Version 1
: Received: 14 June 2023 / Approved: 15 June 2023 / Online: 15 June 2023 (11:19:03 CEST)
How to cite:
Hajisafi, A. Optimizing Marketing Decisions Through a Structured Decision-Making Model Based on Marketing Engineering Principles. Preprints2023, 2023061129. https://doi.org/10.20944/preprints202306.1129.v1
Hajisafi, A. Optimizing Marketing Decisions Through a Structured Decision-Making Model Based on Marketing Engineering Principles. Preprints 2023, 2023061129. https://doi.org/10.20944/preprints202306.1129.v1
Hajisafi, A. Optimizing Marketing Decisions Through a Structured Decision-Making Model Based on Marketing Engineering Principles. Preprints2023, 2023061129. https://doi.org/10.20944/preprints202306.1129.v1
APA Style
Hajisafi, A. (2023). Optimizing Marketing Decisions Through a Structured Decision-Making Model Based on Marketing Engineering Principles. Preprints. https://doi.org/10.20944/preprints202306.1129.v1
Chicago/Turabian Style
Hajisafi, A. 2023 "Optimizing Marketing Decisions Through a Structured Decision-Making Model Based on Marketing Engineering Principles" Preprints. https://doi.org/10.20944/preprints202306.1129.v1
Abstract
Effective marketing decision-making benefits from a rigorous, data-driven process that systematically evaluates alternatives based on insights and analysis. This study develops and evaluates a 5-stage decision-making process model integrating marketing engineering principles aimed to optimize marketing decisions. An experiment randomized 150 participants into groups following either the proposed model or an unaided approach. Results indicate the model-following group achieved significantly higher ROI from marketing decisions (19.3% vs 16.4%) and employed key elements like customer segmentation, experimentation, and optimization to a greater extent. However, limitations including the experiment's scenario, self-report measures, and cross-sectional design constrain implications. Future research employing probability sampling, multiple decision contexts, longitudinal designs, manipulation checks, and objective metrics can further validate the proposed model. Overall, the study advances the understanding of how structured decision processes based on marketing engineering principles may optimize marketing decisions and outcomes.
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
