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

Until recently, many publishers have used icons or tags to mark premium content, informing non-subscribers that the article is premium before they visit the page and get blocked by the paywall. But there are a variety of reasons why this is potentially shooting ourselves in the foot. Lars K Jensen discussed this in an article on Audiencers back in 2023.

“What you are trying to do is to put the conversion (from free user to paying subscriber) into the user journey as soon as possible. Because that’s what you want… By having a nice “beware, premium content ahead” icon (or, should we say warning), you are short circuiting that thought process and user journey. Conversions need to happen based on the article on your website, not how you market it in a newsletter or on the homepage.”

It’s perhaps one of the reasons why we’ve seen increasingly more publishers trying the opposite, i.e. signalling when content is free to access.

The New York Times and The Verge for instance have been testing this for a while now, dedicating a whole section of their homepage to “free to read” content.

The Independent in the UK also has a tag on a newsletter promotion, highlighting the 1 month free.

Subscription without blocking content

Most publishers solve the reader-revenue question by drawing a line through their content. Some of it is free to access, some of it is paywalled. SFGATE deliberately didn’t do that. The web stays entirely free, exactly as it’s always been. What’s new sits next to it, a separate, paid app.

This means that the web traffic and engagement on-site is preserved whilst the brand also monetises their most engaged, loyal readers through an additional product.

> Read the full story of SFGATE’s subscription launch

Subscriptions focused on community and relationship-building

In an age of content abundance, where generic content becomes easier to produce, the advantage shifts to what is harder to replicate: original journalism, trusted relationships and communities that give audiences a reason to return. It’s what FT Strategies has labelled as the Community Era, and it’s exactly what many publishers are moving towards with subscription products built for lasting relationships.

The Economist launched Play, a $15/month audio and video-only tier that sits inside the existing Economist app. It sits alongside a second, deeper relationship-building product: The Economist Insider, a premium video series featuring the editor-in-chief and senior journalists interviewing figures like Christine Lagarde and Mark Carney. A year after launch, 75% of subscribers have engaged with Insider at least once, and churn drops further the more deeply someone engages with it, holding even through a recent price increase.

Together, Play and Insider represent two ends of the same strategy: Play is built to start a relationship with a reader who isn’t ready to commit to the full product yet, Insider is built to deepen the relationship with a subscriber who already has.

WIRED also relaunched their subscription offer in Summer 2025, priced at $48 a year (roughly $4/month) with a focus on connecting “our humans to all of you humans.” (Katie Drummond, WIRED):

• A five-newsletter subscriber suite. Two entirely new verticals launched alongside three previously free, popular newsletters moved fully behind the paywall. All five are written conversationally by senior correspondents and built to work as standalone “inbox reads,” not link round-ups, which is what makes them habit-forming rather than skimmable.
• Structured, cadenced livestream AMAs, running at least twice a month, with a staff moderator and two senior beat reporters per session. The tight panel format, rather than sporadic ad-hoc video, self-selects WIRED’s most loyal readers and puts them in direct, live conversation with the reporters covering their beat.
• Expanded commenting, rolled out across a much wider set of articles than before, specifically to turn passive readers into visible, active participants in the conversation around a story.
• Subscriber-exclusive narrated articles, giving the subscription a genuine audio dimension rather than treating audio as an afterthought.

Alternative ways to bridge the gap to subscription

Anjali Iyer, Global Head of Subscriptions at The Washington Post, delivered a session at the Audiencers’ Festival in London focused on how news organisations must adapt to modern consumer habits by expanding beyond rigid subscription structures.

Anjali shared how data exposes a massive gap between the willingness to pay and the willingness to commit. Modern readers, particularly younger cohorts, operate in a fragmented digital landscape shaped by streaming services, search loops, and social algorithms. They demand short-term choices and are often willing to pay a premium price for temporary access over a recurring subscription package.

The Washington Post’s solution is flexible access models. By testing micropayments (weekly and pay passes, as well as pay-per-article with an in-paywall mobile payment module), they’re uncovering an untapped market: 42% of flexible access buyers are brand-new, anonymous users outside the existing ecosystem.

Interestingly, this also serves as a nurturing mechanism for long-term subscriber growth. Over a 180-day period, up to 8% of week-pass buyers, 4% of day pass buyers, and 3% of pay-per-article buyers eventually became full subscribers.

> Read the full story from The Washington Post

A handful of publishers are also testing “pay what you can” subscription models.

For instance, Canada’s Les Coops de l’information, a cooperative of six Quebec news outlets, let readers name their own price for the first three months.

Against a C$1 minimum, the average paid was C$3.12 a month, and some new subscribers chose more than the regular subscription price. The suggested figure in the price field did the work – suggesting C$5 lifted the average to C$3.39 and suggesting C$6 lifted it to C$3.50, with more people simply taking the suggestion each time.

84% of the acquisitions were from readers who had never subscribed before, which is the number that matters most for a local publisher wondering whether it has exhausted its market.

> Read more about Les Coops’ pay what you can campaign

Selection over unlimited access

Faced with information fatigue, unlimited access as a subscription product is not always 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.

> Full article on Les Echos’ “La Sélection” here

Not trying to sell on day 1

When benchmarking 100 subscription businesses, including the NYT, Figma, Miro, Calm and Masterclass, we noticed that conversion almost never happens in one step. It happens through micro-conversions, each with its own objective. And more importantly, even conversion itself seems secondary. The real priority for those companies is to create value for their user as a first goal. To build engagement. To make the product feel indispensable before asking for money.

The critical principle behind this model: each step has one single goal.

Figma: $12.5B company.
Homepage: “Get started for free.”

Then the onboarding starts: What’s your name? How do you plan to use Figma? What do you do for work? Where do you work? Have you used Figma before? What do you want to make first?

Six questions before you see anything about pricing. And when the plan page finally appears, it includes a free Starter option, so even the pricing step isn’t a hard sell.

The New York Times even tested this earlier in the year. The only action button in the top right corner (on the left) says “LOG IN”, because at this stage, the goal is registration, not subscription.

Countdowns

Whether on the paywall, subscription offer page or in the registration onboarding journey, publishers are using countdowns to create urgency and encourage conversion. It’s a small change but one that’s proven to convert.

> Find 100+ paywall benchmarks here

> 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

Code is now accessible to anyone with a laptop and an internet connection. The old bottleneck, engineering resources, is gone. That should mean launching more ideas, iterating faster, and learning about our audiences and our mistakes along the way.

At the Minnesota Star Tribune, where I launched our first two audience-facing AI products, the most useful lessons came from how readers responded, including when the products missed the targets we’d set for them.

So how do you hold the line on credibility while building products you’re willing to let fail? A few suggestions:

Build a public sandbox

Separate the experiment from the main product. A subdomain with a clear label (beta, labs, sandbox) tells readers what they’ve found and resets the expectations they’d bring to the homepage. The readers who show up are opting in, and they still give you real behavior data. From there, the experiments that work can graduate to the main site.

