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Contextual Report 3

For this digital artefact (DA), I created a Pinterest account aligned with the media brand I’ve developed throughout BCM114, using it to test formats and techniques designed to maximise shareability and algorithmic reach. Using a mix of format experimentation and decisions dependent on analytics, I explored how content becomes viral. This project brings understanding on how virality is shaped by timing, emotion, structure and strategy. Drawing on Module 2’s ideation, prototyping and testing stages, this report shows how I developed, measured and refined my approaches to enhancing engagement and virality, and how this was supported by external tools including Tailwind, platform-specific research and peer feedback. This project details how I learned how to reverse-engineer visibility and understand the feedback loops which highly influence online culture.

9/11/2025

The ideation stage focused on strategies on how best to inform my target audience, most notably driven by platform selection. Although Instagram and Facebook were initial ideas, research highlighted Pinterest’s strengths in public discovery, trend-driven categorisation and high user intent. Pinterest’s Audience Insights emphasises how users actively search for templates, aesthetics and guides, content directly aligned with my brand, and I focused on audience definition as a foundation for growth. External resources reinforced Pinterest’s potential, highlighting the platform’s search-based algorithm and strong analytics tools that support strategic experimentation. To expand my ideation process, I drew on structured methods recommended by the Interaction Design Foundation, the most effective being mind mapping and SCAMPER, which encouraged generating multiple alternatives rather than defaulting to the first idea.

This pushed me beyond familiar platforms and strategies, encouraging a more systemic ideation process where I generated multiple platform prototypes before choosing the most suitable option. I compared these options based on user intent, algorithmic behaviour and engagement potential. Whilst Pinterest took the longest time to gain traction and an audience, it was revealed to be the most effective in holding engagement over a long period of time. Supported by Everywhere Marketer’s analysis of Pinterest’s metrics and engagement strategies, strengthened Pinterest as the most suitable for a visibility-focused DA. Defining my target audience early sharpened my creative direction and clarified which formats would be beneficial to test, leading toward iterative experimentation.

I began by uploading screenshots of my Notion templates and simple Canva exports, using simple prototypes to gain knowledge on structural issues. Cluttered text blocks performed poorly, while minimalist designs gained impressions earlier. I incorporated Tailwind, a tool frequently recommended for Pinterest creators, to schedule prototypes and test posting times and subsequent effects on engagement. This allowed for a consistent output schedule without overinvesting in any single design, methods highly recommended.

Early findings included:

  • Pins published directly from Canva received higher user engagement than those uploaded directly.

  • Clean, modern and monotone layouts consistently outperformed more creative or “unique” post designs, which contradicted my assumption that originality would drive engagement.

Prototyping revealed patterns I couldn’t have predicted in the planning stage, proving the value of rapid and basic iteration. I was able to gain feedback and insights faster than planning, allowing for more time to explore the key drivers that create viral media.

Testing provided the most effective feedback and knowledge on viral media. Using Pinterest Analytics and Tailwind insights, I monitored how real users engaged with my post over time. Rather than simply utilising raw views, I analysed view-to-click rations to gauge genuine interaction. Key insights included:

  • Music-related pins using trending keywords, particularly those targeting emotions (e.g. “yearning”) received the highest early impressions and view-to-click ratios.

  • Template-style pins exported from Canva performed consistently across multiple posts, confirming that repeating successful layouts is beneficial in Pinterest algorithms.

  • Outbound clicks were minimal, indicating that my designs needed stronger prompts to encourage engagement beyond the visual pin.

These insights reflected viral media theory, where circulation isn’t random, but rather shaped by emotional triggers, trend alignment, clarity and repeated recognisable formats. Everywhere Marketer’s insights highlighted this idea, where consistency and trend responsiveness outperform novelty.

Testing using real audiences highlighted a gap between my intentions and user actions, revealing that algorithmic success is directly connected to using platform-native aesthetics, rather than trying to stand out stylistically.

-Key Takeaways-

This project taught me how viral media relies on strategic repetition, trend alignment and data-led iteration. Using Pinterest, Tailwind and analytics tools helped me understand visibility as something engineered, not accidental or random. My understanding of social utility evolved, emphasising how effective media is more often successful when suited to user habits, platform norms and algorithmic structures, rather than personal intuition.

References

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