I went straight to user trust and seller verification flows, which felt right, but I think I underweighted the supply chain angle.
Start by clarifying the goal: to build trust and reduce counterfeit goods on TikTok Shop, balancing buyer protection with seller experience. Then propose a multi-layered solution combining proactive verification (e.g., AI, blockchain) and reactive measures (e.g., user reporting, audits), and outline how you would measure success and iterate.
Pro tip: Emphasize a risk-based approach: not all items need the same level of verification. Focus resources on high-risk categories (e.g., luxury, electronics) and high-volume sellers to maximize impact while minimizing friction.
Clarify what 'authenticity' means for TikTok Shop, the scope (categories, regions), and success metrics (e.g., reduction in counterfeit reports, buyer trust).
Consider buyers, sellers, TikTok, and brands. Understand their pain points and incentives to design a solution that works for all.
Generate ideas across prevention (seller verification, AI image recognition), detection (user reporting, AI monitoring), and enforcement (penalties, removals). Prioritize by impact and feasibility.
Outline how the solution integrates into the TikTok Shop experience: seller onboarding, product listing, purchase, and post-purchase. Include feedback loops.
Establish KPIs (e.g., % counterfeit items, time to removal, buyer satisfaction) and a plan for A/B testing and continuous improvement.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.