Mark Ghuneim

I will be introducing Trendrr at tomorrow’s April 2008 Meeting of the NY Tech Meetup. Its exciting as we are in good company with the other presenters including Muxtape and Bricabox to name two. Plus my desktop will be like a block long on the wicked cool IAC lobby screen.

There are several reasons we wanted to introduce our service here first. One – is that we love that we are a technology driven company from New York City. Representing the east coast and downtown NYC.

Two, this is where the developer community hangs and Trendrr is a service with an open API that allows users not only to use what data we are populating it with but to create their own graphs with their own custom data.

We hope this will:

- Drive API adoption
- Enable and encourage third party applications that use our API to provide unique web services and middleware.

How following and tracking social behavior allow for more trusted sources to emerge…

I was playing around with social network icons the other day, pairing ones that I liked to form word phrases. The one I liked best was the Hype Machine´s
heart (for when you heart a song) and Tumblrr´s follow (the plus sign
followed by the word “follow”). Combined they become “follow your
heart.”

When I first started social bookmarking on Delicious I realized that if
you subscribed to someone´s feed you could in some ways follow their
train of thought – viewing their fluctuating interest levels through
what they were viewing and reading over time.

Users can build a loyal set of followers by establishing themselves as
a credible and trusted source of information. These “alpha users” have
strong, well-established profiles on various social networking
platforms, micro-blogging, and bookmarking sites, with loyal followers
that subscribe to their feeds across these outlets.  

These followings create networks that are not based on inviting, but
more so a reciprocal relationship between the user and the follower,
based on the user being a trusted source and innovator either online or
off and establishing themselves as someone others are interested in.

Online one’s innovator status can be exhibited through their social
network behaviors; what they are bookmarking, how it is being tagged,
how early they identify new information, sites, and trends. With
tags, one’s skills are further refined, and in the case of Delicious,
the more thorough a user is at parsing the single most relevant line in
a story, the more quickly that user can evolve into a trusted source
and establish themselves as someone other users look to for the latest
news and innovations.

This following process has enabled a natural selection-like structure
of networked intelligence to emerge that is vastly different from the
friending process of social networks and the associated social
relevancy and importance of users with high friend counts.  It is
amazing how much one can see and learn by following the social networks
and the related web-tools utilized by their peers.

A good way for users to gain credibility and increase awareness of
their existence would be for these social networks to free up more
meta-data around the behavior of users.  This would allow for
qualification and rankings around which users might be deemed worthy of
following.  For example, if I want to follow the top ten users who were
the first to identify and properly tag a certain trend or topic, I
should be able to easily access that information through one aggregate
feed or application.

Unfortunately, at the present moment each bookmarking, micro-blogging,
and social networking site has its own ranking and tagging system,
making it nearly impossible for someone to easily migrate their tags or
networks (of followers or alpha users – the users they are following)
from one site to another. 

This is a difficult process as each social network has completely different tools and associated metrics.  For example:

-    On Flickr, my contacts are those that I follow and their
image-specific skills (how good of a photographer they are) is what
determines my following, and I have ascribed a level of trust to their
image feeds on Tumblr as well.

-    On Twitter, much like Delicious there is a natural selection of
signal to noise that determines the follow. The more signal, the better
the follow.  The more noise, the less likely I am to follow

-    Each of these follows have their own hierarchy of adoption. In my
experience the hardest follow is Delicious, followed by Twitter, then
Flickr, than Tumblr, I could go on.

-    Delicious is the hardest because it has taken me over two years and 11,000 +
bookmarks to amass 200 people in my network/follows to my daily
bookmarking.  Over the same period at Flickr, where I have published
2,620 photos and received 173,115 views, I have 417 people that call me
a contact or follow my photos.

As we have been building out social features around Trendrr, I am
pleased that we will be generating cool exportable data and tags around
social behavior and impressions that have not been generated before;
essentially allowing new aggregate values to emerge so actionable
intelligence can be properly followed.

I am excited to be following, followed, and creating new tools in this
exciting space.  We are just on the tip of understanding the importance
of this movement and I look forward to following the progress and
development of these new metrics and associated values.

