> Subscribers keep growing while traffic falls. At the NYT, WSJ and The Guardian, growth now comes from higher prices (up 5% on average in 2025), upgrades and bundles, not bigger audiences

> With monthly churn often below 5%, small retention gains have an outsized revenue impact. Leading publishers treat pricing as a variable and the cancellation journey as a moment to reinforce value

> Bundles pay off through retention, not price. NYT bundle subscribers pay slightly less per user but churn significantly less. At Mediahuis, subscribers who activated the RouteYou app churned around 26.5% less

> Build outward from your journalism. Bundle additions closely tied to editorial content stick, bigger leaps don't. For membership, spell out whether readers pay for access, a cause or a community

> Nearly half of app users are anonymous, and most publishers barely optimise to convert them. James Webb, formerly Apps Group Product Manager at The Economist and Product Director at the FT, reads Pugpig's 2026 Media App Report and argues the value is hiding where standard metrics don't look

> Offline is not an edge case. At both The Economist and the FT, offline was around 10% of screenviews and cellular around 30%. If half those cellular sessions hit patchy signal, roughly one in four opens happens with the app on the back foot, and session analytics are flaky in exactly those conditions, so it is undercounted

> Digital still prices like it is scarce: one person, one paywall, one login, so a household either buys four subscriptions nobody can justify or three people free-load invisibly. The Times gives subscribers free bonus accounts and found those users 76% more likely to say they would stay, with churn under 0.01%. The New York Times charges for the same idea, three extra logins at $30 a month against four separate $25 subscriptions

> Only 26% of publishers say their app's main job is acquisition, yet apps are full of non-subscribers who hit a paywall after a single article. Habits take months, not one read. The Times' £1 for three months gives someone a chance to form one; a seven-day trial does not

The Pugpig report treats cellular and offline usage as a small, almost incidental slice of total screenviews. I find this hard to believe.

For readers who commute, fly and move through cellular blackspots constantly (at least in the UK), that slice is nowhere near as small as the report suggests. At the Economist offline and cellular usage represented around 10% and 30% respectively, and at the FT it was broadly the same. If half of those cellular sessions hit a patchy signal, then roughly one in four opens happens with the app on the back foot. Before we rebuilt The Economist app natively, that experience was painfully brittle. And our best subscribers were paying the price (and letting us know about it in volumes).

Think about who reads on a tube platform, in a station concourse or at thirty thousand feet. They are people who deliberately brought your content with them, likely your most loyal base, and it is exactly where too many apps still don’t handle things gracefully: articles that half-load, images that spin, editions that won’t sync, glitchy ad reloads, saves that fail without telling anyone. 

There is a measurement problem too. Session analytics under these conditions are notoriously flakey, so are probably undercounted. The report’s engagement numbers are likely a floor, not a ceiling, for audiences who read on the move. Treat offline and low connectivity as a retention issue, and test your edition, save and caching with the network switched off, as well as just slow.

Of course, Product Designers always design for state (eg connectivity), but in these constrained times, this is often the polish that gets deprioritised, and then forgotten about, by teams under pressure to deliver to tight deadlines. Publishers who’ve raised the bar won’t necessarily be the ones with the flashiest new formats – though they’ll likely have deeper pockets. Pugpig’s own data half-confirms this: mobile-plus-editions apps, the least fashionable format in the report, still outperform mobile-only apps on almost every engagement measure. My hunch is the same pattern holds for reliability under poor connectivity. It’s unglamorous, it won’t make your slideware pop when selling it to stakeholders, but it’s exactly the kind of thing that decides whether a reader who trusted you on a delayed train chooses to open your app tomorrow, and the next day. 

2. Digital still prices like it’s scarce

Pre-internet, it was fair game for anyone else in the house to read the primary reader’s copy of the newspaper. In the subscriptions era, digital has done the opposite from day one: one person, one paywall, one login, and a family of four either buys four subscriptions nobody can justify, or one person subscribes and everyone else free-loads, unconverted and invisible.

Very few publishers seem to have figured out that this is simply a design choice, and a poor one for tracking true engagement behaviours.

