> 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.

> Le Monde does 4 million video views a day against 6 million site visits. Followers are only 25% of that traffic, so YouTube works as a discovery engine rather than a loyalty channel

> Views grew 25% in 2025, driven by long formats. The deliberate choice was to invest in the newsroom and in staff journalists on camera, not a standalone TV channel or external creators

> YouTube reports 15 billion news views a year, and its revenue share leaves publishers with more than half of what they monetise on the platform

> YouTube is building paywall integration with publishers' own subscription systems, with Le Monde as a pilot partner. Today there is no way to gate video on the platform at all

> YouTube's advice: be personality-driven and direct to camera, don't copy-paste a TV format, and hire someone with real channel-management experience to train the in-house team

> SFGATE didn't add a paywall to its free site. It built a separate paid app instead, so the free web experience that built its audience stays untouched

> The name "Friends of SFGATE" beat "subscription" and "membership" in testing specifically because it read as warmer than a transaction but less formal than a cause people feel obligated to fund

> Nearly 90% of readers who took the 7-day free trial converted to paying Friends, an exceptionally high trial-to-paid rate relative to widely accepted industry benchmarks, though it's still early days

> The North Star metric shifted from total visits to repeat visits: can a reader who showed up once be brought back a second and third time, not just once by anyone

> The launch required real org change, not just a new app: a first-ever Community Engagement Program Manager role, a second video hire, and an existing editor with app experience redirected to lead app programming

> The Economist has launched a dedicated subscription tier for access to audio and video formats, priced lower than the core digital subscription to attract new audiences

> The "Halo Effect": From early signals, the team has observed an uptick in core Premium sign-ups. Seeing a cheaper, format-specific option next to the full product acts as an anchor, nudging undecided visitors to pay a little extra for the full suite.

> Net-New Audience: Conversions through Play have been driven almost entirely by brand-new readers rather than existing subscribers trading down.

> Play is part of The Economist's wider strategic development (alongside podcasts, short-form video and "Insider") towards building deeper, more "human" relationships with audiences

> According to OpinionWay, the average French person consumes about 6 media families a day, 20% already use a generative AI to get informed, but a majority say they feel overwhelmed, even fatigued, by the news

> Faced with information fatigue, unlimited access is not the answer. Les Echos is instead making the opposite bet, that of selection

> "La Sélection" consists of between 9 and 15 articles a day, chosen from a daily output of 120 to 150 pieces, following a reader-needs grid (inform, reflect, get inspired, develop, unwind) and a strict quota discipline

> Three platforms (a dedicated tab in the app, a section on the site, a daily newsletter), a weekly long read on Saturdays, and 5 shareable articles a month. Early bird price sits at €9.90 a month

> Le Monde has wanted to launch an English edition for 50+ years, but technological advances (AI translations) and digital subscriptions (delivering value internationally with ease) finally made the project possible in 2022. It's also an important move for the brand to continue to grow beyond the limited French market

> Content: Le Monde in English doesn't create original content; it exclusively translates a curated selection of the French edition (30% of all articles from Le Monde)

> Because an international reader is highly likely to already hold a primary subscription to a domestic title like The New York Times or The Guardian, Le Monde in English positions itself as a "second read," with a highly accessible pricing structure (starting at €2.50/month for the first year)

> Performance so far: around 16,000 active subscribers, 50% of whom are from the US. Approx. 5 million monthly visits, and subscriptions are the core business driver, representing 66.2% of revenue, followed by licensing (16.9%), B2B (12.3%) and advertising (4.6%)

> "It's not about translation": success comes down to content market fit, product distribution, and understanding the distinct profile of your international audience. And whilst AI has driven productivity gains on this front, it's also created journalism jobs - AI handles the mechanical translation, freeing human journalists to focus purely on editing, nuance, and curation

2. The tech triggers

Two key factors finally made the project feasible in 2022:

• Artificial Intelligence: AI finally matured enough to drastically lower the financial and temporal costs of translation.
• The digital subscription model: Unlike the 1970s print attempt, which incurred immense logistics costs to print and ship copies across the UK and US without any guarantee of sales, digital distribution completely eliminates the friction of international expansion.

