Growth hacking: A scientific approach for data-driven decision making

被引:3
|
作者
Cristofaro, Matteo [1 ]
Giardino, Pier Luigi [2 ]
Barboni, Luca [3 ]
机构
[1] Univ Roma Tor Vergata, Via Columbia,2, I-00133 Rome, Italy
[2] Univ Trento, Via V Inama, 5, I-38122 Trento, Italy
[3] Growthhackers com, Partner 247X Your Dedicated Growth Team Partner &, Via A Brisse,19, I-00149 Rome, Italy
关键词
Growth hacking; Decision making; Scientific management; Data-driven; Choice; BIG DATA; MANAGEMENT; RATIONALITY; TECHNOLOGY; STRATEGIES; MODELS; FIT;
D O I
10.1016/j.jbusres.2024.115030
中图分类号
F [经济];
学科分类号
02 ;
摘要
Today's businesses necessitate data-driven decisions to continuously adapt (and even shape) their environment to stay competitive. Growth hacking, with its emphasis on experimentation and data analysis, offers a promising approach to meet this need. Even though interest in growth hacking is increasing, the literature on the topic is still developing, and notclear guidance in how to implement it has yet been provided. Combining the scientific method and Taylor's scientific management principles with growth hacking insights from academic research and practice, we present growth hacking as a scientific approach for data-driven decision making in organisations. Through its iterative cycle of analysis, ideation, prioritisation, testing, and evaluation of prerequisites and facilitators, growth hacking empowers companies to make data-driven decisions, enabling them to navigate uncertainty, identify and seize opportunities, and transform their operations to adapt to or shape their environment. We also provide point out tools for the real-world business applications of growth hacking.
引用
收藏
页数:13
相关论文
共 50 条
  • [41] A Modern Approach to Security: Using Systems Engineering and Data-Driven Decision-Making
    Cano, Lester A.
    Staid, Andrea
    2016 IEEE INTERNATIONAL CARNAHAN CONFERENCE ON SECURITY TECHNOLOGY (ICCST), 2016,
  • [42] Sustainable supply chain decision-making in the automotive industry: A data-driven approach
    Beinabadi, Hanieh Zareian
    Baradaran, Vahid
    Komijan, Alireza Rashidi
    SOCIO-ECONOMIC PLANNING SCIENCES, 2024, 95
  • [43] eHealth-as-a-Service (eHaaS): A data-driven decision making approach in Australian context
    Black, Alofi
    Sahama, Tony
    Gajanayake, Randike
    E-HEALTH - FOR CONTINUITY OF CARE, 2014, 205 : 915 - 919
  • [44] Data-driven decision-making for lost circulation treatments: A machine learning approach
    Alkinani, Husam H.
    Al-Hameedi, Abo Taleb T.
    Dunn-Norman, Shari
    ENERGY AND AI, 2020, 2
  • [45] From Data to Optimal Decision Making: A Data-Driven, Probabilistic Machine Learning Approach to Decision Support for Patients With Sepsis
    Tsoukalas, Athanasios
    Albertson, Timothy
    Tagkopoulos, Ilias
    JMIR MEDICAL INFORMATICS, 2015, 3 (01)
  • [46] Data-Driven Decision Making: Components of the Enculturation of Data Use in Education
    Mandinach, Ellen B.
    Gummer, Edith
    TEACHERS COLLEGE RECORD, 2015, 117 (04):
  • [47] A Data-Driven Decision Making with Big Data Analysis on DNS Log
    Jung, Euihyun
    INFORMATION SCIENCE AND APPLICATIONS 2017, ICISA 2017, 2017, 424 : 426 - 432
  • [48] DATA SCIENCE AND ITS RELATIONSHIP TO BIG DATA AND DATA-DRIVEN DECISION MAKING
    Provost, Foster
    Fawcett, Tom
    BIG DATA, 2013, 1 (01) : 51 - 59
  • [49] The Learning Model for Data-Driven Decision Making of Collaborating Enterprises
    El-Nouty, Charles
    Filatova, Darya
    HUMAN AND ARTIFICIAL RATIONALITIES, HAR 2023, 2024, 14522 : 345 - 356
  • [50] Data-driven decision-making in emergency remote teaching
    Maya Botvin
    Arnon Hershkovitz
    Alona Forkosh-Baruch
    Education and Information Technologies, 2023, 28 : 489 - 506