When major news breaks, every publisher can report the facts. The challenge today is giving audiences a reason to stay.

For The Baltimore Banner, that meant rethinking what live coverage should look like. Rather than viewing a live blog as simply a faster way to update a story, the newsroom wanted to create an experience that felt immersive, collaborative and uniquely local, one that readers would actively return to throughout the day.

Anyone can report breaking news. The Baltimore Banner treats its live blogs as an audience product, not a faster way to publish updates

> Prepare before the event. More than 50 journalists were trained ahead of Maryland's primaries, and a dedicated editor preloads background, links and context so readers get value from their first visit

> Put one editor in charge. During the event, they prioritise updates, pin key developments, adjust headlines and decide which moments deserve their own standalone story

> Build an experience, not a feed. Several reporters, photos and video let readers follow the Turnstile concert from different perspectives. That blog drew 62% new visitors, and registration take rates were 2.2x higher than the rest of the site

> Connect the live blog to the wider journey. A voter guide, an election night live blog and a results page worked as one product, delivering record subscription conversions, and returning readers viewed the live blog 2.05 times each

Moving beyond the single-author live update

As The Banner’s live coverage ambitions grew, so too did the limitations of traditional workflows.

Major events increasingly required reporters, photographers and videographers to contribute simultaneously, while editors needed a simple way to coordinate coverage and publish updates quickly from the field. Just as importantly, the newsroom wanted to make live reporting easier to execute without adding operational complexity.

Rhe Banner uses Tickaroo, which lets several journalists contribute at once, publish from their phones and push updates into Arc XP.

The result was not just faster publishing, but broader, richer coverage.

“We’ve been able to activate more contributors and be able to cover more angles during events than we were previously,” explained Nicole Orders, SVP of Product at The Baltimore Banner. “That’s definitely helped us broaden our live event coverage.”

Preparation starts long before the event

Technology was only part of the equation, however. The Banner quickly learned that successful live blogs are built before the first update is ever published.

Ahead of major events, newsroom staff are trained on both the platform and the editorial workflow. During Maryland’s primary elections, more than 50 journalists received hands-on training, from logging into the tool to understanding how best to contribute text, images and video. 

Every live blog also has a dedicated editor responsible for orchestrating the coverage. That editor prepares the live blog before publication by adding background reporting, links to existing stories and contextual information so readers immediately receive value when they arrive. During the event itself, they become the central coordinator, prioritising updates, pinning key developments, adjusting headlines and deciding when important moments deserve their own standalone article.

As Breaking News Editor Eileen Holliday explains, “Once everyone has their positions, like in a game, then it’s really seamless.”

Creating experiences, not simply updates

For The Baltimore Banner, successful live coverage isn’t measured by how quickly information is published. It’s about making readers feel as though they’re experiencing the event alongside the newsroom.

In October, coverage of several major Maryland events from the Power to the People Festival to President Trump’s visit to Tradepoint Atlantic to the Turnstile concert near the Washington Monument demonstrated this approach. Rather than relying solely on written updates, the live blogs combined reporting from multiple journalists with photography, video and behind-the-scenes moments to create a genuinely immersive experience. For the Power to the People Festival, readers could watch as the concert unfolded and major acts took the stage, including Bruce Springsteen, Foo Fighters, Dave Matthews and many more. At the Turnstile concert, readers without a ticket could follow commentary from different perspectives and experience the concert  live as it unfolded.

“The more immersive we can make that experience,” Nicole explained, “and the more that we can meet audiences where they are, the more likely they are to keep coming back. Our live blogs reach more subscribers and more new readers than the average news story. Since August, live blogs have drawn 2x – 5x the site share of organic search traffic. Visitors to the Turnstile live blog were 62% new audience, and we saw registration take rates at 2.2x higher than the rest of our site, and 3.4x higher than the live blog average. We’re truly excited about the new audience we are reaching and the engagement momentum that we have built.” 

Building audience value beyond the live moment

The Banner has also discovered that the most successful live blogs rarely stand alone.

For Maryland’s primary elections in June, the newsroom combined its election night live blog with a voter guide published in advance and a dedicated election results experience on polling day. Rather than treating each as separate products, they became complementary parts of one editorial journey.

That approach delivered record subscription conversions from election coverage with over 380,000 pageviews while also producing record page views for the live blog itself. Users spent almost 6 minutes on the voter guide, and the election night live blog drove more return visits than any other in 2026–returning readers viewed it 2.05 times each.

Throughout every event, the editorial team continually links readers to deeper reporting, creating standalone stories from significant updates and driving audiences into the wider journalism surrounding the story. This recirculation strategy extends the value of live coverage well beyond the event itself.

The Banner’s live blog has been effective at driving traffic from Google search and currently sits behind a dynamic paywall. When used in combination with other content, it can also be a driver of subscriber conversions. Based on audience engagement growth this year–October has set another record in terms of live blog pageviews at The Banner. They are actively exploring additional ways to experiment with it as a source of advertising revenue.

> Benchmarking: how to turn an election into subscribers

Lessons for publishers

The Baltimore Banner’s experience illustrates that successful live blogs depend on far more than publishing technology. They require thoughtful planning, clear editorial ownership, collaboration across departments and a commitment to creating distinctive journalism that audiences cannot easily find elsewhere.

Most importantly, they demonstrate a shift in thinking. At a time when breaking news reaches audiences almost instantly through countless channels, publishers no longer compete simply on speed. Anyone can report the latest developments. What distinguishes trusted journalism is the experience and expertise to explain what those developments mean, add context, connect the dots and help audiences make sense of events as they happen. That’s what keeps readers coming back.

By combining collaborative newsroom workflows with immersive storytelling and strong local journalism, The Baltimore Banner has transformed live blogs from a publishing format into a strategic audience product – one that strengthens engagement, supports subscriptions and keeps readers coming back long after the biggest moments have passed.

The post The Baltimore Banner: Turning live coverage into a product audiences want to return to appeared first on Audiencers.

What's working this week is Audiencers' weekly round-up of the numbers, tests and benchmarks that show what's working in digital reader revenue right now. It's a curated selection, not a news digest: we only keep insights on the topics we cover, engagement, conversion and retention, and the models behind them, from subscriptions and memberships to registration, newsletters and apps. This edition draws on data collected up to 6 November 2026. Some figures come from earlier reports in our archive, and each one shows the period it covers. Each insight comes with a “What this means for you” takeaway written by the Audiencers team.

Theme of the week: publishers are building around the core content subscription offering…

• NRC (Mediahuis, Netherlands) gave every subscriber unlimited New York Times access. More than half switched it on, cancellations fell across all subscription types, and daily new subscribers doubled during the launch.
• Il Post (Italy) moved its free daily podcast Morning behind the membership, and 70–80% of its listeners paid. Listening to the podcast is closely linked to renewing.
• Stylist (UK) moved 90% of its previously free puzzles behind the paywall, and puzzle players read 51% more articles. App users who listen to any audio are 9 times more likely to come back, and the app converts subscribers better than the website.
• The Hill (US) added interactive election tools to subscription offering ahead of the midterms. More than 75% of trial members of The Hill’s new subscription went on to pay. Subscribers get extra tools such as election dashboards and live race calls, while the journalism stays free.

