With the tsunami of content that's coming with AI, building communities - where publishers listen, engage and interact with readers - will prove essential to survive. In an incredibly interesting session at WAN-IFRA's World Congress in Krakow, Ryan Y. Kellett, 2025 Nieman-Berkman Klein Fellow for Journalism Innovation at Harvard, Hannah Sarney from Financial Times and Nic Dawes from THECITY shares how they're reimagining journalism as a collaborative, community-driven practice. 

What is a creator-model journalist? 

• Creator journalists combine journalism with elements of content creation
• Creator journalists work independently, in collectives or collaboratively
• Creator journalists run a small business with multiple products and revenue tied to ads, sponsorships and donations
• Creator journalists are accountable to their audience on social media, often by creating a para-social relationship

What can we learn from creators? 

• Be more trustworthy in sharing expertise and having bias
• Become more accessible in online spaces where audiences already consume other content
• Be more reliable in tone and style, including bits of personal life to add a human touch
• More transparency in choices made to run a sustainable model
• Be more responsive to audience feedback 

Creators are often a supplement, a new way of thinking about news, but elevate news organisations because of their resources.

But this doesn’t come without challenges, for the creators and the industry. 

• The algorithm still rules the world around us for both creation and consumption
• Is this actually the uber-fication of news, if journalists are all independent contractors? Who will do BIG investigative or accountability journalism? 
• Curating your own news feed with the creators you like is actually really hard. And it’s hard to elevate new voices
• No incentives for young journalists to train. Inconsistent ethics and editorial judgement
• Does nothing to counter polarization and conflict entrepreneurs

What can your newsroom do to lean into a creator future? 

• Share your favourite creators with colleagues and even bosses
• Identify and uncover talent on staff most comfortable with creator spaces. Give them resources and support to try something new 
• Align talent with aesthetic without forcing brand on them (where a lot of newsrooms go wrong!) – don’t squeeze them in the brand, be creator / person first 
• Empower your audience teams to explore and lead with AI tools
• Partner with non-journalist creators, paid and organic. Try academics or other subject-matter experts you trust first
• Formalise a creator-program that scales affiliate/referrals for subscriptions or registrations
• Start an in-house creator team to extend your brand at the top of the funnel 

Loyalty isn’t built by speaking louder, it’s built by speaking with

Hannah Sarney, Editorial Product Director at Financial Times, highlighted the importance of building a future of trusted journalism. How? Trust builds loyalty, community builds trust, and community is built through two-way engagement spaces.

Curate the story but also the conversation around the story 

Given that owned platforms have the highest trust, it’s here that publishers need to build conversations and interaction around the journalism.

This has already been proved at Financial Times:

• Comment readers are 11x more engaged than those that don’t
• Commenters are 46x more engaged than those that don’t read or write comments

However, research found that although 50% of subscribers read comments, only 4% actually write them. Maybe the door doesn’t feel open, or maybe the door isn’t easy to find?

Encouraging commenting at Financial Times

Protect civility

• Making community guidelines easy to find and understand
• Clearly enforced by the moderation team
• AI-powered moderation bought in to bring up the moderation baseline, re-trained off human comments, with humans still kept in the loop

Spark participation

• “Join the conversation” tag with a question to encourage readers to answer and be more thoughtful in their commenting
• Make it easy for readers to jump down to the comments section

Fostering connection and togetherness

• Upcoming live Q&A feature designed to encourage broader participation and lift conversation into the story
• Only send these questions to journalists, rather than publishing on the site immediately, making the entry-point less intimidating
• Easy-to-use, mobile-first experience
• Clearly defined roles and experts
• Easy to catch up when the event is over

Build direct 1-2-1 relationships to create stability and resilience against big tech platforms 

Nic Dawes, Executive Director at THECITY, a local news nonprofit serving the people of New York City, with a clear mission: provide news for underserved communities.

One way that they achieve this is with targeted, printed postcards via mail, a uniquely non-digital strategy to get in front of the people impacted by these stories. The postcards include explanatory journalism, a QR code to the website and question-and-answer content.

Recipients then share questions, with journalists writing back to thank them and close the loop, showing that readers are part of the process, reinforcing communities. This also helps to better serve readers as content is based on reader questions

These stories have consistently strong evergreen traffic performance, help to acquire new audiences and allow for sponsorship opportunities for different campaigns.

Although they may not be a key driver of immediate donor intent, they have the potential to short-circuit search and AI related challenges (although significant scale-up is required).

Village Media’s North Star: 25% of the total addressable market should be brand lovers

As a media dedicated to “making community news sustainable”, encouraging readers to return, engage and stay is essential to Village Media’s mission. They therefore defined their North Star around the goal of increasing the percentage of brand lovers – anyone who visits their site more than 15 days/month.

