Play Nieman Lab's Paywall! and see if paid online content could work for you
Posted by Emma Heald on January 27, 2010 at 11:23 AM
Nieman Lab's Johnathan Stray has created a paywall calculator, Paywall!, that aims to juggle the different factors that could go into building a paywall and find the best combination that could generate maximum revenue. He takes the New York Times' proposed payment model as a basis: a certain number of articles per month can be viewed free, and after that readers will be asked to subscribe.
"The key to paywall revenue projections is to understand how different portions of the audience are affected differently," says Stray, and his model breaks the audience into five groups based on their number of page views per month: fly-by, occasional, weekly, daily, loyal. Depending on how many articles are offered free, it is unlikely that a huge number of visitors will be affected by a paywall, Stray points out, but the ones that will be are the loyal readers.
"The key to paywall revenue projections is to understand how different portions of the audience are affected differently," says Stray, and his model breaks the audience into five groups based on their number of page views per month: fly-by, occasional, weekly, daily, loyal. Depending on how many articles are offered free, it is unlikely that a huge number of visitors will be affected by a paywall, Stray points out, but the ones that will be are the loyal readers.
According to Stray's tests using different scenarios fed into the calculator, "a tuned paywall can make money for a large free site, but the details matter greatly. Reader reaction is key; small variations in response have big effects on net revenue." There is clearly no sure way to say whether or not a paywall will work: it will always be a gamble.
Any publisher can enter their figures and see if a paywall might work for them. When you arrive at the page, the calculator is set with estimated numbers for the New York Times, which Stray has pulled from various sources. Using "generous assumptions:" that 60% of loyal readers would subscribe at $10 a month and no readers would be lost, he calculates that a paywall allowing 50 free page views per month would net about $500,000 per month in extra revenue. Stray stresses that this number is very fragile, however, with small fluctuations sending it negative.
Given that it is impossible to know how readers are going to react to a paywall, especially considering that so few have been tested on general interest newspapers, it is extremely difficult to say whether or not a paywall is a good idea for any specific publication. The New York Times does seem to have as good a chance as any, given its reputation and the loyalty that its brand attracts. Print subscribers pay up to $770 a year for the paper, and there may well be a significant number of others who would be prepared to pay to keep their online access.
Source: Nieman Journalism Lab
Any publisher can enter their figures and see if a paywall might work for them. When you arrive at the page, the calculator is set with estimated numbers for the New York Times, which Stray has pulled from various sources. Using "generous assumptions:" that 60% of loyal readers would subscribe at $10 a month and no readers would be lost, he calculates that a paywall allowing 50 free page views per month would net about $500,000 per month in extra revenue. Stray stresses that this number is very fragile, however, with small fluctuations sending it negative.
Given that it is impossible to know how readers are going to react to a paywall, especially considering that so few have been tested on general interest newspapers, it is extremely difficult to say whether or not a paywall is a good idea for any specific publication. The New York Times does seem to have as good a chance as any, given its reputation and the loyalty that its brand attracts. Print subscribers pay up to $770 a year for the paper, and there may well be a significant number of others who would be prepared to pay to keep their online access.
Source: Nieman Journalism Lab
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