Number of the week: 40%

According to Piano data, 40% of new subscribers disable auto-renew within 60 days of signing up, and 13% do it straight after subscribing. The same benchmark puts the median publisher’s share of “sleepers” (people who pay but don’t read) at around 40%.

Acquire and register

• El País built 3.7 million registered users before going paid, and about 80% of its first payers came from that pool

El País (Spain) spent a year offering free account creation before its paywall launched, reaching 3.7 million registrations by 2021 (data period 2020-2021). Roughly 80% of early paying readers were already registered. Source: FT Strategies / Google News Initiative

What this means for you: registration is a valuable testing ground to launch pre-paywall, allowing audiences to get used to the value exchange and for you to build a pool of engaged prospects ready to subscribe when you do launch premium. Broadsheet did something similar, and it paid off.

• 21,405 young Belgians took up Mediahuis’s offer of free news until summer 2026

Mediahuis Belgium’s “Wij betalen” campaign opened all its news brands free to 18-26-year-olds (2025-2026), with a €1-a-week youth plan lined up to keep them afterwards. The aim is to move young readers from social followings onto owned platforms. Source: Mediahuis Annual Report 2025

What this means for you: NTM grew its young subscriber base by 500% with the same strategy

Convert

• ABC lifted conversion 46% by showing its paywall on every article to its most engaged readers

Spanish daily ABC (Vocento) tested a full lock for highly engaged users against its standard editorial lock of around 30% of content; conversion rose from 0.26% to 0.38% with little effect on engagement (Subscriptions Academy EMEA 2025). Source: FT Strategies / Google News Initiative

What this means for you: dynamic paywall strategies, where the journey and messaging adapt to the reader’s profile and context really pay off thanks to the improved balance of frustration and engagement.

• A €1 price rise cost Le Parisien 10% of conversions

Le Parisien (France) A/B tested its standard monthly price at €8.99 instead of €7.99 and saw conversions fall 10%. The test followed an 18.5% drop in the package’s ARPU over two years of heavy discounting; the churn effect is not yet known (Subscriptions Academy EMEA 2025). Source: FT Strategies / Google News Initiative

What this means for you: it’s worth evaluating retention and LTV in this situation too however.

• Short, light intro offers convert at 1.4 to 1.6 times the average rate

Subsets (cross-market trends shared by co-founder and CEO Martin Johnsen, November 2026) finds 1-month light and medium offers and 6-month deep offers convert best. Deep 3- and 12-month offers bring in the most subscribers but convert below average.

Source: LinkedIn (Martin Johnsen, Subsets)

Onboard and engage

• Habitual readers are worth 110 times more than one-page visitors

Piano’s client data (Back to Basics report, June 2025) shows median new subscription revenue per 1,000 visitors climbs from $0.23 for one-off visitors to $25.52 for those reading 5+ pages over 4+ days a month. On average, 65% of visitors read one page and leave. Source: Digital Content Next

What this means for you: traditionally, digital publishers have sought to increase RFV (recency, frequency and volume of visits). However, the AI era and changing consumption habits mean that frequency has come out as the most valuable, alongside range of formats being consumed.

The Economist, for instance, shared: “Just getting someone to consume 50 pieces of content of whatever format might not actually be useful. What’s more useful is the depth of that engagement and how many different formats they are engaging with, even if it means a fraction of that volume.”

• 15-20% of new subscribers cancel within 24 hours

Mather Economics’ client benchmarks (Subscription Lifecycle Playbook, current edition) link these instant cancellations to unclear expectations or accidental sign-ups, and recommend clearer trial terms, early value reinforcement and triggers such as no visit by day 7. Source: Mather Economics, Subscription Lifecycle Playbook

• 40% of new subscribers switch off auto-renew within 60 days

Piano benchmark (no sample or period stated): 13% switch it off immediately. On monthly plans, cancellation offers with 70%+ discounts are accepted about 8% of the time, versus 2% for smaller discounts. Source: Digital Content Next

What this means for you: the moments and days post subscribing are essential! You can find onboarding benchmarks here to help build an engaging first experience with your brand.

Retain

• Madsack’s reason-based cancel flow saved 4,281 subscriptions in 2025

German group Madsack answers each cancellation reason with a matching argument and offer; in 2025 it also moved 906 subscribers onto a discounted annual plan, for €70,806 in attributable revenue (Piano case study, no control group). Source: Digital Content Next

What this means for you: adapting the cancellation steps to the reason why someone is churning will always be valuable, allowing you to offer an alternative that is more suited to this specific reader.

• Two habits halve churn

In Mather Economics’ client benchmarks (Subscription Lifecycle Playbook, current edition), subscribers with two or more recurring behaviours, such as daily visits plus newsletter opens, churn 50% less than those with one. Source: Mather Economics, Subscription Lifecycle Playbook

What this means for you: this makes sense, the focus on “variety” over “volume” by The Economist matches this data point.

Product and apps

• Business Insider now asks readers to choose between 3 formats before they browse

A Read / Watch / Listen switch now sits under Business Insider’s masthead (US, observed 4 Nov 2026), with Read as the default and a new “Watch BI” section promoted in the trending bar. Source: Audiencers analysis. (screenshot November 4th)

What this means for you: I found this very interesting – it shows a change in reader habits and how media are adapting, with format choice offered from the top menu.

• Mediahuis Belgium folded its news and e-paper apps into one app per title, with 2 million+ monthly podcast plays

In 2025 Mediahuis Belgium merged its apps and added LUISTER, an in-app audio hub, while putting some podcasts behind the paywall. Its titles publish 120+ new episodes a month. Source: Mediahuis Annual Report 2025

What this means for you: apps and podcasts are brilliant tools for habit forming (the all-important frequency we mentioned before), so it makes sense that Mediahuis is spending time optimising these products and putting them behind paywalls.

The post Nov 9th 2026: What’s working this week appeared first on Audiencers.

Steven Wilson-Beales is a UK-based SEO, AI visibility and content strategy consultant for publishers and brands. The morning after the US midterms, on 4th November 2026, he checked 16 US and UK news sites to see how they asked readers to subscribe. He shares his findings, and recommendations below.