At the Star Tribune, our first audience-facing AI product came out of a four-week experimental sprint. The result was Culinary Compass, a restaurant guide based on a reader’s in-the-moment vibe. We launched it on the primary domain, and readers who found it there expected a Star Tribune restaurant guide, not a rapid experiment. It told us a lot about what our audience expected out of us, and what they expected our use of AI to deliver for them. A sandbox would have taught us the same thing but for less.

Give it an end date before it starts

Decide when the experiment ends before it launches. Better yet, experiment with something that has a natural beginning and end. This helps remove the call of pulling something, so if the end date was always the plan, no one has to make the call to shut it down.

I launched Fair Bot in 2025 for the Minnesota State Fair knowing it would only be useful for the 12 days the fair ran. It let readers use semantic search to explore more than 300 food vendors by craving instead of by name, for example “something fresh” in a sea of fried food, or “sweet but not too salty.”

The tool logged more than 10,000 searches, but a surprising number of them asked where the bathrooms were. We had built a food finder, but our audience wanted a better fair guide. When the fair closed for the year, so did the bot, and the insights we gleaned went into planning for the next one.

Show your work

I miss the blogs newsroom product teams used to keep about how they built something. A lot of thinking goes into a product decision, and most of it disappears once it ships. Writing it down forces the team to say what they believed and why, and it gives the rest of the industry something to build on.

I worked at Vox Media from 2015 to 2022 in roles that straddled the newsroom and product teams, and I had the chance to contribute to a few posts for the Vox Product blog. I was reading the blog before I worked there, and it shaped how I thought about the job long before I was in the seat.

Product blogs are also where a version of a post-mortem can live. We already have a practice for when we need to issue a correction, and a flavor of this practice should extend to our experimental products.

We work in an industry that expects transparency from the people and institutions we cover. Holding the products we build to some version of that standard is a reasonable place to start, and it’s the part of the journalistic mental model that experimental products should look to keep.

Let’s compare notes

I’m the AI Strategy and Product Advisor at Hacks/Hackers, and I want to take the lessons above to independent newsrooms, the ones without a product team to spare. I work with news organizations as they think through where AI belongs in their work, from adoption and training to tooling and audience-facing products. Whatever the project, the work starts the same way: try it, watch what happens, and write down what you learned.

> "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

I’ve run this process with several businesses this year, and it holds up every time. You don’t decide what the subscription includes and test it against the market. You pull apart your own traffic and behavior data, and only decide whether to keep building once that data has told you there’s something worth building. Here’s the process.

1. Start with frequency

The first thing I want to understand is what people already come back for.

For a publisher, that might be a morning briefing, live coverage, a specialist vertical, or a particular columnist. Pull the behavior of your most engaged users and look for repeat patterns. Which products correlate with higher visit frequency? Which newsletters reliably drive people back to site? Which features appear disproportionately among your most active readers?

You’re looking for evidence of habit. A large audience visiting once or twice a month may still support a strong advertising business, but it’s weak evidence for a recurring consumer proposition.

2. Use registration as the next test

Before asking for a credit card, see whether people are willing to identify themselves.

Registration introduces a small amount of friction and asks the user to exchange something of value, usually an email address and some data, for a better experience. That makes it a useful halfway point between anonymous consumption and payment.

Look at registration conversion by audience segment, then compare registered and anonymous users on frequency, depth of engagement and eventual propensity to subscribe. If your most engaged readers still resist creating an account, that’s worth understanding before a payment step goes in front of them.

If registered users who adopt a particular feature come back twice as often, that is a much more useful metric for proposition design than a brainstorm full of ideas about premium benefits.

3. Build paid around behaviors that already exist

Once you know what your most engaged registered users are doing, the paid proposition should give them materially more of it. That might mean greater access, deeper utility, more convenience, exclusivity or a better experience. The important part is that the benefit connects to something the audience has already demonstrated it values.

If live coverage drives unusually high frequency, ask what a paid live product could add. If specialist newsletters show the strongest retention signals, deeper analysis or additional editions may have more value than an unrelated bundle of perks. If users who save articles and follow topics return more frequently, better personalization may be more compelling than another piece of “exclusive content.”

This is also where proposition bloat creeps in. If the pricing page needs six unrelated benefits to make the subscription feel substantial, that’s usually a sign no single benefit is compelling enough. The pricing page should be the output of the proposition work rather than the place where the proposition gets decided.

4. Be deliberate about what remains free

Publishers have an added complication because the free product often drives discovery, advertising revenue and the future subscriber funnel.

Moving too much behind a paywall can reduce the audience available to convert later. Leaving too much outside makes the paid proposition harder to distinguish. The answer depends on what role each piece of content or product plays in the journey.

A story that brings thousands of first-time visitors into the funnel may have a very different job from a specialist product consumed repeatedly by known users. Treating them identically because they both sit on the same website is usually where the trouble starts.

5. Forecast before you keep building

Most subscription models get validated backward. Leadership picks a revenue number it needs, and the team keeps building a forecast toward it without checking whether the underlying audience can actually get there. The proposition work continues on momentum, not evidence.

Build a forecast from the same data steps 1 to 4 produced instead. Take your traffic and frequency segments, apply a realistic registration rate, apply a free-to-paid rate benchmarked against comparable products at a similar price and frequency, then apply a retention curve for that price tier. You’ll get a range, not a number. Compare it to what the business actually needs. If the range falls well short, that’s the signal to stop building and rethink before the pricing conversation even starts. Better to find out before another quarter of work goes into a proposition the audience was never going to support.

Do the work in steps one through five before you launch anything. Some of what comes back will tell you to stop, and that’s a better outcome at step five than a year into a proposition the audience behavior was never going to support.

If those behaviors aren’t there, you’re not a subscription business. Yet.

Jonathan Gupta-Buckley founded The Twenty One and Kaai after a decade leading audience and subscription growth inside News Corp, including building the largest paid subscriber base in Wall Street Journal history. Today, TTO embeds senior operators inside the world’s most ambitious businesses to solve growth challenges in practice, build internal capability, and leave teams with systems they can run themselves. Kaai is TTO’s proprietary AI growth analytics tool. It is currently deployed at a number of global media businesses. 

> 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

We have entered the Age of Experience

Audiences still seek information and fulfill particular needs. But they increasingly choose where to spend their time according to the experience surrounding that value and whether another worthwhile experience follows.

Where platforms provide a compelling sequence, attention accumulates.

Experience is more than a new way to describe “what users need.” It is a different way to organize what an outlet produces, how its products connect, and how audience value translates into revenue.

Asking “How can we improve this product?” therefore misses the sharper question: “What experience should audiences consistently associate with us, and what should lead them to the next one?”

Experience connects audience strategy with revenue

Publishers often move quickly from an audience goal to an output.

“We need to reach younger audiences” becomes “we need more vertical video.” “We need stronger habits” becomes “we need a daily newsletter.”

