> Audience consultant Khalil Cassimally's rule is to use synthetic audiences to narrow, never to decide. They rank options and surface objections, but cannot tell you magnitudes like willingness to pay

> Start with the decision you need to make, not the question you want to ask. Most synthetic research fails by producing interesting material nobody was ever going to act on

> Ground the panel in words real readers actually wrote. Old interview transcripts, cancellation reasons, unread survey comments. The test is whether you can name the humans inside the system

> Most teams only ever count responses. The better question is where your draft conflicts with what you have documented about a reader, which is analysis rather than impersonation

> Tell it to answer as a reader rather than an analyst, and that indifference is valid. Then validate whatever survives with real people, which is where the saving comes from

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

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

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

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

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

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

What a synthetic audience is

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

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

What synthetic audiences are good for

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

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

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

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

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

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

How accurate are synthetic audiences

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

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

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

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

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

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

Where synthetic audiences stop

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

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

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

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

How to start experimenting with synthetic audiences

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

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

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

1. Fix the decision before you write the question

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

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

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

2. Decide what data the panel is standing on

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

There are three levels:

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

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

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

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

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

3. Pick the job

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

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

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

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

Three things to build into the prompt:

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

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

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

Start with the research you already have

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

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

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

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

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

> The Bureau of Investigative Journalism built its own user needs model rather than adopting the standard one. Its four needs are expose, discover, involve and spark, each tied directly to the mission

> Building it in-house was the point. Bringing colleagues through the process turned sceptics into owners, and needs phrased in the newsroom's own language are far easier to accept than a generic framework

> Oliver Kemp is honest that most newsrooms shouldn't start here. A bespoke model takes real work and patience from decision-makers, so begin with the off-the-shelf version and go bespoke once you're ready to commit

> The unglamorous part carries everything. A content audit means tagging hundreds of recent pieces by the need each serves, and the resulting chart of mismatches is what convinces colleagues to act

> The moment they knew it had worked: reporters began pitching stories with the need and the audience already identified, rather than tagging them afterwards

The key insight here is: serving people’s needs. Today’s successful media operations understand that addressing the needs of audiences underpins everything. And many achieve this by integrating the user needs model into their operations.

One media operation that has implemented the user needs model successfully is The Bureau of Investigative Journalism (TBIJ) in the UK. For TBIJ, it’s not about clicks – it has no shareholder to please – it’s about depth and transparency. Its mission is to hold power to account.

Oliver Kemp, TBIJ’s Audience Editor, led the model’s newsroom implementation. When I interviewed him at The Audiencers’ Festival in London, he shared valuable insights about the journey and critical factors for successfully rolling out user needs in a newsroom.

> Here’s a step-by-step guide to implementing user needs in a newsroom of any size.

TBIJ’s unique set of user needs

Integrating user needs opened new pathways for TBIJ to provide value to people. While its core journalism seeks to show abuses of power, talking directly to its audiences convinced Oliver and team that there are opportunities to provide more value by serving more needs.

This led them to create their own user needs model – one deeply representative of their audiences’ needs and fully aligned with TBIJ’s mission.

Creating a unique model helps bring a newsroom closer to its audiences. Beyond exposing abuses of power, TBIJ now aims to help people discover and learn about complex topics (often related to their investigations); involve people by being transparent about their journalistic process; and spark people into realising the good journalism can do by showing the real-world impact of their work.

A unique model can also win over more ambivalent colleagues for two reasons:

• Collectivity: creating the unique model offers an opportunity to bring colleagues along in the process. Oliver highlighted how he addressed scepticism and fostered stronger ownership of the final model.
• Relatability: the unique user needs tend to align closely with the operation’s mission, making them more relatable to colleagues and improving acceptance.

“The first step is to talk directly to your audience … These conversations can take many forms – surveys, interviews, focus groups, or even informal chats – but the goal is the same: to hear in their own words what they value, what they struggle to find elsewhere, and how your journalism can serve them better. This insight becomes the foundation for tailoring the model to fit your unique context.”

Creating a unique model requires considerable work and initial good faith from decision-makers, as results don’t typically appear in the short-term. This is why most media operations aren’t best-placed to create a unique model when beginning to implement user needs. For most operations, allocating resources is a constant battle, and the need to show results quickly is essential for future investment.