Aubron contrasted this with The New York Times, which launched a Spanish edition in 2016 but shuttered it a few years later. The New York Times relied heavily on manual translation without AI and built a business model dependent on advertising rather than subscriptions, a strategy that ultimately proved unsustainable.

The launch and operational workflow

Following Aubron’s appointment in September 2021, the project launched in April 2022 to coincide with the French presidential elections.

At launch, the assumption was that AI could handle the heavy lifting, followed by a quick proofread. In reality, it required much more human intervention.

1. DeepL translation: The software was used to generate the initial baseline translation. While highly rated, the technology in 2022 still required heavy editing to meet Le Monde’s journalistic standards.
2. Professional freelance translators: To ensure maximum quality for a premium subscription product, two translator agencies were brought in to meticulously review and correct the AI-generated text.
3. In-house editorial team: A dedicated team of 8 journalists was embedded directly within the French newsroom. This physical proximity allowed them to collaborate, anticipate news, and edit translations seamlessly alongside the original authors.

Article selection: quality over quantity

Le Monde in English does not create original content; it exclusively translates a curated selection of the French edition.

• 30% of French articles translated: The team skips live coverage, podcasts, and strictly local French wire stories (e.g., specific domestic politics, local school or banking issues) that hold little relevance to an international audience.
• +10 daily dispatches: They supplement translated deep-dives with roughly ten English news dispatches per day to provide hot news.

Pricing: A “second read” strategy

Because an international reader is highly likely to already hold a primary subscription to a domestic title like The New York Times or The Guardian, Le Monde in English positions itself as a “second read.”

To account for this, the pricing structure had to be highly accessible:

• Introductory price: €2.50 per month for the first year.
• Standard price: Loops up to €10.00 per month progressively.
• Average price point: As of March 2026, the average revenue per user sits at €4.66.

Aubron noted that an attempt to raise prices in 2025 stalled subscriber growth because the price increase came at a time when audience acquisition hadn’t scaled sufficiently to absorb the change. Lesson learned: a secondary read strategy demands highly sensitive, highly competitive pricing.

Four years later: audience & subscription performance

The strategy has paid off, yielding steady traffic and subscription growth since the launch 4 years ago.

• Current Subscriber Base: Hovering near 16,000 active subscribers.
• The American engine: A staggering 50% of all subscribers come from the United States. Aubron attributed this to a stark cultural difference in the propensity to pay for news: while only roughly 10% of French consumers pay for digital news, closer to 20% of Americans do. The subscriber base consists heavily of students, retirees, and individuals with an existing personal, educational, or marital link to France.
• Global traffic footprint: The platform pulls in approximately 5 million monthly visits.
  • United States: 38.5%
  • United Kingdom: 12.2%
  • Canada: 9.5%
  • France: 7.3% (although access to the English edition from France is limited to subscribers on higher-tier plans)
  • India: the fastest-growing traffic segment
  • Germany: the first non-anglophone country.

What do they read?

True to Le Monde’s core strengths, the most read sections are international news, French politics, and economy. Surprisingly, culture and lifestyle pieces perform exceptionally well; articles about new Parisian restaurant openings or art exhibitions see massive engagement from readers living as far away as Texas and Florida.

Subscriber acquisition strategies

To recruit international subscribers, Le Monde deploys a three-pronged approach:

1. Leveraging LeMonde.fr’s SEO hierarchy: Internal debates weighed whether to launch on a standalone domain (LeMonde.com) or a subdirectory (LeMonde.fr/en). Choosing LeMonde.fr proved to be a major SEO blessing, allowing the English edition to instantly draft off the massive domain authority of the main French site.
2. Paid acquisition adjustments: Google advertising is a core driver. Aubron also highlighted a massive breakthrough in their Meta acquisition costs: after peaking at an expensive €86.90 cost-per-acquisition (CPA) in late 2025, optimization strategies successfully drove that CPA down to just €9.30 by March 2026.
3. Strategic publisher partnerships: By setting up reciprocal marketing exchanges with premium Anglo-Saxon publishers (The New York Times, The Guardian, The Atlantic), Le Monde cross-promotes free trial offers directly to highly qualified audiences who are already accustomed to paying for premium journalism.