The morning after the US midterms, Steven Wilson-Beales checked 16 US and UK news sites. Most showed their standard subscribe messaging, with very little tied to the election itself

> Tie the pitch to the moment. Open the article with a call to action linked to the news event, and give readers a one-line reason to subscribe that relates to the story in front of them

> Put free facts above the paywall and paid analysis below it. A "Related" strip near the top of the page links the news to the analysis that subscribers get

> Keep the offer simple. One discounted offer, plus a free account to read a single article, gives readers who aren't ready to pay a way in

> Change the page for each reader. First-time visitors see the moment message and free sign-up, registered readers see newsletter and follow prompts, and subscribers see no pitch at all, only gift articles and what to read next

The post The anatomy of a news article built to boost subscriptions appeared first on Audiencers.

During an election, audiences surge and reading intent is at its peak. People who rarely visit come looking for results, explanations and somewhere to follow the night as it unfolds. For a subscription business, that’s a rare opportunity. 

We looked at 21 initiatives from 12 publishers in France, the UK, the US, Spain and Germany, across elections and other big moments like Euro 2024 and the Paris Olympics, which raise the same questions. The publishers getting the most out of these moments work towards four goals, in order:

• Attract: make your site the place to follow the election, with hubs, live coverage, data and service tools
• Engage: get readers taking part, with votes, predictions and contributions
• Recruit: capture an email address or another owned channel in exchange for a service
• Convert: time-limited offers and a value message that follow the electoral calendar

Here’s what each one looks like in practice, and how to adapt it for your next election.

The post Benchmarking: how to turn an election into subscribers appeared first on Audiencers.

KCRW is betting on direct connection to its listeners.

Founded in 1945 at Santa Monica College, KCRW is a nonprofit public radio station serving Los Angeles and is a member station of National Public Radio (NPR), the United States’ largest public radio network. Like many American public media organizations, it relies heavily on listener donations, a need that’s become even more acute with the loss of federal funding in 2025.

In September 2025, KCRW redesigned its app, adding registration for the first time and launching new music features. In the following months, the team migrated its local event ticket giveaway program into the app and added a citywide activities guidebook. 

Beneath all of these changes is KCRW’s people-centric thesis: building ways to connect DJs and reporters to listeners will grow audiences and revenue.

> Only about 10% of a public radio audience donates. With pledge drives weakening and federal funding gone, KCRW rebuilt its app around one idea: connecting listeners directly to its DJs and reporters

> Map your audience before you build. KCRW found three segments, music fans, national news listeners and Los Angeles locals with "cultural FOMO", and gave each its own products and its own reason to pay

> Make the entry point a need, not your brand. Mood Mixes for commuting or working out bring in new listeners, appeals are tied to specific shows, and events like PieFest bring all three segments together

> Let people register for free first. Saving songs and building playlists drove sign-ups, and listeners who go through free account onboarding convert well to membership. KCRW had 55,000 paying members by June 2026

“We need to try something different”

The post Music fans, news listeners, and “cultural FOMO”: How KCRW built memberships around three audiences appeared first on Audiencers.

Here’s an awkward truth about running Audiencers: we spend all year analysing, deconstructing, and benchmarking other publishers’ registration walls, paywalls, and onboarding journeys. Which means every time someone lands on our site, they’re quietly judging our own reader journey. And quite rightly so.

But, over time, our walls had gathered digital dust. They didn’t reflect our latest research, our messaging asked for commitment without proving value, and there was definitely room for some innovation.

So, I decided to open the bonnet and rebuild the entire Audiencers reader journey from scratch.

Our current tech stack:

• CMS: wordpress
• ID/login: Auth0
• Subscription management: Hubspot (not my fave for this task)
• Payment: Stripe
• On-site journeys: Poool

A quick disclosure before we dive in: Audiencers was created by the team behind Poool, and our on-site reader journey runs on Poool. I’m not going to pretend otherwise. But this piece isn’t a canned product brochure; it’s an honest, operational teardown of our funnel mechanics, why we structured our access rules the way we did, and what happened when I used Poool’s MCP to rebuild our touchpoints based on what already converts for publishers. The Poool journey is what I’ve optimised so far, the Hubspot and Auth0 UX is definitely next on my list!

1. The strategy: the value of the archive

Before designing a single button, you have to establish an unambiguous access model. We settled on a 4-tier matrix centred around one publishing reality: content published today needs reach, but archived evergreen insights hold genuine commercial value.

Why 14 days of free access?

When we publish a deep dive on how to optimise your paywall, you might not be actively reworking your paywalls that week. But, in three months, when your team is tasked with overhauling your paywall, you will look for reliable benchmarks. At that exact moment, our archive solves an immediate, operational problem. That is the moment a free member (hopefully) converts into an annual subscriber.

Meanwhile, putting up a hard paywall immediately would kill discovery, peer sharing, and habit formation. Not to mention remove the important registration step for data collection, newsletter sign up and thus engagement. As we’ve seen time and time again, newsletters are a key step in convincing readers to subscribe.

2. The audit: what wasn’t working

When I audited our existing funnel against our own published benchmarks, the flaws were obvious:

• The registration wall asked for friction without showing return: the headline was a standard demand: “Create a free account to continue reading.” It framed registration as a hurdle rather than a valuable membership.
• The subscription landing page buried the price: visitors had to click a radio button just to see the €299 rate, while company plans showed no baseline figure at all.
• The offer page copy was repetitive and bloated: the headline “Make more informed decisions” appeared twice on the same page, and didn’t apply many of the best practices we see on the market today.
• Walls hadn’t been updated in a while: not to mention the fact that we talk (a lot) about needing to continuously change the design and messaging to increase conversion rates, but we hadn’t done this in a while.

3. The rebuild: applying “what works” via Poool’s MCP

Normally, rebuilding an entire reader journey, including registration walls, paywalls, onboarding tours, and offer pages, means weeks of friction: product briefs, Jira tickets, waiting for frontend developers, and endless design review cycles.

As a lean team of one on Audiencers, I don’t have weeks. And I definitely don’t have the tech knowledge (ask ANYONE who’s worked with me). So I connected Claude to our Poool dashboard using Poool’s new MCP.

Many generative AI tools build whatever you ask for, starting from a blank canvas. But Poool’s MCP builds what works. Every journey step it creates draws from two distinct sources of built-in expertise: 10 years of experience at Poool with 250+ publishers, and Audiencers’ analysis of 1,000+ more. Instead of guessing copy or starting with a blank template, it filters requests through proven layouts, structures, and messaging patterns, building the optimised version directly in your dashboard while teams retain full approval control.

So here I am, optimising Audiencers’ reader revenue strategy using our own content, inception.

Here is how that workflow redesigned our funnel touchpoints:

A. The registration wall: from passive form to dynamic playbook

I prompted the assistant to inspect our current wall and cross-reference it with our benchmarking analyses:

“Using Audiencers content, especially the benchmarking articles and strategic recommendations
on messaging, redesign the current registration wall in access.”

In its first pass, the assistant pulled our research and adjusted the copy:

• Before: “Create a free account to continue reading”
• V1: “This article is free. You just need an account to read it.”

