> "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.

> Product decisions at most news organisations get made by whoever's loudest in the room, or by instinct built from experience. To overcome this, Germany's FUNKE Mediengruppe built a framework to replace that with something structured and evidence-led instead: a KPI tree

> Start with one metric at the top, not a dashboard. Funke picked Engaged Time, then broke it down layer by layer: what drives sessions, what drives active users, what drives new vs. returning. Each layer down gets more specific, until you hit something Stefan calls a movable metric, one a single product decision can actually shift

> Once you have even a partial tree, run a basic sensitivity check. Move two input variables by 5% each and see which one moves the KPI above it more. That tells you where to run your next sprint, not guesswork

> Only after the tree exists, build the org around it. Funke's four pillars (Reach, Engagement, Monetisation, Infrastructure) came directly from the tree's branches

> Don't start with the whole tree. Stefan's advice is to instrument one branch, show your team what it reveals, and let that build the internal case before you expand

How are product decisions actually made?

It is worth asking this question honestly, about your own organisation.

Is it the most senior person in the room? Is it instinct built from years of experience? Or is it different departments each pushing their own priorities, without a shared picture of what the product actually needs? Some news organisations have already built strong product cultures, where decisions stem from shared data and clear goals. For many others, the reality is messier.

The problem is not that any single input is wrong. The problem is the absence of a shared view, one where decisions are understood in relation to each other rather than evaluated in isolation. In practice, what often happens is that each team or department sees a part of the picture and acts on it. Everyone is doing something, but it does not click together, and a lot of value gets lost in the gaps.

When data is also missing from the equation, the risks compound. In product discussions about what to build next, maybe the loudest voice, the most confident person, or the most senior stakeholder gets their way. And maybe their suggestion is the right one. But without a shared framework to evaluate it against, there is a real chance that a lot of value gets lost. Hello, agency theory.

There is also a structural challenge specific to product development: new features are expected to have long-term impact, but evaluating that long-term impact after release is genuinely hard. Who hasn’t heard the famous “We simply have to implement Feature X, then in 6 months PV will have increased by xy%”. Too many things change between shipping a feature and having enough time to assess results. This makes it easy to keep moving by instinct, because instinct, in this environment, is rarely held to account.

So ultimately, the goal is to ground product decisions in evidence, not to take judgment out of the process.

Build the tree first

To solve this, the first thing we did was build a KPI tree.

For us as a news organisation, the metric at the top of that tree is Engaged Time, the total time users spend meaningfully interacting with content in our apps. Users choose to spend their time with us rather than somewhere else. The more intentionally they do that, the more value we deliver to them, and the more value we can generate commercially. When Engaged Time grows, the downstream indicators follow: ad impressions increase, subscription conversion improves, churn decreases.

Starting from Engaged Time, you break it down: what drives it? Session length and session frequency. What drives sessions? Active users. What drives active users? New users coming in, and existing users returning. How do new users arrive? Through different channels, direct, social, newsletter, search, each with its own conversion rate and return behaviour. How do existing users come back? Through habit, through push notifications, through content that gives them a reason to return tomorrow.

At each level of the tree, what you are really working with are ratios. How many visits convert to downloads? How many downloads become active users? How many active users encounter the paywall, and what share of those convert? Each ratio is a product decision waiting to be made. And there is something important about working with ratios rather than absolutes: it is easier to move five ratios by a small amount than to move one ratio by a large amount. The more levers you identify in the tree, the more opportunities you have to create meaningful impact.

The further down you go, the more granular the metrics become, and the closer you get to what we call movable metrics. These are metrics specific enough that a concrete product decision can actually shift them. You cannot ship a feature which directly increases Engaged Time. But you can ship a feature that improves how the homepage surfaces content, which changes the click-through rate to articles, which increases time in session, which moves Engaged Time. If you understand the chain, you know exactly what you are building toward.

Building the tree also means filling it with data. Some of it already exists. Some requires new tracking. The more granular you go, the more likely you will find instrumentation gaps. But as the picture fills in, something useful happens: you stop describing what the product produces and start understanding how it produces it.

The tree can also be filtered by user segment. The ratios look different for subscribers, registered users, and anonymous visitors. Understanding where those differences appear tells you where the most valuable product work is concentrated.