This is easy to understand, specific, measurable, and can guide action across the company.

Nation Media Group’s North Star: to become Africa’s premier news destination, with a 500 million daily interactions across all digital platforms by 2027

Speaking to WAN-IFRA, Pamela Sittoni, Group Managing Editor, says their North Star will see the company “transforming into the most trusted, empowering and innovative content platform in Africa; we want to reach 500 million daily interactions or engagements across all our digital platforms.”

Research and analysis into this target suggests this could be achieved thanks to two major transformation moves: newsroom integration into a single team with one editorial direction and central decision making, and a “quality over quantity” content strategy.

This North Star helps inspire the whole company, not just business teams, towards becoming truly digital and continuously thinking about how to ensure the customer is the central focus.

The Irish Independent’s North Star: secure the future of independent Irish journalism, reaching X digital subscriptions by 2025 and generating X revenue

Although slightly outdated, this example from The Irish Independent (shared at an INMA conference) provides a nice visual of the North Star framework in action.

To reach its North Star goal, teams created metrics and then broke them down into smaller, achievable tasks.

Financial Times’ North Star: from engagement to LTV to Global Paying Audience

Since 2016, Financial Times has defined a single metric that matters, a North Star, that they consider essential to digital transformation – the process of evolving an organisation to a point where it can use technology, data and strategy to fulfill its mission, as Tara Lajumoke, Managing Director of FT Strategies until August 2023, shared in an interview.

When defining this first North Star, the team considered the biggest factor that determined whether an FT subscriber would continue to use their subscription. The answer was determined as their level of engagement with content and amount of value they’re getting out of it. 

Thus, FT’s first North Star: RFV (recency, frequency and volume)

The metric looks over a 90-day period and calculates a score based on the number of days since a user’s last visit (recency), total number of visits in that period (frequency), and the number of “counted content pages” viewed.

A user is considered engaged when their RFV score is more than 18.2, and becoming engaged leads to a 10 percent reduction in cancellation rates. 

This became a very concrete, strategic piece of information that people across the organization could understand, use to prioritize their work and rally behind.

For example, with this North Star in mind, the team set out to build a feature called myFT, a simple but effective personalization mechanism based on a subscribers preferred topics. They discovered that the mere act of becoming a myFT user increased engagement by 86%.

Evolving towards a more comprehensive North Star

From a focus on engagement from 2014-2019, FT has since developed their strategy to develop a North Star metric of lifetime value (LTV) in 2020.

Although, it’s worth noting that LTV didn’t replace RFV, but built on it, especially as the latter is an important predictor of the former.

LTV does however support better decision making when assigning resources or prioritizing tasks for acquisition vs retention, or B2B vs B2C for instance.

> Get started with measuring LTV

GPA: Global Paying Audience

From 2023, the North Star was developed to Global Paying Audience, designed to help grow audience revenue beyond its core journalism offerings and across its broader portfolio of products and services, aiming for 3 million by 2028.

The GPA metric encompasses not only the core digital product but also live event customers, FT Specialist and print newspaper circulation, seeking revenue from across a range of sources.

> 100k downloads later, what I learnt from launching FT Edit

Metrics that matter

The Global Paying Audience North Star is part of a wider framework called “metrics that matter”, outlining a range of metrics that sit underneath GPA and used more regularly by different teams across the organization.

In short, Metrics That Matter (MTM) gives all departments at FT a shared framework & common language so everybody in the company can see how their day-to-day work impacts GPA.

• Reduces focus & pressure on the North Star
• Mirrors a simplified customer lifecycle
• Put more focus on retention
• Encourages evaluation of costs
• Links volume and value in all pursuits
• Uses existing metrics where possible

The new metrics “solar system” framework:

This has been established for 7 departments, hosted on FT Intranet for full visibility, with links to the Data Cataglogue (a space to support consistency and ownership of definitions across the business). KPI checklists help measure performance and take action using data.

For example, the consumer revenue group (B2C): (GPA = Global Paying Audience, FT’s current North Star as of 2023)

And Editorial team:

Looking to get started on Metrics That Matter? FT Strategies has created a dedicated playbook!

At the Financial Times, we believe that engaging with this space is crucial to address opportunities and threats emerging for media outlets. Through a lot of experimentation in 2024, we uncovered some promising generative AI use cases (a topic for a separate post) and leveraged predictive machine learning to support computational journalism in uncovering and developing stories that would otherwise stay untold.