It replaced a demand with reassurance, explicitly addressing the primary reader concern at a registration gate. Then we pushed it further with a follow-up prompt:

“Be innovative, using Audiencers’ benchmarks and adapting to our customer profiles and their goals.”

Five minutes and three prompts later, the MCP built an interactive diagnostic wall directly inside our dashboard:

1. The question: “What are you working on right now?”
2. The dynamic options: Readers select their current focus—Paywall & conversion, Registration & data, Retention & churn, or Engagement & newsletters.
3. The playbook: Selecting a topic dynamically updates the wall to display an editor-curated reading list for that specific challenge (e.g., clicking Registration & data surfaces Radio-Canada’s registration case study and 50+ annotated benchmarks).
4. The action: The primary CTA changes to “Unlock my registration playbook,” with a discreet “Skip, just create my free account” fallback underneath.

Before:

After:

Play around with the wall here.

The wall no longer acts as a gatekeeper, it functions like an on-demand consulting intake.

B. The subscription offer page: translating industry benchmarks into high conversion

Our main subscription offer page was overdue for a complete rethink.

“Be innovative and try to build a high converting page inspired by benchmarks, especially Audiencers’ Subscription offer page benchmarks, whilst still maintaining the light, fun look of Audiencers”

Running this brief through Claude and the Poool MCP generated a full redesign directly inside our dashboard. Instead of guessing what might work, the build borrowed structural wins from top-performing publisher benchmarks:

• Plans side-by-side above the fold: Benchmark data shows that leading offer pages present comparative options immediately above the fold. The redesign surfaces the Individual plan and Team plan side-by-side on screen one, featuring the Individual plan prominently with a playful, tilted yellow “Most popular” sticker.
• Unapologetic price transparency: The hidden radio button is gone. The rate is displayed front and centre alongside a relatable anchor: “less than £1 a day” (or €1 / $1 depending on geography)—followed by an immediate CTA button and a clean checklist of core membership perks.
• Sharper, action-oriented messaging: Out went “Make more informed decisions”. In came “Stop guessing. Steal what works.” a direct nod to our popular Steal this strategy series, accented with Audiencers’ signature teal highlight.
• Credibility without visual drag: Rather than burying readers in 24 dispersed logos, the layout condenses social proof into a single, high-contrast dark band featuring 8 key industry logos (including Politico, NZZ, Axel Springer, Estadão, and South China Morning Post).
• Bite-sized benefits and social quotes: The core value proposition is distilled into three pastel benefit cards using our recognisable editorial illustrations (Steal what works, Meet the people doing it, Put it into practice), paired with quick quotes from peers across Condé Nast, Globo, and Hearst, providing social proof spotted as a trend on benchmarked landing pages.
• Friction-reducing accordion FAQ: An interactive 4-part toggle addresses real buying objections right above the final checkout banner: Festival ticket eligibility, Poool client entitlement, group team volume discounts, and subscription duration terms.
• Protecting the brand identity: Crucially, the page didn’t end up looking like a stiff enterprise SaaS template. It retained the warm, distinctive Audiencers tone.

After:

With 3 targeted currencies

The page is live here. (p.s. of course, please feel to subscribe if you like what we do;)

C. The archive paywall: selling the library

When readers land on articles older than two weeks, they’re hunting for reference blueprints.

The new paywall opens with “Unlock the reader revenue library,” instantly cataloguing the inventory: 200+ case studies, 160+ step-by-step guides, 75+ benchmarks, and 160+ decoded paywalls.

Directly below, it presents three clear options side by side:

• Individual Plan (£299/year)
• Team Plan (multi-seat access for newsrooms)
• Poool Client Fast-Track (a dedicated pathway with a 3-step guide detailing how dashboard users link their existing account for immediate access)

As with the other use cases, this paywall was built in a few minutes. The prompt I’m liking in particular at the moment: “Be innovative using Audiencers’ trends”.

Before:

After:

Play around with the paywall here.

D. Onboarding new registered members: the 30-second tour

A registration model only succeeds if readers return, and for a while I’ve been very keen on implementing a product tour to show new free members around our media and community.

Drawing from our onboarding benchmarks, the assistant analysed patterns from publishers like The Washington Post and The Irish News:

• A separate welcome screen introducing the tour before launching into steps.
• Centred modals with one clear headline, one supporting line, one visual, and one button.
• A quiet “Maybe later” text link under the button to eliminate frustration.

The result was an on-site, 3-step onboarding flow highlighting Case Studies (with direct jumps to SFGATE and DER SPIEGEL), Directories (50 tools, 148 industry consultants), and Festivals (community passes for London, Paris, and Berlin).

Before:

Nothing

After:

If you’ve got a free account on Audiencers, you’ll see this journey next time you log in! Or play around with the onboarding journey directly here.

The display conditions mean that each users sees this only once, that it’s not shown to subscribers or Poool clients and not on certain pages, such as the subscription offers page.

4. Operational takeaways

Whether you configure journeys manually or leverage an AI assistant, the MCP provided a few key takeaways to apply to your reader journey, based off Audiencers expertise:

• Assign one job per wall. Our previous registration wall tried to pitch our newsletter, promote live events, and explain our company all at once. One step, one goal.
• Turn barriers into personalised utility. Letting readers select what they’re working on turns an administrative gate into a personalised diagnostic and adapted reason for converting. When set up properly it can also give you rich first-party intent data from day one.
• Lead with transparent pricing and clear anchors. Hiding annual rates behind radio buttons or burying team plans behind vague forms creates friction. Put the price front and centre, anchor it to something easily comparable (“less than £1 a day”), and display individual, team, and partner licences side by side. But of course, testing and adapting to the reader’s profile and context is essential.
• AI builds the steps, but your team retains the steering wheel. The MCP generated functional, styled, dynamic modules in minutes, but human editorial judgement remains essential. When Claude initially proposed heavy bullet lists on the registration module, we trimmed them to protect visual focus.

Test it out for yourself!

The updated reader journey is live across Audiencers today. You can test the interactive playbook on our latest articles, examine the newly streamlined offer page, browse the archive library paywall, or take the onboarding tour.

For existing Poool clients, Poool’s MCP is already available in your plan at no extra cost, connecting your preferred AI assistant directly to your dashboard to turn ideas, URLs, or Figma files into optimised journeys based on 10 years experience with 250+ media brands and Audiencers’ analysis of 1,000 more.

The post We write about paywalls for a living. So I rebuilt ours with an AI trained on everything we publish appeared first on Audiencers.

In two years, Svenska Dagbladet (SvD) has gone from 98,000 to more than 158,000 digital subscribers. With growth of over 60% the Swedish daily hit its target for end-of-year 2026 by August.

It would be easy to look for the clever paywall trick behind numbers like that. But when I sat down with Christoffer Lindvall, who heads subscriptions at SvD, he kept coming back to the same point: subscriptions start and end with journalism.

