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Generative AI for growth hacking: How startups use generative AI in their growth strategies

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  • Rezazadeh, Arash
  • Kohns, Marco
  • Bohnsack, René
  • António, Nuno
  • Rita, Paulo

Abstract

This study explores how startups and scaleups in Europe and the US use generative AI in their go-to-market strategies across product-led, sales-led, and operational efficiency-driven growth. Through interviews with 20 cases spanning pre-seed to Series E funding stages, we 1) analyze generative AI’s role in growth strategies, 2) identify large language model use cases for tackling growth challenges such as customer churn, and 3) develop a framework for AI capabilities that guides managers in building, refining, and reflecting on their knowledge of using generative AI for growth hacking. Key findings include the implications of generative AI for technical and non-technical content creation in product-led growth, promotional content creation and repurposing, and customer experience personalization in sales-led growth, and market research, market entry strategies, and customer engagement in operational efficiency-driven growth. Findings empower managers to develop effective generative AI-driven growth hacking strategies while proactively managing unintended organizational, competitive, and societal consequences.

Suggested Citation

  • Rezazadeh, Arash & Kohns, Marco & Bohnsack, René & António, Nuno & Rita, Paulo, 2025. "Generative AI for growth hacking: How startups use generative AI in their growth strategies," Journal of Business Research, Elsevier, vol. 192(C).
    • Handle: RePEc:eee:jbrese:v:192:y:2025:i:c:s0148296325001432
    DOI: 10.1016/j.jbusres.2025.115320
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    2. Wobst, Janice & Lueg, Rainer, 2025. "Avoiding algorithm errors in textual analysis: A guide to selecting software, and a research agenda toward generative artificial intelligence," Journal of Business Research, Elsevier, vol. 199(C).

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