Building a Scalable Solution

We’ve been privileged to lead a multidisciplinary team with the mission to bring focus to and accelerate the development of AI products at the Financial Times. One of our primary challenges lay in making data-driven reporting more scalable. In the FT newsroom, there were some very tech savvy data journalists who could do case by case data ingestion and analysis of complex data. But the process was often manual and isolated to specific stories or datasets. To overcome this, we needed solutions to enhance our journalists’ ability to mine data for insights consistently.

We launched an experiment focused on the UK Register of Parliament Members’ Interests — a complex, unstructured dataset listing MPs’ affiliations and financial interests. The important bits of information are often hidden there, obscured or presented as free text that is hard to run analysis on. By building a data pipeline, our team automated the extraction of key information, adding intelligent metadata and clustering entries for deeper insights.

What it allows us to do in practice is for instance search one of the new UK government members and see their links to people and companies at a glance. Their interests can be aggregated into groups and entities that are not available in the original dataset. It also enables reverse querying of the data — asking questions like “Which MPs receive the most football tickets?” — as well as analysis of changes over time.

This capability allowed our journalists to quickly identify patterns and connections between UK government MPs and external entities, resulting in two published stories on MPs with previously hidden connections.

The first story raised critical questions about potential conflicts of interest as Labour advances its rental reform agenda. The second story revealed that Unite had shifted its financial support away from the central Labour party following Sir Keir Starmer’s move toward the centre, directing over £573,900 to individual left-leaning MPs. This analysis highlighted shifts in political and financial influence within the party.

By leveraging automated data pipeline paired with editorial analysis, the team was able to quickly and efficiently uncover these insights, accelerating the journalists’ ability to tell the story.

Expanding the Horizons

Having proven the hypothesis for ingesting and transforming a single dataset to unearth new stories, we are now expanding the capability to additional data sources and functionality. We’ve already replicated this success by adapting the model for U.S. data — starting with the Federal Election Commission records, which present a similar level of complexity and can benefit from a similar pipeline.

Additionally, we are developing a “fuzzy matching” capability, which would allow us to draw connections within and across datasets where exact matches might not exist. Fuzzy matching, or close proximity matching, helps identify subtle patterns by matching similar, though not identical, data points, such as variations in names or addresses. To implement this, we created a prototype pipeline that uses techniques like indel distance (measuring differences between text strings) and name normalisation processes. The initial testing has been promising and further refinement could generalise this tool for broader applications, enabling journalists to investigate a variety of datasets more efficiently.

Through this work, we’ve also identified several key challenges that need to be addressed in order to continue advancing data-driven journalism:

• Growing Data Complexity and Volume. As data complexity and volume increase, our ability to maintain a competitive edge in storytelling requires more advanced capabilities.
• Varied, Inconsistent and Unstructured Data. Journalists often face difficulties working with unstructured and inconsistent data sets. This is particularly challenging when working at scale and with multiple data sources, and when very few data sources have broad utility across various reporters’ subject-matter specialisms.
• Time-Consuming Data Acquisition. Obtaining large datasets can be a labour-intensive process, which slows down the newsroom’s ability to discover insights more rapidly. Often, as in the case of the Register stories, it takes far longer to simply get the data than to analyse it enough to tell the story.
• Difficulty in Connecting Insights. Discrepancies across various data sources make it harder to rapidly link key insights, potentially leaving important gaps in our stories and limiting our ability to uncover hidden narratives.

Looking into the future, we see three main opportunities areas to further enhance story finding in the FT newsroom:

1. Information Extraction & Insight Generation. Using AI to extract names of individuals and organisations mentioned in unstructured data, group related data, and conduct connection analysis to uncover trends and patterns in large datasets that might otherwise go unnoticed.
2. Signal-Based Notifications. Developing services that alert journalists when new, relevant signals emerge from data sources, ensuring early detection of important events or trends.
3. Automatic Data Processing. Building pipelines for the automatic ingestion, cleaning, and parsing of unstructured data sources to establish a strong data foundation for sophisticated, exclusive analysis other news organisations will struggle to match.

Conclusion

Our experiments have highlighted the transformative potential of automated robust data pipelines and AI-driven analysis in story finding. Data pipelines uncover hidden narratives within complex datasets, while AI equips journalists with advanced tools to perform more sophisticated analyses, elevating their story finding capabilities.

We have seen the potential of AI-driven discovery while identifying challenges, such as managing growing data complexity and volume. As we refine these tools, we remain committed to responsible AI adoption, helping journalists tell richer, data-driven stories that reinforce the FT’s commitment to rigorous, impactful journalism.

