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LTK Search & Disco
overview
Introducing a personalized discovery experience designed to help Creators efficiently find products that resonate with their audience, thereby simplifying the posting process. Simultaneously, it strategically positions LTK as the primary destination for content inspiration, fostering the creation of impactful posts.
Many Creators struggle with knowing what to post and interpreting performance analytics to develop effective content strategies.
Guide Creators' posting with data-informed product and brand discovery recommendations, strengthening LTK's position.
Empower Creators to adopt a more strategic approach to content creation by discovering relevant products and new brands to post.
Increase in unique brand click-outs, products saved (a precursor to post), and daily active users (DAU) returning to LTK.
A personalized discovery experience that delivers unique product suggestions aligned with audience interests enables content creation with high-impact.
LTK
Lead Product Designer
Strategy & Vision
Release iterative value
The core strategy for this project centered on gaining a comprehensive understanding of Creators' needs and objectives in the context of brand and product discovery. This involved a review of user experience research data collected over the past few years, extracting relevant quotes and observations, and analyzing underlying needs to define essential modules that would effectively address user needs. Subsequently, I facilitated a content strategy and prioritization workshop with the team to establish an initial framework, recognizing the need for iterative experimentation with module order and hierarchy based on performance data.
The design process began with aligning the strategic framework with stakeholders, followed by translating it into UI mockups. Leveraging and extending the existing design system allowed for multiple component variants to add visual interest to the dynamic feed. The UI was broken down into four phases to balance iterative delivery of value, data-driven iterations, reducing change aversion, and data collection for machine learning recommendations. Further technical collaboration with data science revealed a brand recommendation system was ready first, which was prioritized before product recommendations to enhance the first few phases.
Phase 2 of the project is now complete, and consistent engagement and traffic patterns indicate positive momentum toward achieving the overall vision. Within the initial months following the second release, significant increases across key metrics were observed, including a 219% increase in unique brand click-outs, a 400% increase in products saved, and a 41% increase in DAU.
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