Achieving break-even

The most significant milestone of the session was the announcement that Le Monde in English has officially achieved financial break-even in 2026, a full year ahead of the initial business plan.

Revenue breakdown:

• Subscriptions: 66.2% (The core driver of the business).
• Licensing: 16.9%.
• B2B: 12.3%.
• Advertising: 4.6%.

Product evolutions: what’s new today?

Since its 2022 launch, the operational model has undergone substantial changes, heavily driven by advancements in generative AI:

AI-driven productivity gains

• The transition to ChatGPT: Le Monde migrated its primary translation workflow away from DeepL to customized ChatGPT protocols. This allowed the team to deeply bake Le Monde’s distinct editorial tone and style guides directly into the translation layer.
• Eliminating the translation layer: The quality of generative AI advancements and the know-how developed by the team of journalists completely eliminated the need for external freelance translators.
• Creating journalism jobs via AI: Instead of using tech to downsize, Le Monde redirected those cost savings into expanding its internal editorial team from 8 to 10 full-time in-house journalists. AI handles the mechanical translation, freeing human journalists to focus purely on editing, nuance, and curation.

Platform and multimedia expansion

• A bilingual app: They integrated a native toggle inside the core Le Monde app, allowing users to switch effortlessly between the French and English editions. While app users represent just 4% of total English traffic, they account for a massive 49% of all English subscribers.
• A dedicated YouTube channel: Rather than merging video content, they launched a standalone English YouTube channel. High-end video investigations have achieved breakout success: their video on Dubai money laundering garnered 760,000 views (vs. 1 million for the French original), and their documentary on the November 13 Paris attacks pulled in 2 million views (nearly matching the 2.2 million views of the French version).
• Apple News+ integration: Le Monde in English is now available directly within the Apple News+ ecosystem to capture a broader layer of casual readers.
• M International magazine: In March 2025, they went “back to paper” by launching a premium, biannual print magazine called M International (themed “The French Touch”). They distribute 25,000 copies across 350 elite newsstands in 20 countries and 37 global cities.

Key takeaways

Aubron concluded his presentation with three definitive lessons learned over the four-year journey:

• It’s not about translation: Translation is now a commoditized utility. Success comes down to content market fit, product distribution, and understanding the distinct profile of your international audience.
• Hard to imagine working in another language: Due to global curiosity around French art de vivre, culture, and global politics, in the US, the English edition works beautifully. However, Aubron noted he would not attempt to launch a Spanish or German edition of Le Monde yet, as those markets lack the critical size and same unique cross-border structural demand.
• AI can create jobs: The project stands as living proof that when publishers embrace automated productivity tools like generative AI, they can reinvest the dividends directly into hiring more human journalists to elevate the final product.

> The Sun launched Sun Club in 2025 as a £1.99 freemium membership, layering subscription revenue and first-party reader data on top of an already large free audience

> A year in, 65% of members are women, and TikTok has opened a genuinely new audience: those subscribers are 85% female and 15 years younger than the average member

> First-week app activity is the strongest predictor of long-term retention. The most loyal members read over 100 articles a week, almost entirely in the app

Lessons:
> Value-driven perks are the core of the proposition, and for this audience they're the retention tool that matters

> Freemium can serve advertising and subscription goals at once, because a known, logged-in audience strengthens both

1. The strategy gap: moving engagement to the heart of the business

Newsrooms are recognizing that deep audience engagement is the primary lever to unlock long-term financial sustainability. In fact, audience engagement was the most frequently selected top-three goal for 2026.

However, a significant execution gap remains: 25% of newsrooms still make daily editorial decisions purely on instinct or reactivity, and 42% operate with only loose structural alignment.

Furthermore, the data proves that editorial growth stems directly from operational discipline. Newsrooms that systematically review and discontinue low-impact initiatives are nearly twice as likely to experience budget growth compared to those that manage portfolios on an ad hoc basis (50% vs. 28%).

