Digital Marketing for Fashion Startups: Measuring Influencer Impact on Growth

Abstract

This thesis investigates the conditions under which influencer marketing strategies outperformed brand-managed digital advertisements for fashion startups by improving unit economics, specifically Cost per Acquisition (CPA), Return on Ad Spend (ROAS), incremental ROAS (iROAS), payback, and, when feasible, lifetime value (LTV). The main goal is to figure out what conditions (creator tier, brand–creator fit, format, and platform context) make influencer activity result into better incremental outcomes instead of just superficial gains or last-click noise. The research was carried out from a pragmatic, mainly deductive perspective, applying secondary data sources such as academic literature, public case materials, platform documentation, and practitioner reports. All while organizing the evidence around unit economics guardrails and using SWOT and PESTEL frameworks to connect the startup's internal realities to changing outside factors. Quantitative and qualitative inputs were combined to help founders make decisions.