Max Moné is co-founder and CEO at Poool, the dynamic journey builder to boost subscription conversion, engagement, and loyalty.

This article is the second in a 6-part series where Max shares what he learned from studying 100 subscription business models across 15+ industries. If you haven't read the first one yet, start here. 

Why we’re focusing on media for this one

In most subscription businesses, the rules for accessing the paid offer are simple. You subscribe to a box, you get the box. You hit a usage limit on Claude, you’re asked to upgrade. You want to get access to Calm for guided meditation? You get 1 week, then have to pay. Each model has its logic, but it’s tied directly to the product.

In media, it’s different.

Media online was historically free (almost always). Then publishers moved to paid models, and products got more complex: apps, newsletters, podcasts, games, bundling, community. Because of this, an enormous variety of access models emerged.

So we decided to go deep into media alone in today’s episode. Because the question “what’s free and what’s paid” is one of the most consequential product decisions in our industry.

And there’s no right or wrong answer. The right model depends on your organization, your editorial DNA, your content, your business model… It’s specific to each media, each newsroom, each audience.

The goal here isn’t to tell you what to do. It’s to show you the range of what exists, so you can figure out where the opportunities are for your unique context.

The engagement-frustration trade-off

The data on this has been clear for a while now.

In 2021, we published the Digital Media Review (DMR – a research initiative with Google and Le Geste – a french association for publishers) at Poool. One data point stood out: the relationship between the share of traffic exposed to paid content and conversion. The correlation is clear: the more you expose, the more you convert. But only up to a threshold. Past that, conversion plateaus or falls. Frustration alone is not a sufficient reason to subscribe.

And the other side of the coin is just as important: the DMR also showed a strong correlation between traffic on paid articles and the share of users who simply leave the site when hitting a paywall. The more you frustrate, the more people bounce. That’s not just a missed conversion. That’s a direct hit on engagement, on page views, ad revenue, and your ability to build any relationship with that user.

In the same year, Mather Economics published a study (part of a GNI LatAm Subscriptions Lab report) looking at paywall hits per user. Subscription probability follows a bell curve: up with each hit, peaks, then back down. Same conclusion, different data.

Then in 2022, the New York Times shared what brought all of this together (Audiencers analyzed it here). When they launched their paywall in 2011, the meter limit was the same for everyone: a fixed number of free articles, a regwall, some more articles, and finally a paywall.

Over the years, they developed the Dynamic Meter, a machine learning model that sets personalized meter limits per user.

The core insight: the model optimizes for two metrics simultaneously, engagement and conversion. And these two metrics have an inherent trade-off. More paywalls → more subscriptions, but less readership. They proved this with randomized control trials: as the meter limit goes up, engagement increases but conversion drops.

With a fixed approach, you’re forced to choose. Maximize engagement, or maximize conversion. Not both. There’s always a window of lost revenue that a single rule can’t capture. The Dynamic Meter expands that window by personalizing: more frustration for high-propensity users, more openness for low-propensity users.

None of this is new. DMR: 2021. Mather Economics: 2021. NYT: 2022. We’ve known this for years. And yet, the vast majority of publishers still use a uniform paywall for all users.

If you don’t personalize, you’re forced to make a choice between engagement and frustration. And that choice won’t be the right one for all users. That’s why more and more sophistication has developed. And the jump from “manual” to “1-2-1” isn’t binary. There’s a whole spectrum in between.

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The 6 levels of paywall sophistication

Being at level 1 doesn’t mean you’re behind. But it means there are probably missed opportunities. The question is whether these missed opportunities are big enough to justify the resources needed to reach the next step.

• Level 1: Fully manual

Free and paid decided by hand, article by article. An Atlas & Audiencers’ study confirmed this is still the norm: the decision is based on the article’s perceived value. Breaking news free, analysis behind the wall (to simplify it to its core).

• Level 2: Manual + automated rules. Paid content still decided manually, but rules modify access based on additional criteria.

Foreign Policy shared their approach on The Audiencers. They segment by publish date (under 7 days: hard-walled; older: standard meter), but also by cohort. Users with high search intent and churned subscribers are hard-walled (high subscription propensity). Social referrals see a registration wall with a free page view (low subscription propensity, but conversion on registration is 100x higher than on subscription). That registration data became their strongest conversion driver. No AI needed. Just smart segmentation.

• Level 3: Manual with override.

The base decision is manual, but the system can override contextually: closing a free article for a high-propensity user, opening a premium one for a segment you want to engage. A first step toward personalization without losing editorial control.

