November 18, 2013

Unique Visitors May Be Unique, But Are They Enough?

 Unique Visitors May Be Unique, But Are They Enough?


By Rob Norman, Chief Digital Officer GroupM Global


Consider the following. In September 2013 Comscore, for the USA, reported that six web properties had more than 100 million unique users, 15 more had at least 50 million uniques and that a further 29 had 25 million or more. Of these 50 sites, over 40 of them are funded by advertising. In addition, Comscore also publishes the number of unique visitors to inventory represented by ad networks and exchanges and lists 20 with more than 120m unique visitors.


In this case unique is defined as “un-duplicated single user visits to a given property”. These users are not unique or exclusive to that property.


Other than confirming that there are many very large sources of internet audiences this information is uniquely useless because it tells us ridiculously little about real user engagement with the properties in question other than the raw reach of any one site; if we buy a page in Vogue we buy the whole reach, when we buy Yahoo, for the most part we don't. Of course we can de-duplicate audiences in the pursuit of optimizing reach and frequency and apply behavioral and other data but it would be helpful to know more.


More pertinent data, in addition to monthly unique visitors, would be:


1.Daily unique visitors – an indicator of the frequency of visits and by extension the value placed on that property by the user; three quarters of Facebook users visit daily, one half of LinkedIn users visit monthly - do the math.

2.Average time spent per daily unique within five user quintiles from most to least time spent – an indicator of the depth of the relationship between property and user.

3.A frequency distribution with accompanying geo-demographic and device data by user quintile from the heaviest users to the lightest users – indicating the characteristics of the most and least committed users; such as the relationship between the relatively small cohort of active Tweeters as opposed to the larger cohort of passive followers and the relationship between YouTube devotees and those that view occasionally.

4.Volume of content shared to Facebook, Twitter, LinkedIn and YouTube per unique visitor - showing the likelihood of that property being a source of influence. The majority of the10 most shared sources on Facebook and Twitter are news organizations with their roots in television or newspapers (happily 3 are British!). 2 of the other 3 are Buzzfeed and the Huffington Post. What might this imply? At another level two recipe sites with a vastly different sharing profile may indicate a difference in value to advertisers.

These four data points, if made available as standard measures, will paint a far more textured view of the web’s leading properties than exists today and infuse audience data with real meaning.


Even in this data driven age, buyers of media, creators of advertising and owners of brands have an interest in the composition and characteristics of the environments in which their advertising and brands appear. Knowing why someone does something and how often is every bit as interesting as knowing how many do it. This is particularly relevant when the pursuit of long term marketing effect and brand health are priorities supported by the need to tell stories rather than a simple focus on immediate actions.


The most likely beneficiaries of these data sets are publishers, platforms and aggregators that have deep and frequent purpose driven interactions with their audiences. That might infer that the data could hold significant advantages for the creators of original content who often don't top the unique user charts as well as high utility destinations like Google. Frequently a perceived lack of scale disguises the value inherent in strong relationships and the influence of those relationships on both the formation of opinions, decision making and the creation and transmission of influence.


Ultimately efficiency and effectiveness in advertising lies in the content of the message, Its context, its relevance to the recipient and the price and timing of its delivery. If it is true that context and relevance are elevated by user engagement with adjacent content the data sets proposed above are likely to be contributors to success or at least a valuable price modifier to available inventory. If this is not true the data will tell us soon enough.


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