will get to in a minute what I mean by “most popular people” and how that is measured as this is critical for this scheme to work. Any person who is connected to this most popular person will get his/her rank increased a bit. So, a person connected to more and more popular people would him/herself get better rank.

Now, if we just go by how many people a person is linked to others, then that doesn’t really give a good picture. Mainly because, there are so many recruiters on LinkedIn who have more than 500 connections. Does that mean these recruiters should get a better LinkedIn Rank than others? What about those who just keep accumulating LinkedIn connections just for the heck of it? One guy for example, who was from my Alumuni and some 10yrs elder to me connected with me and I later realized that he did that just to promote his published books! The guy didn’t even bother to send a response to my personal email I sent as part of accepting his invitation! I wish LinkedIn has a way to retrieve back an earlier accepted connection.

Anyway, back to the topic at hand. I think, popularity should be determined by profile search and visits. That is, when people are looking for a particular profile, then that person is likely to be more popular. In this case, how many connections a person has doesn’t matter (ofcourse, they matter in calculating their rank indirectly based on the above scheme of connections from popular people, but that wouldn’t be linear and cumulative). Infact, popularity can be a combination of number of page visits + links to popular people. In other words, it’s a hybrid of Alexa’s Traffic Rank + Google’s Page Rank.

Let’s call it PersonRank.