There are only three that spring to mind. The Times led with “bonus accounts” some time last year, letting subscribers share access with family or friends at no extra cost, essentially recreating the breakfast-table version of the newspaper digitally. This was inspired by Le Figaro’s play to reduce password sharing. The New York Times has introduced a paid family tier, up to three extra logins for $30 a month total rather than four separate $25 subscriptions, each with its own personalised account and its own Wordle streak. 

The Times treats sharing as a retention feature you don’t charge extra for. Everyone in a shared account has their own login. A family plan with individual, personalised accounts isn’t just cheaper access, it’s four separate chances to turn an anonymous user into a known user, to learn what they read, deepen the relationship with features suited to them, rather than one subscriber and one or two freeloaders who never show up in a single engagement analysis. 

From there a loop forms. An invited member gets their own login, activates on their own terms, and their usage compounds. Household engagement makes cancelling harder, and a member with a year of habits behind them is a far better candidate to subscribe in their own right than a cold prospect.

It may take time, but it works. The Times found people using shared bonus accounts were 76% more likely to say they’d stay subscribed, and churn on those accounts ran under 0.01%. Shared use turns a single subscription into a sticky household relationship.

NYT’s reasoning for the family tier was explicit: people with a lighter relationship to the paper become far more likely to convert once a friend or family member brings them in. When they convert or renew the household plan, they can invite someone else, expanding reach household by household.

Put this next to the anonymous-user problem from earlier and the opportunity becomes obvious. Nearly half of app users are anonymous, and most publishers barely optimise to convert them. A family member added to a household plan is the friendliest version of that anonymous user: someone already inside the tent, already reading, and one step away from being known.

The ones building this loop have figured it out. They’re turning the thing publishers tend to treat as leakage into the fastest lever they have for both retention and growth.

3. Be more generous with sampling/trials

According to the Pugpig report, only 26% of publishers say the main role of their app is subscriber acquisition, yet many apps attract plenty of non-subscribers who are far less engaged than payers. I’ve read countless angry reviews from people who followed a prompt to download the app and hit a paywall after a single article. Habits take weeks or more likely months to bed-in, not one read. The Times’ £1 for three months is a far better way to let new entrants get a feel for the product than a seven day trial ever will.

Apps are getting better at keeping the people they already have. Meaningful opportunities are hiding where standard metrics don’t bother to look: the reader with no signal, the family member re-using a login, and the anonymous user we ask to pay before they’ve had a chance to connect with your journalism. How can we expect younger people to adopt news apps that weren’t even designed for how they want to consume media, if we don’t even give them a decent chance to try it out?

James Webb has led mobile and web product groups at The Economist and the Financial Times. For a deeper look at why 82% of publishers misdiagnose their delivery bottlenecks, read The capacity myth: why your product bottleneck is actually a focus problem, on Substack.

> Metrics look objective. They are not. Every metric carries the business interests of whoever designed it, so a newsroom running on off-the-shelf analytics is optimising for someone else's goals

> Platform analytics are built to maximise ad inventory and time on network. If you sell subscriptions, you are measuring success with a tool designed for a different business model

> The free analytics smaller outlets use are modelled on what the biggest outlets negotiated with platforms. The advantage compounds: winner-take-all monetisation, hierarchies that are hard to challenge, and incentives to consolidate

> Chasing volume shows up in the copy. Articles translated into several languages, wire reports pasted in, headlines bent towards trending keywords. In the AI era the suggestions are no longer keywords but whole sentences, which risks framing social issues more stereotypically

> Goodhart's law: once a metric becomes a target, it stops being a good metric. The argument is not to abandon analytics, but to ask of every number on your dashboard who designed it and what behaviour it rewards

Based on the PhD research of Sophie Chauvet.

What I have found, in a nutshell, is that the bigger the size, the bigger the wins. But to find out about this simplified insight, I first had to take a step back from newsrooms, find the right tools, and ask key questions in order to open the black box of analytics the best I could.  

First, where do metrics come from? A genealogical approach proved to be deeply informative.  

Second, who profits from metrics? Following the money, and identifying who defines them, helped identify the main technical and political dynamics at their core. That’s where the macro approach became fruitful. 

Third, how realistic are metrics? There, I investigated what happens when a quantified measurement captures a social process, and what gets erased.  