What your team can do:

• Diversify the strategy table: Break down the church-and-state silos. Include audience engagement and platform leaders directly in your long-term strategy and investment conversations. Structurally aligned newsrooms over-index significantly on bringing these non-traditional roles to the table.
• Implement “portfolio discipline”: Set up a formal quarterly review to audit your newsletters, verticals, and content formats. Be ruthless about killing initiatives that fail to build a loyal public so you can redirect scarce editorial resources toward high-impact journalism.
• Shift from destination-first to audience-led commissioning: Currently, 64% of newsrooms still design stories for a single primary legacy channel (like print or a homepage template) and adapt them later. Instead, adopt commissioning frameworks—potentially assisted by internal AI tools—that force editors to identify a defined user need or specific audience group before a story is assigned.

2. The audience trust gap: rethinking participation and storytelling

Trust has evolved from rigid, institutional authority into relatable, relational signals. While newsroom leaders heavily prioritize audience relationship building, their workflows tell a different story: reporters spend a staggering 38% of their week on technical production drag, but a meager 11% on post-publication work like community building and responding to reader feedback.

When it comes to distinctiveness in an abundant content ecosystem, the ‘evergreen explainer’ of the past is being disrupted. Publishing teams are pivoting toward deep background reporting (+6pp in structurally aligned newsrooms) while actively steering away from low-margin daily breaking news (-10pp).

What your team can do:

• Bring community in-house: Stop relying on volatile, algorithmic social media comment sections to house your audience network. Take inspiration from pioneers like Newpress (building algorithm-free spaces for co-creation) or the Financial Times (leveraging interactive digital forums like “Ask an Expert”) to turn passive readers into highly engaged, recurring subscribers.

• Lean into service-oriented co-creation: Consider launching a reader-initiated reporting vertical. New Zealand’s Stuff Digital launched “Solving Stuff,” an interactive project prompting readers to submit local problems for journalists to actively investigate. This approach generated massive reader investment, with average time-on-page and completion rates soaring well above site averages.
• Be candid and transparent: Build ‘Behind the Story’ formats into your editorial mix to pull back the curtain on how your journalism is sourced and verified. Relational trust relies on showing your audience your journalistic motivations and methods.

3. The capability gap: shifting AI from efficiency to strategic maturity

Publishers are widely trapped in a Level 1 AI maturity mindset: 42% use time savings as their primary KPI for AI success, focusing strictly on automating existing administrative tasks like transcription or copyediting.

However, the biggest barriers holding back AI integration are people-based rather than technical, namely skills gaps (61%) and cultural skepticism (52%). Crucially, the study found that 57% of organizations do not have explicit AI representation in the newsroom, which directly correlates to the lowest literacy and adoption rates.

What your team can do:

• Embed AI roles directly in editorial: Do not leave technology procurement solely to isolated corporate IT or CTO functions. Move your tech budget closer to production by placing editor-coders, hybrid technologists, or dedicated newsroom engineers directly within your reporting desks. High AI usage and literacy jump dramatically (to 46%) when technical leads are embedded in the newsroom.
• Modernize backend metadata: Your AI tools are only as powerful as your archive. Publishing teams that systematically structure and tag their content assets with robust metadata report exponentially higher levels of successful AI integration and content retrievability.
• Reframe the AI conversation: Move beyond generic, mandatory tool-click training. Adopt a strategy like Bonnier News, which ran voluntary, playful workshops focusing on demystifying what AI fundamentally is and cannot do. This organically transforms open-minded early adopters into internal ambassadors who win over newsroom skeptics by proving how technology frees up capacity to do more investigative work.

4. The skills gap: transitioning from generalists to sharper specialists

Confidence in the future readiness of editorial talent drops sharply when media leaders look three years ahead. To thrive in a visually and technically demanding landscape, the industry is transitioning from broad generalism to a tightly bundled set of hybrid skills.

The primary skills priorities identified for future readiness include tech-enabled journalism (e.g., open-source intelligence, AI prompting), audience data fluency, and production expertise (short-form video editing, visual design). Yet, 61% of newsrooms still provide no formal training for these vital new competencies.

