Play Paywall!, The new web game sweeping the newspaper industry

Nieman Journalism Lab ha creato uno strumento, un simulatore, che consente di calcolare l’impatto economico generato dalle barriere all’accesso eventualmente erette dal New York Times, e dai quotidiani più in generale, per far pagare l’accesso ai contenuti on line delle loro versioni digitali.

Il simulatore è settato sui dati raccolti da Neiman Lab con specifico riferimento al quotidiano newyorkino ma è possibile variare i dati ed effettuare una elaborazione a proprio piacimento, variando i cpm per view [settati per default sui livelli alti del mercato], la percentuale di adesioni scomponendola in 5 sottogruppi di utenti e numerosi altri elementi che impatteranno sul risultato finale.

Lo strumento è funzionale e funzionante per lo scopo per il quale è stato creato, ma in realtà rappresenta una modalità geniale di illustrare l’enigma che molti editori, perlopiù senza arrivare ad una decisione, stanno affrontando attualmente.

L’autore, non a caso ironizza, a partire dal titolo dell’articolo, sul dilemma che divide l’industria editoriale per la difficoltà delle prese decisionali che, se favorevoli alla creazione di paywalls, fino a questo momento hanno decisamente portato più insuccessi che altro.

Praticamente tutte le ricerche, in qualsiasi nazione del mondo, hanno confermato che l’utenza non è attualmente disponibile a pagare i contenuti dei quotidiani on line. Talvolta, è proprio il caso di dirlo, si vuole sbattere la testa contro il muro a tutti i costi.

How do you play? First, hit “Turn Paywall On!” From there, “Views before paywall” is the most fun slider, and the number that many paywall discussions focus on. This sets the number of free pageviews (not the same as stories) that are allowed for each reader before requiring them to subscribe. As the number of free views decreases, the net revenue jumps as each audience segment hits the paywall, then falls from lost ad impressions. Somewhere, there’s a sweet spot.  The key to paywall revenue projections is to understand how different portions of the audience are affected differently. The model used in this calculator breaks the audience into five distinct segments. These can be given names such as “Fly-By” and “Daily,” but for accounting purposes each segment is completely described the number of unique visitors (readers), the number of pageviews per month, and the fraction of readers who will subscribe when they hit the paywall. (Of course, in the real world, people aren’t so neatly divisible into segments.)  The main graph shows these five segments as five bars. The height of each bar is the number of pageviews per month for that segment, and the width is the number of readers. Each pixel on this graph corresponds to a fixed number of pageviews times users, and therefore the same amount of advertising revenue. Ads shown to unsubscribed readers are in blue, ads shown to paid subscribers are in red, and ad sales lost due to non-subscribers stopping at the paywall are in gray.  The scroll bar at bottom of the graph zooms the display for better viewing. The calculator starts zoomed in for clarity, but by zooming all the way out you can see that only a very small fraction of readers will be affected by most paywalls. The crux of the paywall issue is that these are also the most valuable readers, the ones that a publisher can least afford to turn away. In terms of ad revenue, one Loyal may be worth a hundred Fly-Bys.  Ad revenue is captured in the “CPM per view” slider, measured in dollars per 1000 pageviews; it can be thought of as the per-ad CPM times the number of ads on each page. Some pages have higher CPM than others, so this value is an average across all pages actually served.  When a reader hits the paywall, several different things can happen. They may subscribe; they may come back next month when they have free views again; or they may never come back. The “Subscribed” and “Never came back” sliders model this.  “Subscribed” is the fraction of the most loyal readers who subscribe when they hit the paywall — that’s the width of those red “paid” bars on the graph. This figure is necessarily a guess, and the real world subscription rate will also vary by segment, with loyal readers far more likely to subscribe. That’s why there are segment-specific subscription rates in the boxes at bottom. The slider up top sets the maximum possible subscription rate, the rate for a segment with a relative subscription rate of 100 percent. Paywall revenue is very sensitive to subscription rate, because every non-subscriber also represents lost advertising impressions.  “Never came back” represents the fraction of the audience that simply disappears when the paywall goes up. Some regular readers will hit the paywall and switch to a source of free news — but even readers who wouldn’t hit the paywall may be lost, because the existence of a paywall can discourage linking. In the Times’ case, they’ve said that articles arrived at via links from other sites won’t count towards paywall metering — but that might just encourage people to browse Times content through an aggregator instead of the front page, which still amounts to a loss of casual readers. In any case, this slider subtracts readers from all segments in the same proportion.  Below the graph are the audience segment definitions. Each of five segments is described by the number of unique readers in that segment, the number of monthly pageviews of each of those readers, and the subscription conversion rate relative to the most loyal readers. The subscription rate slider and the relative subscription rate are multiplied to get the final subscription rate for each segment. A bit tricky, I know, but I wanted to make it possible to visualize global changes in subscription rate with one slider.  The number of pageviews for each segment is also calculated; note that the Loyal and Fly-By readers both represent a large fraction of pageviews. Again, this is the difficulty with a paywall.

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Archiviato in Scenari Editoriali, Vendite Editoria


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