After analysing hundreds of white papers, events, and interviews with practitioners across the data monetisation ecosystem, including ad tech, platforms, measurement and analytics companies, there was a single metaphor that helped to understand the stakes of metrics, and why they may negatively re-shape media landscapes: confusing the map for the territory, or how metrics tend to be self-fulfilling prophecies, thus impacting reality based on biased premises. 

The crux of metrics: Self-fulfilling prophecies, or why we confuse the map for the territory

Confusing the map for the territory is an expression borrowed from Alfred Korzybski, which is often used in quantification studies to describe the nefarious impacts of ill-used measurement. It means thinking the measurement model associated with an object is as real as the object. In the context of metrics, this helps to understand why there might be some confusion as to whether clicks mainly represent the audience’s interest, when in fact, they also represent the interests of those who built the model of metrics.  

Let’s break this down further, by answering the first question: where do metrics come from? Here, the history of audience measurement, as told among others by Cécile Méadel, Philip Napoli, and Josiane Jouët, is particularly enlightening. To practitioners in companies such as Médiamétrie and Nielsen, who historically developed studies on audience measurement on television and radio thanks to their Audimat and Peoplemeter, the audience was understood to be more of a convention than a reality, a means to gauge advertising revenue, with measurements mostly agreed upon by the different media involved.  

As such, measurements hold a kernel of truth, but they also serve a function of coordination between players in order to synchronise, predict and project financial benefits, with measurement containing an added layer of filters based on the parties’ main interests, which are also audited in order to ensure some degree of fairness within the ecosystem.  

With the rise of the internet in the late 90s, the first analytics companies, such as Urchin in 1998 that was acquired by Google, emerged with the promise of more realistic measurement. The internet was sold at the time as a new medium where clicks served as the new proxies for direct and real-time feedback from the public, through personal computers.  

This promised to help bypass the often heavy, costly, and slow studies of more traditional audience measurement. This promise was then thought to be fulfilled exponentially with the rise of social media and their troves of personal data waiting to be collected at a scale never seen before.  

This helps explain why the public is sometimes conflated with analytics, or why the map is confused for the territory, as analytics tools entered the newsroom in the 2010s, a field traditional measurement companies hardly ever dared to enter so as to protect journalistic integrity from advertising’s interests.  

Who profits from confusing metrics? 

The rise of analytics in newsrooms coincided with the rise of platforms and real-time data collection at scale. Here, the answer to the second question, who profits from metrics, is answered. Indeed, one aspect that contributed to making platforms so powerful is that their models rely partially not only on providing real time data on website use, but also on collecting this data back to improve their own models, by maintaining a more global overview on all the websites they are implemented on.  

With this broader perspective on audience flows, they are able to optimise advertising models which are key to their financial monopoly over the internet. These models however are black-boxed, as platforms are rarely audited.  

This has severe consequences on the internet ecosystem. For example, recent trials have proved that Google have abused this position of power over data flows by choosing specific, opaque algorithms that prioritised them over news outlets in an undue fashion, leading the latter to lose millions in revenue.  

But beyond the potential for fraud, these black boxes, this network of metrics used by many newsrooms, also contain the different ties of coordination between a slew of internet players that monetise data across the ecosystem.  

This, added to the complexity of the technical layers that sustain online consumption, and the un-audited incentives for inflating numbers, not to mention spam and bots, contribute to a phenomenon of entropy, which help explain why metrics do not always make sense. Disclosing this entropy is not in the interest of platforms. 

Overall in that regard, metrics, when provided by bigger, non-journalistic players, could be regarded not as direct public feedback, but rather as a reflection of bundled interests of the technical infrastructure of the internet, and the political economic negotiations that happen often behind closed doors. 

Metrics have therefore kept their function of coordinating the interests of the players involved, but without much transparency, and with an added layer of realism which is heavily advertised by many internet players, whether in online advertising, or via analytics companies.  

The benefits of scale: Supercharging the Goodhart’s law  

Now there is perhaps one factor that is the most significant when it comes to further understanding why metrics are confused for reality, and why they tend to become self-fulfilling prophecies with deep impacts on media landscapes: scale. This is due both to the fact that metrics are made of statistics, and because online audience flows are dominated by platforms.  

First, the statistical nature of online data flows highlights the importance of scale in monetising data online. This is caused by the fact that most predictions online are based on probabilities, and their associated deterministic patterns.  

