CrossMind – One Hub for Influencer Workflows

I conducted semi-structured interviews with five content creators aged 19–34 who actively post on at least two major platforms.

The qualitative insights were clustered and turned into an empathy map for the primary persona. This helped capture what creators think, feel, say, and do throughout their workflow and provided a clear foundation for defining the problem statement.

The main persona, Lena Torres (27), is a lifestyle & travel creator who juggles Instagram, TikTok, and YouTube. She wants to stay authentic and consistent but feels overwhelmed by manual analytics, platform switching, and keeping a coherent brand across formats.

I started with rough paper sketches and quick user flows to explore different dashboard layouts and posting journeys. Then I used Walt Disney’s Creative Strategy (Dreamer, Realist, Critic) in a structured brainstorming session to turn user needs into concrete concepts such as a unified dashboard, cross-posting assistant, and consolidated analytics.

The most promising ideas were turned into low-fidelity wireframes in Figma and later into an interactive high-fidelity prototype. This allowed me to validate the concepts with creators before committing to detailed UI design.

Several UX principles informed the layout and interactions in CrossMind:

Hick’s Law: complex choices were simplified by grouping analytics and actions into clear modules, reducing decision time.

Fitts’s Law: frequently used actions (posting, scheduling, switching platforms) were placed in large, easy-to-reach areas.

Nielsen’s 10 Heuristics: especially “Consistency & Standards,” “Recognition over Recall,” and “Visibility of System Status.”

Information Architecture: content was structured to match influencers’ real workflow: Create → Publish → Analyze.

The prototype was evaluated with the same five target users who participated in the initial interviews, using task-based usability testing and heuristic review.

Usability Testing: participants were asked to perform core tasks such as scheduling a post across multiple platforms, comparing analytics, and checking engagement trends. Their behaviour and comments helped refine labeling, navigation, and the overall flow.

Heuristic Evaluation: the interface was reviewed against Nielsen’s usability heuristics, focusing on visibility of system status, consistency & standards, recognition rather than recall, and error prevention.

Feedback & Analysis: all user feedback — both task performance and heuristic observations — was consolidated and analyzed. The quantitative results (e.g., task success rates and rating questions) were processed in Python to calculate means, standard deviations, and generate simple graphs as required by the course guidelines.