“At the end of the day, it’s all about the content. If you don’t have the right content and the right profiles, the rest doesn’t matter really.”

What’s interesting is how that belief shapes the way his subscription team works, and how SvD got its newsroom and business side pulling in the same direction.

> Svenska Dagbladet went from 98,000 to more than 158,000 digital subscribers in two years, over 60% growth, and hit its full-year 2026 target by August

> One North Star: publisher Lisa Irenius replaced separate goals for reach, logged-in users and social with one, more subscribers. A weekly reach of 1.7 million Swedes against roughly 100,000 subscribers was read as headroom, not a problem

> Content has two jobs. Quick news builds habits and traffic, while "aha" stories convert. During the Swedish election traffic spiked, but many of the best-converting articles weren't political: readers came for the news and subscribed for something else

> Paid by default: most articles publish behind the paywall, so the newsroom decides what to leave open, not what to lock. An algorithm flags trending open articles so they can be closed mid-life

> Around 80% of SvD's audio subscribers are new to the brand, people text had never reached. And content commissioned for impact rather than conversion often brings in the most subscribers

Content first: what that means for a subscription team

The post How SvD grew digital subscriptions by 60% in two years by putting editorial first appeared first on Audiencers.

Khalil A. Cassimally is an audience consultant and coach who helps organisations align teams around change, user needs and AI. The user needs audit is a prerequisite for putting the model to work, yet it often stalls. Tagging hundreds of pieces by hand is slow, and the people doing it work from instinct they’ve rarely put into words. Drawing on hundreds of headlines he tagged himself and then with AI, he shares a seven-step process to:

• Build a tagging rubric: an LLM drafts it, you tag a sample of about 100 headlines, and the LLM turns your disagreements into written rules you approve
• Tag your whole archive with an LLM instead of hand-tagging a sample
• Check 20 headlines at random to surface the rules you haven’t written yet
• Set what you publish against whichever performance measures your newsroom values

In his tests, the LLM agreed with his own blind tags around nine times in ten.

> Plenty of newsrooms use the language of user needs but never audit against it. Tagging 75 pieces per need by hand is slow, and taggers work from instincts they've never written down

> Let an LLM take the labour. Tag a calibration sample of about 100 headlines yourself, with notes on the hard calls, then have the LLM tag your whole archive against a written rubric

> The bigger win is alignment. Ask the LLM to turn its disagreements with you into rules, such as "a research finding is UNDERSTAND, however flatly the headline states it", then approve each one as a newsroom

> In Khalil A. Cassimally's tests, the LLM agreed with his blind tags around nine times in ten. Re-check 20 random rows after every rubric change: agreement under about 14 out of 20 means a rule is missing

The user needs model asks one question of everything a newsroom publishes: what is this piece for, for the person reading it? Answer it consistently, and we can see which needs we serve well and which we neglect, and commission accordingly.

Before any of that can happen, we need to know where we stand. That’s the audit: tagging a body of recent content by the need each piece serves, then putting those tags next to how each piece performed. It’s a key prerequisite for implementing user needs, because every decision that follows depends on it. And it produces the chart that tends to change minds in a newsroom: what we publish set against the value people get from it.

In my experience, plenty of newsrooms use the language of user needs without ever having audited against it, and others did it once, on a sample, and never went back. There are two reasons for that, and AI can help with both.

Why the audit matters

The audit matters, first, because it gives us a baseline. It shows how our content serves each need today, so there’s something to measure any change against later.

It also shows us the gaps. Some needs perform far better on whatever measure we care about than others do but they’re rarely the needs most of our content serves. That mismatch, laid out in one chart, is what I often find persuades editors and leadership to act. It tells us which needs to prioritise, and which to produce less of.

Plus, the tagging itself is how a team learns user needs. In my experience, discussing whether a headline is there to update or to explain teaches the needs better than a training session does. Done together, the audit can leave a newsroom with a shared sense of what each need looks like in its own content.

> You should also sign up for Khalil’s user needs course: implement user needs in your newsroom

The two challenges that block an audit

Two things tend to stall the audit. The first is the sheer labour. Tagging means someone reads each piece, decides which need it serves, and logs that tag alongside the piece’s details and its performance figures. To get a reliable picture, I recommend tagging 75 pieces of content per number of user need. For a newsroom working with eight user needs, that’s 600 pieces. Each one is a judgement call that takes time. It piles up.

The second challenge is alignment: getting everyone who tags to apply each need the same way. If one person does all the tagging, the tags stay consistent, but the work is slow. If a small team shares it, everyone needs training, plus about a month to absorb the definitions.

Both challenges have the same root: experienced taggers work from instinct. Dmitry Shishkin, who championed the model at the BBC World Service, pointed out that it feels familiar to journalists from the start since the user needs correspond to story angles they already use. I’d add that the same familiarity means the rules people tag by rarely get written down. An unwritten rule can’t be checked or taught, and a newsroom can’t explicitly agree on it.

How AI helps

AI can ease both the labour and the alignment problem.

The labour is the obvious one: an LLM can tag an entire archive of headlines in a fraction of the time it would take a person. It also changes the maths. The 75 pieces per user need is the size of the audit. Once the LLM does the tagging, it can take on your whole archive rather than a sample. What you still tag by hand shrinks to a calibration sample of about 100 headlines, used to build and check the rubric the LLM works from.

The alignment help is, I think, the more valuable. When an LLM’s tags differ from yours, you can ask it why, and ask it to turn the differences into rules. It takes the scattered notes you made while tagging, and the calls you made without explaining them, and writes them up as general rules anyone can apply. Once they’re written down, a newsroom can discuss them and agree on them.

Doing this sharpened my own definitions of each user need. Two examples, from the definitions I’d been teaching with to the rules the process produced:

KNOW. 

Before: give me the who-what-when-where; let me know the latest. 

After: the headline states an event in the world and stops there. A research finding isn’t an event, however flatly the headline states it, so it’s UNDERSTAND. A poll of what people think this week is a fact about the world, so it’s KNOW.

DO. 

Before: take action; find useful information for my day-to-day. 

After: the reader has to be the one acting, and the headline has to instruct them. Advice aimed at governments is UNDERSTAND, and so is a question about the reader’s life that weighs the options rather than telling them what to do.

When I ran the rubric on headlines from a publisher, the LLM agreed with my blind tags around nine times in ten: 91% on Claude and 89% on Gemini.

More publishers are looking into using AI this way. Le Nouvel Obs, for one, is exploring AI to classify content though it’s “still very much in the R&D phase”. 

Here’s the process I’d recommend after testing it on 275 headlines from two publishers, all of which I also tagged by hand.

Using AI to audit your content: a seven-step process

Each step exists because the next one depends on it.