This statistical technique helps to observe long term audience consumption patterns and how to optimise the most fine-tuned, cost-efficient audience trajectories according to a specific click architecture. In order to fulfil the promise that predictions become true, scale is a sine qua non condition in order to decipher what historical patterns are the most profitable.  

A corollary of this is that this deterministic parameter tends to cement historical repetitions, and to further benefit media players who existed first, as both benefits of scale tend to generate cumulative effects, and to provide a reputation for reliable profit which is highly valuable when it comes to organising and predicting budgets.  

This one main parameter is what allows platforms, whose main objective is to accumulate data, to maintain a stronghold on media landscapes, by adopting this meta-deterministic perspective on data patterns, and therefore, holding a monopolistic, black-boxed overview of what performs best online. 

By using probabilities and providing metrics as a map, platforms may supercharge the effect of self fulfilling prophecies online, through metrics that often tend to become objectives rather than simple indicators. 

This mechanism is called the Goodhart law. When metrics are used as objectives, they cease to be good metrics. In the case of metrics in news, it means that using scale in order to fulfil the deterministic parameter of finding truth through statistics tends to create self-fulfilling prophecies. By incentivising scale, volume, clicks, and page views, there are some negative macro effects that come as a consequence of mis understanding the power of metrics.  

Macro impact #1: The winner-take-all effect  

First, there is a winner-take all effect. The bigger the media, in terms of volume of clicks and data, the bigger the possibilities of monetising the data fruitfully. This winner-take-all effect is similar to the one observed in the business model of platforms, where scale advantages tend to become cemented and supercharged once a certain volume is reached. The bigger the media in terms of data volume, whether through subscriptions or through the accumulation of page views, the bigger the monetisation opportunities. The benefits of owning historical data tend to be cumulative as the predictions are more reliable and more precise.  

Furthermore, metrics models provided by platforms are often based on those bigger media outlets, as platforms are naturally more interested in those large pools of data that benefit their own meta-deterministic models of data analysis. In that sense, adopting the metrics provided by platforms tends to mean, that one’s strategy becomes symmetrical to platforms’ interests.  

Macro impact #2: Media hierarchies  

A second macro effect of this model is that it tends to also favour media hierarchies that are difficult to challenge. Bigger outlets remain at the top in terms of traffic and monetisation, because platforms tend to model their metrics and software according to those levels of scale and volume, which is what is of interest to them. Bigger outlets are also those who benefit more from in-person negotiations with platforms as to what metrics matter, and those models tend to be those that are then provided in free versions of analytics software that are more accessible to smaller outlets.  

Furthermore bigger outlets with more financial resources are more able to tweak analytics parameters, whereas smaller outlets tend to have more ready-made, black-boxed tools that may not be that adaptable to their own needs. Those outlets who have higher volumes of data also tend to be favoured in negotiations with other players of the data monetisation ecosystem, such as ad tech companies, as they benefit from a solid monetisation reputation.  

This model is not necessarily new, as traditional measurement companies also used similar hierarchical, rankings-based models, where media outlets with more resources and established historical deterministic patterns, have access to more precise and efficient measurement. 

In this digital analytics model therefore, new players have fewer chances to compete in terms of data monetisation because they lack the historical reputation and the reliability of accumulated historical patterns that unlock trust in predictions and funding opportunities. Therefore, the parameters of determinism, scale, and self-fulfilling prophecies are revealed by the simple fact that measurement and metrics are based on statistics and projections.  

Macro impact #3: Media concentration  

The data monetisation benefits provided by scale include a third significant macro impact on media landscape: media concentration incentives. In order to compete in this quantified, rankings-based system, some media outlets have adopted strategies to access those scale benefits. By creating alliances, accumulating media outlets, and re-joining databases, more volume can be reached. In that sense, media conglomerates can be incentivised to optimise online advertising and data monetisation opportunities, with a potential risk for media diversity.  

Macro impact #4: Devaluing journalism 

The final macro consequence that derives from the volume incentive, which is perhaps the most significant of all, is the devaluation of journalistic standards. From a strictly volume-oriented data monetisation perspective, it makes sense to produce more content which translates into more clicks, at the expense of journalistic standards. While clicks and webpages can easily be inflated from a quantitative perspective, the labour that tends to come with quality, original reporting, which often takes time, tends to be disregarded, from the perspective of this quantified financial model.  

