In our new world of constant, personal streaming data points such as followers, retweets, pins, comments, shares, plus ones, watchers, likes, likes, and likes it only seems fitting that at some point, we are going to have to have some new way of measuring the intersection and union of all of these data points and more importantly, their relevance. To me, measuring something that is only in the web context, a website’s pageview, is not only archaic and irresponsible but screams for the undeniable need for new network metrics.

The archaic pageview

Since the near inception of anayltics around websites and their respecitve influence and/or reach, pagviews, be they unique or total, were for the longest time a visual representation of a website’s popularity or a blog post’s impact.

However, with the birth of apps like Flipboard and Instapaper, those pageviews aren’t really page views as much as they are a reformatting of the page’s content.

Moreover, something could have low pageviews and have loads of likes or shares on Facebook or LinkedIn. This is not typically the case but it is possible and doesn’t that increase the influence or reach of that article or site? What really needs to be measured isn’t the actual pageview, but the network effect.

An example of a new network metric

Upon some technical introspection in Amsterdam a few weeks ago, I posited some new concepts around measuring efficacy, influence and network reach around content available via the internet. The crux of nearly all of them involves social networks and the ability of those networks to impact the virality or “network effect” of the content provider’s content.

First, let’s posit something that not only captures the immediate network effect of Twitter, but also the residual effect measure by pageviews, or obtusely named the “Rapid and Residual Impact”, or RRI.

In a nutshell, the RRI, is a rather straight forward metric: A webpage’s total number of views + the number of tweets for that page’s URL. This is merely pageviews++ but underscores the importance of where the page is surfacing around the Internet, particularly via Twitter and more importantly, tweets, like pageviews, can be measured against a timestamp.

One item to note here is that the sharing of a link can occur completely outside of the web context, meaning, URLs can be tweeted and retweeted from within a native application from a smartphone, tablet or even a traditional PC-based desktop app. That’s why, from my perspective, the pageview isn’t the only bit of importance for a webpage’s URL – its ability to create an impression in a user’s twitter feed has value as well.

Scalable network metrics

The concept of something like RRI can be mixed and mashed with any number of social media or even website data points. Imagine combining Instagram likes with Pinterest pins of a particular photo. Brands could be and should be leveraging this channel, but they have no way of measuring its efficacy. Note, that until just recently, Instagram was a mobile only service. The data coming form the sensors of mobile devices (e.g. GPS location) coupled with more direct data points (e.g. Instagram likes) only exacerbates the need for the ability to start more comprehensively measuring all of these consumable data points.

Imagine an administrative control panel where you can select and finely tune the data points and social media sharing capabilities to come up with your own custom metrics. You decide that Facebook likes, LinkedIn shares, Retweets and unique pageviews within a geo-fenced region of Southern California are your core metric for measuring network reach for a piece of content your company is surfacing on the web, or shall I more appropriately call it the network. This is doable. There is nothing technologically barring someone or some company from building this. This scales horizontally as more data sources and social networks surface. Have a look at If This Then That and how they continue to add more service providers for one’s custom web hook “recipes”. The same thing would apply for adding Github followers, Etsy likes or any other consumable data point from a site or service.

Whoever gets there first will have a data-backed goldmine that nearly every brand, retailer and service provider in the world will pay for.

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