The first step gives you a draft rubric, so you aren’t tagging against bare definitions. But a rubric drafted by an AI alone holds its judgement, not yours, so the second step captures yours: you tag a sample and note how you decided on the hard ones. The third step is where the alignment value comes from. The LLM compares its tags with yours and turns the differences into written rules. The fourth step makes sure every one of those rules is your newsroom’s decision before it’s applied to hundreds of pieces. The fifth applies the rubric to your whole archive, which is where the labour saving comes from. The sixth checks a random sample of the results, mainly to find the rules you still haven’t written, which go back through step four. The seventh turns the tags into the chart.

You only tag by hand in steps two and six: just over 100 headlines to build and check the rubric. The LLM does the rest.

1. Draft a rubric with AI, or start from mine

All you need coming into this step is your archive and the user needs definitions you already work with, however rudimentary at this stage.

A rubric sets out, in writing, how to decide the headlines that sit on the line between two needs. Most archives have them: a research finding stated like news (KNOW or UNDERSTAND?), an event headline with an explanation added after the dash (KNOW or UNDERSTAND?), a feature with an emotive headline that’s really an explainer (FEEL or UNDERSTAND?). The rubric is where you settle those decisions.

Give an LLM a few dozen headlines from your own archive, so the rules fit your content, and ask it for a rubric with three things: a definition of each category, the order to test them in, and a decision rule for each kind of borderline headline, with a worked example for each rule.

If you’d rather not start from scratch, the rubric I ended up with is included in full below. You can paste it as a prompt.

Either way, you now have a draft rubric. The next step captures your own judgement to set against it.

2. Tag a sample yourself, then have the LLM tag it too

The draft rubric holds the LLM’s judgement. This step captures yours, which is what turns a generic rubric into your newsroom’s.

Export your archive from your CMS or analytics tool, if you haven’t already: headlines, sections, dates and the performance metrics you care about (you’ll need those in step seven). From that export, draw around 100 headlines, spread across topics and formats. This is your calibration sample. It’s small enough to tag by hand, and it’s separate from the audit itself, which the LLM will run on the whole archive.

Tag the sample yourself first, without looking at any LLM tags. Wherever the rubric doesn’t settle a headline, write a short note on how you decided. The notes matter as much as the tags, because they’re what the LLM will turn into rules. Then give the LLM the draft rubric and the same 100 headlines, and have it tag them too.

Ideally, several colleagues tag the sample on their own as well. Where they disagree with each other, you’ve found your alignment problem at a size you can work with.

You now have two sets of tags for the same headlines. The next step turns the differences between them into rules.

3. Ask AI to turn your disagreements into rules

With your tags, your notes and the LLM’s tags side by side, this step turns scattered judgement into rules people can see and discuss. Give the LLM all three. Ask it to group the disagreements by cause, and to draft one general rule for each group, with a worked headline that shows the reasoning. Ask it as well to flag anywhere your tags contradict your own notes.

Expect a few kinds of rules. Some will generalise reasoning you’d already written in your notes, one headline at a time, into a rule for any headline. Others will write down calls you made consistently but never explained. The second kind can be the more useful, because it’s knowledge that hadn’t previously been put into words.

What comes out is a list of proposed rules. The next step decides which of them go into the rubric.

4. Decide on every rule

The proposed rules are still the LLM’s drafts, and this step makes them your newsroom’s. Every one needs a human decision: accept it or reject it.

Keep in mind that not every pattern deserves a rule. Some disagreements are genuine house calls, better left for a team to talk through. And even when a rule is written down, people can argue with it, and I’d expect those arguments to be how colleagues come to apply the needs the same way.

The rules you accept go into the rubric, which is now ready to apply at scale.

5. Tag your archive

With a rubric that holds your newsroom’s calls, this step applies it to everything you want to audit. This is where the labour goes. Paste the rubric into the prompt, then give the LLM the headlines from your export (and sections, if you have them). Because the LLM is doing the tagging, you can go well beyond 75 pieces per need if you want to – perhaps to the whole archive. Ask for one tag per headline, a confidence level, and a short reason naming which test in the rubric decided it. The reasons make the next step much quicker.

You don’t need to add many worked examples in the prompt. In my tests, adding them moved agreement by a few points at most, because the rubric already holds the detail.

Also: which LLM you use matters less than you might expect. Claude and Gemini were a few points apart in my tests.

Every piece now has a tag. The next step checks how far to trust them.

6. Check 20 at random, to surface the rules you haven’t written yet

The archive is tagged, but however good the rubric, there will be calls it doesn’t cover yet. This step finds them. Pull 20 tagged rows at random, tag them yourself without looking, then compare. For each disagreement, ask who’s right: did you slip, did the LLM, or is this a call the rubric doesn’t cover? The third kind becomes a new rule.

If you can, run the same 20 through a second LLM. Where both LLMs agree with each other against you, check your own tag first. Where both LLMs make the same mistake, there’s probably a rule missing. As a rule of thumb, under about 14 out of 20 suggests the rubric is missing a rule.

Tip: pick the rows at random, not by the LLM’s confidence flags. Some LLMs mark nearly every row high confidence, including the ones they get wrong.

Any new rules go back through step four. Once the check comes back clean enough, the tags are ready for the chart.

7. Plot what you publish against how it performs

With tags you trust, this step sets what you publish against how it performs. That’s why the export in step two needs your performance metrics as columns next to each headline: pageviews (which is what I used), time spent, conversions, return visits, or whichever measures your newsroom values. For each need, set its share of what you publish against its share of each measure. Look at the median per piece as well as the share, so one exceptional piece can’t inflate a whole category.

That chart is your baseline. The next audit will show you whether editorial changes have moved things in the right direction.

Keeping it running

The rubric is a living document. Every random check can add a rule, so date and number each version, and you’ll always know which rules a batch of tags was made under. Mine has been through four versions so far.

From there, the natural next move is tagging new content at publication, with the same rubric, rather than in a big audit after the fact. The mix can then sit on a dashboard, and you see it shift month by month. A settled rubric should also help new staff and new desks learn how the newsroom applies the needs.

What the AI and I tagged differently, and what came out of it

When I compared my tags with the LLM’s, the differences fell into three groups, and each produced something useful.

• Rules generalised from my own notes.
  • The LLM’s early drafts tagged research findings as KNOW. My notes on those headlines kept giving the same reason for disagreeing, and the LLM turned that reasoning into a rule for any headline: a research finding is UNDERSTAND, however flatly the headline states it.
  • On a piece about how a government could protect people from heatwaves, I’d noted that it would only be DO if the action were the reader’s own. That became the rule that DO needs the reader to be the actor.
  • And I’d kept asking myself whether a headline’s emotional register was strong enough for FEEL. The LLM swapped my test for a better one: FEEL needs the piece itself to be the experience, like a review or a personal essay.
• Calls I’d made without a rule.
  • Some of my tags were consistent, but I’d never said why. I’d tagged “How late is too late for dinner?” UNDERSTAND without a note. The LLM wrote down the rule I’d been applying: DO needs an instruction, not a question about the reader’s life. I’d tagged a poll of public opinion KNOW without comment, and the rule now says a poll of what people think is KNOW, while a study of how the world works is UNDERSTAND.
• My own slips.
  • The LLM also held me to my own rules, flagging places where my tags contradicted my notes. I’d tagged a footballer’s sprained ankle FEEL because it was football rather than event news. The LLM had the injury as KNOW, and it was right. The rubric now says sport and celebrity coverage isn’t automatically FEEL.

