Case Study 05 · SKY Brasil · Previous 2023

Audience
Strategy

Audience strategy and experience architecture for SKY Brasil. The design work was building the logic between data and experience: translating behavioral signals into audience segments, and segments into personalized navigation flows that aligned with subscriber intent.

Audience Strategy Experience Architecture Data Design Behavioral Analytics Personalization
Client SKY Brasil
My Role Senior UX · Audience Strategy & Experience Architecture
Role scope My role focused on interpreting behavioral analytics, defining audience taxonomy, and translating data signals into experience strategies that the data and engineering teams could implement through the DMP infrastructure.
Year Previous 2023
Outcome 3 audience clusters mapped. Personalized navigation flows deployed. First-party data strategy documented for ongoing DMP use.
3 Audience segments
1st Party data interpreted
DMP Data strategy
Personalized Navigation flows
Audience StrategyData DesignBehavioral AnalyticsExperience ArchitectureSKY BrasilPersonalization Audience StrategyData DesignBehavioral AnalyticsExperience ArchitectureSKY BrasilPersonalization
Section 01
Context

Data without context is just noise.

SKY Brasil had the infrastructure. The gap was context: no one had translated behavioral data into experience decisions.

SKY Brasil operated a Data Management Platform collecting first-party behavioral signals across their website, app, and media properties. The infrastructure existed. The gap was interpretation: no one had translated that data into experience decisions. The UX opportunity was to become the layer between analytics and architecture, defining what the behavioral signals meant for how the site should respond to each type of user.

◎
The Design Opportunity

DMP data enables personalization of the website itself. Different consumers see different navigation flows and content prioritization based on their behavioral signature. The design challenge was defining what each of those variations should contain.

→
The Translation Layer

Behavioral data only drives experience if someone defines the rules. My contribution was the audience taxonomy: which segments matter, how they are constructed from behavioral signals, and what the UX response to each segment should be.

∑
Strategic Progression

Behavioral data → Audience segmentation → Experience strategy → Personalized navigation flows. Each layer depended on the clarity of the one before it.

Behavioral clustering framework
Behavioral clustering framework — mapping data signals to audience structure
Section 02
Research

Reading behavior as intent.

Cross-functional alignment first. Then a deep read of behavioral signals across four data layers to understand not what users clicked, but why.

The discovery phase started with cross-functional alignment: strategy, design, and data science mapping SKY's subscriber journey from aspiration to conversion. The objective was not to understand what consumers clicked, but why they clicked and what they expected to find when they did.

I then worked through SKY's analytics layers: shopping funnel drop-off points, organic traffic intent signals, and behavioral flow paths between content areas. The behavioral analysis was the most revealing. Consumers arriving via sports content behaved entirely differently from those arriving via price comparison searches. These were not the same customer and the site was treating them as if they were.

"The goal was to understand audience affinities by crossing aspirational signals with attitudinal data — what they wanted to own versus how they made decisions."
01
Shopping Funnel Analysis

Mapped drop-off points across the e-commerce funnel. Identified which stages were losing potential subscribers and whether the cause was content, UX, or audience mismatch.

02
Organic Traffic Intelligence

Analyzed search terms driving organic arrival. Surfaced intent signals that revealed fundamentally different consumer motivations arriving at the same homepage.

03
Behavioral Flow Mapping

Tracked navigation paths between content areas. Identified behavioral signatures distinguishing high-intent converters from browsers, the foundation of the segment taxonomy.

04
Attitudinal Layer

Cross-referenced behavioral data with content engagement patterns, what sports they followed, what entertainment they consumed, to build the reasoning behind the behavioral signatures.

Behavioral data interpretation diagram
Behavioral data interpretation — mapping user signals to audience intent
Behavioral flow analysis and analytics data
Behavioral flow analysis — analytics data informing audience segmentation
Section 03
Audience Model

Three audiences. Three experiences.

The data resolved into three distinct behavioral clusters, each with a different decision model and a different UX requirement.

The data resolved into three distinct behavioral clusters, each with a different relationship to SKY's product, a different decision-making style, and a different UX requirement. The segments were not marketing personas. They were interaction models, each one mapping to a different homepage layout, a different content prioritization, and a different conversion path.