Credits

The multidisciplinary team working on these capabilities includes: Chris Cook, Martin Stabe, Oliver Hawkins, David Djambazov, Ivan Nikolov, Zdravko Hvarlingov, Laura Paul, Jorge Sanchez-Cano, Desislava Vasileva, Erin O’Handley, Chris Mears, Liz Kessick.

Instead of being a threat, successfully retaining subscribers can often happen thanks to easy-to-cancel user experiences (as well as retaining throughout the user journey of course). As Travis Bernard, Executive Director, Growth Marketing at LA Times put it, "brands focused on long-term growth prioritize trust over short-term barriers". 

Let's dig into the topic.

Over in France, a recent “termination in 3-clicks” law requires that consumers should always be able to end a contract 1) online and 2) in 3-clicks or less.

Other countries are already implementing similar restrictions, or set to do so soon, and publishers are getting worried…

If someone can unsubscribe in just a click or two, what’s going to happen to my retention rates?!

On the contrary, these regulations could be the push that publishers need to put audience needs first, implement retention strategies throughout the user experience (not just as an afterthought) and allow for positive, lasting impressions that leave the door open to potential re-subscription in the future.

> If you’re interested in learning about how publishers have already reacted to these regulations, in France and elsewhere, check out this article

When should you think about retention?

Or rather, when shouldn’t you think about retention…

Retention shouldn’t just be an afterthought at the moment of cancellation, nor as a ‘post-conversion problem’. In fact, many successful digital reader revenue teams are mission-based to avoid siloed acquisition and retention teams, ensuring the former’s KPIs (high conversion rates but little thought for engagement/retention after this moment) don’t create problems for the latter (high churn rates).

Newsletter subscription, free registration, apps, games, or even simple access to content can all have a positive impact on retention even before a reader subscribes. For instance, increasingly more publishers with a registration model are leading new members through a 3-step onboarding journey, just like they do for subscribers, with the goal of boosting engagement and ticking off some of those important actions that lead to long-term loyalty.

South China Morning Post has a 3-step onboarding journey for newly registered members, encouraging them to sign up to one or more newsletters (habit forming) and build a personalized news feed (support engagement, and this is likely a feature that maximized retention).

The point I’m trying to make is that easy-to-cancel laws are not the nail on the coffin for publishers, because it’s simply this single moment that matters for retention. There are so many other opportunities to keep a reader engaged. And perhaps these easy-to-cancel laws are exactly what’s needed to remind us of this fact.

What’s more, with simple, transparent unsubscription journeys, readers won’t lose trust or confidence in a publisher and may even consider coming back, especially if they know that they could choose to leave again easily without having to phone 5 helplines.

Using data to discover which features correlate with high engagement

In order to think about retention even before a subscriber considers cancelling, you need to understand which features or actions correlate with high engagement, and thus retention.

There are generally two key metrics used to evaluate the value of a feature or product in a reader revenue model:

• How popular a product is (proxied by the average proportion of users that use it at least once over a 30-day timeframe)
• How impactful a product is on engagement (proxied by how engaged a user of that product is 30 days after using it)

DER SPIEGEL did a similar analysis, shared in an article on The Audiencers:

1 – Identifying all available platform features, defined as functional elements that enhance user experience. As news constantly changes, editorial content is excluded.

2 – Analyzing consistently available features and highly engaged users (based on engagement score) to ensure reliable data.

3 – Regular usage means engaging with a feature weekly over four weeks.

They also ran guided in-depth interviews to gain insights into which features users are aware of, which they use and how they rate them. 

According to FT Strategies, you can then rank features by how ‘likely’ they are to drive adoption and generate engagement. Below, an example from a Portuguese publisher.

DER SPIEGEL’s version looked like this:

This research can then be used to strategically nudge those with lower usage towards engagement drivers by highlighting them in mailing, on-site, during onboarding, etc.

The example of Financial Times

At FT, data analysis has shown that the following habits have the greatest impact:

• Signing up to the app & receiving push notifications
• Organically following topics on myFT
• Signing up for newsletters
• Engaging with comments
• Reading on a Saturday or Sunday
• Reading the FT across multiple devices
• Viewing graphics / visual journalism

If we take the example of a subscriber building up their myFT, by following topics and saving content, we can see this being nudged throughout the user journey, from early life, in-life, growth and renewal / cancellation.

In the onboarding journey:

I really like these bundles of content (“newsletters, podcasts and more”) that allow for content diversification around a topic that interests the reader, and all in a single click.

Whilst exploring the website (‘Add to my FT’):

On the subscriber account page:

Readers are congratulated for completing this action (excuse the bad screenshot):

And finally it’s included in the cancellation journey for loss aversion:

Reminder of the goals in the cancellation journey: 

TL;DR: to both retain the subscriber whilst not harming potential future re-subscriptions or referrals.

