Notes on dynamic meters
In my notes on membership, I referenced a few types of meter experimentation, like testing different meter rules based on content or geography. Also more complex tests, like changing rules based on referral or whether the user has an ad blocker enabled.
These meter approaches are essentially deterministic. Given a set of conditions the meter should respond in the same manner. These rules are most familiar to users who experience them on sites like the New York Times or Washington Post. While user data may be used in marketing and retention algorithms, it is not generally used in meter logic.
The Wall Street Journal was reported in February to have launched a personalized meter for the first time “after years of testing”:
Non-subscribed visitors to WSJ.com now each receive a propensity score based on more than 60 signals, such as whether the reader is visiting for the first time, the operating system they’re using, the device they’re reading on, what they chose to click on, and their location (plus a whole host of other demographic info it infers from that location).
Scandinavian publishing house Schibsted has also explored this approach. And as the Lab notes, the Financial Times has a long history of using user data to target offers.
Dynamic meters are becoming more feasible for small and medium publishers. Largely due to startup activity in this space. The dynamic meter space has a complementary idea in “Value Walls”, a term used to describe situations where a user may provide an email, follow a social media profile, or do some other valuable action to gain access to a restricted content or feature.
Piano, formerly TinyPass, has a tool called Canvas which can be used to lay out rules and potential actions/responses for a dynamic value wall. Here are some product images from their support site which help site managers lay out the business logic for their value wall using a graphical flow interface.
Startup Pico takes a CRM approach to the same problem, allowing site managers to design audiences and then set rules and membership offerings against each audience (like US vs non US or ad blockers vs non ad blockers).
In France, Google has funded Poool, operating in the same space as Pico and Piano.
Conversion tools like Optin Monster also have options to setup rules similar to the above, although without any paywall or checkout integration (focusing primarily on newsletter signups).
One open question: How will consumers respond to this price discrimination? News would not seem to fall under the protection of the Robinson-Patman Act. Assuming that we are able to deploy ethical algorithms that adjust price based on user data, what can we do to prevent the consumer from feeling gamed? While profit maximization may be greater due to price discrimination, I see some risk for consumer blowback as well. Perhaps because news companies have to meet a higher than average ethical standard.
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