The Role of Growth Hack Strategy in Influencing Customer Purchase Intention in a New EdTech Start-up: a Case Study of Smartz Centre - Ayunda, Batari; Dellyana, Dina (Paradoks : Jurnal Ilmu Ekonomi, 2025)
Customer purchase intention is a critical metric for measuring sustainable growth in educational technology (EdTech) startups. This study aims to examine which growth hack strategy variables, based on the AARRR framework by Bohnsack and Liesner (2019), significantly influence customer purchase intention and to provide strategic recommendations for Smartz Centre, a new EdTech platform in Indonesia. A quantitative approach was applied using Partial Least Squares Structural Equation Modeling (PLS-SEM), with five main variables: acquisition, activation, revenue, retention, and referral. Data were collected through an online questionnaire involving 211 respondents, consisting of both internal users (students and parents) and external potential users. The model analysis demonstrated that all variables have a significant influence on customer purchase intention, with the highest path coefficients recorded for revenue (β = 0.412), retention (β = 0.401), and referral (β = 0.387). Acquisition (β = 0.201) and activation (β = 0.195) also showed positive and statistically significant contributions, although to a lesser extent. The model explained 87.2% of the variance in customer purchase intention (R² = 0.872), indicating a strong predictive power. These results suggest that while acquiring and activating new users remain essential, greater emphasis should be placed on monetizing existing users, maintaining loyalty, and utilizing word-of-mouth referrals to drive sustainable customer intent to purchase. Theoretically, this study contributes to the development of a data-driven growth hack framework adapted to the EdTech context. Practically, the findings support Smartz Centre in designing targeted strategies, such as premium service bundling, personalized retention programs, and structured referral incentives, to increase purchase conversion. Limitations include the absence of deeper segmentation and qualitative insight. Future research should consider mixed-method approaches and longitudinal data to capture changes in purchase behavior over time.