> Create a fair amount of friction to prompt reconsideration
> Not be too obstructive to cause frustration
> Highlight the value loss
> Understand why they’re leaving to be able to…
> …offer relevant alternatives
> Encourage them to commit for a longer duration
> Ensure they’re confident in the fact that they’ve cancelled but can come back if they want to

For Romain Lhote of L'Équipe, personalized offers, messaging and showcasing relevant usage stats (such as number of articles read) during cancellation proved highly effective.

> More benchmarks on the topic in this dedicated article.

What other strategies should you try to keep readers from visiting the cancellation page?

> Understand the conversion killers and address them

It’s not enough to understand why someone wants to use your product. You also need to know why not?

At Freeletics, they realized early on that many people don’t have much space to train at home. So they developed special workouts that can work in small spaces. Other users have neighbors who complain about the noise, so they need quiet workouts.

Applying this to the media industry, EL PAIS has addressed the “Lack of time” restraint and created an “Exprès” feature, offering readers a curated update on the most important news stories of the day in 7 headlines, explained in 7 minutes.

FT Edit was launched with a similar goal, also providing a lower-cost subscription offer.

> Lock-in effects

This refers to the idea of making it a hassle to switch between products or cancel a subscription. But, be warned, it’s a careful balance – if you overdo it, customers will feel trapped and frustrated, thus damaging the brand. Or, in the worst case, you end up creating dark patterns that really aren’t recommended (slash are illegal).

But there are little innovative tricks to keep your customers loyal…

For example: personalization

Spotify is the prime example here. The app knows your music tastes from the last 10 years and uses this data to recommend new music and create tailored playlists. Spotify itself says that its promise is not just access to music, but more importantly discovery. Switching to Apple Music means the app has to get to know me all over again before it can make recommendations on a comparable level.

A second lock-in example: price guarantee

Average monthly spend is increasing rapidly as prices soar. This makes old contracts all the more valuable. This can be the flat lease from the 90s or, as with the Washington Post, a price guarantee for the next 50 years. For the Watergate anniversary, they offered a subscription for $50 per year with a price guarantee until 2072 – which, importantly, expires if you cancel or pause the subscription.

> The brilliant Lennart Schneider shares 9 lock-in effects in his article on The Audiencers

> Habit forming

Subscription models live off habits. Just look at the success of newsletters in retaining subscribers, or the buzz around Wordle, returning to complete the game on a daily basis.

But hardly anyone is as good at forming habits as Duolingo.

A large part of this success is thanks to streaks, a feature where users are rewarded for completing an exercise a day.

But this strategy wasn’t built overnight – the team have run over 600 tests on optimizing streaks. In Lenny Rachitsky‘s podcast, Jackson Shuttleworth (Group PM, Retention Team) reveals what they’ve learned through testing:

Find a goal that people want to achieve

Streaks don’t work for every digital offering. They’re designed to build habits that users are proud of and that are difficult to achieve.

E.g. at beehiiv: “I would like to write a newsletter every week”

Remember that streaks rely on two psychological effects:

> Loss aversion: psychologically, it’s worse for people to lose something than never to have had it.
> Sunk costs: the more time, money or work we’ve already invested, the more likely we are to hold on to things

That’s why the perceived value of your Streak increases with each passing day. And so does the effect on retention.

Find the right balance between ambition and flexibility

Jackson sees a great danger in streaks becoming too easily accessible and therefore worthless. At the same time, retention drops abruptly if a streak is lost.

Hence why they introduced “streak freezes”. If you take a day off, you can still continue your streak.

> Have a flick through my LinkedIn post on the topic for more recommendations from Lenny.

> And if all else fails, you always have the grace period and post-cancellation

Prioritizing former subscribers in your strategy, especially reaching out to them within the first few days after they cancel, is crucial. At L’Équipe:

• 70% of re-subscribers re-engage within seven days of cancellation.Former subscribers acquired during promotions are particularly responsive to targeted relaunches with attractive pricing.
• On average, former subscribers are 18 times more likely to re-subscribe than members, while members are 18 times more likely to subscribe than anonymous users.

It’s for this reason that they target subscribers during their grace period and immediately post-loss of access.

For instance, a banner warns churned users that they soon won’t have access to this content:

Or a pop-up with a message from the Editor-in-chief sharing what’s coming up at L’Équipe:

Article written by Nicolas Galland, with Loïc Jalmin. Thanks to Madeleine White and Maria Frih for their fantastic reviews and feedback.

