Summary:

• Time is the constraint. If you want to understand audience interests, you have to understand where audiences spend attention, not just what they do on your platform.
• Affinity makes that actionable by measuring audience overlap between titles, brands, and talent.
• Three practical moves: use content affinity to plan acquisition vs retention, use taste clusters to personalize messaging, and use brand affinity (Affinity + Reach) to pick partnerships and talent.

Hidden in Plain Sight: How to Unlock Audience Interests

Most audience insight work answers “what’s popular?” The harder question is “what else does this audience care about, and what should we do with that?” Affinity analysis is built for that second question: it connects the rest of an audience’s attention footprint so decisions on programming, marketing, and partnerships stop relying on guesswork.

Audience interests are “hidden” because attention is finite

In an attention economy, platforms and brands compete for a fixed time budget. That makes “where else does this audience go?” as important as “how big is this audience?” Reed Hastings put it bluntly: Netflix competes with sleep.

Parrot’s framing starts with supply and demand. On the supply side, you map what audiences can engage with: titles, talent, distribution, and metadata. On the demand side, you capture expressions of engagement across many signals, which is what lets you build a complete picture of what audiences care about and how those interests cluster.

What “Affinity” is, and why it’s the bridge from insight to action

Affinity measures synergy between audiences by quantifying overlap. If the audience for A shows up disproportionately in the audience for B, you have a useful relationship you can plan around. That holds whether A and B are TV shows, brands, or talent.

This matters because popularity alone does not tell you purpose. Affinity helps you answer “what does this title do for us?” and “where should we show up to reach the right people?” It also scales across the most common pairings teams care about: content + content, brands + content, and audience + content.

Use case 1: Use content affinity to prioritize subscriber acquisition vs retention

Content affinity answers two questions at once: what else is this audience watching, and where are they watching it? That single view helps you separate titles that primarily bring in new audiences from titles that primarily keep people engaged. It also helps you model churn risk if a title pulls in viewers who do not connect to the rest of your catalog.

Start with “audiences are also watching”

The simplest starting point is a “people who watch this also watch” view. For The Mandalorian, the top 10 shows most watched by its audience include sci-fi, superhero titles, and adult animated comedies across multiple networks. Even in that small cut, you see a mix of Disney+ titles and off-platform viewing.

That mix is not good or bad on its own. It’s a clue about the job the title is doing.

The launch window shows you who you actually acquired

At launch, The Mandalorian struggled with affinity to other Disney+ content, showing higher affinity to Prime Video, Netflix, and Apple TV+. In plain terms: the show was pulling in an audience whose broader viewing habits largely lived elsewhere.

That pattern can be a feature when you need growth. It can also become a retention problem if you keep acquiring “isolated” audiences without building enough adjacent content to keep them watching on-platform after the tentpole ends.

Affinity changes over time, and that shift is strategic

Affinity can change week to week because the market and catalog change. The webinar shows that affinity for new launches on Disney+ increased for more recent tentpole launches. At the time of launch, The Mandalorian had 0 of the top 25 “high affinity” titles coming from Disney+. Later launches show more internal overlap: WandaVision (1), The Falcon and the Winter Soldier (4), and Loki (5).

The takeaway is that platform strategy is not only about owning the right tentpoles. It’s also about building a catalog web that makes retention easier.

Where this becomes actionable: programming, recommendations, and licensing

The webinar frames the end goal well: use affinity to inform programming and licensing decisions based on a show’s contribution to a platform.

A few practical applications from the discussion:

• Recommendation engines and merchandising: first-party data only sees on-platform behavior. Content affinity adds cross-platform context, which can change what you promote and what you recommend next.
• Platform fit: two shows can share a genre label and still behave differently. The examples highlight how Ted Lasso and It’s Always Sunny in Philadelphia separate when you look at which platforms their audiences overlap with, and how crime dramas like Ozark and Peaky Blinders cluster toward different platform ecosystems.
• Exclusivity scenarios: when you change availability through licensing, you are forcing a choice. Affinity helps you anticipate whether the audience is likely to follow the title elsewhere or stay because the rest of your catalog is still a good fit.

Use case 2: Social affinity taste clusters for personalized audience strategy

Taste clusters translate social behavior into distinct audience profiles. Instead of describing an audience by age or income, you segment them by what they follow, what else they engage with, and what conversations they respond to. That lets you tailor creative and placements to different sub-audiences inside the same fandom.

