Personalisation has been a stated priority for media, entertainment, sports, and gaming brands for the best part of a decade. Yet the gap between intent and execution remains wide.
Most brands collect plenty of engagement signals. Fewer can identify which signals matter. Fewer still can act on them in a coordinated way across channels, screens, and lifecycle stages.
That gap is one of the central themes of a new report from Braze, the Media & Entertainment Personalisation Report.
Drawing on direct practitioner experience from streaming, sports, gaming, and music brands across Asia, ANZ, and the GCC, the report examines the pressures driving change in the industry, the capability gaps holding brands back, and the personalisation strategies, data foundations, and AI applications delivering measurable results.
This article, which features excerpts from the main report, looks at what high-performing brands are doing differently. It covers how they identify meaningful first-party data signals, the personalisation strategies that consistently deliver results, and the organisational foundations that allow personalisation to scale.
Building unified fan profiles from interactions
Most brands already collect plenty of data. The problem is identifying which signals are actually useful for engagement decisions. Every interaction is a signal, but not every signal is equally meaningful. What people watch, click, buy, ignore, and return to all tell a story, and separating useful signals from noise is where most brands fall behind.
Peter Filopoulos, Chief Marketing and Digital Officer with Canadian Soccer Media and Entertainment (formerly with Football Australia), frames the goal in terms of quality over quantity.
“The goal isn’t more data. It’s better signals. Who is this person? What do they care about? What’s valuable to them now?” Filopoulos said.
The starting point is distinguishing meaningful signals from noise. Content consumption, frequency of engagement, channel preference, and purchase behaviour all indicate intent, preference, or loyalty. Any single interaction may be inconsequential in isolation, but stitched together, the right signals create a profile that allows brands to activate data in real time and deliver experiences that feel relevant and timely rather than generic.
Viewing behaviour and depth of consumption are the strongest signals, but brands also benefit from monitoring help site visits, customer service interactions, subscription data, and discoverability patterns, such as whether a user is browsing without finding something to watch.
Machine learning models can synthesise these signals into an understandable and operationalisable view of each customer, driving practical use cases around increasing viewing time, extending platform sessions, preventing churn, and reactivating lapsed users.
Start narrow, then scale
The temptation with first-party data is to try to personalise everything for everyone at once. That approach personalises nothing for no one.
A more effective path starts with progressive profiling. Begin with email and basic preferences. Add signals based on what people do rather than what they are asked. Watch content consumption patterns, monitor engagement triggers, and track what drives response.
The sequence matters. Pick a segment. Define what “engaged” means for that segment. Identify the signals that predict engagement. Build personalisation that responds to those signals and prove value. Then expand.
Personalisation should not rely solely on passive data. Asking fans directly, through preference centres, quizzes, ratings, and interactive experiences, adds declared intent to observed behaviour.
These zero-party data signals give brands explicit information about what a fan cares about and where they are in their relationship with the brand. The value exchange dictates that when fans share preferences, they receive better, more relevant experiences in return.
With the data foundation in place, the question becomes which personalisation strategies actually deliver results. The approaches that work consistently share three traits. They are relevant to individual context, coordinated across channels, and grounded in real behaviour rather than assumptions. Brands that achieve all three tend to outperform those that excel at only one.
Effective personalisation often starts simpler than brands expect. The strategies delivering results tend to be practical rather than complex. Relevant alerts. Content that reflects real interests. Offers driven by behaviour. Journeys that adapt based on loyalty and engagement. Recommendations based on habits rather than marketing priorities. Real-time personalisation is about context, not speed.
Three approaches consistently work. Showing different content based on preferences, coordinating messages across channels, and triggering actions based on behaviour. Dynamic content does not always need machine learning. Showing basketball content to basketball fans can outperform complex algorithmic recommendation in some cases.
Behavioural triggers deliver real return on investment. Browse abandonment, inactivity re-engagement, and milestone celebrations all work because they personalise responses to what customers do rather than what a marketing calendar dictates.
“Stop overthinking it. Start with triggers, add dynamic content, coordinate channels. The sophistication can come after the basics work,” Tim Armstrong, Director at Mangrove Digital, said.
The coordination piece is where most brands fall down. Omnichannel personalisation means email, push, and in-app messaging work together. Most brands send three separate personalised campaigns that do not correspond to one another. The same principle applies across screens. The goal is a cohesive experience that follows the fan, not a collection of isolated messages competing for attention.
Where to invest to get more out of personalisation
Understanding what personalisation can do is one thing. Knowing where to invest to make it work is another. The most durable gains come from organisational enablers: team structure, clear ownership, and an operating model that treats personalisation as a standing capability rather than a series of campaigns.
Personalisation fails when it is treated as a marketing project. When it is owned by one team, measured by campaign metrics, and disconnected from product and data decisions, it cannot scale. The strongest returns come from foundational capabilities rather than features. Unified data, a single fan identity, clear ownership, simple journeys, and organisational alignment consistently outperform more sophisticated but poorly grounded efforts.
The most important foundational investment is unified identity across the business. Without knowing who the customer is, and without consistent data across channels, coordinating efforts becomes impossible.
From there, the priority is enabling automation that can personalise at scale, including journeys that respond to real-time behaviour and the ability to coordinate across channels. A single unique ID is the baseline. One that allows data sharing across platforms and touchpoints, with connected systems that deliver consistent experiences across web, app, and communications.
“Personalisation succeeds when it’s treated as a capability, not a campaign. You can only scale personalisation so far without a solid data structure,” Josh Jones, who leads CRM Capability and Enablement at Sportsbet, said.
Organisational capability matters as much as technical capability. Clear ownership of data, alignment between teams, solid priorities in what to build next, and a test-and-learn culture that ties efforts to measurable outcomes all determine whether personalisation scales or stalls. The most effective starting point is use cases that clearly impact the customer experience and align with business goals, rather than trying to personalise everything at once.
The brands turning engagement data into engagement action are not relying on more sophisticated algorithms or larger data sets. They are doing the foundational work that most brands skip.
They are deciding which signals matter for their audience. They are starting with a narrow, defined use case and proving value before expanding. They are coordinating across channels rather than running parallel campaigns that contradict each other. And they are treating personalisation as a permanent capability with clear ownership rather than a series of one-off projects.
For the full picture, including the market pressures driving these shifts, the tooling decisions that support or constrain personalisation, and the AI applications delivering measurable results, read the complete Braze Media & Entertainment Personalisation Report.