Those may be sensible decisions, but format alone does not explain why audiences will value the product, return, or move deeper into the publisher’s environment.

This is the logic behind the AXR framework: Audience → Experience → Revenue.

We developed AXR to connect audience strategy to revenue through the experience layer. That layer defines the audience moment, the promise, how the product fits into a routine, and the behavior it should create. Revenue can then follow through advertising, sponsorship, registration, or subscription.

For example, “a daily video briefing for younger audiences” is still mainly a format description.

A stronger definition would describe the audience moment, why the briefing is worth returning to, what should follow it, and which elements, such as the host, pace, promise, or format, must remain recognizable across the sequence, beyond this particular item.

That difference matters, because formats produce outputs. Experiences create behaviors. Repeatable behaviors (staying longer, returning more often, and moving to the next item) connect audience value to commercial value.

The most valuable experience is the next one

One strong experience is rarely enough.

Audiences do not open a social platform for one excellent post and then move elsewhere. They stay because another relevant experience is immediately available. Games create the same continuity from one level or challenge to the next.

Publishers still tend to optimize the individual item. Even when related content is offered, the connection is often topical rather than experiential. The next item may cover the same subject but feel completely different in format, pace or purpose.

In our work developing recurring video experiences, we see a passive-active pattern. After one video, many viewers prefer to remain passive and let the next one begin. But when the experience changes too sharply, they must become active again: reassess, choose, and decide whether to continue.

That decision point is often where they leave.

Continuity does not mean showing people more of the same. It means preserving enough of the experience promise while introducing new value.

Before launching a new product or format, publishers should therefore ask:

• What experience begins here?
• What is the most natural next experience?
• What must remain consistent so the next experience feels like a continuation rather than a new decision?

These questions change the unit of planning. Instead of producing one engaging item and hoping the audience finds another, the publisher begins designing the sequence itself.

The bottom line

The strategic question is no longer only which products to build, but how those products combine into a recognizable experience system.

The practical shift is to evaluate every product on two levels: the experience it creates on its own, and the role it plays in the sequence around it.

Publishers that design both levels can turn journalism into more time spent, stronger habits, and clearer paths to revenue. In the Age of Experience, the advantage will belong to outlets that make every experience valuable, recognizable and naturally connected to the next.

> 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

Few newsrooms are commissioning representative surveys. Meanwhile interviews cost real money and real weeks. Plenty of newsrooms also treat editorial instinct as the senior evidence, with audience feedback consulted after instinct has already decided. The effect is the same either way. When there is appetite for research, it goes to the bigger decisions while everything else gets settled without evidence.

That leaves a whole category of question permanently unanswered. The recurring ones, where we need to know:

• How a piece of content will serve the users we are trying to reach
• Which of several options is weakest, before we invest in any of them
• What objections a change is likely to run into
• How different groups of users will react to the same thing

To take one example: whether a new onboarding flow feels like a data grab. Too small to commission a study for, and it creates uncertainty that festers within the onboarding team. So somebody decides on instinct, usually whoever is most senior, and the instinct becomes the decision. It might well be right. Nobody will ever know though because nothing was tested.

This is the gap synthetic audiences fill. They don’t have to replace the research we already do well. What they change is the dozens of small calls a month that currently run on somebody’s hunch. And for the many newsrooms doing no audience research at all, the comparison isn’t between synthetic feedback and properly-designed research. It’s between synthetic feedback and nothing, and synthetic feedback done well beats nothing.

The value is that a whole class of decisions moves from opinion to evidence, at a cost and speed that makes asking routine.

What a synthetic audience is

“Synthetic audience” is an umbrella term, and the field hasn’t settled on what sits under it. You’ll see people use synthetic personas, synthetic users, digital twins, synthetic panels and silicon samples loosely and sometimes interchangeably. I’m using three here because the differences between them change what we can trust.

• A synthetic user is an LLM given a description of a type of person and asked to respond as they would. We write the description – the segment’s age range and media habits, whatever we know – put it in the prompt, and ask the question. Nothing sits underneath it. What comes back is the model’s averaged sense of what somebody matching that description would say, assembled from everything in its training data about people like that.
• A digital twin is built from one real person’s own material: an interview transcript, their survey answers, their support emails, their behaviour on our site. That material goes into the prompt and the model answers new questions as that person. It’s also possible to fine-tune a commercially available LLM on the material instead of putting it in the prompt. Academic studies from Stanford and Columbia tested that route, and neither found it beat simply placing the person’s words in the prompt. Which is good news for small teams: building a twin should not require any substantial engineering lift.
• A synthetic panel is either of the above, multiplied. We can build twenty or fifty across our segments, put the same question to all of them, and read the pattern rather than any single answer.

What synthetic audiences are good for

A number of use cases have emerged. These four have the clearest evidence behind them:

Testing language before it goes out. Paywall messages, membership appeals, onboarding copy, campaign framing. We put the draft in front of a synthetic panel and ask what they find unclear and what would make them stop reading. This is the highest-value use, partly because language is what these models handle best, and partly because it’s a decision we make most often.

Narrowing a field of options. Six newsletter concepts, or four pricing structures. The panel won’t tell us which one to launch. But it will reliably tell us which ones are weakest. So, the real research budget goes to the survivors. Spindrift Beverage Co., a US drinks brand, used a synthetic panel to choose between new product concepts. It reached the same answer in a week that a 500-person consumer study had taken two months to produce.

Surfacing objections we haven’t thought of. Ask the panel what would stop them subscribing, or what they’d assume about us from our about page, and we get a list of concerns to check. Where synthetic panels have been compared against real research, they have identified the same themes and the same objections. The magnitude and the proportions differ, the list doesn’t.

Comparing groups against the same thing. Put one paywall message to a synthetic lapsed-subscriber panel and a synthetic loyal-reader panel, then read the difference. Relative comparison is where these systems are most useful.

You may have spotted that all of the four use cases narrow or pressure-test rather than decide.

How accurate are synthetic audiences

A figure of around 85% circulates in coverage of this field, usually without much explanation. Read quickly, it suggests a synthetic version of a person gets 85 out of every 100 answers right. That isn’t what was measured.

The figure comes from the Stanford study. Researchers interviewed just over a thousand people at length. They built a digital twin of each person from their interview transcript. Then they tested whether the twin could predict that person’s answers to a survey the person had already completed.

But they didn’t score the twin’s answers against the persons’ answers and leave it at that. Because if we ask a person to fill the same survey again two weeks later, they match their own earlier answers only about eight times in ten. (Memory shifts!) So, the fair question is not whether a twin matches perfectly. It’s how a twin does against that eight-in-ten human benchmark.

Researchers found that the synthetic twins reached roughly 85% of that human benchmark – so, not 85% of the answers.

The figure tells us that synthetic twins built on rich individual material carry genuine signals about the people they came from.