Using the plug-and-play user needs model is a perfect starting point for most operations, with creating a unique model more feasible when they are ready to double down on user needs.

Acting on the user needs

With the model in place, it’s time to identify how well (or not!) the newsroom is currently serving the user needs. The aims are to create a reference for gauging the impact of user needs-informed journalism, and to understand which user needs to prioritise (and deprioritise).

For most media operations, TBIJ included, the content audit forms the bulk of this work. The audit involves tagging hundreds of recently-published content with the user need each serves. 

Once the tagging is completed, the charts that change everything can be generated. These charts are the artefacts that will convince colleagues to take action. They showcase:

• The reference: how a media operation’s content currently serves user needs.
• The actions to take: where mismatches exist between content production and serving user needs.

Note: the chart is for illustrative purposes only, but it mirrors findings from many media operations that conduct content audits.

As you can see, doing the content audit, and subsequent analysis, can be a painstaking process. But it is of utmost importance. The insights from the audit forms the basis of every downstream decision.

User needs at the basis of strategy

Oliver shared how user needs now informs TBIJ’s content and engagement strategies, leading to even better journalism and growing audiences.

• More needs, better served: TBIJ tracks the volume of content it produces for each user need and identifies those that may be under-served. This informs content strategy, enabling them to act strategically to rebalance content production with user needs.
• Increase exposure of their investigations: certain themes and user needs are more accessible to specific target audiences than others. Reframing some investigative work around these creates more accessible entry points for more people.
• Increase engagement with their journalism: while certain user needs make good entry points, others suit deeper engagement or conversions. Understanding the user funnel through user needs, using tools like Poool’s Engage element, opens up possibilities for better-optimised user experiences.

User needs also became the basis for identifying both missed and new opportunities at TBIJ.

The “spark” user need has untapped potential, according to Oliver. To better inspire audiences about the impact good journalism can have, reframing this need so it more clearly guides TBIJ’s work may be on the cards.

This illustrates the evolving nature of user needs. It’s necessary to frequently revisit the model and iterate to remain close to audiences and aligned with the media operation’s purpose. Audiences’ needs, interests, behaviours, and the problems they face all change over time. And sometimes, it may be worth adjusting certain user needs to make them more actionable within newsrooms. The point is we must continually adapt to remain valuable.

Oliver also revealed that a fifth user need may be in the works at TBIJ – one that would be explicitly action-oriented. This speaks to extensive research showing that people seek information that helps them do something about what’s happening. There may well be an opportunity to serve this need by engaging people more directly with investigations, whether through participation or advocacy.

When user needs leads to culture change

Culture change can seem impossible – it’s hard and takes time. But at some point, it becomes inevitable.

Oliver knew TBIJ had reached this point when reporters started pitching ideas with user needs already in mind. No longer was user needs something they ticked off at the end of the process. Now, user needs directly shaped their journalism. Reporters were thinking about the “why” (need to serve) and “who” (target audience) of a story from conception.

“[Reporters] identified whether [their stories] would meet a need to inform, explain, inspire, or provoke action, and tailoring their approach accordingly. This not only produced stronger, more focused ideas, but also fostered a more collaborative editorial culture, where conversations about audience and impact became part of the creative process rather than an afterthought.”

This is the culmination of a user needs-first newsroom – where the virtuous cycle of value, quality, trust, and connection is fully activated to provide journalism that is genuinely valuable to people.

> Download all slides from Khalil & Oliver’s session here

What you'll get from this article:

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

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

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

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

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

1. We are the facilitators of strategic vision

Audience practitioners and product-minded people get things done. More importantly, we get the right (strategic) things done and deliver value internally to colleagues and the broader business, and externally to people.

This is no mean feat. Especially in the media, an industry that is typically so hierarchical and where teams are so siloed.

The consequences of this structure are evident:

• Excessive hierarchy often impedes the flow of information within organisations, achieving the opposite of streamlining.
• Siloed functional teams create inevitable internal misalignment.

These factors breed strategic ambiguity throughout many media organisations.

Strategic ambiguity causes various problems:

• Lack of confidence in decisions: second-guessing of decisions by teams and others forces constant validation, which is inefficient.
• Resource allocation failures: pursuing multiple simultaneous goals spreads resources too thin, preventing breakthroughs in any area.
• People pulling in all directions: without seeing the bigger picture, teams work on overlapping or conflicting projects, stalling progress.