• Level 4: Manual + a third automated category.

Free and premium decided manually, but a “grey zone” is managed automatically based on user profile, device, or source. Ideal for publishers in transition between fully manual and more automation, while still giving the editorial team a great deal of control (yes, the business side gets some autonomy, but only on categories validated with editorial).

• Level 5: Fully automated for conversion and engagement.

The system decides everything: whether to show a wall, which type, what meter limit, per user. The engagement/frustration trade-off managed dynamically.

Business Insider does this with AI: reading habits, traffic source, content genre propensity. The algorithm decides: paywall, registration wall, or nothing.

The NYT does it with the Dynamic Meter (as we talked about earlier). It learns from first-party engagement data (no demographic or psychographic features), adjusts per user, and continuously runs randomized trials to improve. This is the model that expanded the opportunity window we described above (source).

• Level 6: Fully automated for conversion, engagement, AND advertising.

The decision also integrates ad revenue. Fortune’s CCO at the time, Selma Stern, explained that if a user’s subscription probability is too low (first-time visit, smartphone, social media), the paywall opens and the page view is monetized through ads. The goal is to maximize total user value, wherever it comes from (which I personally think is an amazing model for short term profitability – because yes, sometimes long-term profitability might require less revenue in the short-term).

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What you can take away from this (and test tomorrow)

Figure out which level you’re at. If all your users see the same paywall regardless of behavior, source, or profile: what are you missing?

Try one rule-based variation before thinking about AI. Foreign Policy’s 100x stat didn’t require machine learning. It required a hypothesis and a way to test it.

Start by looking at your bounce rate on paywalled content. If a significant share of users leave the site when they hit your paywall, that’s revenue you’re losing twice: no subscription AND no engagement. The DMR, Mather Economics, and NYT data all showed the same thing: a uniform paywall always forces a trade-off. Even a simple first step (like showing a registration wall instead of a paywall to low-propensity users) can start closing that gap. That’s what Poool makes it easy to test.

See you next week for Article 3, where we leave media and look at how the best subscription businesses across all industries think about conversion.

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PS: This article was about the decision logic (who sees a paywall, and when). We didn’t cover the blocking method: how the paywall is technically implemented, front-end or server-side, and what that means for content protection, AI scraping, and SEO. That’s a separate topic, and we covered it here: “Paywalls & SEO“. All 6 levels above work with all blocking methods.

The post What’s free, what’s paid, and why it’s really an engagement trade-off: episode 2 appeared first on Audiencers.

The Next Gen News Report 2 (NGN2), a collaboration between FT Strategies, Medill Knight Lab, and the Google News Initiative, provides a roadmap for news organizations to reach younger audiences. The core of the report is the 7 Modes of Engagement, which distinguishes how "next gen" consumers (ages 18-25+) discover, consume, and share news.

To reach the next generation of news consumers, publishers must stop thinking about “the audience” as a monolith and start designing for specific “modes” of behavior. The second Next Gen News report reveals that younger consumers are not passive; they are active “information curators” who cycle through seven distinct mindsets.

Let’s breakdown the 7 Modes of Engagement and uncover how you can win in them.

1. The “Sift” Modes: how news is discovered

In sift modes, users filter through their chosen information environments to separate news from noise. 

Across all markets studied, the report finds that young consumers actively curate their information ecosystems, seeking to reduce the daily mental load from sifting through endless content. Consumers intentionally mold their environment to refine and filter the news sources they encounter.

Observing this shaping process in the field showcases how sifting is richer than originally thought, now defined through three distinct modes of news discovery: 

Mode 1: Scroll

 The vibe: passively encountering news whilst browsing social feeds
 The energy: engagement is often spontaneous and fleeting, shaped by the emerging news producers they follow and platform algorithms.
 The strategy: intentionally create content that feels native to the platform and find patterns that break through to capture fleeting attention in the few seconds before viewers scroll to the next post

How to win at Scroll mode:

• Post consistently and predictably 
• Balance the tone to make news entertaining without trivializing serious events
• Develop repeatable storytelling templates.

Case Study: Local News International. Using “Dave,” a recurring character who plays the role of the viewer, they use skits and satire to cover complex news in 40 seconds. By using a consistent personality and humor, they stop the scroll and build a recognizable brand identity without formal news constraints.