The evidence in this field is still early, and most of it comes from outside media. For me, that’s another reason to use synthetic audiences for narrowing rather than deciding, which is what the rest of this piece argues for.

Where synthetic audiences stop

They can’t tell us how much. Synthetic responses cluster. Real people spread out more across opinions, while synthetic ones converge towards each other. A panel can tell us which option comes out ahead. It can’t tell us by how much, which rules out adoption rates, willingness to pay, satisfaction scores and anything else where the size of the number is the finding.

They can’t show us the small segments that matter. Clustering also means that the users at the edges disappear: the share of subscribers already on their way to cancelling, or the small core of superfans who account for a disproportionate slice of revenue. Those groups are small because they’re specific, and specificity is something a synthetic panel loses.

They tilt in a consistent direction. Synthetic respondents tend to be WEIRD (Western, Educated, Industrialised, Rich, and Democratic). They come out more comfortable with technology, less worried about privacy, better informed and more agreeable than the people they’re modelled on.

They shouldn’t speak for communities we haven’t spoken to. A team in the US building a WhatsApp media-literacy service for the Indian diaspora tried synthetic personas and got the diaspora as Westernised media imagines it. They caught it because they knew what the actual community sounds like. This is the kind of stereotype that synthetic responses can perpetuate.

How to start experimenting with synthetic audiences

Running a synthetic panel takes four steps, and each exists to make the next one worth doing.

The first fixes what you’re actually trying to settle, so you don’t generate material nobody acts on. The second decides what evidence the panel stands on, which sets how much weight its answers can carry. The third picks the kind of question you’re putting to it, which determines whether the answer is usable at all. The fourth gets an honest answer out, then records it in a form a colleague can judge for themselves rather than take on trust.

Set up once, the marginal cost of the next question is close to nothing. That’s the point. You’re building something you put questions to whenever they come up.

1. Fix the decision before you write the question

Most synthetic research fails before a panel is ever built, by producing interesting material nobody was going to act on in the first place.

Understand what you’re trying to achieve – what decision you’re hoping to make. Then determine the insights you need to inform the decision. Then figure out how to get those insights from a synthetic panel.

This ensures that the work you’re going to do is aligned with what you’re working towards. This is the step that stops the output becoming a slide nobody uses.

2. Decide what data the panel is standing on

What a panel stands on sets the ceiling on everything after it. The same question put to a well-grounded panel and an ungrounded one produces answers that may look similar but instead mean completely different things.

There are three levels:

• Nothing. A prompt describing a reader, with no evidence underneath it. What comes back is the model’s general impression of people like that.
• Demographics. Market or audience data about the segment you’re aiming at. Better, and still a description of a category rather than of anybody.
• Real material from real readers. Words that actual people you can name said or wrote.

The test is whether you can name the humans whose words are in the system. If you can’t, you’re on one of the first two levels.

Reaching the third level may be easier than it looks, depending on what your newsroom has kept. Worth looking for:

• Past interview transcripts, from any research project, however old
• Open-text answers from surveys (which usually go unread)
• Subscriber cancellation reasons
• Support and complaints emails
• Comments and community posts

Prepare the material by anonymising the person and keeping the detail that makes them specific: their circumstances, and what they came to you for. Spread it across your segments rather than piling it up. Fifteen people covering five reader types beats forty of the same type.

3. Pick the job

There’s more than one kind of question you can put to a synthetic panel. Two are worth setting out here, because most teams use the first but never the second. Choosing deliberately between them is what separates a usable answer from a merely plausible one.

• Count them. Put the same question to the whole panel and use the aggregate: which of six concepts is weakest, or which objection comes up most often. Read the ranking and ignore the margins. This is the workhorse, and it’s subject to the tilt described earlier.
• Analyse against them. Instead of asking how users would react to a membership appeal, ask where that appeal conflicts with what you’ve documented about them. The model isn’t being asked to be anyone. It compares your draft against a profile you wrote and can check, then hands back specific points of friction you can verify or throw out. Most teams never try this, and it’s the job best matched to what these systems genuinely do well, which is analyse language.

4. Make it argue back, then record what you did

Left alone, these models are agreeable, and an agreeable panel tends to tell you what you already believe.

Three things to build into the prompt:

• Tell it to answer as a reader rather than as an analyst. Otherwise the responses arrive sounding like a dull report, with tidy conclusions no human user would share.
• Tell it that being indifferent is a valid answer. Plenty of readers genuinely don’t care, and a panel where everyone holds a strong view is already wrong.
• Show it a few examples of how real people answered a similar question, with any polished phrasing taken out.

Then narrow with the panel and validate the surviving one or two options with real people. You’re only testing what has already survived something, which is where the saving comes from.

Finally, write up what you ran: who the panel was defined as, what it was grounded in, what you showed it, what you asked, what came back, and what still needs checking. Synthetic output is convincing whether or not it happens to be right. In one study, experienced researchers reviewing persona material could not reliably say which of it had been written by people. Making the output less convincing doesn’t help either, because results that read as obviously machine-made don’t get acted on. Since you can’t remove the believability, the write-up is what stops a plausible answer becoming a fact by repetition.

Start with the research you already have

The case for synthetic audiences isn’t that they closely match real readers. Academic testing has consistently found that they don’t. The case is that most audience questions in most newsrooms are currently answered by whoever is most senior in the room, and a synthetic panel grounded in things our actual readers said is better than that.

So, use them to narrow, not to decide. Ground them in real material, and know which level you’re on. Ask them to compare and to analyse. Keep them away from magnitudes, from the small segments that may matter most, and from communities you haven’t met. And, finally, validate what survives with people.

To get started: find the research your newsroom already commissioned and hasn’t opened since – the launch interviews, the subscriber survey, last year’s churn study – and work out which of this month’s decisions it could answer, if only you could ask it questions.

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

Let’s take a closer look at how it works in this excerpt from the book:

• Reach

Reach includes every interaction with potential subscribers, whether through your own channels or through partners, referrals, paid advertising, or other touchpoints.

a) Anonymous contacts: most of these contacts remain anonymous. They have interacted with your brand, but you cannot identify or contact them directly.

b) Leads: Leads are potential customers whose identity is known and who can ideally be contacted directly. Examples include newsletter subscribers, freemium users, registered accounts, or prospects collected through sales or telephone campaigns.

• Trials

Trial users are people who are testing the subscription through a free or discounted offer while deciding whether the product provides enough value to justify becoming paying subscribers.

• Active subscribers

Active subscribers are customers who have paid at least one regular subscription fee. Later in this article, I’ll explain in more detail where I draw the line between trial users and active subscribers.

Within the subscriber base, customers often move between different pricing tiers through upgrades and downgrades, increasing or decreasing their customer value.

A subset of active subscribers consists of customers with expiring subscriptions. When subscribers cancel, their subscription usually remains active until the end of the current billing period. In other words, the cancellation has been submitted but has not yet taken effect. During this remaining term, there is still an opportunity to persuade customers to reverse their cancellation.

