Attend any digital subscription conference these days and invariably you’ll hear about the topic of dynamic metering. This initiative usually involves scoring users based on known and predicted attributes and then adjusting your paywall’s metre limit accordingly.

Here at Tribune Interactive (a business unit inside tronc), we are also working on this endeavour. Our scores will analyse a range of user events like whether they’ve seen the paywall before, what sections they typically read, and what referral sources they came from. But our ambitions about how to use these scores for greater monetisation are much bigger than just a dynamic metre.

As colleagues in data science, technology, and product development partner with my marketers on building this capability, here’s a starter list of ways we can put this innovation to good use:

  • Varying the metre: This is the most commonly discussed application as far as I can tell. Likely subscribers might enjoy two or three free articles per month so the paywall is triggered sooner than normal. Those who seem less likely to convert get five to 10 articles, so we can habituate them in our content and reap ad impressions along the way.
  • Print or no print: If we detect a user lives in our local market’s delivery zone — as forecasted by her IP address — then a subscription landing page would offer a choice of print and digital or digital only. However, if she’s out of the zone, let’s eliminate the friction of that choice and send her directly to a digital-only checkout page.
  • Different market offerings: Building on the geographic idea above, what if a Miami-based user visits the Chicago Tribune? Perhaps before he leaves, we’ll recommend a subscription to the Tribune’s sister product, South Florida’s Sun Sentinel.
  • Free trials when needed: If our scoring suggests a low likelihood of subscribing, maybe we’d replace our standard offer with a completely free trial. We’d pair this message with “No credit card needed,” and then use this sampling opportunity to acquire an e-mail address and introduce our full digital experience.
  • Reallocate ad inventory: Conversely, when we encounter a user with a high likelihood of subscribing, we could reallocate some ad inventory from paid ads to house ads. In this case, we’d want to prove those house impressions drive better yield.
  • Double-down on paid marketing: For those likely converters, let’s make sure we aggressively re-market to them on Google, Facebook, and elsewhere to drive a return visit or direct purchase. The cost will probably be worth it given a higher-than-average response rate.
  • Exit interstitials: We use Bounce Exchange’s exit intent technology to pop up interstitials before a customer leaves our sites. Here we can leverage user scoring to suggest when BounceX should show a subscription promotion because the user is high-intent, versus an e-mail capture box for less likely converters.

Obviously managing these scores and scenarios takes time and precision, so in each case we’ll carefully measure the incremental lift to ensure the juice is worth the squeeze. But we are confident that at least some of these dynamic experiences will beat the old-fashioned, one-size-fits-all approach!