What your team can do:

• Formally define hybrid reporting profiles: Modern reporting roles require a multi-disciplinary approach. Audit your hiring pipeline to look for cross-functional profiles, such as Open Source Intelligence (OSINT) reporters who combine coding with investigative craft, or “Podcast Correspondents” capable of owning a niche category across written text, audio recording, and visual presentation end-to-end.
• Invest heavily in creator-like on-camera training: 41% of newsrooms want to upskill existing staff into multi-platform visual creators, yet 68% of them offer no formal video or presentation coaching. If your strategy relies on launching visual content on TikTok, YouTube, or Instagram, you must systematically allocate budget to presentation, vocal coaching, and platform algorithm fluency.
• Evolve compensation models to retain niche talent: Because audience affinity is increasingly shifting from legacy institutional corporate brands to recognizable individual personalities, media companies must rethink retention. A dominant 78% of media executives agree that alternative payment structures, such as bespoke revenue shares, hybrid contracts, or performance-linked bonuses, are becoming essential to protect and incentivize your most prominent, community-driving journalists.

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Download the full Future Newsrooms Study 2026 report from FT Strategies here.

> Le Nouvel Obs cut its article output by nearly 30% and everything went up: pageviews rose 30% year on year, median pageviews per article rose 50%, and digital subscriptions rose 35%

> The cuts were structural rather than across the board. Raw newswire reposts went, print-shaped cultural reviews became digital-first compilations, and premium content doubled as a share of the feed

> The mechanism is simple: fewer pieces competing for the same homepage and distribution means each surviving article reaches more people, which lifts propensity to subscribe

> A daily morning briefing puts editorial and data in the same room, and deliberately spotlights what worked rather than what flopped. They chose positive reinforcement over chasing underperformers

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

When I talk to newsletter creators, it usually doesn't take long before one of two questions comes up:

1) What actually constitutes a good open rate?

2) My newsletter has an open rate of 40%. That's great, isn't it?

The honest answer is: There is no such thing as a "good" open rate, and the open rate really says nothing about the quality of your newsletter. 

In this article, I want to clear up this misconception and explain when you should still pay attention to it.

P.S. I've written a book! The playbook for successful subscription models: With growth strategies and best practices from 50 leading companies. You can pre-order here (or on Amazon).

I completely understand the need for benchmarking and comparability. With newsletters, you have relatively few metrics, so you try to draw as many conclusions as possible from what you do have. Often, however, you draw more conclusions than are actually useful.

Let’s start with the basics: open rate is a fraction. You calculate it by dividing the number of unique opens by the total number of recipients. So far, so simple.

However, this leads to a small detail that is often overlooked. You can increase the open rate in two ways: more openers, or fewer recipients.

The latter point is often overlooked, but it’s crucial. People who don’t open your newsletter are often the same ones. And especially with older mailing lists, you often have a large proportion of inactive subscribers who have lost all interest.

So, if you have a 50% open rate with 10,000 recipients, then there are probably several thousand among them who haven’t opened a single email in six months. Let’s say there are 3,000 of them. If you remove them from the mailing list, your open rate immediately jumps from 50% to 71.4% (5,000 opens/7,000 active recipients).

Has this improved the quality of your newsletter? Are your readers more satisfied? No!

Has the quality of your distribution system improved as a result? Absolutely!

Why would you delete valuable leads?

Admittedly, I’ve recommended this to many newsletter creators, but the enthusiasm is usually lukewarm. They’ve put a lot of money and effort into acquiring these subscribers, so why would they just delete the addresses? Perhaps the reach is even important for advertising sales (even if nobody sees the ads).

But here are a few reasons why it’s worth it:

1. If someone hasn’t opened your emails for a long time, the likelihood of them returning is relatively low
2. You increase the risk of being perceived as spam
3. Depending on the newsletter tool and contract, costs increase as you send more emails
4. Your brand suffers when customers perceive you as pushy
5. Your advertisers are surprised when clicks fail to materialize on a supposedly large distribution list, and become suspicious
6. …

Many of the world’s leading newsletters therefore place great importance on the quality of their mailing lists and remove users after a long period of inactivity. The New York Times even discloses this quite transparently:

How can I find out who is inactive?

Good question! And not so easy to answer. Since Apple’s Mail Privacy Protection, it’s often difficult to track who actually opens an email – Apple automatically opens emails before they reach the recipient, and therefore your email program thinks these users are active. Other users block tracking and are registered as inactive, even though they enthusiastically read every issue. So if you’re unsure: just ask the users.

Clicks can be tracked more reliably than opens, and if readers don’t click after (repeated) requests, you can remove them from the mailing list with a clear conscience.