Thankfully, audience practitioners and product-minded people are stepping up as proactive leaders, bringing strategic clarity to media organisations.

This strategic clarity delivers important benefits:

• Amplifies impact by multiplying the effect of effort and resources through focus on high-leverage work.
• Reveals transformation opportunities by revealing novel ways to capitalise on challenges.
• Creates competitive advantage over the many organisations operating without strategic clarity.
• Unlocks sustainable growth, moving beyond tactical hacks to deliver consistent, long-term value to people.

Audience practitioners and product-minded people provide this strategic clarity through several means:

• Crystallising direction by translating vision into actionable strategies for teams. We align around clear problem statements rooted in the needs of audiences and ensure colleagues understand the strategic rationale.
• Implementing prioritisation frameworks that address the most critical issues. We focus on outcomes over outputs and use a systematic approach: collating ideas, rating them by impact-to-effort ratio, and creating roadmaps through collaborative discussion.
• Fostering organisational alignment around both decisions and decision-making processes. We manage key stakeholders by bringing decision-makers along early, explaining the “why,” maintaining transparency about tradeoffs, and genuinely addressing concerns.

The media organisations that truly value their audience practitioners and product-minded people are the ones reaping these rewards most abundantly.

2. We foster collaboration

Egos, siloes, woes: this toxic combination breeds strategic ambiguity and its associated calamities. The solution is creating strategic clarity, as we’ve seen. And audience practitioners and product-minded people deliver this through mastery of collaborative working.

Effective collaboration breaks down barriers across seniority levels, functional divisions, and other divides. Its not-so-secret ingredient is empathy – the ability to truly understand challenges from multiple perspectives.

Consider resistance to change. Rather than defaulting to frustration when facing opposition, audience practitioners and product-minded people dig deeper to uncover the real concerns:

• Loss of control: often interchanged with loss of power but also includes a threat to our sense of autonomy.
• Too much uncertainty: people tend to choose familiar misery over an unpredictable path.
• Too massive of a difference: when the change feels too big, people lose their bearings and the new normal feels impossible to navigate.
• Fear of losing face: people become defensive when what they worked on gets changed.
• Insecurity about competency: change can make people worry their skills are outdated – or worse, no longer needed.
• More work: change is too often linked to giving people even more work.
• Unintended consequences: the fallout may hit those who had nothing to do with the decision.
• The vibe is off: sometimes, they just don’t like you, sorry.

We excel at detecting these underlying concerns to inform collaborative work.

Audience practitioners and product-minded people systematically embed collaboration into organisational DNA through:

• Purpose-driven cross-functional teams that unite diverse talents (and perspectives) around shared goals. We assemble teams that work together physically, share objectives, and are rewarded by common outcomes. We maintain teams small initially (no more than 7-8 people) with regular touchpoints to ensure adequate support and alignment.
• Structured documentation processes like Product Requirement Documents (PRDs) that build credibility bridges between technical and non-technical groups. We implement review processes with peers to preserve strategic cohesion.
• Project frameworks that connect individual contributions to broader strategic objectives. We deploy design sprints and hackathons for problem-solving sessions.

By serving as collaboration catalysts, we don’t just facilitate better teamwork, we fundamentally transform how media organisations function – which gives them the strategic clarity they so desperately need.

3. We bring newsrooms closer to people

The media industry has grown too disconnected from the people we’re meant to serve. And we see the consequences all around us.

It would be naive to think that the rise of fascism, a loneliness epidemic, an increasingly fracturing web aren’t at least partly due to our failure to connect with people in a positive way.

Audience practitioners and product-minded people strive to rebuild this broken connection. We believe that truly serving people requires deeply understanding their needs, expectations, and problems. And then ensuring this understanding permeates into newsroom decisions.

Audience practitioners and product-minded people reconnect newsrooms with people in various ways:

• Championing perspectives of audiences: we build organisational empathy by democratising audience understanding across teams. We support colleagues to deepen their understanding of audiences with AI tools.
• Elevating audience value creation: we ensure that creating genuine value for people becomes central to every conversation – not just editorial discussions but also business strategy. We map content directly to the needs of different audiences, creating high-value content pillars.
• Anchoring product development in audience insight: we ground every product firmly in our understanding of real people – which we achieve through audience research – rather than building based on assumptions. We maintain internal research backlogs and help connect audience research to decision-making.