The table sets the definitions I’d been teaching with against what the rubric now says:

Category	The definitions I started with	What the rubric now says
KNOW (update me, keep me engaged)	Give me the who-what-when-where. Let me know the latest. Keep me up to date with the latest trends. Catch me up with what my tribe is talking about.	The headline states an event in the world and stops there. Research findings aren’t events. A poll of opinion is. Official warnings and service announcements are KNOW. Sport and celebrity news is KNOW like any other event.
UNDERSTAND (educate me, give me perspective)	Help me learn something. Teach me about the wider world. Broaden my horizons. Challenge my opinions.	The piece explains, analyses or argues, including every research finding. “How it works” and “why it happens” both count. An explainer stays an explainer whatever adjectives it wears. Advice aimed at institutions is UNDERSTAND.
FEEL (inspire me, entertain me)	Inspire me to be the best person I can be. Give me hope. Show me a not-so-serious side of a story. Give me something to smile about.	The piece itself is the experience: a review, a human story, a personal essay, a celebration of a person, something told purely for pleasure. Reviews are FEEL. Arguments about culture are UNDERSTAND.
DO (help me, connect me)	Take action. Find useful information for my day-to-day. Help me become part of something bigger than myself.	The reader is the one who acts, and the headline instructs them. A question about the reader’s life that weighs the options is UNDERSTAND.

What to watch out for

AI doesn’t get this right on its own. The process works because the human and the LLM check each other. A few things are worth watching.

• A new rule can fix some headlines and break others. When I added rules, they fixed a few headlines and pulled others the wrong way. So, re-check a sample every time you change the rubric.
• Don’t defer to the LLM, even when it’s often right. Where two LLMs agreed against me, they were usually right. That makes it tempting to accept every disagreement. Every correction I made went towards the LLMs’ answer, so it’s worth asking each time whether you’ve genuinely changed your mind, or just been outvoted.

The real limitations

Beyond those watch-outs, two limitations are worth being clear about.

• One person’s tags were the ground truth. Every number in this piece measures agreement with me. Yours will measure agreement with whoever tags in step two, which is a good reason for more than one of you to do it.
• Some headlines stay contested. A few serve two needs equally, and a rule may not settle them. Those are for a team to talk through.

Start with 100 of your own headlines

The audit is the foundation of user needs work, and it stalls for two reasons: tagging hundreds of pieces by hand is slow, and the people doing it work from instinct they’ve rarely had to put into words. AI takes on most of the tagging. More usefully, it turns the notes and unexplained calls behind that instinct into written rules, which a newsroom can argue over and agree on. The judgement stays with us, in the tags we make and the rules we approve.

You don’t need a data team or a big project to start. Take the rubric below, draw 100 headlines from your own archive, tag them yourself, and see which rules your newsroom would write differently. That calibration sample is the beginning of a rubric that’s yours. From there, the LLM can tag the whole archive, and you can run the audit again whenever you need to.

***

The rubric in full

The four categories follow the top level of smartocto’s User Needs model 2.0. Paste everything below into your prompt, then adapt the rules to your own newsroom’s calls.

What you are tagging

Every piece of content gets one tag: the need it serves for the person consuming it. Not what it’s about (that’s topic), not what shape it is (that’s format). What it’s for.

Four categories, each covering two of the eight needs in the model:

Tag	Covers	The reader comes to…	The headline typically…
KNOW	update me, keep me engaged	find out what happened, or what everyone’s talking about	states an event in the world: a decision, an announcement, a result, a development, a trending moment
UNDERSTAND	educate me, give me perspective	learn how something works, or make sense of what it means	promises an explanation, a background, an analysis, an argument, a verdict
FEEL	inspire me, entertain me	be moved, uplifted, amused, delighted, taken somewhere	signals the register: a review, a human story, something funny, curious or beautiful
DO	help me, connect me	act, in their own life or with others around a cause	addresses the reader as the one who will act, decide or take part

The tests

Run them in this order. Stop at the first clear yes.

Does the headline address the reader as the one who will act? Tips, how-to, should-you, what-to-do, here’s-how-to, join, sign up – aimed at the reader’s own life or their part in something collective. If so, DO.

Does the headline state an event in the world, and stop there? Something happened, was decided, announced, won, lost, or is trending, with no promise to explain it. If so, KNOW.

Is the piece itself the experience? A review, a human story, a personal essay, a celebration of a person, something funny or told purely for pleasure. Strip it to a neutral summary and the value disappears. If so, FEEL.

Otherwise the piece explains, analyses or argues. How it works, why it happens, what a study found, the background, what it means, what the evidence says, the case for or against. That’s UNDERSTAND.

Decision rules for the cases that cause arguments

A research finding is UNDERSTAND, however flatly the headline states it. KNOW is for events in the world. A study result is something the reader learns (educate me), not something that happened to them. “Collagen supplements can help your skin and joints, large new study finds”: UNDERSTAND. “Horses really can smell fear, new study claims”: UNDERSTAND. It becomes DO only when the headline addresses what the reader does with it: “How eggs can help you come off Wegovy”.

A poll of opinion is KNOW. A study of the world is UNDERSTAND. “Three out of four Americans say AI firms are not doing enough to prevent disaster, Reuters/Ipsos poll finds”: KNOW, because a poll reports what people currently think, which is a fact about the world this week. “Collagen supplements can help your skin and joints, large new study finds”: UNDERSTAND, because a study reports how the world works, which is something the reader learns.

An event with an appended promise takes the tag of the promise. The event is the peg; the clause after the dash is the need. “Ebola outbreak declared a global health emergency”: KNOW. “…– what you need to know”: UNDERSTAND. “Taiwan Travelogue wins the International Booker – a deftly translated tale of food, love and history”: FEEL. “Keir Starmer has resigned – but what could a new PM actually do differently?”: UNDERSTAND.

“How it works” and “why it happens” are both UNDERSTAND. Mechanism and meaning sit in the same category: educate me and give me perspective. “Why sepsis is becoming harder to treat in Europe” and “Why climate action stalls, despite widespread popular support” are both UNDERSTAND. You don’t have to decide which of the two it is.