You might have heard the term Customer Lifetime Value (CLV or LTV) being tossed around more and more in the digital publishing universe. Its application, though, can really differ. Some media outlets might only use it every now and then, perhaps in an occasional analysis, while others, like the Financial Times, have truly made it their guiding light (AKA North Star Metric).

There’s no denying the buzz around it, but the topic can get a bit tricky. If you start asking questions, you might get a mixed bag of answers. Everyone seems to have a different opinion on what you can do with it or how to calculate it. In fact, the popular methods of calculation have even been  strongly criticized by academics.

Used properly, this metric helps companies to understand the medium and long-term effects of their actions, and steer their digital subscription business model. Misused, it can lead to harmful decisions.

The goal of this article series is therefore to try and clear up as much as possible about LTV, and ensure you’re not making any of these harmful business decisions!.

1. How LTV can help you make better marketing, product and business decisions 

The most common use case for CLV is to use it to cap Customer Acquisition Cost. The idea: If a customer will bring us $x in their lifetime, we can spend $y on acquisition. 

This use is possible in the media world, but it’s not the most useful. LTV is one of the best tools at your disposal for taking into account the medium and long-term effects of your decisions. That’s why it’s a good idea to use it for a variety of purposes: 

• Comparing different pricing strategies. Pricing not only affects acquisition, it also affects short- and long-term retention. LTV is a tool for better understanding the full effect of a price change, and sometimes makes you realize that a price decrease can, under certain conditions, also be profitable.
• Estimating the ROI of promotional offers and introduction offers: The long-term view offered by LTV enables you to better appreciate the value of your promotional offers.
• Estimating the ROI of future projects: If a project can help you acquire X number of new subscribers, reduce churn by Y% or increase your conversion rates by Z%, then the various forms of LTV can help you quantify the expected financial gain over different time horizons.
• Estimating the damage caused by an outage/bug (especially if it’s the responsibility of a service provider). For example, if a partner causes a problem that costs you X number of acquisitions, then the long-term financial loss can be calculated using LTV
• Perform a customer-based corporate valuation
• Make revenue forecasts: using LTV to forecast future revenue by estimating the expected value of current and potential customers over a given period
• Emphasize the importance of loyalty and evaluate the financial impact of retention actions
• Serve as a metric for product management. The Financial Times refers to  “[LTV as] a way for us to measure how successful we establishing longer term relationships and keep them with us for longer”
• Sizing up Customer Success efforts: Knowing a customer’s potential LTV can enable the customer service department to better size efforts for that customer.
• Customer Segmentation: Using LTV to segment customers based on their predicted lifetime value. This allows media companies to target the most profitable customer segments and tailor marketing strategies accordingly.
• Advertising Campaign Optimization: Using LTV to optimize advertising campaigns by targeting audiences with the potential to generate high lifetime value and adjust advertising messages accordingly.
• Partnership Evaluation: Using LTV to evaluate the profitability of partnerships with other companies or brands. Media companies may choose to collaborate with partners who have access to high LTV customer segments.
• …

> You might enjoy: Breaking the silo: bridging editorial and commercial teams, the essentials for success

2. But it’s critical to properly define your LTV

There are many ways to understand the term “Lifetime Value”.

The first thing to ask yourself is what are you going to put behind the word “value”?

You’ve got a few options here. Some consider it to be the sum of revenue generated by the customer (also known as Customer Lifetime Revenue). Others will subtract the cost of acquisition (CAC) from this figure, and others still will also consider all other variable costs (setup costs, server costs, …). This last definition seems to be the one adopted in the academic world, but not necessarily the most common in practice. The most important thing is to be clear about the choice you are making within your company. 

For digital publishers, an interesting definition is sum of revenues (Customer lifetime Revenue), because CAC is an unclear notion for a media that generates a lot of traffic on its own. Conversely, for a print-based medium, a definition that includes the cost of print production will maybe be more relevant for analysis purposes.

The second thing to define is what we mean by “lifetime”. 

Some customers may stay subscribed for more than 10 years, but does it make sense in your analysis to take into account the revenue that a customer will generate in 10 years time? Sometimes it does, sometimes it doesn’t, depending on how you’re going to use CLV. It’s not uncommon to limit the CLV horizon to 2 or 3 years. This is known as “Finite Horizon CLV”.

Defining CLV vs LTV

Most people use the terms CLV (customer lifetime value) and LTV (lifetime value) interchangeably, but others make a difference between the two. In this instance, CLV would be the value of a specific customer, while LTV would be the average value of a pool of customers. In this article, we’ll use CLV in the “average value” (=LTV) sense.

Finally, CLV can be separated into 2 parts The value that has already been collected (Historical Value or Earned Value) and the future value (Residual Value). We’ll investigate both these use cases below.

