To paywall or not to paywall has been and remains an issue for content publishers, as a phenomenon it is interesting to study the data from a web analytical standpoint. Crucial is understanding the tradeoff in setting the threshold levels low or high, the optimal setting means more revenue.
Paywall as a term surfaced in volume during 2009 (Google Trends) when content publishers needed to find a way of monetizing their free content and replace the declining revenue of print.
One key item is to understand the optimal paywall trigger value, this typically is set on a maximum of X page views. Reporting of the consumption of page views per unique browser on a per weekly basis gives a clear indicator and what browser volume the paywall setting will give.
For the sake of simplicity in the examples in this article 10 page views is the maximum weekly quota that any browser can request (click images to enlarge).
A quick analysis using a page views per week report item shows that the paywall message would have targeted almost 29% of the browsers visiting the site over the week. Depending on the assumptions on user behavior it is always wise to consider a paywall trigger based on weekday / weekend usage.
Reports filtered on weekdays vs. weekends setting shows that for that same week the difference in page views per browser based on these 2 time periods is rather small.
Depending on the type of content published, as well as the frequency of new articles being published, this might call for a more granular analysis on a day of week study to find differences.
An alternative data source can be to study the consumption of page views by creating hour of day grouping consumption (morning, afternoon etc) for clarity on when the browsers consume the content.
The typical paywall will when hit prompt the visitor to pay in order to continue reading the site contents for a subscription fee, a typical fee is about the cost of a lunch, and for the next month be able to browse the content but also get access to a value added section typically named "Plus".
With very basic tracking it is easy to determine the conversion rate of such a mechanism, and over time the renewals of the subscriptions will show in the data. In essence it is a count of number of browsers exposed to the paywall message and the number of browsers that complete a subscription purchase transaction.
However rather large loss of browsers in the subscription conversion funnel means a lot of visitors are "getting away". For publishers a gold mine of information can be provided by the visitors if a free subscription entitling more free page views is given.
Free lunch is a myth so visitors are expected to give back something for an extension freebie...
In exchange for an extended free page view volume a visitor would submit metrics such as age, gender etc which for the publisher extended their web analytics with valuable user data. One can see this as a rather obvious A/B test if giving the visitors 2 options (or more) to choose!
This data can be used to provide advertisers with demographic information in order to better target the right audience within the right content sections.
For the publisher a better understanding on which user groups consume what content and more is then within reach.
The optimal paywall trigger setting depends on the business model of the publisher, but web analytics can provide insight in the expected volume at different levels.
Remember that the visitors using extended free content are all potential future subscription clients.