How the clustering workflow works

The workflow in the webinar is clear: start with a show’s social following, map that network, then segment it into clusters and profile each cluster by its community preferences (content, brands, talent, and conversation drivers).

This is especially useful for upcoming releases, where the audience is already forming socially before there’s any viewing behavior to analyze.

Two clusters from House of the Dragon that show what you get

Segment 12 represents 21% of the audience and is labeled “Sci-fi Show and Movie Fans.” Their interests include DC/Marvel shows, sci-fi and fantasy, and new or upcoming releases.

Segment 6 is labeled “Game of Thrones Die-hards” (10%). This segment skews toward deeper fandom behaviors: cast interest, memes, quotes, and lore.

If you treat these groups as the same audience, you end up with one generic campaign. If you treat them as different segments, you can build different creative hooks, different adjacency targets, and different influencer and talent angles.

Taste clusters vs demographics: when to use which

Demographics are still useful because they’re widely understood and easy to operationalize. Taste clusters add the missing “why” and “what else,” which is where personalization comes from. In practice, demographics set guardrails and clusters guide creative choices.

Use case 3: Brand affinity to pick partnerships, placements, and talent that actually fit

Brand affinity applies the same overlap logic to marketing and partnerships. By plotting affinity (similarity) against reach (scale), you can spot the shows and talent that match a brand’s audience, separate niche fits from scalable fits, and avoid mismatches. When the trade-offs are not obvious, a compatibility score turns the two dimensions into a single rank ordering.

Affinity + Reach: the decision map

The deck introduces reach as audience size and affinity as overlap, then plots them together for brand-to-content decisions.

What the map gives you:

• Titles in the top right: strong fit at scale.
• Titles in the bottom right: strong fit, smaller audience.
• Top left: broad reach, weaker overlap.
• Bottom left: low priority.

In the luxury auto example, the expected auto-related titles surface strongly, but the more important thing is that you can see the whole field, including the shows that are simply not your audience.

Use the same method to shortlist talent

The webinar applies the same approach to brand-to-talent matching.

In the example, Richard Hammond and Max Verstappen appear as high fit, high reach options for an auto brand, while Jon Rahm appears as a strong fit even though he sits outside the obvious “auto talent” category. That’s the point: you are matching audiences, not stereotypes.

When affinity and reach disagree, Brand Compatibility Score gives you a ranking

The Brand Compatibility Score converts the two dimensions into a single metric, with weighting determined by objective. The example weights affinity at 75% and reach at 25%.

This is what makes the output usable across teams. You can defend why a niche, high-affinity option should outrank a large, low-affinity option, or vice versa, depending on whether you’re optimizing for precision or scale.

FAQs:

Is affinity still useful if we already acquired the content?

Yes. It shows what your audience watches across other platforms, which is context your first-party systems cannot fully capture. It also helps you model exclusivity decisions. When availability changes, affinity helps you anticipate whether the audience follows the title or stays for the rest of your catalog.

Taste clusters vs demographics: do we replace demographics?

No. Demographics are the common language for buying and reporting. Taste clusters make them smarter. Use demographics to define the addressable pool, then use clusters to tailor creative and placements to what sub-audiences actually care about.

Can affinity change over time?

Yes. It changes as new content enters the market and as availability shifts. That’s why the most useful view is often the change from launch to later in the title’s lifecycle.

Can we do this for content concepts that don’t exist yet?

Yes. If you can describe a concept in terms of its “genes” (the attributes that define it), you can build a hypothetical title, then run affinity to identify likely audiences, comparable titles, and potential taste clusters before production.

Investor section: What can audience overlap actually tell us about value: which titles, platforms, brands, and talent are most likely to drive subscriber acquisition, strengthen retention, and create partnership upside, so we can underwrite content decisions on more than headline popularity alone?

For content investment underwriting, audience overlap tells you the job a title, platform, brand, or piece of talent is likely to do. Content affinity shows whether something is more likely to drive subscriber acquisition or strengthen retention by revealing what else that audience watches, where they watch it, and whether the title connects to a broader catalog web or risks a higher churn rate once the tentpole moment passes.

The same signal shows platform fit, makes licensing and exclusivity decisions more informed by showing whether audiences are likely to follow a title elsewhere or stay for the rest of the catalog, and helps surface which taste clusters need different positioning. On the partnership side, brand affinity plus reach shows which brands and talent are real audience fits, and a compatibility score helps rank scale against precision. Put simply, overlap turns popularity into purpose: what acquires, what retains, and what actually fits.

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