Do I really need to delete them?

No. You can also pause them for now, or reduce the frequency. A good example is the sports newsletter “The Gist”. They temporarily pause inactive subscribers, and then when a major event is coming up (for example, the Olympics), they try to reactivate them.

However, you shouldn’t overdo it. If you haven’t contacted them for 1.5 years, you should continue to refrain from doing so. After that time, their consent to contact them also expires.

Even more reasons why open rate is misunderstood

Your open rate depends on numerous factors, and the quality of your content is just one of them. Here’s a (likely incomplete) list of factors that affect open rates:

Mailbox display:

• Subject lines (Do they encourage clicks?)
• Pre-header (Do you tease the content well?)
• Sender’s name (Do they trust you and look forward to your emails?)
• Sender images (Will they stand out in the inbox? Only works with certain clients)

The quality of your distribution:

• Cleanup of inactive users (Are non-openers regularly removed?)
• Segmentation (Do you always send emails to the entire mailing list or do you select recipients based on interests?)
• Preference Center (Can users configure which emails they receive and how frequently?)
• Age of the addresses (How long ago did someone sign up?)
• Lead campaigns/address origin (On which channels and with which promises were the users acquired? Did they want the newsletter, or did they just share their email to participate in a competition, for example?)

Deliverability

• Advertising tab in Gmail (Are emails from Gmail classified as advertising and not delivered to the main mailbox?)
• Spam (Do the emails often end up in the spam folder?)
• Bounces (Can the emails not be delivered? What are the different types of bounces, hard and soft?)
• Image sizes (are the images too large?)
• Email size (Will the email be truncated, for example, by Gmail, which happens from 102 kb upwards?)
• Shipping time (Is the shipping time chosen?)

Contents

• Good quality & relevance
• Diversity (do you cover different needs so that every issue contains an “aha” moment?)
• Continuity & predictability (Do readers know what to expect and why each issue is worthwhile?)

Technology

• Email tool or measurement technology (each mailing tool measures opens slightly differently, which affects the KPIs)
• Tracking Opt Out (Have users disabled tracking of clicks and opens?)
• Auto Opens on iOS (Are open rates inflated because emails are automatically opened due to privacy settings?)

So I can ignore the open rate?

Not quite. Since we don’t have many metrics, it’s still a valuable signal, as long as you’re aware of what it tells you, and what it doesn’t.

What you shouldn’t do is compare your absolute open rate with competitors who use completely different tools and whose lead generation works differently. This apples-to-oranges comparison is completely pointless.

What you can do instead:

• Observe long-term trends: Is my own open rate constant? Have there been any sudden changes I should understand? (But keep in mind that these changes could be due to technical reasons, e.g., more users with tracking protection.)
• Optimize your open rate with A/B testing: e.g., send each email with 3 different subject lines to a sample group beforehand and send the best version to the rest of the distribution list.
• Examine outliers: Were there any particular issues that were opened more or less frequently than average? What can I learn from them?
• Re-contacting those who didn’t open the email: Some companies send a newsletter a second time if it wasn’t opened the first time. I find it a bit spammy, but it seems to work.
• Use net reach (recipients x open rate) as the basis for advertising deals: This is fair to your advertising clients and you can also justify why the CPM (cost per thousand contacts) is higher if you are contacting a cleaned distribution list.
• Comparing segments: When running different lead generation campaigns, you shouldn’t just focus on the short-term cost per lead (CPL), but also consider whether the campaigns generate active recipients in the long run. A sweepstakes often generates a large number of addresses cheaply, but the open rate drops significantly after just a few campaigns.
  For better comparability, you can calculate a cost per lead after, for example, 10 campaigns. If you generate 1,000 leads per campaign for €1,000 in each of two campaigns, the short-term CPL is €1. If, after 10 campaigns, 70% of the leads in campaign A are still active, while only 30% remain active in campaign B, you get a clear picture: In campaign A, an active lead cost €1.43, while in campaign B it cost €3.33.
• …

PS: My newsletter has an open rate of 51-58%. That’s great, isn’t it?…

PPS: To hear more from me about subscription and newsletters, sign up to my own newsletter, Subscribe Now, here

This article was originally published in German on the Subscribe Now website, translated and republished with permission.