This systematic integration of audiences-informed thinking transforms both media organisations and the communities we serve, not least by enabling the creation of true content/product-audiences fit. The remarkable successes of newsrooms that implement the User Needs model – an audiences-informed way of creating content and experiences –demonstrates this approach’s power.

Here are a few ways in which newsrooms can better leverage audience practitioners and product-minded people:

• Empower us: get comfortable allocating us more resources while maintaining ROI accountability. Our influence delivers greater impact per dollar than most other investments.
• Support us: have our back, especially at the onset of projects. Your backing builds confidence – both ours and the organisation’s – resulting in quick wins and early momentum.
• Trust us: our work can take time to bear fruits. Embrace iterative experimentation and substantial longterm gains it produces.

By bridging the gap between newsrooms and people, we don’t just improve media business models, we help restore the essential social contract between journalism and society.

By facilitating strategic vision, fostering collaboration and bringing newsrooms closer to people, audience practitioners and product-minded people are defying the odds to breathe fresh air into an industry struggling for oxygen. The organisations that elevate these roles from peripheral to central will be the ones that not only survive but thrive. In an industry desperately in need of reinvention, we are the architects of a more sustainable, audiences-informed future.

Most media organisations struggle to tap into the creative potential of their entire staff. This guide presents a practical template that helps anyone in newsrooms come up with promising ideas and turn them into strategic solutions, breaking through traditional hierarchical barriers. In doing so, it can reshape organisational culture, creating an environment where each staff feels valued, heard, and empowered to contribute to meaningful innovation.

Many news organisations do offer staff opportunities to share their ideas – usually digital forms with submissions triaged by product managers or product-minded staff. However, these systems frequently result in high rejection rates, with most submissions being dismissed. While some view this as a marker of idea quality, the assumption that rejected ideas are inherently bad is fundamentally flawed.

Most ideas are not. Most ideas stem from genuine experience and intuition, but they often lack critical elements essential for organisational success.

Common reasons for idea rejection are:

• Lack of focus: the ideas are vague in scope with unclear expected outcomes.
• Misalignment with organisational priorities: it’s unclear how the ideas contribute to strategic objectives.
• Insufficient end-user consideration: value propositions are poorly-defined. This is especially prevalent in cases when submissions are from staff who are not the intended end-users.
• Resource underestimation: assessment of implementation requirements (for example, human hours needed) is inadequate.

These limitations create certain challenges: 

• Ineffective use of the organisation’s collective intelligence, which leads to fewer high-potential ideas, which leads to a smaller surface area for creative solutions.
• Erosion of trust in feedback mechanisms as staff perceive the exercise as ineffective.
• Diminished morale as staff feel little institutional appreciation for their ideas.

A template to collaboratively unlock and execute ideas

Product managers are uniquely positioned to address those challenges by socialising idea generation, design, development and even deployment.

Product managers have the skills to problem-solve in a strategic way, work across functions, create alignment, and more. They are also conscious that execution is fundamental – and that executing effectively and efficiently is enhanced by the right frameworks and templates.

Here, then, is a template I created for use by product managers in news organisations for this very use case.

The template is an adapted design sprint that gives staff visibility and agency over the design thinking process.

There are three sets of collaborative exercises which respectively create alignment about goals and key challenges to overcome, develop hypotheses for solutions, and validate the hypotheses with research.

People who have gone through the sprint come away with three main benefits:

• Alignment and focus. The sprint creates a shared understanding of objectives and problem-solving approaches. “The sprint helped us comprehend a problem more clearly and break down the solution into manageable chunks,” wrote a commissioning editor who went through the sprint.
• A repeatable system. The sprint converts abstract ideas into concrete requirements for strategic execution in a structured way. “I feel we will be able to integrate this streamlined approach to planning into all aspects of our work, not just this project itself,” a senior editor said.
• Collaborative empowerment. The sprint motivates staff and ensures equitable participation. Another senior editor said: “we all got a chance to contribute and feedback, it wasn’t dominated by more senior team members …”

Assembling a good team

Before you use the template, you need to assemble a team of half a dozen members or so. Your aim here is to form a team that will work collaboratively in a creative, productive and fulfilling manner.