Mode 2: Seek

 The vibe: actively looking for information on a specific event or topic
 The energy: a highly intentional mode of engagement, where consumers look for efficient, information-rich content that delivers depth and relevance without distraction
 The strategy: anticipate information needs and make depth accessible without friction, ensuring that credibility, clarity and efficiency guide every interaction.

How to win at Seek mode

• Guide discovery
• Deliver curated briefings
• Offer live formats that capitalize on immediate interest.
• Create seamless, intelligent discovery experiences (such as Ask FT or Ask The Post)
• Design intuitive interfaces within owned and operated platforms
• Provide high utility & control, such as personalization and tailored formats based on preferences, such as listening or watching

Case study: Particle News. Using AI, Particle gives readers the option of discplaying a news story in 6 different ways, including the 5 Ws, opposite takes on the same story or “Explain like I’m 5”

Mode 3: Subscribe

 The vibe: receiving news from trusted, curated sources (newsletters, podcasts, SMS) they have deliberately chosen
 The energy: people expect timely, important or novel updates from sources whose judgement they rely on and do not want to miss
 The strategy: timely, important or novel updates from reliable sources

How to win at Subscribe

• Sync with daily habits: design delivery around audience routines and preferences, not output cycle, and consider offering personalized delivery
• Make subscribe content dense and easy to complete
• Standardize the skeleton for instant scanning
• Keep it short, but provide options to go deeper

Case Study: The Edinburgh Minute. Short paragraphs, plain language, softer tone in the introduction. A long list of key stories, each with two sentences max, along with an emoji to introduce the topic, and a follow-up link.

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2. The “Consumption” Modes: how news is processed

In consumption modes, users have chosen to explore a subject, topic or piece of content more deeply.

Audiences are more likely to engage further when a story feels personally relevant, emotionally resonant or sparks their curiosity. How they first encounter that story does not dictate what happens next. Differing motivations shape how individuals choose to go deeper, whether by verifying facts, exploring context, or interpreting different perspectives.

The 2025 findings reinforce the three modes we previously identified that capture how audiences engage more deeply with the news once they move beyond sifting. These modes are:

Mode 4: Substantiate

 The vibe: verifying if a story is true or finding the "raw" facts.
 The energy: an individual has found a piece of information and wants to quickly and credibly confirm it
 The strategy: create content that is promptly delivered after a breaking news event, focusing on main claims and facts, with simple, clear and digestible evidence at its core

How to win at Substantiate mode:

• Meet your users where they are
• Prioritize accuracy and relevance over speed or volume
• Push information directly to audiences
• Lead with facts
• Create quick, easily digestible formats 
• Show your working: stitch primary source materials into the content and show the process

Case Study: UnderTheDeskNews. They often presents sources behind the presenter, such as maps of military facilities, to promote substantiation. These successful news producers clearly annotate from where information has come using overlays on videos or references in text, and they include links in captions and bios

Mode 5: Study

 The vibe: going deep to master a topic of personal or professional interest
 The energy: a desire to learn, upskill and be inspired. For some, Study mode is a pleasant distraction
 The strategy: producers can create long-form, information-dense content that has high-production value to keep engagement high, while showcasing their lived experience as a means to draw users into the story.

How to win at Study mode:

• Bring the story to life through immersion.
• Show interest
• Open with the motive, not just the topic
• Use mini-brands or shows as a creative sandbox
• Start with a learning goal to orient the audience and prime attention
• Favor evergreen or under-explored themes
• Build concepts progressively and go deep
• Bring it to life: gather original evidence, showcase it in original ways and develop a signature form

Case study: HowTown. 15-plus-minute long-form videos that have clear chapter markers and distill key information while allowing users to easily go back or skip specific sections.

Mode 6: sensemake

 The vibe: looking for context to understand why a story matters and what to think about it
 The energy: exploring different viewpoints to form opinions. This mode is driven by curiosity and a desire for co-learning spaces
 The strategy: create spaces that invite perspective and transparency, helping audiences process disagreement and find orientation amid uncertainty

How to win at Sensemake mode:

• Create open conversations 
• Create open spaces for honest conversation where producers and audiences can think aloud
• Clearly disclose your intention, bias or viewpoint
• Orient and include the audience: vary show formats to deepen engagement and reach audiences across information needs
• Use livestreams to transform news into a shared, real-time act
• Use satire wisely (it should stimulate critical thinking): build recurring inside jokes to make complex topics feel approachable and foster a sense of belonging

Case Study: Morning Brew. Always linking to the original business story to anchor it to one company and how it’s being covered. Satire is created through the use of props, funny captions and over-the-top personas or characters.