• Former subscribers

Once a subscription has expired, customers become former subscribers. If they have given permission to be contacted, the win-back process begins. Former subscribers effectively become qualified leads again and can be targeted with dedicated retention and reactivation campaigns to encourage them to subscribe again.

Let’s go through these stages in detail:

1. Reach

The first step in any subscription business is making sure that potential customers become aware of your brand, your product, and your subscription offering. To achieve this, you should continuously expand the range of channels through which you reach your target audience. In addition to your own website, these channels may include social media, podcasts, public relations, events, partnerships, and many other customer touchpoints.

However, reach alone is not enough. What matters is reaching the right people. Ten million impressions may sound impressive, but they have little value if the audience has no genuine interest in your product. That’s why every subscription business should define an Ideal Customer Profile (ICP)—a description of the customers who are most likely to become successful, long-term subscribers.

Very few products are truly designed for everyone. Instead, companies should focus on the people or organizations that have a real need for what they offer. If you cannot clearly define your target audience, you risk wasting both time and marketing budget on customers who are unlikely to convert. An ICP therefore defines not only who you want to attract, but also who you deliberately choose not to target.

The best starting point is your existing subscriber base. Analyze your most valuable customers, identify the characteristics they have in common, and use those insights to guide future acquisition efforts. At the same time, you should regularly challenge your assumptions and explore whether there are attractive customer segments beyond your current audience.

For the digital subscription of a regional newspaper, an Ideal Customer Profile might look something like this:

Potential subscribers have lived in the region for several years, feel connected to their local community, and are interested in topics such as local politics, schools, transportation, public safety, sports, real estate, and local events. They are typically between 35 and 64 years old, employed, and have started a family. Many own their homes and expect to remain in the region for years to come. Although this profile is still relatively broad, it also illustrates that younger audiences or digital nomads may currently be much harder to convert into paying subscribers and therefore might not be the primary focus of acquisition efforts.

At the same time, additional customer segments may exist outside this core audience. For example, people who no longer live in the region may still have a strong interest in the local football club. The German newspaper Ruhr Nachrichten identified exactly this opportunity and introduced a separate, lower-priced BVB subscription for Borussia Dortmund fans living outside its core distribution area. This represents a second Ideal Customer Profile that can be targeted independently and generates up to 2,500 additional subscribers during a single football season.

Once you have identified the right audience, the next challenge is to create a positive first impression and spark genuine interest. Or, as Lucas von Gwinner and Dirk von Gehlen would put it, to build a strong brand. In their book Macht Marke, they argue that a brand is far more than a company’s name or logo—it is its reputation.

Before people decide to subscribe, they need to understand what your brand stands for and why it solves their problem better than any competing alternative. Strong brands communicate a clear positioning that differentiates them from competitors. As a result, they not only increase awareness but also improve conversion rates and strengthen long-term customer retention.

2. Leads

Before asking people to commit to a subscription, it is often worth getting to know them first and gradually introducing them to your product. A well-designed lead generation strategy helps build a large pool of potential subscribers whom you can engage with on an ongoing basis.

Compared with anonymous visitors, leads are significantly more likely to become subscribers. Because you know more about them, you can tailor your communication and offers to their individual interests, needs, and stage in the customer journey.

One of the key metrics at this stage is the Lead Rate, which measures the percentage of people reached who are willing to share their contact information and give permission to be contacted.

For example, if 5,000 out of 100,000 people reached provide their email address and marketing consent, the Lead Rate is 5%.

Lead generation serves two important purposes. First, it creates an owned audience that you can contact repeatedly without relying solely on paid advertising or third-party platforms. Second, it allows you to build trust over time before presenting a subscription offer. Especially for products that require a high level of commitment or involve a longer purchasing decision, this gradual relationship-building can significantly improve conversion rates.

3. Trials

In most cases, customers can only determine whether a subscription is worth paying for after they have experienced the product firsthand. Free or discounted trial offers lower the barrier to entry and encourage potential subscribers to give the product a try.

There is no universal formula for determining the ideal trial length or discount level. Companies continue to experiment with different approaches, and in my interviews I have seen everything from one-day trials to introductory offers lasting two years. Some businesses prefer short trials that generate revenue quickly, while others use longer trial periods to help customers build lasting habits, making the subscription an integral part of their daily lives before they begin paying the full price.

One of the most important phases of any subscription begins during the trial: onboarding. Effective onboarding helps new customers discover the value of their subscription as quickly as possible. The sooner they experience meaningful benefits and establish regular usage habits, the more likely they are to become long-term subscribers.

The Trial Start Rate measures the percentage of people reached who begin a trial. For example, if 2,000 out of 100,000 people reached start a trial, the Trial Start Rate is 2%.

The Lead-to-Trial Rate, on the other hand, measures how many known leads convert into trial users. If 500 out of 5,000 leads sign up for a trial, the Lead-to-Trial Rate is 10%.

Not every trial results in a paid subscription. Users who decide not to continue after the trial often return to the lead pool. If they have given permission to be contacted, they can be nurtured through future marketing campaigns and may eventually decide to subscribe.

4. Active subscribers

Active subscribers are the foundation of every subscription business. They generate predictable, recurring revenue and therefore determine the long-term health of the business. At this stage, the primary objective is to retain subscribers for as long as possible while increasing their lifetime value through upgrades and cross-selling where appropriate.

The Conversion Rate measures the percentage of people reached who become paying subscribers. For example, if 1,000 out of 100,000 people reached purchase a subscription, the overall Conversion Rate is 1%.

Like most funnel metrics, this KPI can also be broken down by funnel stage. If 200 of those 1,000 subscriptions originated from 2,000 trial users, the Trial-to-Paid Conversion Rate is 10%. Likewise, if 750 out of 5,000 leads purchase a full subscription, the Lead-to-Paid Conversion Rate is 15%.

One important question remains: When does a trial become an active subscription?

In practice, the boundary is not always clear. Many subscription businesses offer heavily discounted introductory pricing before customers begin paying the regular rate. However, not every discounted offer should be classified as a trial. A clear definition is essential because it directly affects the accuracy of key performance indicators such as retention and churn.

Consider the following examples:

1. DIE ZEIT

The German newspaper DIE ZEIT offers a print subscription in which the first 13 issues cost €5 each before increasing to €7.50. Since subscribers pay roughly two-thirds of the regular price from the very first issue and generate at least €65 in revenue, I would classify them as active subscribers from day one.

2. BILDplus

A BILDplus subscription costs €1.99 per month during the first year before increasing to €7.99 and can be cancelled at any time. In this case, I would consider only the first month part of the trial period. Once the subscription renews, customers become active subscribers, even though they continue to receive a substantial introductory discount.

3. The New York Times

The annual All Access subscription is sometimes available for $20 instead of the regular $90. Because these customers pay only around one-fifth of the standard price—and many are likely to cancel before ever paying full price—there are two reasonable approaches. They can either be treated as long-running trial users or as heavily discounted active subscribers.