An explainer stays an explainer, whatever adjectives it wears. “This is why you only breathe out of one nostril at a time”: UNDERSTAND. “How Nasa’s Artemis II mission rediscovered the majesty and mystery of the Moon”: UNDERSTAND, an emotive frame on an explanatory piece. “Why was an Egyptian mummy stuffed with a fragment of Homer’s Iliad?”: UNDERSTAND, a curiosity explained. FEEL needs the piece to be the experience: a review, a story, a celebration of a person, a personal essay, something told purely for pleasure. “Forget flowers: lovers in 19th-century Ireland exchanged hair”: FEEL. “My unsung hero of science: Carolyn Wood Sherif”: FEEL.

Reviews are FEEL. Arguments about culture are UNDERSTAND. “Normal: this quirky Bob Odenkirk caper isDie Hard meets Fargo”: FEEL. “Horror won big at the Oscars – it’s time the genre was taken seriously”: UNDERSTAND.

A trending peg with an explanation is UNDERSTAND. The trend itself is KNOW. “Kim Kardashian’s new caffeine-free energy drink – here’s what the science says”: UNDERSTAND. “Prescriptions for ivermectin soar after Mel Gibson claims it cured friends of cancer”: KNOW, because the conversation is the story.

“What you need to know” is UNDERSTAND, unless the need is a personal decision. “Osteopenia: loss of bone mineral density affects millions – here’s what you need to know”: UNDERSTAND. “Vitamin D: everything you need to know – from when to take it, to how much you really need”: DO.

DO needs an instruction, not a question about the reader’s life. “Eight ways to sleep well in hot weather”: DO. “How late is too late for dinner? Does eating earlier help with weight loss?”: UNDERSTAND, because it asks and explains rather than telling you what to do. “US or UCITS ETFs: which is better for Singapore investors?”: UNDERSTAND, a comparison the reader may act on, but the piece weighs rather than instructs.

An official warning or a service announcement is KNOW. “Bus passengers face fines of up to S$500 for playing loud audio”: KNOW. It reports that something has been said or made available, even when the reader could act on it. DO needs the headline itself to instruct the reader.

DO also needs the reader to be the actor. Advice aimed at institutions is UNDERSTAND. “Eight ways to sleep well in hot weather”: DO. “Heatwaves are now everyday disasters – here’s how the UK government can better protect people”: UNDERSTAND, because it is a case for what someone else should do. “What your local council can actually do to tackle the climate crisis”: DO, low confidence, because the reader has some agency there, which is the connect-me edge of the category.

Topic never decides. Health can be any of the four. Politics can be any of the four. Sport and celebrity coverage is not automatically FEEL: an injury, a squad change or an award is an event, and tags KNOW like any other. If you find yourself tagging by section, stop.

Tag the promise the headline makes, not the piece you imagine behind it. When you only have the title, tag the title. When you have the standfirst too, let it settle the call, not overrule it.

One tag per piece. Most pieces serve more than one need. Tag the one that, if unmet, would make the reader feel the piece failed. If two are genuinely equal, tag the one higher in the test order (DO, then KNOW, then FEEL, then UNDERSTAND) and mark the row low confidence.

Output

For each row: id, tag, confidence, reason

tag is exactly one of KNOW, UNDERSTAND, FEEL, DO

confidence is high or low. Low means a second reader could reasonably tag it differently. Low-confidence rows are the ones worth checking by hand

reason is one short clause naming which test fired – e.g. “addresses reader’s action”, “event stated”, “review”, “appended promise: explains”, “study finding – educate me”

The post How to run a user needs audit with AI, in seven steps appeared first on Audiencers.

The stories newsrooms publish most are, article for article, the least effective, and Dmitry Shishkin thinks the fix starts with how publishers commission. 

Ten years after a piece of BBC audience research turned into the user needs model, its architect used his FIPP Congress slot in Madrid to argue that most publishers still get this the wrong way round. Shishkin, now an independent consultant after senior roles at the BBC, Culture Trip and Ringier, told the audience that topic and format are only two of publishing’s three pillars. The third, the reader’s motivation, drives subscription and loyalty metrics, and most newsrooms still leave it to instinct.

> Article for article, "update me" pieces underperform every other user need, yet newsrooms produce more of them than anything else

> Tag every piece with three things: the subtopic (personal finance, not just finance), the format (Q&A, feature, listicle, data-led) and the single user need it serves. Then commission on all three

> One dominant need per piece. An article that tries to serve every need gets a headline that can only sell one, so most readers never discover the rest

> Don't let the newsroom write its own user needs model: take the needs from audience research. At Ringier, across five countries, conversions, subscriptions and loyalty metrics all increased once teams moved to the model

Measure the angle as well as the story

The post Why your newsroom shouldn’t write its own user needs model, and what to do instead appeared first on Audiencers.

Every year, INNOVATION Media Consulting’s Innovation in Media report, published for WAN-IFRA and FIPP, takes stock of trends in the industry and, in particular, how publishers are making money. The 2026/27 edition’s theme is that the model hasn’t broken, but it has matured.

Reader revenue is now the main source of income for many news businesses, making up 46% of total revenue for the median news business unit, overtaking advertising. The Reuters Institute adds that the share of people paying for (or using) paid online news has nearly doubled over the past decade, to almost one in five adults across 20 markets.

But the report is clear that the most engaged readers have largely already converted, acquisition is slowing and fewer new users are entering subscription funnels. So what comes next? We’ve pulled out the trends and case studies that matter most for reader revenue teams to share 7 lessons to help overcome this challenge.

> Subscribers keep growing while traffic falls. At the NYT, WSJ and The Guardian, growth now comes from higher prices (up 5% on average in 2025), upgrades and bundles, not bigger audiences

> With monthly churn often below 5%, small retention gains have an outsized revenue impact. Leading publishers treat pricing as a variable and the cancellation journey as a moment to reinforce value

> Bundles pay off through retention, not price. NYT bundle subscribers pay slightly less per user but churn significantly less. At Mediahuis, subscribers who activated the RouteYou app churned around 26.5% less

> Build outward from your journalism. Bundle additions closely tied to editorial content stick, bigger leaps don't. For membership, spell out whether readers pay for access, a cause or a community

1. Subscriber growth has decoupled from traffic growth

Digiday’s reporting, cited in the report, shows that The New York Times, The Wall Street Journal and The Guardian have continued to grow their subscriber bases (in some cases, faster than before) while their overall traffic declined over a 12-month period.

That growth isn’t coming from bigger audiences. It’s coming from getting more value out of existing ones:

• Subscription prices rose by an average of 5% year over year in 2025
• Publishers pushed subscribers towards higher-value tiers and bundles
• Meanwhile, median audience reach across the hundreds of publishers tracked by INMA fell by double digits year over year, driven by weaker news cycles, falling platform referrals and competition from creators

The post Subscriptions grow up: 7 reader revenue lessons from Innovation in Media report 2026/27 appeared first on Audiencers.