There are three considerations to keep in mind as you assemble a team:

• Establish strategic alignment. It enables the team to move more quickly through the sprint without the need to rehash key decisions. And it ensures they are more focused on, and able to, deliver value to end-users. Having a certain desk or department department heavily represented in the team is one way of starting with some strategic alignment.
• Incorporate cross-functional expertise. The breadth of knowledge and perspectives is conducive to a cross-pollination of ideas, which puts the team in a position to execute holistic solutions. So, while the majority of your team might come from a certain desk or department, ensure you also have representatives from some other functional groups.
• Ensure team empowerment through stakeholder representation. There’s no point going through the sprint if any execution will be blocked by certain stakeholders. The team should be empowered to deliver. It is for this reason that bringing a decision-maker into the team can be valuable. They are typically more senior staff who may not have the bandwidth to go through the sprint. But ensuring they have visibility in the process and are kept informed about decisions should make it easier to get their backing, empowering the team.

Once you’ve assembled the team, you can start the sprint.

How to use the template

The product manager’s role is as a facilitator. They ensure that team members understand how to carry out each exercise, and how each exercise fits into the larger objectives of the sprint.

The exercises are ordered in eight rows. Most of those eight rows are the equivalent of 60- or 90-minute work sessions.

The first two sessions are all about finding alignment and focus for the sprint.

The first session is strictly focused on end-users.

• The first exercise  (Who are our users? Research) helps team members develop a shared understanding of end-users by sharing and reviewing any information they can find.
• The second exercise (Who are our users? Decision) then helps the team zero in on one target segment they want to be valuable to, using a decision-making matrix.

The second session aligns the team on why they’re doing this work, the challenges to overcome to be valuable to the target segment, and ultimately the scope of work during the sprint.

• The first exercise (What is the longterm goal of our work?) helps create a goal to aspire to.
• The second and third exercises (What are some of our possible challenges?; What are some key themes from our possible challenges?) identify challenges to overcome to reach the goal.
• The fourth exercise (What does the current experience look like?) recentres the team on the current state.
• The fifth exercise (What are the first steps we can take now itself?) helps identify what the team can work on during the sprint.

By this point, the team is clear on the long-term goal and the key challenges to work on. The team is now primed to move to develop hypotheses for solutions, which it will do over the third and fourth sessions.

The third session is all about idea generation. 

• The first exercise (What are our hypotheses?) encourages brainstorming of hypotheses for the identified challenges.
• The second exercise (Which hypothesis do we prioritise?) allows for prioritisation of hypotheses, using a prioritisation matrix.
• The third exercise (How are we defining success of the ideas?) establishes a criteria for hypothesis validation.
• The fourth exercise (What are some of the risks involved?) helps risk mitigation by encouraging the team to think through the assumptions and risks associated with the hypothesis.
• There are two additional exercises which the team can go through if it appears like they are favouring working on a prototype. The exercises are competitor research and heat map.

At the end of the third session, the team has clarity about the hypotheses they will work in an attempt to deliver value to the target segment.

The fourth session is concerned with planning the work. 

• The first exercise (What do we need to do to action the idea?) helps the team break down their big idea into a series of manageable jobs to be done. 
• The second exercise (When do we do what?) is roadmapping.
• The third exercise (Who does what?) is resource allocation.

The team is now ready to go and execute. Following execution, the team needs to decide whether they want to stop, iterate, or deploy what they’ve worked on or built. This decision relies on validating their big idea or hypothesis. To do this, the team needs to conduct research. This is covered by the fifth and sixth sessions.

The fifth session:

• For hypotheses that can be validated by looking at performance metrics, the first exercise of the fifth session (What do our metrics tell us about the idea?) allows comparison with a baseline.
• For more product-related work, user research is needed to validate hypotheses. In this case, the exercises help organise the research and set it for success.

The sixth session helps make sense of the output from the user research.

• The two exercises (What is the feedback from the user testers?; What can we learn from the user testing?) provide canvas for feedback capture and synthesis into actionable insights.

At this point, the team can make an informed decision about how to proceed with what they’ve worked on or built. If the research has not provided enough evidence that it is working, there is no shame in stopping and moving on to something else. (Some teams set fail criteria to help decision-making be more rational.) If the research has provided ample evidence that what they’ve worked on or built is working, the team can choose to iterate and eventually deploy at scale.