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3. The “Social” Mode: how news is shared

Mode 7: Socialize

 The vibe: sharing news to connect with others, express identity, or start a conversation. Sharing news is less a discrete consumption habit and more of a long-term relational action to be considered in its own context.
 The energy: there is no more valued, trusted source than friends and family, and often these connections help cut through content overwhelm.
 The strategy: build for sharing!

How to win the Social mode:

• Make sharing a “product feature” that makes the action effortless for readers
• Create content that people want to send to their peers
• Leading news producers tell human stories
• Consider using memes to condense meaning and inject humor

The takeaway for publishers

The “Next Gen” consumer is suffering from information overwhelm. To win, publishers must transition from being “content factories” to “mode-specific service providers.” Whether it’s winning the first two seconds of a Scroll or providing the deep-dive tools for Study, success in 2026 and beyond depends on meeting the audience exactly where they are.

The post Beyond the feed: mastering the 7 modes of next gen news engagement appeared first on Audiencers.

Newsletters are a powerful engaging, habit-forming and loyalty-building tool for publishers employing a reader revenue model. And there are a huge variety of ways that you can convince a reader to convert.

To inspire your efforts, we’ve created this collection of benchmarks, categorized by format-type.

Landing page

Banner

Side module

Within the article

At the bottom of the article

Pop-up

Newsletter wall

Newsletter quiz

Special section for The Washington Post who built a newsletter quiz for readers to discover the newsletter best suited to their interests.

The post Benchmark ideas to increase newsletter subscribers appeared first on Audiencers.

Anil Chitrapu is a Senior Product Manager at Wirecutter (The New York Times), focused on audience growth, AI, and personalization.

In this article, Anil takes a closer look into the operational choices, guardrails, and tech powering Wirecutter For You, the new email that tailors article recommendations to each reader - without compromising editorial judgment or inbox trust.

Wirecutter, The New York Times’ product recommendation service, shares the same commitment to rigorous journalism that defines our broader newsroom. The mission has always been to help readers make confident, informed decisions about the products in their lives – from the best gifts to the most reliable home appliances – through reporting that is diligent, transparent, and practical.

But reaching readers today requires more than great reporting; it demands a thoughtful approach to how we surface that journalism. As information overload grows and attention spans shorten, the challenge expands beyond what we publish to how we make sure each reader finds what’s most relevant to them. Within Wirecutter, we’ve long debated how to use technology to serve that mission responsibly. Our goal has never been to replace the editorial voice that makes our product recommendations distinct, but to use personalization as a way to extend it: to help every reader find the next right thing at the right time, in a way that still feels unmistakably like us. Every design decision flowed from that principle: personalization should extend editorial judgment, not replace it.

This idea led to the creation of Wirecutter For You, a weekly email that pairs our journalism with a robust personalization model to deliver tailored article recommendations. The system we created is designed for flexibility and oversight, allowing us to refine its rules and outputs in tandem. In the sections that follow, we’ll share why we built it, how it works, and what this taught us about bridging editorial craft and algorithmic systems.

Why we built it

As we entered Q4 (our yearly peak shopping period), Wirecutter wanted to build a resilient and automated channel to help our readers find their way to more of our journalism. Even with our deep library, many readers only ever see a fraction of Wirecutter’s journalism. Personalization gave us a way to surface the right story at the right moment. Our goal was to develop a product that would be able to learn from a reader’s browsing history while maintaining Wirecutter’s standards.

At a high level, we wanted to answer a simple question: how could we introduce personalization to Wirecutter in a way that still felt entirely editorial and intentional? For us, that meant starting in a space where we could test carefully, learn quickly, and retain full control over tone and trust – and email was the perfect place to do that. It gave us a contained environment to experiment with personalization primitives, measure impact, and ensure deliverability, all without changing the on-site experience until we got signal.

We designed the program’s format with three primary goals.

• First, we wanted to build a weekly reading habit that complemented our existing editorial newsletters – adding value without displacing what readers already trusted.
• Second, we used the email as a controlled space to learn: testing personalization logic, tracking engagement and trust metrics, and validating that the signals we captured could scale across unique email sends.
• Finally, we treated this as an early framework for how personalization should operate across Wirecutter: establishing the rules, measurement standards, and editorial guardrails that future products could build on.

Because this was new territory, we defined success less by traffic spikes and more by durable reader signals: quality over clicks, inbox trust and deliverability, and long-term engagement that deepened – not diluted – our relationship with subscribers.