Why does this distinction matter?

Cancellation rates during trial periods are typically much higher than among established subscribers. For that reason, trial users are generally excluded from core KPIs such as Retention Rate and Churn Rate. An inconsistent or poorly defined distinction between trials and paid subscribers can therefore distort these metrics and make performance comparisons misleading.

The Retention Rate measures the percentage of active subscribers who remain subscribed from one period to the next. If you begin the month with 10,000 subscribers and 9,500 are still active at the end of the month, your monthly Retention Rate is 95%.

The Upgrade Rate and Downgrade Rate measure how frequently subscribers move between pricing tiers. For example, if 200 out of 4,000 subscribers on the Basic or Standard plan upgrade to a higher-tier subscription within a month, the monthly Upgrade Rate is 5%.

4.5 Expiring subscriptions

Expiring subscriptions are subscriptions that have already been cancelled but remain active until the end of the current billing period. Although this stage is often overlooked, it can last for a surprisingly long time.

For example, a customer with an annual subscription may cancel on the very first day simply to avoid missing the cancellation deadline. Even though they have already submitted their cancellation, they remain an active subscriber for another 365 days. That also means you have 365 opportunities to convince them to stay. In many cases, a cancellation is not a final decision against the subscription but simply a precaution to avoid an unwanted renewal.

The Cancellation Rate measures the percentage of subscribers who submit a cancellation request during a given period. If 300 out of 10,000 active subscribers cancel within a month, the monthly Cancellation Rate is 3%.

The Cancellation Rate should not be confused with the Churn Rate. The two metrics often differ because a cancellation usually occurs well before the subscription actually ends.

For example, if a customer cancels in January but still has six months remaining on an annual subscription, they contribute to the Cancellation Rate in January but only enter the Churn Rate in June—provided they do not withdraw their cancellation beforehand.

In addition, the Churn Rate includes not only voluntary cancellations but also involuntary churn, such as failed payments or expired payment methods.

The Reactivation Rate measures how many subscribers reverse their cancellation before their subscription expires. If 30 out of 300 cancelling subscribers decide to stay before the end of their billing period, the Reactivation Rate is 10%.

Finally, the Save Rate measures the percentage of subscribers who begin the cancellation process but never complete it. In other words, it captures the cancellations that were successfully prevented before they were ever submitted.

4.6 Former subscribers

Once a subscription has expired, the customer becomes a former subscriber and the win-back process begins. Provided you still have permission to contact them, former subscribers can be reached through email, phone calls, push notifications, or other marketing channels with tailored offers and messages designed to encourage them to return.

From the perspective of the subscription funnel, former subscribers effectively become highly qualified leads again. Unlike new prospects, they already know your product, have experienced its value, and have demonstrated a willingness to pay in the past. As a result, win-back campaigns often achieve significantly higher conversion rates than campaigns targeting first-time subscribers.

As a subscription business matures, win-back becomes increasingly important because a growing share of the addressable market has already subscribed at least once. Over time, acquisition efforts naturally shift from attracting entirely new customers to re-engaging former ones.

For that reason, it is useful to measure how many people you are targeting have previously been subscribers. The Former Subscriber Rate measures the percentage of contacts—such as recipients of an email campaign or visitors reaching a paywall—who have held a subscription before.

This metric provides an indication of how frequently you are “fishing in the same pond.” While it is often impossible to identify former subscribers across third-party channels such as social media, logged-in users on your own website can usually be matched to their subscription history.

In some industries, particularly video streaming, customers regularly pause and restart their subscriptions depending on the content available. In these markets, cancellation is often not the end of the customer relationship but simply a temporary break.

The Win-Back Rate measures the percentage of former subscribers who return within a given period. For example, if 1,000 out of 5,000 former subscribers purchase another subscription within one year, the annual Win-Back Rate is 20%.

Viewed as a whole, the subscription funnel is not a linear process but a continuous cycle. Every former subscriber represents a potential future customer, making retention and reactivation just as important as acquiring new subscribers.

> 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

The exercise: fill in the blank

To find that sense of uniqueness, Goligoski runs newsroom leaders through simple prompts about their organizations:

• First in the subject, region, or world to ___
• Best compared to competitors in ___
• Only organization to ___

The exercise surfaces an origin story fast. But it’s only half the picture, and it’s the easy half — anyone can answer these questions for themselves around a conference table. The real value comes from asking the same questions of the people you’re trying to reach, who often can’t name your staff or your specific value prop but can place you loosely (“oh, that’s the one about the business of fashion”). The gap between the two answer sets is the actual work. It tells you what you think differentiates you and what has actually landed.

Her rule of thumb for this kind of testing: n+1 is better than n. At a minimum, show your prototype to one more person than has seen it already. You’ll walk away smarter from even one conversation, and Goligoski says fifteen years into doing this work, people still thank her for simply being asked for their perspectives.

But watch out for “feature soup.” When you start testing value props and benefits, it’s easy to try to cram everything onto one page or into one pitch. If a first-time visitor needs a magnifying glass to understand what they’re being offered, you’ve already lost the differentiation you were trying to build.

A few examples she pointed to of publishers getting specificity right:

• Caper, a New York food media outlet, prices its annual subscription against hyper-local reference points: “less than a burrata pizza slice at L’Industrie, a potato pancake at Veselka” instead of the generic “less than a cup of coffee a day.” It grounds the price and quietly reminds readers how many small recurring charges they’re already carrying.

• The New York Review of Books and The Paris Review have a once-a-year summer bundle offer for two publications with possible audience overlap and growth potential, a financially compelling deal rather than a permanent merge.
• Capital B, a Black-media outlet in the US, sent an anniversary email that included an actual screenshot of the text exchange between its two co-founders deciding to launch. It reads as human in a way most subscription marketing doesn’t.

Goligoski’s closing asks for anyone doing this work: 

• Limit friction wherever you find it
• Take a less-is-more approach to any data you collect (only ask what you’ll actually act on)
• Keep a running, visible wishlist of the features and coverage areas you hear people ask for. That list becomes a genuine insight bank over time.

The Skift case study: is your product an audience or a feature?

Mines joined Skift, a B2B travel media and events company, two years ago and inherited what she called “a family I didn’t plan for”: four separate subscription products, none designed to sit together.

• Skift Pro: always-on travel news, home-grown, positioned as “the Bloomberg of travel”
• Skift Research: long-form, data-driven reports, also home-grown, aimed at strategy and analytics buyers making a major decision rather than staying informed daily
• Airline Weekly: a roughly 20-year-old acquired product, once faxed, now a newsletter with its own site, read closely by aviation executives (Delta CEO Ed Bastian has called it “the only thing I read every week”)
• Daily Lodging Report: a similarly old acquisition, a daily digest for hospitality and real-estate operators

Three questions before you package anything

1. Identity: does combining these products strengthen each one’s core value, or dilute it?
2. Audience: are these audiences actually growing toward each other, or are they deliberately separate?
3. Operations: can your UX, billing, and support genuinely hold a bundle together, or will the seams show? Mines runs subscriptions with a five-person team, a small product team, and a small design team, capacity is a real constraint.