What the tree reveals

One of the most useful things a completed tree enables is simple calculation.

Once you have your first tree filled with data, even if only some branches, you can start to model the relationships between metrics. Take any KPI in the tree that is influenced by two variables below it. Increase each of those variables by five percent in isolation and see what happens to the KPI above. Which one has the bigger effect? A basic sensitivity analysis like this tells you quickly where the highest-leverage opportunities are in your product, and where effort is likely to be wasted.

Once you have identified a movable metric worth pursuing, the next question is how to actually move it. This is where mapping opportunities and solutions becomes important. For any given metric, there are usually multiple opportunities to influence it, and for each opportunity, multiple possible solutions. A structured approach, which opportunity has the highest potential, which solution is the most feasible, is what connects the diagnostic work of the KPI tree to actual product decisions. That process deserves its own deep dive, but it is the natural next step after the tree has done its job.

With that kind of visibility, prioritisation conversations change. Where should product investment go? What should marketing focus on? The tree does not make those decisions for you, but it makes the trade-offs visible. And visible trade-offs are easier to act on than invisible ones. That is the practical value: better prioritisation, and therefore better return on the investments you are already making.

A concrete example: once the tree was in place, we started observing new users as a specific segment. Sessions and page views among this group were dropping off within the first 30 days, pointing to a retention problem the aggregate numbers had been masking. Looking at what differentiated users who stayed from those who did not, the data pointed clearly to push notification subscription as a key factor. Push subscription rate during onboarding became our movable metric for this branch of the tree. We ran a series of A/B tests across the onboarding funnel, which increased push subscribers as a leading indicator, and significantly grew engaged time for this user segment, resulting in a higher 30-day retention rate as the lagging indicator.

From tree to organisation

Once the KPI tree was in place, the structure of the team followed naturally from it.

The tree has distinct branches, and those branches became four operational pillars:

Reach covers everything connected to bringing new users into the app and converting existing web readers into app users. App store presence, referral channel performance, cross-platform conversion. Reach asks: how do we grow the active user base?

Engagement takes over once a user is inside the app. Its mandate is habit formation: building the product and editorial conditions under which users return regularly and spend meaningful time. Onboarding, notification strategy, content personalisation, homepage design. Engagement asks: how do we make the app worth coming back to?

Monetisation is the commercial result of Reach and Engagement doing their jobs well. Ad revenue, subscription conversion, ARPU. Monetisation asks: how do we convert engaged attention into sustainable revenue?

Infrastructure is the supporting pillar: technical stability, data pipelines, analytics instrumentation. Without it, the other three pillars cannot move with speed or confidence. Infrastructure asks: are the foundations strong enough to build on?

And beneath all four pillars sits journalism, not as a pillar but as the foundation. Great editorial content influences all four. It drives Reach through word of mouth and search. It drives Engagement through loyalty. It drives Monetisation through willingness to pay. It makes Infrastructure worth investing in. The product framework serves the journalism, not the other way around.

The four pillars themselves are not a new idea. What matters is what sits beneath them: clear ownership, defined KPI sets, and the mandate for each team to break those KPIs further down within their branch of the tree. Downloads is a Reach KPI. But how are downloads generated? Through app store visits and the download conversion rate. Which of those can be moved? That is where the actual product work begins.

The pillars are independent in ownership but interdependent in practice. Reach without Engagement produces churn. Engagement without Monetisation produces a great product that does not sustain itself. A decision in one pillar usually affects the others.

Working together across pillars

Having clear ownership per pillar solves one problem. It creates another: how do you keep four teams, each focused on their own metrics, moving in the same direction?

This is where the organisational question becomes as important as the product question. Each pillar needs to understand what the others are working on, how prioritisation decisions are being made, and how a new feature or product change in one area will affect the others. Without that shared understanding, you end up with well-run silos rather than a well-run product.

What we built is a cross-pillar structure where all relevant stakeholders, from editorial and data to advertising, subscriptions, and tech, sit down together with the same data in front of them. Each pillar shares its learnings, its priorities, and its dependencies. Where one pillar needs something from another to move forward, that gets named and resolved together.