In that sense, favouring financial value to access scale benefits tends to empty journalism from its values, and the value of its labour. Some strategies adopted by some media outlets include quickly producing articles at scale, by translating them into different languages, copy-pasting news wire reports, and coordinating with trending online keywords that do not necessarily stem from organic audience behaviours or from journalists’ flair.  

Journalists have sometimes conceded, when interviewed for this research, that they felt they had to twist their instincts in order to ensure content production that may have felt industrial rather than the byproduct of careful listening of their public’s needs. On the brighter side, another strategy also includes the attempt to quantify the value of journalism’s democratic mission, by promoting the quality of the involvement with the public as a selling point.  

Other media employees, involved more in advertising monetisation rather than direct editorial output, have also mentioned how adopting these keyword-tweaking strategies, informed by listening to more global internet trends provided by platforms, tend to be fruitful. Small tweaks, multiplied by thousands of webpages, thanks to benefits of scale, result in interesting profits.  

However, these strategies also tend to benefit platforms’ meta-deterministic needs, with consequences on how social issues covered by journalists are phrased online. With certain keywords being chosen over others, in order to fulfil the internet’s echo-chamber of what gets attention or not, meaning, what is observed statistically as echoing what was said in the past or by others, platforms mechanically end up having quite a say as to how social issues might be framed. 

In the era of AI, this editing power based on historical patterns might be supercharged, as suggestions not only contain keywords but sometimes entire sentences. Mechanically, this also runs the risk of framing issues in a more stereotypical way.  

Re-seizing the power of metrics 

Metrics are rarely neutral tools. They benefit some and devalue others, according to decisions that are black boxed behind their deceptive simplicity. They might contain a kernel of truth, but they often reflect the interests of those who manufactured them. In the context of news, they might be one reflection of the public’s clicks, but this reflection is also heavily mediated by the infrastructure of those who are able to have an overview of the internet’s secret flows and complex behind-the-door negotiations.  

Theoretically, metrics should be seen as indicators, not as objectives. In practice however, they tend to re shape media landscapes in a way that is detrimental to media diversity, journalistic autonomy, and may contribute to journalists’ loss of meaning.  

Following a map provided by non-journalistic players may lead journalism into platforms’ projected territory, one that is biased and mostly driven by scale, where truth is defined according to statistical parameters and their own interests. That’s the price to pay when meaningful, values-oriented work is translated into simplified quantified measurements.  

But I would argue that the potential of metrics to really benefit news remains unexplored. Metrics, when seized with the awareness of what they can and cannot do, can be powerful tools to visualise injustice and quantify the reality of invisible social issues.  

When the self-fulfilling prophecy aspect of measurement is mitigated, and when metrics are seen as simple indicators, they can be used to jolt actions that favour values rather than value. Knowing the limits of metrics, and being aware of what territory is being mapped when choosing them, is therefore key to secure journalism’s interests and social mission.  

This project has received funding from the European Union’s Horizon 2020 research and innovation programme under the Marie Skłodowska-Curie grant agreement No 765140 – based at Samsa and with the University of Toulouse.

Paywall and subscription tests publishers ran in 2026, collected by Audiencers:

> Stop advertising what's locked, start advertising what's free. The New York Times and The Verge both run "free to read" sections on the homepage, reversing years of premium tagging. The logic: conversion should happen on the article itself, not be pre-empted by a warning icon that ends the journey early

> Sell access in smaller units. The Washington Post's day passes, week passes and pay-per-article found an untapped market, with 42% of flexible access buyers brand-new anonymous users. Over 180 days, up to 8% of week-pass buyers, 4% of day-pass buyers and 3% of pay-per-article buyers became full subscribers

> Let readers set the price, then anchor it. Quebec's Les Coops de l'information took an average of $3.12 a month against a $1 minimum for the first three months. The suggested figure in the field did the work: $5 lifted the average to $3.39, $6 lifted it to $3.50. 84% of acquisitions had never subscribed before

> Sell less, not more. SFGATE kept its entire site free and sold a separate paid app alongside it. Les Echos' "La Sélection" is 9 to 15 articles a day chosen from a daily output of 120 to 150, at an early bird €9.90 a month

