From Genre to Motivation
Rethinking Lifecycle Strategy
for
Content Strategy
〰️
Lifecycle strategy
〰️
Workshop Facilitation
〰️
Content Strategy 〰️ Lifecycle strategy 〰️ Workshop Facilitation 〰️
The Challenge
When new users signed up to Paramount’s ad-supported streaming platform, they were dropped straight into the platform’s ad-hoc lifecycle communications.
Emails and push notifications constantly highlighted the latest titles.
The entire lifecycle was shackled to the release calendar. Every message operated on the same unconscious hypothesis: viewers will always engage with what’s new.
The novelty effect.
Yet we all have different viewing habits.
And different motivations.
Trouble in your relationship? Rom-com.
Chilled Sunday? Something with a young Keanu Reeves.
Background noise while you cook? Reality TV.
The platform ignored these motivations entirely.
There was no segmentation around viewing intent.
And no early signal for what type of watcher someone might become.
Why was that a problem?
Because Pluto operates on an ad-supported revenue model.
The more people watch, the more advertising inventory the platform generates (£££).
Understanding the psychological triggers that make someone press play isn’t just a product question.
It’s a revenue question.
Without that understanding, the platform was effectively shooting in the dark.
Early engagement and a large part of the ROI on newly acquired users was being left to chance.
The Result
The first version of this onboarding framework was trialled in LATAM and Brazil.
After presenting the rationale and early findings, the approach was adopted by the global Pluto TV team as a framework for understanding viewer motivations and guiding lifecycle experimentation.
With Pluto TV operating across dozens of markets internationally, this way of thinking about viewer behaviour had the potential to shape onboarding and lifecycle strategy at a significant global scale.
What began as a question about content discovery became a new lens for understanding the audience itself.
The Method
So I started with a hypothesis.
People don’t choose what to watch based purely on genre.
They choose based on how they want to feel in that moment.
To explore that idea further, I ran a series of workshops with the Pluto team titled “Our Watching Habits.”
The premise was simple. We all stream content ourselves. Which meant we could examine our own behaviour and motivations as viewers.
What do we watch on the train to work?
What do we put on as background on a Sunday afternoon?
What do we reach for when we want something intense or escapist?
Through a series of exercises and discussions, we started mapping the emotional motivations behind different viewing behaviours.
Three clear patterns began to emerge.
Comfort
Familiar shows, nostalgic TV, easy watching. The things people return to when they want something relaxing or predictable.
Discovery
Documentaries, knowledge-led content, things that make you feel like you’ve learned something.
Excitement
Thrillers, crime, horror. The kind of content people choose when they want tension or stimulation.
Instead of recommending genre to genre, we began organising content around these viewing motivations.
We then introduced zero-party data capture into the onboarding journey, allowing new users to signal what type of viewer they might be early on.
That signal could then shape recommendations and lifecycle communications across channels - push, email, in-app messaging and content cards.
In other words, the platform could start learning what kind of watcher someone was, rather than assuming everyone behaved the same.