Applying these questions to the Skift context

Airline Weekly: bundled the content, not the experience. Skift already had aviation coverage in both Pro and Research, plus an aviation conference. The instinct was to fold Airline Weekly’s coverage into all of it. But the products stayed on separate platforms — readers had to hop between a newsletter, a website, and (for a much smaller group) a conference ticket. Layering content across products without integrating the actual user experience just produced a disjointed one. Nobody complained loudly about it; it showed up quietly instead, in renewal numbers drifting down. 

Mines’ takeaway: audit the identity of what you’re bundling before you bundle it. Packaging without UX clarity is just confusion with a price tag.

Daily Lodging Report: the right move was nothing. This subscriber base — Mines described them candidly as mostly finance and real-estate professionals — wanted exactly one thing: the digest, in their inbox, undisturbed. Skift gave the product a new site and new data tools anyway. The response was, in effect, I don’t care, just give me the newsletter. 

So, they left it alone, and it’s remained one of their steadiest, best-growing products. Leaving a product untouched is a strategic decision, not a lack of ambition. Some products’ value comes directly from their constraints.

Pro + Research: complementary, not interchangeable. These two live in the same sales conversation but serve different budgets and different personas — daily information versus decision-support research. Combining them into one package would have devalued Research and bloated Pro. Skift’s framing: they don’t need the same package, they need the same conversation.

Mines’ shorthand for thinking about all of this is a marriage metaphor: some products should merge households (full integration makes both better); some should keep separate addresses (related, committed, but each keeps its own front door); some need couples therapy first (real potential, but only after the UX and integration work happens); and some are simply happy as they are, and the kindest, smartest move is to leave them alone.

From the Q&A: a few things worth stealing

Don’t let a new feature justify a price increase — check whether anyone asked for it first. When Skift acquired the Women Leading Travel membership group, the team raised prices and then went looking for features to justify the increase — a research report, an add-on. Mines stopped that process to ask a more basic question: did any of these members actually want the research report? They hadn’t. What they wanted was networking and job connections, and that’s where Skift redirected. Goligoski added a comparable example: ProPublica offers a branded t-shirt only after someone donates, framed as a thank-you rather than a subscription perk — about 17% of donors take it, self-selecting into people who’ll actually wear it and become visible advocates, rather than shipping merchandise nobody asked for.

Test new features free before you charge for them. Goligoski’s suggestion for the free-versus-paid feature question: launch new features free first, watch uptake, get people to describe the value in their own words, and work out the rough edges before you decide whether it becomes a paid add-on or a built-in part of the product.

When you research your gap between positioning and perceived value, don’t blend your audiences. Talk to loyal long-term subscribers, low-frequency readers, and non-subscribers separately, and be explicit — internally — about exactly who any given finding came from. Self-reported research also goes stale fast, so treat it as something to refresh regularly rather than a one-time study you cite for years.

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The takeaway to carry into your next packaging conversation: don’t start with pricing, and don’t start with the bundle itself. Start with audience needs and wants, including the friction points people tell you about. The best package isn’t the one with the most crammed into it — it’s the one your subscriber would actually notice if it disappeared.

Mark Zohar, President and CEO of Viafoura, shares why he believes that Frequency (the F in RFV) is the most important metric to be tracking in an era where content is increasingly a commodity that AI can summarize, paraphrase, and serve without ever sending a click your way.

AI broke the funnel. Community rebuilds it.

The uncomfortable truth is that the open web’s traditional acquisition funnel, SEO at the top, content in the middle, monetization at the bottom, was always borrowed infrastructure. Publishers rented audience from platforms, and now those platforms are keeping the audience for themselves.

The publishers who will win the next decade are the ones who own the relationship, not rent it. And ownership doesn’t come from content alone anymore. Content is increasingly a commodity that AI can summarize, paraphrase, and serve without ever sending a click your way.

What AI cannot replicate is community. It cannot replicate the experience of debating a local election with your neighbors in a comment thread moderated by your newsroom. It cannot replicate a live Q&A with the reporter who broke the story. It cannot replicate the feeling of being recognized, replied to, and known by a brand you trust.

This is where Frequency lives.

The Frequency Loop

When publishers invest in community-building experiences like commenting, Live Q&As, topic-based chats, polls, personalized feeds, and real-time notifications, something powerful happens. Readers stop coming back for the content alone. They start coming back for the conversation.

That shift creates what I call the Frequency Loop

A reader visits for the content. They engage with the community. They get notified when someone replies, when a new live event starts, when a topic they care about is being discussed. They come back. They engage again. They register to keep participating. They subscribe to deepen the relationship. Every loop tightens the bond, increases the visit count, and unlocks more revenue per user.

The economics of this loop are not theoretical. Viafoura‘s data shows that engaged community members visit at a rate that is 6-8 times higher than fly-by users. And the downstream impact is well documented across the industry. Piano’s Subscription Performance Benchmark has found that registered users convert to paid subscriptions at roughly 45 times the rate of anonymous visitors. Frequency is what closes the gap between the two.

More frequency means more ad impressions. More frequency means higher page RPM, because engaged sessions monetize at premium rates. More frequency means a registered-user base that’s actually worth registering. More frequency means subscription offers that land, because the reader has already demonstrated, behaviourally, that they value what you do.

What the F should you actually do?

If Frequency is the metric, here’s the honest diagnostic every publisher should run:

Do you give your audience a reason to come back tomorrow that isn’t a new article? Do you have a place on your property where readers talk to each other and to your journalists? Do you notify them when their participation matters? Do you personalize what they see based on what they engage with, not just what they read? Do you treat your registered users like a community, or like a CRM list?

If the answer to most of those is no, you don’t have a Frequency problem. You have a community infrastructure problem. And in an AI era where every other lever (search traffic, social referral, content differentiation) is getting harder to pull, community infrastructure is the lever that’s getting more valuable, not less.

The bottom line

AI is going to keep getting better at answering questions. It’s not going to get better at being a community. That asymmetry is the single largest strategic opening publishers have right now, and Frequency is the metric that tells you whether you’re walking through it.

So the next time your board asks how you’re future-proofing the business against AI disruption, don’t show them a traffic chart. Show them your Frequency curve.

And if you don’t have one, well, you know what to ask yourself.

What the F are you doing?

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.

What’s working

A new “Join the Conversation” section of The New York Times was launched earlier in 2026, creating a discussion dedicated space for Times readers and reporters.

