We interviewed Johannes Vogel, Chief Product & Revenue Officer at stern (published by RTL Deutschland) about how they relaunched a subscription offering from scratch, with teams & systems designed for digital reader revenue.

TL;DR
- The product & revenue team grew from 7 to 75 people, with early hires focused on product, data, engineering and Ad-/Affiliate and Subscription marketing
- Moving from monthly to weekly pricing lifted conversion, particularly among readers with less prior brand engagement; the app stores were reverted to monthly after weekly renewal prompts created friction
- GEO and Capital perform particularly well on both conversion and retention, with brand profiles addressing very specific user needs

Key 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
- Get editorial positioning right before optimising conversion. The funnel cannot compensate for a weak proposition

When Johannes Vogel, Chief Product & Revenue Officer, joined stern in late 2023, the digital operation wasn’t obviously broken. Traffic was significant, the brands were well-known, and the technology was functional. The problem ran deeper.

The entire organisation had been built on a portfolio model with its core business being mass-reach advertising. “It was a portfolio machine,” Vogel says. “The important thing was not the individual title or the distinctive brand — it was the massive reach.” The distinct editorial identities of stern, GEO and Capital had been quietly dissolved in the pursuit of digital scale.

For advertising, that was a manageable trade-off. For subscriptions, it was fatal. A reader revenue model only works if you are offering something specific enough that an individual reader will pay for it and, importantly, keep paying. stern’s digital setup had spent years doing the opposite.

Subscription sales sat in a separate silo from editorial and product, with few shared goals and little shared accountability. Vogel and editor-in-chief Gregor Peter Schmitz decided early that there was no point trying to reform what existed. “We had the big chance to build a new team,” Vogel says. “We could work on a new philosophy and a new culture.” They started with seven people, one business, and product organisation.

Building the team

Seven became 75, now heading toward 100. The priority among those early hires was product managers, UX/UI designers, data analysts, software engineers, and subscription marketing specialists. In the editorial areas, the focus was more an transformation the organisation toward a digital-first mindset and way of working. The journalism already existed across three established brands. What was missing was the capability to build, iterate and measure a digital product around it. A subscription business requires product thinking, data infrastructure and commercial mechanics that most legacy editorial operations had never needed to develop.

The post Stern+: How one of Germany’s biggest magazine brands rebuilt a subscription business from scratch appeared first on Audiencers.

In this guide, we share the various ways that publishers can segment their audience to develop a dynamic paywall model, with examples for each type of segmentation: 
> By content type: free vs premium, user needs, date of publication...
> By user profile: level of engagement, user status, location...
> By acquisition channel: Google, social media, newsletters...

Some quick definitions: 
> Audience segmentation: grouping users based on their profile or context, i.e. data that could impact their behaviour, inform us on what could convince them to subscribe or make them more or less likely to convert in that moment
> Dynamic paywall: a paywall that adapts for different audience segments. This could be in terms of the paywall design, messaging or even the journey itself (e.g. seeing a paywall or not). Note that "dynamic" doesn't necessarily equate to AI-driven. It's often rule based - i.e. a user reading a "politics" article sees a different paywall to a user reading a "sports" article.

There are 3 main buckets of audience segmentation with the goal of increasing conversion rates:

• By content type
• By user profile
• By acquisition channel

In each of these cases, the form of segmentation dictates who sees or experiences what. For instance, if we segment based on location, someone in Germany might see and experience something different than someone in the UK.

What can change based on the segment, in terms of the conversion strategy?

• The fact that the article is blocked or not
• The conversion journey (e.g. how many articles for free before the paywall, the use of a registration wall)
• The wording on the paywall
• The design (colours, image, etc.)
• The pricing and subscription offers pushed

Let’s dive into these 3 buckets of segments, with examples for each:

1. Segmenting by content type

For many publishers, this is the simplest starting point. It allows for extensive testing to discover what content converts best without risking overall traffic. It also helps to put editorial teams in control (to an extent) as the paywall strategy is based around their work instead of a user’s profile. This can be valuable when first launching a dynamic paywall to get buy-in from these teams.

