User-Needs Content Strategy Webinar

The discussion centers on the survival and evolution of the news industry through three distinct but interconnected lenses: the theoretical framework of “User Needs,” the practical application of format innovation in legacy media, and the rigorous application of data analytics and AI.

Here are the three core arguments extracted from the session, elaborated in detail.

Argument 1: The “User Needs” Model as a Cultural and Strategic Shift for Newsrooms

The Core Premise

The media industry is facing a trifecta of existential threats: declining user engagement and trust, stagnation in digital subscription growth, and the disruption of production and distribution caused by Artificial Intelligence. The traditional “broadcast” model—where newsrooms decide what is important and publish it in a standardized format—is failing to resonate with modern digital audiences. To survive, newsrooms must pivot from a product-centric mindset (producing content for the sake of news cycles) to a user-centric mindset (producing content that solves specific problems for the audience). This involves adopting the “User Needs” model, a framework derived from the “Jobs to be Done” theory in product design, which posits that users do not just “buy” a product; they “hire” it to fulfill a specific emotional or functional need in their lives.

The Theoretical Framework: Beyond the “Update Me”

Historically, journalism has focused overwhelmingly on one specific need: “Update Me.” This is the standard news report: What happened? Where? When? While this remains the foundation of journalism, Ola argues that in a digital environment saturated with free information, the “Update Me” function is a commodity. It is necessary but insufficient for building loyalty or driving subscriptions.

To capture the full spectrum of human interest, newsrooms must treat their content like a grocery store. A customer might go to a grocery store primarily for food (the basic need), but they also have underlying motivations: satisfying a craving, preparing for a celebration, or learning a new cooking skill. Similarly, a reader comes to a news site not just to know what happened, but to understand why, to feel connected to others, or to find utility for their daily life.

The User Needs model categorizes these motivations into four main axes, subdivided into specific needs:

  1. Fact-Driven (The “Know” Axis): This includes the traditional “Update Me” (knowing what is happening) but extends to “Keep Me Engaged,” which follows trending topics over time.
  2. Context-Driven (The “Understand” Axis): This is where journalism adds value beyond the headline. It includes “Educate Me” (explaining complex mechanisms) and “Give Me Perspective” (offering analysis or diverse viewpoints on a controversial topic).
  3. Emotion-Driven (The “Feel” Axis): This is often neglected in “hard news” environments but is crucial for engagement. It includes “Inspire Me” (stories of human triumph or innovation) and “Divert Me” (entertainment or lighter content).
  4. Action-Driven (The “Do” Axis): This transforms the reader from a passive observer to an active participant. It includes “Help Me” (practical advice, e.g., road closures) and “Connect Me” (stories that foster a sense of community or shared experience).

Implementation and Cultural Transformation

Implementing this model is not a “quick fix” or a simple box-ticking exercise; it requires a fundamental cultural shift. The transcript highlights that simply knowing the theory is useless without rigorous application. For instance, a newsroom cannot simply tell journalists to “write better.” They must restructure how stories are commissioned.

The process begins with a Baseline Analysis. Newsrooms must audit a significant sample of their historical content (e.g., 500 to 1,000 articles) and tag them according to these user needs. The results almost invariably show a “supply and demand” mismatch. Publishers usually produce a massive surplus of “Update Me” stories (often 70-80% of output), yet the data frequently reveals that readers spend more time and engage more deeply with “Inspire Me,” “Educate Me,” or “Give Me Perspective” pieces.

Once this baseline is established, the organization must engage in Growth Hacks. This involves small-scale experiments—perhaps a specific desk or section commits to producing diverse formats for a month—to prove the concept. The goal is to move away from a “one size fits all” approach to treating audiences as plural. Different demographics have different needs at different times of the day.

Case Studies of Success

The argument is bolstered by evidence from diverse publishers:

  • Der Spiegel (Germany): Their analysis showed that while their brand identity was tied to “providing context,” their actual output was heavily skewed elsewhere. By realigning output with their “context-driven” promise, they met reader expectations better.
  • Blick (Serbia): They discovered that negative emotions in headlines were actually deterring engagement. By pivoting to “Inspire Me” stories, they saw a 3.7x increase in readership for those specific pieces. This proves that the “if it bleeds, it leads” mantra of traditional journalism is often factually incorrect in a digital subscription context.
  • South China Morning Post (Hong Kong): They combined User Needs with the “Hooked” model of product psychology. They realized that certain user needs (like “Give Me Perspective”) acted as investment vehicles—readers who engaged with these stories were more likely to subscribe, effectively turning content consumption into a habit-forming loop.
  • Svenska Dagbladet (Sweden): They took the most radical step of restructuring their entire newsroom. Instead of traditional “Politics” or “Culture” desks, they created hubs based on needs, such as a “Snapshot” hub (Update Me) and a “Spotlight” hub (Give Me Perspective). This structural change forces the organization to prioritize the user’s intent over the topic’s nature.

