Ongoing debates surrounding the topic of “free news versus digital media pay walls” present an interesting opportunity for web analytics professionals.  If there’s one thing I learned during my stint in new media, it’s that news organizations have historically had a poor grasp on establishing sustainable revenue models to support substantial distribution costs and sizable staff counts.  I say “historically” because savvy media companies are becoming increasingly capable of correlating content consumption to revenue streams and optimizing their advertising models for greater ROI.  The latter have been forced to cut back on content acquisition or workforce, and even now consider the use of pay walls.  But that’s not necessarily a bad thing! For analysts, pay walls introduce a great opportunity to test the efficacy of both models.

Model 1: Ad-supported content

Needless to say, there are significant challenges facing media sites that operate on ad-supported content.  Recent months have given rise to the worst decline in media jobs the industry has seen in it’s history.  News rooms are cut to the bone, local affiliate stations are closing their doors, the public in general is changing the way it consumes the news — but I digress.  The main problem ad-supported sites face is correlating visitor engagement to true ROI:

  • Integration of ad-serving platforms with web analytics systems may not be straightforward
  • Costs associated with ad serving platforms are minimal and relatively fixed/known, but custom advertising sponsorship deals have many moving parts
  • Publisher and advertiser goals may be in conflict, i.e. visitor engagement with a media site sells ads but visitor engagement with an advertiser reduces engagement
  • Calculating true ROI involves knowing the operating costs of the content provided, including but not limited to manpower for article creation, multimedia distribution rights, bandwidth, etc.

Clearly, savvy media sites that are able to manage the complexities of calculating and optimizing user experiences and maintain positive net ROI can make this model work, however it takes a brilliant analytics ninja to implement the myriad of data required to make it truly sustainable.  Avinash’s 90/10 rule might have to be magnified a bit… maybe 98/2 would work?

Model 2: Pay walls

The reason pay walls are such an appealing model for large media sites is that they can reintroduce a subscription model akin to selling newspapers, magazines, and specialty channels on TV.  Logically, using a pay wall follows the age-old business model that built large media empires.  In addition, pay walls provide a number of measurement opportunities that seldom exist on media sites:

  • Actual direct revenue numbers from paying customers, and optimizing landing pages and user experience based on dollars and cents. I.e. rather than loosely correlating advertising dollars to visitors, getting real-deal dollar numbers on specific users.
  • Measuring distinct user behavior and profiling and recommending packages or custom subscriptions based on user preference or actual consumption. I.e. the iTunes approach; selling content per song, per episode, or per movie.
  • Engagement essentially becomes less ambiguous, users are authenticated, they pay for access to certain assets, and a relationship is established.  Data comes to the rescue and can assist in decision making to establish and nurture that engagement.  I.e. visitors now have skin in the game and a voice.

Regardless of the approach, it appears as though several key media destinations will carry-out testing some kind of pay wall model this year.  It’s uncertain what they will be measuring and how sophisticated the approach to maximizing ROI will be, but given this approach, the barriers to testing should be significantly lower, if and only if these same companies abandon ulterior motives and truly adopt data-driven decision-making.

The key is that there is no key.  There is no right way, there is no magical bullet.  Each site’s audience will be different, each traffic segment will be different.

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