Mark Ghuneim, with contributions from Seth Salomon and Mari Katsunuma
I. Introduction
II. What Engagement Means
III. How We Measure Engagement
IV. The Value of Understanding Engagement
V. Conclusion
VI. Next Steps
I. Introduction
For more than a decade, web users have actively bookmarked their favorite websites and forwarded interesting articles to their friends. These actions are a form of consumer engagement that has changed rapidly in scope, degree, and importance during the onset of Web 2.0. In the world of YouTube, MySpace, Second Life, and blogs, users are now more active and in-control than ever. Consumers can not only interact with brands, but also influence marketing strategies and performance. Users have been responsible for changing movies during production (Snakes on a Plane) as well as getting bands signed through MySpace engagement (Shiny Toy Guns).
Marketers and advertisers have struggled to define this behavior of engagement as well as assign a value to the various user actions associated with it. The desire for clarity and understanding has created the need for a methodology that can measure connected engagement and the varying levels of associated attention and interaction.
II. What Engagement Means
In the traditional sense, engagement is the period between proposal and marriage. This definition can also apply to engagement in terms of marketing and advertising. In an initiative conducted by the Advertising Research Foundation, the need for a measurement of engagement is described as the “search for the 21st Century gross rating points.” The ARF later refined their definition. “Engagement is turning on a prospect to a brand idea enhanced by the surrounding media context.” I am more inclined to define engagement as, “a consumer based measurement that regards interaction with an aspect of a brand or media property.”
Web 2.0 Engagement could include, but is not limited to:
* Publishing
* Creating and Publishing to a Group
* Posting
* Subscribing
* Favoriting
* Adding Friends
* Bookmarking
* Emailing
* Distributing
* Streaming
* Networking
* Creating Mash-up Content
When measuring engagement, the level of user interaction (i.e. 200 vs. 2,000,000 streams) is an obvious and important component. Yet engagement is complex in that it is not comprised solely by clicks, but also a range of involved user actions. The true measurement of this principle must factor in the saliency of each application in relation to other forms of engagement. For example, what is the importance of MySpace friend adds as opposed to YouTube views? Additionally, it must be noted that these metrics are goal-based in the sense that they will be of varying strengths and importance across different sectors (music, television, consumer products, etc.). This implies that in order to accurately derive a useful measure of engagement, one must not only possess the necessary Web 2.0 data, but also a unique understanding and perspective of each surveyed application by industry.
III. How We Measure Engagement
As we evolve our methodology and explore the determinants and ultimate value of Web2.0 engagement, we must look closer at the activities that help define engagement. We must also determine the level of attention and actionable experiences for each application once a user is engaged. These levels of engagement can be measured by the complexity and ultimate depth of user actions and the related amount of attention associated with each. This measurement also allows for the understanding of the time spent with the message or the causal action stemming from the attention. For example, a user might be engaged enough to read an article as well as related user comments following the text. But how is this situation different from a user that does the same, but also leaves a new comment of their own? The action of regarding content, followed by an action of engagement is the causal agent that defines this principal.
Also worth noting is that each user action can provide different levels of attention as well as influence further interactions. While the viewing of a video online is considered an impression, there are different and unique tiers of attention and engagement in each play such as the length of active viewing as well as subsequent sharing, rating, favoriting, forwarding, and adding which are all secondary interactions that form the engagement layer of behavior. These actions are also important as they involve users recommending and approving products which ultimately affects both their own reputations and levels of trust amongst peers.
This idea falls naturally into a formula of engagement being action over attention.
Measuring the levels of interaction surrounding the communication is one way to quantify this relationship.
Engagement = Interaction/Attention
This equation illustrates the combined activity of regarding; or paying attention to, and then acting on. While this is an important relationship to understand, one must remember the inherent limitation it presents in that each interaction is not as valuable as others.
IV. The Value of Understanding Engagement
Engagement is important as it can lead to web traffic, CRM, sales, and brand loyalty. These outcomes can be directly attributed to time spent and subsequent actions with a product’s message, related media, and the Web 2.0 applications in which they are communicated. The ability of marketing to engage and endear consumers will ultimately determine whether a user digs deeper and/or engages friends.
In order to measure these factors, it might be beneficial to begin to with Web 2.0 services such as social networks and media sites. The functionality of these applications is ideal for generating data surrounding engagement activity. We can use benchmarking to create weights for each attention and interaction type. This is the trickiest piece of the equation as it requires both a strong understanding of Web 2.0 applications as well as all the industries in which a product lives. Yet when performed with precision, this method should allow for the understanding of online engagement value.
Engagement must be understood by type, and the value associated with each in terms of ultimate adoption, sales, and brand loyalty:

V. Conclusion
Once product owners understand the value of each type of engagement in their industries of play, they will know how to best market their products. By creating milestones and targets of user involvement in each application, performance can be measured actively and strategies can be altered during a campaign to meet ultimate goals. This creates the need for an owner to understand their marketplace and related Web 2.0 applications. Additionally, they must possess the capabilities to measure presence across related applications over time and the ability to enact marketing that will achieve desired results.
VI. Next Steps
* Top-20 engagement activities
* Further definition of engagement types
* Produce algorithm (compose related activity weights)
* Incorporate feedback/comments
Comments are welcomed and appreciated