In a nutshell, using CLV correctly sometimes starts with choosing a more precise term.

3. For each use case a different definition  

We’ve seen that CLV can be used for a wide range of decision-making purposes, but we also understand that a single definition won’t be enough to cover all uses.

Let’s take a few examples:

• If you want to know whether your CAC is correctly sized, you won’t be interested in what a customer will bring in over their WHOLE lifetime, because you want your marketing costs to pay for themselves over a few months or years at most. A more appropriate CLV in this case would be a Finite Horizon CLV of 12/18 months for an average media or 6 months for a younger company.
• In the case of the estimation of the financial damages that a partner has caused, on the other hand, you’ll be concerned about the money lost over a longer period, and a Finite Horizon CLV of 4, 6 or even 10 years (depending on the maturity of your market) will be more appropriate.
• As a final example, for revenue forecasts or estimates of pricing changes, we can use Customer Lifetime Revenue By Age, which we’ll discuss in another article.

Rounding up the tour

As you can see, a single definition of CLV won’t cover every need because the needs can vary across companies, among teams within a single company, and even within the same department. So, whilst it’s acceptable (and normal) to have several definitions coexisting in your company the important thing is to choose the right definition for the job and name things precisely.

For example, the acquisition department may decide to use a “finite horizon lifetime revenue of 2 years”, the product department may opt for a “cumulative net profit over 4 years”, the legal department may occasionally opt for a net profit over 8 or 10 years, the customer success department may use the “earned value” of each customer to better gauge the effort needed for a specific customer.… 

This is the end of part 1. In future posts, we’ll look at the other keys to using CLV properly: how to avoid magic formulas, how to calculate it properly, how not to over-interpret it, knowing when not to use it… Stay tuned!

We are Loïc Jalmin and Nicolas Galland, we have both been helping media companies optimize their revenues and make decisions using data for 8 years (Le Monde, Radio France, Mediapart, CMI, Les Inrocks, …)

FT Edit and the Financial Times

FT Edit is a new app by the Financial Times that offers 8 in-depth articles a day. The app aims to replace the endless scrolling on social media and news sites with more thought-provoking reading time. It’s the first new app the company has launched in a decade.

Contrary to popular belief, I think there’s virtue in launching something new on a decades-old foundation. The team felt like a startup, while our 135-year-old organisation is far more comfortable launching new products now than a year ago. Here is what I’ve learnt:

1. Identify your baseline

We had a good starting point – the FT already had a strong internal belief that:

1. FT content has much larger audience appeal than our subscriber base (look at our 7m LinkedIn followers), and
2. It’s hard to attract new customers at scale with our price of +£35 a month.

But launching new products requires a new mindset. Drawing on my previous experience launching new features at Google Arts & Culture and running my own startup, I knew a lot needed to change.

Being an intrapreneur always requires cautious patience which can, at times, clash with an entrepreneurial spirit. I mapped in my head the areas that were critical to change immediately; areas I could change later; and things I needed to accept and appreciate.

2. Pick your battles

When launching a new venture, you need to be ready to make decisions every minute. Inevitably, there will be arguments — a positive sign you have surrounded yourself with thoughtful, critically-thinking individuals. However, as you have limited time and energy, being wise on which battles to pick is essential for success (and your sanity).

My rule was that I aimed to influence situations that were vital for validating product-market fit.

For example, the FT’s initial understanding of a low-cost product was £19 a month. While it’s nearly half the price of the current subscription, it’s still more expensive than any other news subscription on the market. Our main job with the FT Edit was to understand if users would engage with the product and be willing to pay for it, at scale. If we set a price that was too high, we wouldn’t know if it was the proposition that didn’t resonate with readers, or the price was too high for that proposition. It was a “make or break” decision and we decided to push the organisation to agree on a cheaper price (currently £0.99 for the 6-month trial).

As you have limited remit (and energy) as a Product Manager, there were areas that I challenged but decided to park for later. These were mainly areas which, prior to launch, we had no data on, therefore it was pointless to engage in long discussions. For instance, we took the decision not to include new articles on the app during the weekend; instead we would feature “best articles of the week”. One year after launch, we are seeing weekend engagement rapidly decreasing for new users and the team is now revising this approach.

Finally, there were the situations I had to work with. In our case, it was whether we could launch a fake-door paywall instead of spending time developing a working paying solution. One of the worries was it would impact our brand. I sensed that the organisation was not ready for this type of crude experimentation. A year later, this is no longer the case!