This form of segmentation can take various forms:

• Content divided into free (often general news or commodity pieces) and premium (generally longer-form, investigative articles which can’t be found elsewhere)
• Format-based: e.g. articles vs games vs videos
• Topic- or tag-based: sports, politics, news, lifestyle
• User needs based: update me, give me a perspective, etc.
• Date of publication: e.g. articles published more than 30 days ago

Free vs premium

Organising content based on its value:

• Free content: keep open to maximize reach (often focused on ad revenue)
• Premium content: close to convert readers into subscribers

Some publishers are developing this a step further by adding 2 additional segments:

• Grey content: sometimes open, sometimes closed (based on editorial decision)
• Super Premium Content: with a hard paywall

This segmentation is often driven by two business goals: advertising and subscriptions. However, the articles used for advertising revenue (which aim to gain page views) can also serve the role of engaging audiences to move them through the funnel toward subscription.

A good place to start is a matrix analysis, placing articles in different buckets based on their “strengths”. A goal can then be established for each bucket, making the most of each article.

For instance, Mather suggests the following matrix:

• Mass appeal / top of the funnel: advertising revenue focus, so left open
• Premium content / bottom of the funnel: subscription focus, so behind a paywall
• Mission journalism: often left open
• Under-performers (that require further analysis)

A slightly more complex approach is illustrated by FT Strategies below, who established 4 categories based on topic popularity vs willingness to pay:

The post Guide: how to segment audiences for a dynamic paywall appeared first on Audiencers.

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

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

Why we’re focusing on media for this one

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

In media, it’s different.

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

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

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

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

The engagement-frustration trade-off

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

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

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

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

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

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

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

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

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

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

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The 6 levels of paywall sophistication

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

• Level 1: Fully manual

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

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

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

• Level 3: Manual with override.

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

• Level 4: Manual + a third automated category.

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

• Level 5: Fully automated for conversion and engagement.

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

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

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

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

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

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

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

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

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

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

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

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

According to the 2025 Reuters Institute Digital News Report, digital subscriptions have hit a plateau. Across 20 countries, only 18% of people pay for online news, a figure that has barely budged since last year. So if you feel like you’re hitting a ceiling with your core audience, you aren’t alone.

However, in their latest report on the power of registration, the 2025 FT Strategies and Google News Initiative (GNI) Subscriptions Academy found that the most successful publishers are stoping the “anonymous-to-subscriber” leap and focusing on the “middle ground”: Registration. By converting anonymous visitors into known users through a lightweight value exchange, you aren’t just collecting emails, you’re building the resilience needed for a privacy-first, platform-fragmented market.

The post Building resilience with registration appeared first on Audiencers.

Khalil A. Cassimally is an audience consultant and coach who helps organisations align teams around change, user needs and AI. 

Audience and funnel work succeeds or fails on internal communication, yet few people get structured support to improve it. Drawing on recent research and his own practice, he shares a practical three-step way to use AI to:
> Understand your intervention style
> Identify strengths and gaps in how you communicate
> Take small, intentional steps to improve how your communication skills

Most audience and product work lives or dies on internal communication.

Yet, communication is also where many people struggle. Doing it well can be hard! And while senior executives may have access to coaching and leadership programmes, the people doing much of the day-to-day work rarely do.

This is where AI becomes interesting.

In an article published by Harvard Business Review (HBR) last year, researchers Katharina Lange and José Parra-Moyano explored whether AI could support intervention improvement in a way comparable to human coaching. Working with 167 global executives, they used AI to analyse real conversations and provide feedback, which participants then compared with feedback from human observers.

The results were telling. About 30% of participants received feedback that largely validated what they already believed about their intervention styles. More importantly, around 55% landed in what the researchers called the “zone of learning”: the feedback was both surprising and useful, sparking new insights.

My takeaway from this research isn’t that AI replaces human coaches but that reflective feedback can be made far more accessible than traditional coaching ever allows.

Inspired by their research, I built a repeatable process for using AI chatbots like ChatGPT and Gemini to improve my own interventions.

The research-informed process breaks down improvement into three phases:

• Understanding intervention style
• Identifying strengths and gaps
• Taking steps to improve how to intervene

Phase 1: understand your intervention style

Understanding how you currently communicate is the first step to improvement. To achieve this, you have to understand your preferred style – which you can do systematically with some anonymised transcripts and specific prompts.

