Optimizing online marketing efficiency by analyzing the mutual influence of online marketing channels with respect to different devices

• Autores: Ole Nass
• Directores de la Tesis: José Albors Garrigós (dir. tes.), Klaus Peter Schoeneberg (dir. tes.), Hermenegildo Gil Gómez (dir. tes.)
• Lectura: En la Universitat Politècnica de València ( España ) en 2019
• Idioma: español
• Tribunal Calificador de la Tesis: Ángel Peiró Signes (presid.), Antonio Hidalgo Nuchera (secret.), Michael Sievers (voc.)
• Programa de doctorado: Programa de Doctorado en Administración y Dirección de Empresas por la Universitat Politècnica de València
• Materias:
  • Ciencias económicas
    • Economía del cambio tecnológico
      • Innovación tecnológica
• Enlaces
  • Tesis en acceso abierto en: RiuNet
• Resumen

  • What does attribution in an omni¿channel environment look like? A major distinction can be determined in contrast to attribution in a multi¿channel environment. Besides providing the Marketing Analytics Process, a specification of the Cross¿industry standard process for data mining (CRISP¿DM), a sequential mixed method approach is utilized to analyze the main research question.

    Within the first step of this presented research characteristics, and requirements of efficient attribution in an omni¿channel environment are analyzed. Based on semi¿structured expert interviews and a holistic structured literature research process, the lack of an omni¿channel attribution approach is clearly identified. Existing attribution approaches are identified by conducting the structured literature review process. Those identified approaches are evaluated by applying the results of the semi¿structured expert interviews - the requirements and characteristics of efficient omni¿channel attribution. None of the identified attribution approaches fulfill a majority of the analyzed omni¿channel requirements.

    By having the research gap - the lack of an omni¿channel attribution approach - clearly identifed, an omni¿channel attribution approach is developed in the second part of this presented research. Utilizing the MAP methodology, the main research gap is filled by providing the Holistic Customer Journey (HCJ): an omni¿channel ready data foundation and a corresponding omni¿channel attribution approach. Among other things the developed attribution approach consists of a machine learning classification. This presented research is the first to utilize information from almost 240.000.000 interaction data sets, containing crossdevice and cross¿platform information. All underlying data sources are provided by one of Germany's largest real¿estate platforms.