• On the article page, a featured comment modal directs readers toward the conversation window, which includes a refreshed layout and reader profile icons
• On some articles, Times reporters have shared thread-style breakdowns of their story and reporting process, adding context for readers who jump straight to the comments. These summaries, along with new inline headshots, build on the “enhanced bylines” introduced in 2023, which were aimed at highlighting journalists’ work

• Conversations are led by a direct question, followed by a comment from one of The Times’ journalists.

• The best contributions are highlighted and surfaced

This strategy helps conversations to feel valuable, and participation feels meaningful. Instead of noise, comments become an extension of the journalism.

What you should steal from The New York Times’ model

• Reframe comments as an editorial space, designed for quality over scale
• Lead the discussion with a direct question and a trusted voice
• Involve journalists directly in the conversations, ideally with a tag (think X’s blue tick), text or photo to highlight their position

Dive deeper into The New York Times’ model

• The New York Times is becoming subscription infrastructure for other publishers
• The New York Times dynamic paywall model, analyzed

2. Financial Times: loyalty isn’t built by speaking louder, it’s built by speaking with

Communities are built on trust, and for FT, this sits at the center of the brand. What’s more, it’s specifically recognized as a business driver. 58% of subscribers chose the FT specifically because they needed a reliable news source they could trust. To sustain it, they must strike a balance between trust in what they say, through quality and credibility of journalism, and trust in what they do, through the experience delivered at every touch point.

What’s working

Commenting has been a significant part of FT’s community-building efforts, especially as those who read the comments are 11x more engaged than those that don’t, and commenters themselves are 46x more engaged than those that don’t read or write comments.

• Community guidelines are easy to find and understand
• AI-powered moderation is being used to bring up the moderation baseline, re-trained off human comments, with humans still kept in the loop
• Editorially-reviewed AI-generated question encourage readers to join the conversation and be more thoughtful in their commenting

• A live Q&A feature, designed to encourage broader participation and lift conversation into the story

The team have also worked hard to cultivate “evangelical” communities, creating spaces for “authentic” and “nerdy” interactions that go beyond standard corporate communication.

The FT Alphaville section for instance has developed a core of evangelical fans by maintaining an authentic tone that appeals to readers on a personal level.

• It’s free to read
• Journalists engage directly with readers
• It doesn’t feel corporate or that the tone is being moderated
• You can meet the journalists in real life at small, unashamedly nerdy events!

These micro-communities focus on specific niches where belonging forms most naturally.

To deepen this advocacy, the FT also launched the Trusted Voices marketing pilot in 2025.

• Personal branding: By elevating individual writers and creating personal brands, the FT encourages readers to defend and promote specific journalists, which in turn reinforces their loyalty to the parent publication.
• Broadening inclusivity: Initiatives to make the FT feel less like a “gendered entity” (moving from 80% male in 2016 to 92% non-gendered perception in 2025) have been essential in making more people feel they belong, thus empowering a more diverse group of advocates.

What you should steal from FT’s model

• Move beyond engagement metrics towards trust, belonging and advocacy
• Use AI to reduce resources needed on moderation, but keep humans in the loop in the right places to build relationships with readers
• Consider building, or making use of existing, external, micro-communities that focus on specific niches – this is where belonging builds most naturally

Dive deeper into FT’s strategy

• Smarter discussions thanks to AI-generated talking points
• Building and serving communities with Ryan Y. Kellett, Financial Times and THECITY

3. Der Spiegel: from comments to conversations

The team at Der Spiegel found that whilst commenting was a huge part of their audience engagement strategy, conversations on a single topic were happening in multiple places across the site, diluting the potential value of this feature.

What’s working

The solution: a debating product inside of their website

• Qualitative research found that users associate trust and safety with these conversations when they happen under the DER SPIEGEL branding, so clear branding and a custom design ensures audiences feel confident that they’re in a safe space
• No possibility of “disliking” a comment, only liking

• They decided against real-name commenting after research that suggested users wouldn’t feel comfortable with this. They also have an account verification process, even for free members
• The feature is promoted in subscriber onboarding to increase regular usage amongst subscribers.
• And debates are integrated into editorial content and vice versa to encourage recirculation. Notice the high visibility of premium (S+) content here to promote the value of subscription.

Mexico’s Milenio has done something similar but with video content, fostering deep discussions on everyday life topics (Mexican lifestyle and social topics).

What you should steal from Der Spiegel’s model

• Test new, innovative formats to maximize the value of community engagement and encourage more guided interaction
• Integrate community features into the registration and subscription funnel
• Even if the community isn’t commenting at the end of articles, link community to content to encourage recirculation and continued engagement

Dive deeper into Der Spiegel’s strategy

• Going further with interactive engagement: introducing the debating feature
• Audience Research: identifying Key Engagement Drivers at DER SPIEGEL

4. The Times: reader-led journalism

The leading publishers have created an eco-system where readers fuel journalism that then fuels reader conversations, and so on. This is exactly what they’ve established at The Times & Sunday Times in the UK.

What’s working

After seeing conversations spark from a certain topic or article in the comments section, The Times’ Reader Comment Journalists reply directly to readers or set up interviews to dive deeper into the topic, with the goal of creating reader-led articles.

“Reader-led articles give a direct voice to our readers, but it also improves our journalism! The piece on the right below came from a call-out at the bottom of an article that got an amazing response from people telling us about how they beat their diagnoses. We ran it as a story and it really humanized the journalism. The other piece came from the simple idea of asking why people were running the London marathon. It pulled very touching stories together, and you could see in the comments section how positive the reaction was. These reader-led articles often bring yet more valued conversation within the community.” – Peter Jackson, Reader Comment Editor at The Times and Sunday Times

Live Q&As with journalists are another important engagement tool to directly connect The Time’s readers and writers.

The Times works with polls and embedded carousels to elevate comments, and they also feature reader contributions in their journalism, such as “Reader’s recipe”. This has a positive impact on the brand, and highlights how The Times’ values its readers.

What you should steal from The Times’ model

• Make use of reader comments to fuel journalism
• Consider strategies that help readers feel part of your brand and connected to journalists
• Use polls, quiz’s and Q&As to establish 2-way interaction

Dive deeper into The Times’ strategy

• Developing community beyond commenting, with The Telegraph and The Times

5. Other publisher relationship-building strategies of note

• The Globe & Mail’s live Q&As with journalists, call-outs and reader-led content
• The Economist’s Substack experiment: “Off the charts” is a newsletter on Substack, designed to engage new audiences who are already on this platform
• Digital Spy’s forums

• WIRED’s subscription re-launch, which now focuses heavily on community-building, in particular with their exclusive, subscriber-only newsletters, each with a writer at the forefront

• Handelsblatt Circles: lessons on community from Chief Growth Officer Jan Kleibrink
• Village Media have launched “Spaces” their own chatting platform, for users to connect with others based on topics of interest or joining in on conversations

> Daily Maverick built a database of contactable readers and their expertise

> The Guardian US meets underrepresented communities where they are

And the article you HAVE to read if you’re moving towards community-building: Why the most valuable newsroom KPI is no longer reach but belonging