> Use a shared language: John Heron’s categories of intervention

Most of us have default patterns of intervention. We need to name those patterns otherwise “communicating better” remains vague. To do this well, we need a shared language.

The post Improve how you communicate with colleagues: an AI method tested by researchers appeared first on Audiencers.

Lennart Schneider, founder of Subscribe Now podcast & newsletter, and expert on digital subscription models, interviews Stefan Bader, CEO and Co-founder of Cello, to discuss how to build a referral program that ensures sustained growth.
-> Why visibility is the decisive factor
-> How to choose the right rewards
-> Why fairness wins
-> and how you can reduce the cost risk in customer acquisition

For years, companies have been struggling with the same challenge. Specifically, rising customer acquisition costs are making it increasingly difficult to win new subscribers at affordable prices.

There are many reasons for this – paid marketing is becoming increasingly expensive, SEO has less reach since the spread of LLMs, and social media algorithms play a significant role on who sees which content, and when.

However, there’s one growth lever that’s often overlooked, one that can help decrease dependence on external players… your own users. Satisfied, loyal fans are already recommending you, and if implemented correctly, this can become a valuable and inexpensive acquisition channel.

At least that’s what Stefan Badar, CEO and Co-founder of Cello, and guest on my Subscribe Now podcast shared, and he makes some great arguments. Cello, a company based in Munich, has built a referral software that’s now used by some of the world’s leading tech companies, including Miro, Typeform, and Riverside.

In this article, summarizing my conversation with Stefan, you’ll learn how to build a referral program that ensures sustained growth:

• Why visibility is the decisive factor
• How to choose the right rewards
• Why fairness wins
• …and how you can reduce the cost risk in customer acquisition

By the way: You can try out Cello directly. Using my referral link will save you €1,000 and I’ll receive a commission if you become a customer, a great opportunity to support my work.

1. Stop hiding referral programs

The biggest mistake for a referral program is hiding it in a sub-menu. Permanent visibility at the top of the app leads to the strongest user engagement.

The example above, from Neotaste, illustrates this very well. The recommendation program appears on the first screen when you open the app and above the map in the Discover tab.

Stefan gives five tips for optimal visibility:

1. If the recommendation icon is visible on the first level and not hidden in a submenu, the usage rate increases by 8x.
2. In addition to a gift symbol, the monetary value of the recommendation should also be displayed. A currency symbol increases performance by 3.3x.
3. A small animation also draws attention to the referral program – even if we’ve seen a screen many times before and are therefore blind to changes.
4. Moments of delight: Specifically look for moments when your user has had a positive experience, such as at the end of an article, or after giving a good rating in the NPS survey. These moments are the perfect time for a recommendation.
5. CRM integration: You should also regularly remind your users about the referral program via email or other push channels.

2. Cash beats most other rewards

A referral program is only as strong as the rewards it offers. There are many options: free months, discounts, raffles, vouchers, gifts, etc. But Stefan has seen that money works best for most customers.

There are several reasons for this:

1. Money is universally usable and therefore interesting for every user.
2. Money remains an incentive even after the tenth or twentieth recommendation, while free months eventually lose their appeal. Many users recommend a product not just once, but on average six to seven times. Money is therefore the perfect incentive for heavy recommenders.
3. Money is attractive even to non-subscribers. In freemium models, roughly 90% of users don’t have a subscription, but that doesn’t mean they won’t recommend the product. A successful referral program mobilizes all users, not just paying ones.
4. Money also works in the B2B sector: A free month may only benefit the employer who pays for the subscription, whilst a cash bonus goes directly to the employee making recommendations.
5. Money can be paid out over a longer period. And that brings us to the next point…

3. Rewards grow with lifetime value

Paid subscription advertising always carries a risk: Acquisition costs are incurred when a subscription is signed, but it’s only months later that you see whether a new customer has recouped those costs. That’s why a short payback period and a good ratio of Customer Acquisition Costs (CAC) to Lifetime Value (LTV) are so crucial.