The Future of the Argument

Ultimately, the User Needs model is about survival through relevance. In an era where AI can write a basic news summary in seconds, the “Update Me” function faces zero-marginal-cost competition. However, AI currently struggles to provide the human connection of “Inspire Me” or the deep, localized nuance of “Connect Me.” Therefore, shifting to a User Needs model is not just an editorial strategy; it is a defensive moat against technological disruption. It forces journalists to ask “Why am I writing this?” before they type a single word, ensuring that every piece of content justifies its existence by serving a distinct human requirement.

Argument 2: Operational Transformation and Format Diversification in Legacy Media

The Core Premise

For legacy media organizations—specifically print-native giants like The Hindu, founded in 1878—the transition to digital requires more than just “putting the newspaper online.” It demands a complete reimagining of the relationship between the publisher and the reader. Pundi Sriram’s argument posits that the shift from an advertising-based model to a subscription-based model fundamentally alters the product requirement. Subscribers demand both breadth (a wide variety of topics) and depth (a profound understanding of issues) presented in formats that respect their time and device preferences. To achieve this, legacy organizations must break down the silos between “print” and “digital” teams, create “full-stack” journalists, and embrace platform-agnostic storytelling.

The Challenge of the Subscription Relationship

In the pre-2019 era (before The Hindu’s paywall), the publishing flow was linear and print-centric. A story was written, edited for the next day’s paper, and then dumped onto the website. This resulted in a massive distribution inefficiency: 70% of stories were published after 6:00 PM, missing the entire digital news cycle of the working day.

The subscription model broke this workflow. A subscriber paying for content has a higher threshold for satisfaction than a casual reader. They are not just buying news; they are buying a relationship. To sustain this relationship, the publisher must offer value throughout the day and across different cognitive states. Sometimes the user wants a quick podcast while commuting; other times they want a deep-dive visual investigation on a tablet. If the publisher only offers text articles published 12 hours after the event, the value proposition collapses.

Visual Storytelling and “Scrolly-telling”

A major pillar of this argument is the necessity of Format Diversification. Text is no longer the default sole carrier of information. The Hindu’s experiments with “scrolly-telling” (interactive, scroll-based visual narratives) demonstrate this.

  • The Man-Animal Conflict Story: Instead of a dry report on elephant poaching or habitat loss, The Hindu created a visual, interactive experience. It mapped conflicts across states, integrated illustrations, and explained mitigation strategies dynamically.
  • The Gaza Crisis: Rather than just a series of articles, they produced a “scrolly map” tracking the evolution of the battle, detailing attacks on specific sites, and visualizing the destruction.
  • The Liquor Tragedy: Even for hyper-local stories (like an illicit liquor tragedy in Tamil Nadu), complex chemistry and supply chain issues were simplified through visual explainer formats.

These formats are not merely aesthetic upgrades; they are retention tools. Sriram notes that these immersive stories garner 2.5 to 3 times the engagement of standard web stories. They signal to the subscriber that the publication is investing effort to help them understand complex issues, which justifies the subscription cost.

The “Full-Stack” Journalist and Organizational Fluidity

The most difficult part of this transformation is human capital. The argument insists on the dissolution of the “caste system” within newsrooms that separates print journalists from digital creators. The Hindu is moving toward “integrated teams” where distinctions are blurred.

  • The Flow Reversal: Historically, print stories became digital assets. Sriram highlights a milestone where a story on migrant labor in New Delhi was conceived as a digital project first—planned with video, data visualization, and specific digital formatting—and then adapted for print. This reversal proves the organization is finally putting the digital user first.
  • Cross-Functional Skills: This shift necessitates the rise of the “full-stack journalist”—a reporter who can write, shoot video, understand SEO, and conceptualize a data graphic.
  • The Collaboration of Tech and Editorial: In this new model, developers are not just IT support; they sit inside the editorial teams building the visual stories. Product managers are not just business analysts; they facilitate editorial goals by building personalization engines that surface the right content to the right user (e.g., offering a summary view vs. a deep dive).

Platform Agnosticism

Finally, the argument extends beyond the publisher’s own website. The “User Needs” concept must be applied across platforms. A story about the Gaza war needs to be packaged differently for YouTube (a 3-minute explainer), for the website (a live blog), for the app (a notification), and for social media (a vertical video). Each platform has a different user intent. A user on Instagram might want “Divert Me” or “Inspire Me,” while a user on the website might want “Update Me” or “Give Me Perspective.” The modern legacy newsroom must map a single topic against this matrix of platforms and needs, executing a coordinated content strategy that meets the user where they are, rather than forcing the user to come to the print edition.