What we launched

After a small hardcoded pilot earlier in the year gave us confidence in the format, we began developing a dynamic weekly email that could scale – one capable of dynamically tailoring article recommendations for readers while staying true to our editorial standards. The goal was simple but ambitious: to create a product that felt more personal than automated, guided by the same standards our newsroom applies to any published work.

We launched Wirecutter For You as a weekly Sunday send, designed to deliver personalized story recommendations alongside a concise, editor-written lede. Each version draws from a reader’s recent engagement and a curated, editor-approved content pool. We started with a small, highly engaged audience to monitor performance and maintain deliverability as we introduced a new sender identity.

The post How NYT Wirecutter shipped a personalized newsletter in 4 weeks appeared first on Audiencers.

Jason Langsner is Group Product Manager, Data + AI, at The Washington Post. In this article he shares how they built The Post's in-house generative AI chatbot tool that uses RAG to answer user questions.

The Washington Post may be best known for our world-class and award-winning reporting, but we also pride ourselves on being industry leaders in technology and innovation. In addition to producing journalism, we also develop products and solutions in house to improve our users’ experience, including through our expanding AI Pod Research & Development (R&D) lab and newly launched WP Incubator.

The post The Washington Post: how and why we built “Ask The Post AI” appeared first on Audiencers.

This article summarizes the session given by Max Leroy, Audience & Product Strategist, formerly of The New York Times, CNN, and POLITICO, at The Audiencers’ Festival in Paris on September 16th, 2025.

This session in 5 bullet points:

• The real danger to the media? Not AI or Google, but our disconnect from readers’ needs.
• Plummeting trust, dwindling audiences, refusal to pay: in any other industry, we would be talking about the collapse of product-market fit.
• Urgent action is needed: we must stop pointless obsessions and return to users’ needs.
• This series of articles presents five toxic obsessions and offers five useful obsessions for the sustainability of the media: short circuits, communities, respect for audience needs, experimentation, and advertising/UX balance.
• This second chapter covers obsession number two: refusing to interact with audiences

What if the greatest threat to the media were neither AI, nor the “big bad wolf” Google, nor the decline in social traffic, not even the crisis in publishers’ business model? What if the real problem were us? Us and our persistent inability to take an interest in those we are supposed to serve: our readers.

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Heiko Scherer is the founder and CEO of tchop. In this article, Heiko discusses how the homepage has changed - it's no longer about one screen. It’s about shared intent that runs through the entire product experience.

Homepage traffic has been drifting downward for more than a decade. In most newsrooms, this is no longer up for debate. The signs are clear: visits have declined, session durations are shrinking and the ways people arrive are more fragmented than ever. What used to be the front door is now, for many users, a side entrance they rarely walk through.

Still, what’s more interesting than the data is how publishers have responded.

Some tried refreshing the homepage with new layouts, hoping a better design might bring users back. Others added personalisation layers, believing algorithms might rebuild the kind of editorial judgement that once guided readers. A few removed the homepage altogether, shifting toward infinite scrolls or card-based structures that resemble social platforms.

The post The homepage isn’t dead, it just stopped being a page appeared first on Audiencers.

Over the past few months, the team at Hearst Newspapers has been experimenting with AI tools to create “Connecticut Today”, CT Insider’s weekday audio news briefing. 

At first the team thought they could summarize a few stories, top with a quick introduction and finish with a closing sentence or two. Those initial scripts were fine but not really something you’d want to listen to daily to get caught up on the news. 

The post Launching a weekday audio news briefing with AI at CT Insider appeared first on Audiencers.

I'm Alexander Held, Senior Data Scientist at DER SPIEGEL, and this is the third article in a series on our audience engagement strategy. 

> In part one, we explained how we built our engagement score, a blended metric for measuring reader interaction (link to part one). 

> In part two, we showed how combining the score with surveys and interviews helped us identify our most important engagement drivers (link to part two). 

> In this article, we describe how we apply the score across our product and channels, and why a one size fits all approach to a diverse audience is inefficient for sustainable revenue growth. 

Short recap: Our engagement score ranges from 0 to 100 and gets calculated for  every user who has visited spiegel.de in the last 30 days, subscribers and non subscribers alike. By combining signals such as visit frequency and reading time, the score provides a holistic view of involvement and defines four audience segments:  Occasional (0–25), Regular (25–50), Active (50–75), and Fans (75–100). 