> Don't ask on day one. Figma asks six onboarding questions before showing pricing, and The New York Times tested a homepage whose only button top right said "log in". Each step gets a single goal, and early on that goal isn't the sale

> Journalism has to be right the first time. Product work learns more from what doesn't work. Frank Bi, AI Strategy and Product Advisor at Hacks/Hackers, argues that when products are held to the journalistic bar, nothing half-baked ever launches, so nothing ever teaches you anything

> Build a public sandbox. A labelled subdomain (beta, labs) resets the expectations readers bring to the homepage while still producing real behaviour data. Bi launched the Minnesota Star Tribune's AI restaurant guide Culinary Compass on the main domain instead, where readers judged a four-week experiment as a Star Tribune guide

> Set the end date before launch, ideally by picking something with a natural start & end. Bi's Fair Bot ran only the 12 days of the Minnesota State Fair, so nobody ever had to make the call to shut it down

> Show your work. Newsrooms already have a practice for issuing corrections, so extend a version of it to experimental products and publish what the team believed and why, before that thinking disappears at launch

> "Subscription fatigue" is the easy explanation when a paid product struggles. The more useful question is whether there was enough recurring value to begin with, rather than benefits assembled to justify a monthly charge

> Start with frequency: what do people already come back for. Then use registration as the halfway test, because if your most engaged readers won't create an account, a payment step will not fare better

> Build the paid tier around a behavior that already exists, giving people materially more of it. If the pricing page needs six unrelated benefits to feel substantial, no single benefit is strong enough

> Be deliberate about what stays free. A story that brings in thousands of first-time visitors has a different job from a specialist product consumed repeatedly by known users, and should be treated differently

> Forecast before you keep building. Apply realistic registration, free-to-paid and retention rates to your own segments, and if the range falls short of what the business needs, stop before the pricing conversation starts

> The Age of News Experience argues that for social platforms and games, experience is not a feature added to the product. It is the product, and that is what holds attention

> Strong products do not add up to a strong outlet. Readers still meet a fragmented set of things unless you design how they move between them, an approach the authors call Experience Islands

> The AXR framework runs audience to experience to revenue. Skipping the middle turns "reach younger audiences" into "make vertical video". Formats produce outputs, experiences produce behaviors. And repeatable behaviors connect audience value to commercial value

> In video there is a passive-active pattern. Viewers happily let the next item start, but if it shifts too sharply they have to actively decide again, and that decision point is where they leave

> So the unit of planning changes. Before launching anything ask what experience begins here, what the natural next one is, and what must stay consistent so it feels like continuation

> Audience consultant Khalil Cassimally's rule is to use synthetic audiences to narrow, never to decide. They rank options and surface objections, but cannot tell you magnitudes like willingness to pay

> Start with the decision you need to make, not the question you want to ask. Most synthetic research fails by producing interesting material nobody was ever going to act on

> Ground the panel in words real readers actually wrote. Old interview transcripts, cancellation reasons, unread survey comments. The test is whether you can name the humans inside the system

> Most teams only ever count responses. The better question is where your draft conflicts with what you have documented about a reader, which is analysis rather than impersonation

> Tell it to answer as a reader rather than an analyst, and that indifference is valid. Then validate whatever survives with real people, which is where the saving comes from

Lennart Schneider is Founder of Subscribe Now, helping decision-makers in the subscription economy attract subscribers and keep them happy.

> When you start testing value props and benefits, it's easy to mix and match features, and try to cram everything onto one page to make subscription "worth the cost". But research has proven that the biggest driver of loyalty isn't usually price or feature count. It's a sense of uniqueness, something the reader couldn't get anywhere else

> Your team can answer this easily, but the real value comes from asking the same questions of the people you're trying to reach. At a minimum, show your prototype to one more person than has seen it already

> Once you know your differentiation, packaging decisions get easier. Rachel's 3-question test before bundling anything: does it strengthen or dilute each product's identity? Are the audiences actually converging? Can your ops hold it together without the seams showing?

> Sometimes the research tells you to do nothing. One of Skift's four products stayed untouched, on purpose, and it's now one of their steadiest, best growing subscriptions