01
Segment 01
Opportunity Taker

High-intent, price-sensitive, comparison-driven. Arrives via promotional content or price comparison searches. Needs immediate clarity on current offers and competitive advantages. Decision will be made fast.

Conversion: hero offer → plan comparison → subscribe
02
Segment 02
Price Oriented

Value-maximizing, deliberate, research-heavy. Spends time comparing plans, reading FAQs, calculating value per channel. Needs comprehensive information architecture with detailed plan breakdowns.

Conversion: plan builder → FAQ → contact or subscribe
03
Segment 03
Movie Lover

Content-driven, aspirational, entertainment-first. Arrives via movie or entertainment content, not product searches. Needs a content-led experience where brand affinity precedes transaction.

Conversion: content discovery → what's included → subscribe
Audience segmentation model
Audience segmentation model — behavioral signals mapped to clusters and experience variations
Section 04
Experience Strategy

From cluster to creative strategy.

With the three segments defined, I built decision frameworks for each, translating behavioral logic into DMP rules and interface variations.

With the three segments defined and validated against behavioral data, I built a creative strategy document for each cluster, defining the communication tone, the content prioritization rules, the visual hierarchy, and the navigation flow the DMP should serve to each identified profile. These were not wireframes. They were decision frameworks that the technology team could translate into DMP rules and the design team could execute as interface variations.

Design Principle 01
Segment at entry, not at checkout. Personalization that only activates at the conversion step is too late. The DMP logic was designed to identify behavioral signals within the first two page views and adapt the navigation from that point, before the consumer had to declare themselves.
Design Principle 02
The homepage is not one page. The fundamental design insight of this project: SKY's homepage was actually three different pages, served to three different audiences. The structural design work was defining what each of those three versions contained and how the DMP rule set would trigger the right one.
Design Principle 03
Data must be classified before it can be creative. The most important document I produced was the segment classification matrix — the rules that translated behavioral signals into audience assignments. Design thinking applied to data taxonomy.
Personalization strategy showing audience segments triggering different navigation experiences
Personalization flows — how each audience segment triggers a different navigation experience
Section 05
Operational Impact

Strategic output with measurable reach.

The audience strategy produced operational outputs that extended beyond the UX brief into data, marketing, and technology teams.

The audience strategy produced a set of operational outputs that extended beyond the UX brief. The segment taxonomy and experience rules became shared infrastructure used across data, marketing, and technology teams.

Behavior-driven homepage personalization. Enabled the DMP to serve three distinct navigation architectures based on behavioral signals identified within the first two page views.
Reduced intent-navigation mismatch. Aligned the site architecture with the actual decision models of each audience cluster, reducing friction between arrival intent and available content paths.
Shared audience taxonomy. The segment classification matrix became a working reference for marketing, data, and creative teams — a shared language across functions.
Documented UX rules for personalization. Defined the decision logic for which behavioral signals trigger which experience variation, making personalization reproducible and extensible.
Established a template for data-informed UX. The methodology became a repeatable model for subsequent personalization initiatives.
Retrospective

What this project taught me
about design and data.

I worked on this project in a Senior UX role focused on experience architecture, early enough in my career that the lessons were still arriving in real time. Looking back, I can name what this project was actually about: translating complexity into clarity, and clarity into action.

The instinct in data-heavy projects is to defer to the analysts. This project showed me that the harder and more important skill is constructing the questions that make data meaningful, and then converting the answers into experience decisions that non-technical stakeholders can act on.

Lesson 01 · Data Literacy
UX strategy requires data literacy, not data dependency
This project required reading analytics platforms, interpreting behavioral flow data, and constructing taxonomies from first-party signals. The capability required was not data science but disciplined translation: asking the right questions of the data and converting the answers into experience decisions.
Lesson 02 · Organizational Design
Personalization is an organizational capability, not a feature
The DMP strategy only worked because SKY's data, creative, and technology teams were aligned on the same segment taxonomy. The design work was creating that shared language. Personalization at scale requires organizational infrastructure, not just technical capability.
Lesson 03 · Strategic Visibility
The most strategic design work is often invisible
The most impactful work — the segment classification matrix, the behavioral signal taxonomy, the creative strategy documents — was entirely conceptual. This shaped how I think about demonstrating the value of strategic UX: not with outputs, but with outcomes.
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