The seventh, and last, session (Does the prototype meet our standard?) is meant to act as a final internal check before deployment to reassure the team, and the wider organisation, that what the team worked or built is ready for the big time.

By following this structured approach, product managers can transform the idea generation process in their media organisations, creating a culture of innovation that values and leverages collective intelligence.

Which can in turn help shift the culture to one where creativity can flourish, staff feel heard, and meaningful innovations can emerge.

News organisations that want to deliver more value to their audiences now have a plug-and-play model they can use.

Dmitry Shishkin is back with an updated version of the user needs model, six years after posting his seminal article detailing six user needs. The updated model, user needs 2.0, so comprehensively maps people’s news needs that practically any news organisation can just use it off the shelf.

The user needs model essentially provides news organisations with a set of needs that most people have when they’re consuming news content. It is premised on the idea that news organisations can be more valuable to audiences by addressing their news needs better and more systematically.

The original model comprised six needs:

User needs work

The model works and that’s why it’s been so effective across many news organisations. It delivers more value to people. And it delivers more reach and user engagement for news organisations.

Views to BBC Russian content nearly tripled in five years as the newsroom focused more on the news needs of their audiences. At The Conversation, focusing on the news needs of young professionals led to a 45-50% increase in the portion of people who read whole articles and helped secure partnerships with other publishers including Fast Company, Público and others.

I suspect another big reason user needs has been so effective within newsrooms is that it doesn’t so much as solve problems many newsrooms face when they try to become more informed by their audiences as it negates them entirely.

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An argument I hear often as we support teams within The Conversation to become more audiences-informed is: “we’re just going to publish clickbait articles if we just listen to what people want”.

But familiarity with the user needs model has consistently assuaged this concern. Because the user needs model doesn’t condescend to journalists by minimising their journalistic intuition and expertise. It doesn’t tell them what they must commission. Instead, it augments the impact of their ideas and enables them to produce content that is even more valuable to people.

And once they see it in action, once they see how anchoring their ideas around relevant news needs supercharges the reach and impact of their work, journalists become advocates. ​​

At The Conversation, we started using user needs in 2020 with our UK newsroom to make its COVID coverage more valuable to people. The success of this project – despite our rather crude approach back then – has been eye-opening. We’ve since been deploying user needs across more teams to support them in becoming more audiences-informed.

User needs 2.0: “help me” and “connect me”

User needs 2.0 better captures people’s news needs today. The original model mapped people’s needs to know and understand what’s happening, and their need to be entertained or inspired.

But people are also looking for news content that they can use to do something, to take action. Over the years, audience research carried out by multiple news organisations has made this increasingly apparent. And this insight has informed the news needs that those organisations have looked to address with their journalism – from Vogue’s “make me feel responsible” to The New York Times’ “make my life easier” and The Conversation’s “motivate me”.

User needs 2.0 now incorporates this insight with the addition of two new action-driven user needs: “help me” and “connect me”.

“Help me” is more concerned with the personal. It is the need to find information that solves a problem, that details how to do that thing that will improve their lives.

“Connect me” is more societal in scope. It is the need to feel part of something bigger and act for the sake of not just themselves but others too. In one of our research with some of The Conversation’s climate readers, we kept hearing that they wanted to know more about what neighbouring communities were doing to mitigate or adapt to climate change. The need to be connected and to do something about the biggest crisis of our time was clear.

With the introduction of these two action-driven needs, this new model is comprehensive. It covers people’s needs to know, understand, feel and do. So comprehensive that the barrier for news organisations to implement user needs is now really low. Most news organisations can now just use the model without doing much user needs audience research of their own. The needs of their audiences is very, very likely to be represented in the new model.

Getting started with user needs

The user needs 2.0 model is detailed in a whitepaper just published by Shishkin and SmartOcto. An entire section of the whitepaper is devoted to implementing the model in newsrooms (addressing the “help me” need!).

It boils down to:

1. know your audience (at least a little bit) and determine their news needs
2. create a baseline to understand how well (or not) you’re addressing those news needs
3. start focusing on user needs to create more value to people

You can also read this detailed piece which explains how we implemented user needs with The Conversation’s UK newsroom.

The user needs model can be transformational. It’s an actionable model that helps news organisations elevate the power of their journalism and be more valuable to people. And the more valuable we are to people, the more they trust us, the more they come back to us. It’s a win-win. Implement it.

Download the full user needs 2.0 report here.