The goal is not process for its own sake. The goal is to get genuinely data-oriented as a group, not just in individual teams, but across the whole organisation. When everyone is looking at the same KPI tree and asking the same questions about what is moving and why, the conversation shifts. It becomes less about opinions and more about what the numbers are actually showing.

That shift is harder to achieve than it sounds. But it is where the real value of the framework sits.

A new way to think about what comes next

The KPI tree has changed how we make product decisions. The next opportunity is to apply the same thinking to strategic planning.

A user-centric planning approach starts with an understanding of how users move through the product: where they drop off, where they convert, and what drives them to return. Leading indicators first, active users, retention rates, engagement depth, push notification reach. The lagging indicators, including revenue, follow from those.

A subscriber target becomes meaningful when it is anchored to retention rate assumptions, conversion rate assumptions, and active user trends. A combination of leading and lagging indicators, connected through the KPI tree, gives every team a clear view of which part of the tree they are responsible for moving and how their work connects to the overall goal.

But the KPI tree also points toward something bigger. Every data point collected across all KPIs is a training signal. The logical next step is a statistical or AI-driven model that operates on top of the tree and surfaces the highest-leverage actions automatically. Imagine a system that tells you: sending four percent more push notifications related to sport, at 4:30pm, has a 78 percent probability of increasing MAU by two percent, which flows through to a 1.5 percent revenue effect. (Numbers are illustrative to get the point across.) The tree helps you understand your product and it can become the foundation for a system that actively tells you where to act next.

Even if we are not fully there yet, the tree gives us the language and the structure to get there.

Where to start

If you are thinking about building something similar, start with the first draft of the tree.

Start with one branch, not the whole tree. Instrument what you can, show your team what it reveals, and let the results build internal confidence in the approach. The framework earns trust the same way any good product does: by demonstrating value before asking for full commitment. Then expand.

It does not have to be complete. It will not be. Gaps in tracking will appear. Assumptions will turn out to be wrong. Metrics you thought were independent will prove to be connected. Correlation is not causation.

That is the process, not a problem with it.

The value of the tree is not in its perfection. It is in the discipline of building it, because building it forces you to ask, at every level, the question that product decisions too often skip: what needs to happen for this metric to change?

Ask that consistently. Break each number down until you find something specific enough to act on.

And when you find it, build toward that.

> 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.

Why prioritize segmentation?

Segmentation can be mind-bogglingly complex and wildly impactful.

The word comes from biology, where it refers to the process of cell division from a fertilized egg to a full organism. In business, it refers to identifying the constituent parts of a whole – ideally, a set of mutually exclusive and collectively exhaustive groups of customers. It can’t be perfect by definition, but even an imperfect segmentation can massively improve your bottom line.

At its core, segmentation addresses three fundamental business needs:

• Stickier content: By clearly defining  Ideal Customer Profiles (ICPs), newsrooms learn not just what to write about, but how to speak to their readers so the content truly resonates. If you replicate your ICPs as agents and train your newsroom to use them, you can get endless customer feedback at any time of the day.
• Smarter monetisation: Without segmentation, a dynamic paywall is only based on recency and frequency – which is fine, but does not capture your full market potential. . With it, you are “fishing where the fish are,” targeting the right users at the right time.
• Better marketing: Different segments live on different channels and respond to unique triggers. Segmentation tells you exactly where to find your users and helps advertising brands connect with theirs.

Choose your lens:

To build a complete picture of a user, like “Amanda, 40, VP of Marketing,” organizations can look through several analytical lenses:

Segmentation type	Focus/use case	Signal examples
Behavioral	UX Optimization	>5 visits per month, scroll depth, newsletter opens
Needs-based	Product Development	Prefers summaries, explainers, or opinion pieces
Value-based	Revenue Optimization	LTV, subscription tier, payment history
Technographic	Paywall Optimization	Device type (Mobile vs. Desktop), OS, Referral (LinkedIn vs. Facebook)
Demographic	Ad Sales / Strategy	Age cohort, geography, seniority

> Spektrum’s evolution of audience segmentation and testing

Five tips for succeeding in segmentation

1 – Hire strong analytics: This is the “single most important lever.” You need talent that can bridge the gap between high-level business strategy and deep data modeling.