A referral program can reduce this risk: The bonus doesn’t have to be paid out as a fixed amount at the start, but is distributed monthly as long as the referred user remains a subscriber. In Miro’s example, as the referrer, I receive 10% of the ongoing subscription fees. When the referred subscription ends, my bonus also ends. This eliminates the risk of spending more on a customer than you earn from them.

These ongoing payouts have another advantage: they remind users every month on their bank statement that the referral program is worthwhile, thus motivating them to make further referrals.

4. Bonuses must be worthwhile for both sides.

Another key to success is fairness: When I recommend a product, I’m investing my social capital. If my friends are disappointed or feel exploited, the relationship and my social standing can suffer. So it’s essential that users feel comfortable with the recommendation.

Rewards should therefore not be one-sided. Opt for a balanced reward rather than a particularly high bonus for the referrer, something that also motivates the receiving party to click through the link and use your product. Ultimately, a large number of invitation links sent out are useless if the offers aren’t redeemed, and an attractive, exclusive discount increases the acceptance rate.

However, you should ensure that the discount is time-limited – ideally for the first three or six months. If you offer an indefinite discount, you risk negatively impacting your ARPU in the long run.

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

The post How to build a referral program that drives subscription growth appeared first on Audiencers.

Khalil A. Cassimally, Consultant in Audience Development, User Needs & AI, and regular contributor to Audiencers, shares 7 strategies to not only become data-informed, but data-led.
1. Data is a compass, not a map
2. Connect every role to the bigger goal
3. User value first; business follows
4. Don't focalise on tools
5. Create one source of truth
6. Start with one analysis, and keep going
7. Optimise the funnel; small lifts compound

There is no shortage of data in media operations. There is also no shortage of stress and overwhelm in media operations – and an abundance of data is now contributing to it.

“There’s so much data – I don’t know what to look at, I don’t know what’s important.”

Data is an opportunity for media operations. It improves four things at once:

• Audience understanding (the foundation on which everything else lies)
• Content strategy
• Business performance
• Impact measurement

Unlocking these improvements rely on appropriately leveraging the data in meaningful and actionable ways. And when done systematically, harnessing data becomes a natural part of operations. That’s a data culture to strive for.

Building such a data culture doesn’t happen overnight. And some newsrooms are certainly further along on their data maturity ladder than others.

The data maturity ladder is made up of three rungs:

• Data-informed: we have a basic understanding and visibility on what’s happening
• Data-driven: we are actively using data to support strategic decisions, and ultimately growth
• Data-led: we are putting data in service of business outcomes, having systematised its leverage

The post 7 ways media can build a practical, empowering data culture appeared first on Audiencers.

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

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

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

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

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

Why we built it

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

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

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

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

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

What we launched

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

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

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

Ali Mahmood is a Digital News Strategist, helping publishers navigate the evolving digital landscape to build sustainable, audience-first revenue strategies—even in the most challenging environments.

This article was written off the back of a conversation from The Audiencers' Whatsapp group about how to prevent password sharing and account misuse within organisations. 

Many news and media publishers reach a point where subscription growth stalls even when all the basics seem right – a strong product, smooth onboarding, effective newsletters funneling readers in. Often an unseen culprit behind this plateau is revenue leakage: people accessing paid content without paying (or without paying correctly). This leakage tends to hide in the data – for example, odd spikes in “active users per account,” lots of free-trial users who never convert to paid, or unexplained churn. If left unaddressed, it quietly undermines growth, cutting into revenue and even skewing audience metrics. It can also create fairness issues that erode trust between your newsroom and paying subscribers.

Why revenue leakage happens (and why it hurts)

The post How to stop subscription revenue leaks: technical and strategic solutions appeared first on Audiencers.

This article was written by Lennart Schneider, founder of Subscribe Now podcast & newsletter, and expert on digital subscription models.

Everything is getting more and more expensive, including subscriptions. Those who don’t adjust their prices regularly will lose revenue in the long run due to inflation. But of course, with a price increase comes churn risk. So how do you balance the two: increase revenues and retention despite price increase?

To help answer this question, I spoke to pricing expert Florian Bauer in my Subscribe Now podcast where he explained to us when you should increase prices, how to communicate this gently and why you should always offer alternatives.

Here are his 7 biggest mistakes to avoid.

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