Conclusion

Sriram’s argument concludes that this is a game of change management. It is about overcoming workflow friction—such as videographers being overwhelmed because reporters didn’t check capacity before shooting—and cultural resistance. However, the survival of legacy media depends on this operational fluidity, transforming from a newspaper that has a website into a multi-platform digital news brand that happens to print a paper.

Argument 3: Data-Driven Decision Making and the Symbiosis of AI and Human Insight

The Core Premise

In the modern media landscape, intuition is a liability. Edmund Chua’s argument is that the days of editors deciding content based on “gut feeling” or anecdotal experience (“You think, I thought, who confirms?”) are over. To survive in an attention economy dominated by algorithmic giants, publishers must adopt a rigorous, data-first approach. This involves a symbiotic relationship between Media Analytics (looking backward at what happened) and Market Research (looking forward at what people want), all supercharged by Artificial Intelligence. The argument posits that data does not stifle creativity; rather, it provides the guardrails that ensure creative efforts actually reach and resonate with the intended audience.

The Limitations of Human Intuition

Traditionally, editorial decisions were driven by seniority and experience. An editor might claim a specific angle works because “it worked six months ago.” Chua argues this is dangerous because digital audiences are volatile and trends are ephemeral. Without hard data, newsrooms are flying blind. Furthermore, humans are incapable of processing the volume of data required to make accurate decisions at scale. A human cannot manually tag 700 articles a day with consistent metadata regarding sentiment, entity, and user need without making errors or succumbing to fatigue.

The Role of AI in Standardization and Metadata

This is where AI becomes an indispensable operational tool. The foundation of actionable data is Metadata Tagging. If content is not tagged correctly, you cannot analyze why it succeeded or failed.

  • Automated Tagging: AI systems can scan thousands of articles and tag them not just by topic (e.g., “Politics”), but by sentiment (“Negative”), style (“Opinion”), entities (“Prime Minister”), and User Need (“Give Me Perspective”).
  • Standardization: AI ensures that tags are applied consistently, removing the subjectivity of different human taggers.
  • Predictive Analytics: Once this robust dataset exists, the newsroom can query it to make predictive decisions. Before a story is commissioned, editors can look at historical performance of that specific combination of Topic + Sentiment + Format to predict its likely engagement. This moves the newsroom from “post-mortem” analysis (checking why a story died) to “pre-natal” optimization (giving the story the best chance to live).

The “Looking Forward” vs. “Looking Backward” Approach

Chua distinguishes between two types of data necessary for a holistic strategy:

  1. Media Analytics (Backward): This uses AI to analyze traffic and consumption patterns. It tells you what users did. However, reliance solely on this creates an echo chamber; you only know what users liked among the things you offered them.
  2. Market Research & Social Listening (Forward): To break the echo chamber, publishers must look outside their own ecosystem. This involves Social Listening—using AI to scan Reddit, social media, and forums to understand what the audience is talking about before the newsroom covers it.

Case Study: The Singaporean Ministerial Housing Scandal

Chua provides a potent example of this methodology in action regarding a corruption investigation into Singaporean ministers renting state properties.

  • The Event: Two ministers were investigated. The traditional news approach would be to report the investigation and the outcome.
  • The Data Insight: Through social listening, AsiaOne discovered the audience wasn’t just interested in the corruption aspect; they were specifically obsessed with the valuation process—how the rental price was calculated compared to the market rate. There was also confusion about the specific type of housing involved.
  • The Execution: Armed with this insight, they crafted a video specifically explaining the valuation guidelines and the property type. They even found a data point suggesting their audience loved pets, and realized one of the influencers in the story had a dog, weaving that element into the narrative.
  • The Result: A highly technical policy story garnered 580,000 views and a 4-minute engagement time in a market (Singapore) with a population of only 6 million. This success was not luck; it was engineering.

The Integration of Hardware and Process

The final pillar of this argument is Integration. Data cannot sit in a silo with the “analytics guy.” It must be integrated into the Content Management System (CMS) and the Customer Data Platform (CDP).

  • Operationalizing Data: When an editor opens the CMS, the data regarding trending keywords, successful formats, and audience sentiment must be visible at the point of creation.
  • The Triad of Success: Chua summarizes successful content as the answer to three questions, all answered by data:
    1. Content: What do we include? (Driven by social listening/interest clusters).
    2. Format: How do we package it? (Driven by historical performance of user needs).
    3. Channel: Where do we put it? (Driven by audience segmentation data).

Conclusion

Edmund Chua’s argument ultimately reframes the relationship between AI and Journalism. It is not about AI replacing journalists. It is about AI handling the cognitive load of pattern recognition and metadata management so that journalists can focus on the creative execution of stories that the data proves the audience actually wants. It moves the newsroom from a “supply-side” mindset (we publish what we have) to a “demand-side” mindset (we publish what you need), verified by algorithms.

Leave a Comment