2 – Make it cross-functional: Segmentation is a “Left Brain + Right Brain” exercise. Editorial, product, marketing, and data teams must own the model together; no single department can succeed in a silo.

3 – Quantify benefits early: You must build a business case or risk losing organizational priority. Use “Napkin Math” to show the potential uplift: for example, if a newsletter with a defined audience grows twice as fast as a vague one, what could the vague one be worth with a properly defined audience?

4 – Remember the customer: Segments should feel like people, not rows in a spreadsheet. Give them names like “Jane, the CMO” or “Joe, the Sales Lead.” Talk to them, build agents around their profiles, and ask, “Would Jane actually read this?”.

5 – Keep it simple (but useful): Aim for 4–6 mutually exclusive, collectively exhaustive (MECE) segments. If your team can’t name the segments from memory, your model is too complex and will fail to be actionable. Even if your goal is to build a dynamic paywall algo – lose (human) control of complexity, and you won’t know what’s working, which means you won’t be able to adjust next time the world changes.

Real-world impact: fewer paywalls, more subscription revenue

A US publisher’s journey from a rigid freemium model to smart segmentation highlights the power of behavioral data. They discovered that just 1% of traffic drove 25% of conversions.

By analyzing attributes, they found that newsletter click-throughs and direct repeat traffic were high-value, while Facebook and Android traffic converted at near-zero rates. By stopping the paywall for near-zero segments and doubling down on high-value behavioral and technographic signals, they achieved:

• A 2x increase in conversion rate.
• 50% fewer paywall exposures, leading to a better user experience for the same number of new subscribers.

Did this publisher get it completely, fully right? Absolutely not. But the model was a dramatic improvement over the simplistic frequency and recency model they were using before.

As statistician George Box famously said: “All models are wrong, but some are useful”. Be pragmatic—start with the data you have, build empathy for your readers, and iterate until your segments drive measurable growth.

> stern, published by RTL Deutschland, had been built as what Chief Product & Revenue Officer Johannes Vogel calls "a portfolio machine", optimised for mass reach with the distinct identities of stern, GEO and Capital dissolved into digital scale. Workable for advertising, fatal for subscriptions

> Weekly pricing beat monthly on conversion, especially among readers with little prior attachment to the brand. The app stores went back to monthly after weekly renewal prompts created friction

> GEO and Capital now outperform on both conversion and retention, because a sharply defined brand profile answers a specific reader need rather than a general one

Lessons:
> For a general interest publisher, personalisation is not a feature — it will be the product

> The article that converts is not always the one that retains. Content consistency matters more than acquisition volume

> Duolingo's streak counts consecutive days with at least one exercise and resets if you miss. Nine million people hold streaks of a year or more, and the feature has run through 600 tests in two years

> They optimise for CURR, the share of today's active users who were also active in the past week, rather than daily actives. Focusing on it since 2018 grew daily actives 4.5 times in four years

> The wins are often tiny. Changing a commitment button from "next" to "I'll commit" measurably lifted retention, and reminders fire 23.5 hours after the last lesson

> Streak Repair used to cost money. It is now free but requires extra exercises, choosing long-term loyalty over the easy revenue

> Lennart Schneider's warning matters more than the tactics. Streaks work because every Duolingo user shares one goal. News audiences don't, and a comic animation rewarding an article on the Middle East confuses rather than delights

What you'll get from this article:

> The root cause of slow teams: teams without clarity on organisational purpose struggle with unfocused discussions, overwhelming strategic possibilities, and difficulty prioritising.

> Key warning signs of purpose misalignment: including lack of clear articulation of what makes your organisation unique, vague mission understanding, and strategic decisions that miss key opportunities.

> The "work alone together" technique: a structured alternative to chaotic brainstorming meetings that generates more focused ideas by giving team members dedicated time to work independently before collaborating.

> A complete three-session alignment process: Session 1 aligns on users, problems, and competitive advantages; Session 2 identifies differentiators and decision-making principles; Session 3 prioritises the best approach using multiple evaluation lenses.

> The foundation hypothesis statement: or how to consolidate all team alignment into one powerful framework: "If we help [customer] solve [problem] with [approach], they will choose it over [competitors] because our solution is [differentiation]"