We use these segments to tailor subscription offers, adjust our paywall to balance reach and conversion, prioritize product features that drive engagement (especially during onboarding), and improve targeting on external platforms. The aim is a more relevant experience for casual readers and our most dedicated fans, which supports short term conversions and long-term retention.

The following is a collection of our key use cases. 

Dynamic offers 

We built a dynamic offering system that adapts subscription offers to audience  segments. Experiments showed three things:

• Each engagement segment responds best to different offers
• Higher engagement correlates with interest in discounted long term plans
• A notable share of orders, especially among higher-engagement readers, comes from former subscribers. 

Based on these findings, we introduced dynamic pricing. Footer banners and paywalls now present tailored offers aligned with each reader’s engagement score. This moves us away from one size fits all and toward a strategy that maximizes both initial conversion  and long-term retention, ultimately capturing the highest possible revenue per reader  over time. 

From left to right:

• Paywall with tailored offers for low engaged users
• Paywall with tailored offers for mid engaged users
• Paywall with tailored offers for high engaged users

While dynamic offering has shown promising results, challenges remain. For occasional readers, while conversion rates improve with low-barrier offers, retention rates remain low, underscoring the need for improved onboarding and engagement strategies to  sustain these subscriptions. 

Dynamic paywall 

We use the engagement score to adjust access to premium content by reader  segments. Our experiments showed:

• Highly engaged users had a 54% higher conversion rate when exposed to more paywalled articles on our front page
• Even the lowest engagement segment saw a 19% lift.
• But as expected, paywall exposure also reduced article views and time spent, especially among highly engaged readers.

Overall, conversion gains outweighed the losses, but we are still working to optimize this tradeoff  (more on that in the outlook). 

Nevertheless, the results show that a dynamic paywall converts highly engaged users efficiently while not (too) significantly deterring less engaged readers. It helps us  balance reach and monetization. 

Prioritizing product features that drive engagement 

To focus development on features that matter, we ran audience research using  interviews, surveys, and behavioral data analysis. This work surfaced the key drivers of engagement. You can read more about our approach here. 

We are now increasing interaction with these drivers through product changes, including targeted nudges in onboarding journeys and greater visibility on the front page. In A/B tests, a  tooltip-guided tour outperformed a banner-based onboarding, lifting the average engagement score by 9 percent and increasing usage of the features introduced during onboarding. ** 

We also restructured our email onboarding based on the study’s insights. These tests have not shown a meaningful uplift yet, but we are evaluating next steps. 

Other use cases and the path forward 

We export the engagement score to external platforms such as Meta, enriching platform profiles with on-site signals for more precise audience targeting. Furthermore, we use the score internally to enrich surveys with engagement data and to report how segments evolve over time, giving marketing, editorial, and product a shared metric to align growth, retention, and reactivation. 

Next, we are combining the engagement score with additional signals, such as content interests, so personalization reflects both involvement and intent. In parallel, we are moving beyond a short lookback by incorporating long-term engagement and the increasing reality of re-subscriptions. To support this, we are testing a proof-of-concept for a customer lifetime value model that feeds into the same use cases: offers, access, onboarding, marketing (and even more). The goal is a longer-term perspective that captures sustained engagement and repeat subscriptions while making the product more relevant for every reader.

Thanks to Simon Schwandner for proofreading and Valentin Espagné for the images (and for driving so many of our A/B tests and innovative experiments at SPIEGEL).

The post How DER SPIEGEL uses its engagement score: our key use cases  appeared first on Audiencers.

In a strategic move to engage a new and distinct audience, as well as take note from the creator economy, The Economist has launched its data journalism newsletter, “Off the Charts,” on Substack. This decision to venture beyond its traditional paywalled ecosystem offers a fascinating glimpse into how a legacy publisher is adapting to the evolving digital landscape. 

I sat down with Aaron Coultate, Head of Newsletters at The Economist, to discuss the thinking behind this exciting initiative.

Context: an innovative idea for an established product

The idea to launch “Off the Charts” on Substack was born from a great collaboration between the editorial and commercial teams. The core idea? To find a new way to reach potential readers who are interested in a specific area of The Economist’s content but might not be ready to commit to a full subscription just yet.

“Off the Charts” was the perfect candidate for this experiment. It originally launched as a free newsletter back in 2021 before being moved behind the paywall as part of the core subscription. But the vibrant data journalism community that had grown around it was too compelling to ignore. This new Substack launch, which was in the works for several months, is a deliberate effort to both create a new revenue stream and nurture that niche audience.

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