3. Get your team comfortable with ambiguity

There will be many moments in the new product development journey where you need a team that is comfortable saying, “I don’t know, let’s try and see” or “Yes, why not”. It’s an attitude that is significantly different from the requirements of a regular job, no matter the department. In a normal setting, you are praised in your job if you know all the answers and you are detail-oriented.

Doing new product development requires the opposite virtues: distilling signals from noise, and knowing what’s important to focus on right now, and what you should worry about later.

For example, the FT Edit experience is no different for users who have been paying for 2 months or for 8 months. We decided that life-time value is something we will optimise for only if we validate there are enough users ready to pay for the product after using it for a month. This is not to say that long-term retention is not important, but we made the conscious decision that our resources were limited, and there were more important things to solve right now.

I was lucky enough to be surrounded by people who, although they had no experience in launching new products, were curious and excited and wanted to challenge the status quo. This mentality made our team move much faster.

4. Take the team on a journey with you

If you are someone who has launched multiple new products, you probably have the whole playbook in your head, but don’t assume people from your team will know what’s coming next. It’s the job of the Product Manager to ensure everyone in the team understands the decisions you take and feels confident that we are building the right thing. I aimed to be systematic and transparent on the product decisions I’m making and what would follow based on different results and scenarios. I aimed always to articulate to the team what our product strategy was and why we had prioritised one feature over others.

We did a few things that helped tactically: we have a weekly team meeting with everyone working on the project. All qualitative or quantitative research is shared in that meeting, we discuss the results and we make decisions together.

5. Everything revolves around the value proposition

Our team spent months surveying users, doing diary studies and user interviews, before we designed even one screen. Articulating clearly what problems your product solves and where it adds value into people’s lives is the foundation upon which everything else is built. Still, if this information is mentioned once in a meeting and then collects digital dust in a shared folder, it all goes to waste. I quickly understood that this was not a one-off activity. Particularly in the early days, we mentioned the value proposition at every single discussion we had around the product.

One way we ensured alignment was involving all departments from inception — marketing, brand, PR, editorial, product, technology. They were surely annoyed with me and Maia Bridi (our stellar researcher at the time) asking the whole team to attend user interviews or sit through key insight sessions. We filtered any decision through the lens of our value proposition. This resulted in marketing coining the slogan “Time well read” for our launch in March 2022, as well as editor-in-chief Roula Khalaf describing the app as a way to “Read less, understand more”. It’s exciting to see how little by little everyone is playing to the same tone for a new product you have created together.

> Discover value proposition benchmarks from successful publishers

6. Are we there yet?! Defining success before launch

Another thing that empowered the team to stay focused and move quickly was having a clear definition of success from the very beginning of the product development journey. Reaching product market fit is an elusive term if you don’t know what you want to achieve. In our case, we know we would be successful if FT Edit reached the industry benchmark for Download-to-Paid rate on mobile news apps, and if we did this at scale.

Having a high Download-to-Paid rate with few users would mean we were a niche product; having a lot of users with low willingness to pay would mean we were not offering anything new or valuable.

On the latter, consider how you will reach product channel fit: how to optimise your product to work with the top communication channels your audience is using. It would be a mistake to focus exclusively on growth before you have validated your product is valuable to users, but it’s important to consider embedding one or two growth loops into your product early on. In our case, we over-relied on paid marketing activities at launch which should not be the only growth driver to any new product as marketing budgets are never endless.

7. The perfect MVP doesn’t exist

It’s a paradox: you cannot create a perfect product, if you haven’t launched a scrappy minimum viable product (MVP) first. The process of new product development is a iterative, sometimes chaotic journey that requires you to launch unfinished products to learn how to make them better. By contrast, the FT has a reputation as one of the most trustworthy organisations in the world for the opposite reason: perfection is the aim with everything we publish. This could easily have created tension between the product and editorial teams.

Luckily, the editors on the app, Malcolm Moore and Hannah Rock, were open-minded, and they trusted me with adopting a new approach to work.

While we kept our perfectionism in the editorial realm, we learnt how to be more experimental with testing our assumptions and launching experiments, from new push notifications to product improvements.

8. Validate your biggest assumptions as early as possible

Before the minimum viable products, there were the minimum viable tests. I often share this article by Gagan Biyani, which discusses a leaner approach to testing new product concepts.

It asks you to list all the things that need to be true for your product to succeed.

FT Edit is an app offering long-reads, we therefore needed to validate if people would be willing to do that long reading on their phones. We did this in a diary study before we had a working product and no user has brought it up as a problem.

You don’t need a team of engineers and extensive A/B testing to validate whether your product has a chance of success.

Once you have that initial validation and knowledge, it becomes much easier to pitch internally for more resources.

Hope this was useful!