Mar’atus Solikhah, Rudi Ferdiansah, Arif Rohman Hakim

Data Science Utilization in Consumer Trend Prediction: A Qualitative Study on an e-commerce Market Research Team in Indonesia

Introduction

Data science utilization in consumer trend prediction: a qualitative study on an e-commerce market research team in indonesia. Explore data science utilization by e-commerce market research teams in Indonesia for consumer trend prediction. This qualitative study reveals tool use, challenges, and impact on marketing.

Abstract

In the era of digital transformation, the e-commerce industry in Indonesia faces great challenges in understanding and responding to changes in consumer behavior that are very dynamic. Data science is a strategic approach that can help companies analyze consumer data deeply and predict market trends accurately. This research aims to explore how market research teams in e-commerce companies utilize data science in the process of predicting consumer trends. A qualitative approach with a case study design was used in this research, involving in-depth interviews, questionnaires, and observations of market research teams from three major e-commerce companies in Indonesia, namely Tokopedia, Bukalapak, and Blibli. The results show that tools such as Python, Tableau, and BigQuery are widely used in the analytics process, from data cleansing to trend visualization. The research team has a good conceptual understanding of data science, although there are still gaps in coordination between divisions. The implementation of data science has proven to have a positive impact on the accuracy of marketing strategies and the efficiency of business decision-making. Obstacles faced include limited technical human resources and lack of standardized documentation. This research provides a practical contribution in developing a data-driven market research ecosystem in Indonesia's e-commerce industry and serves as a basis for further research with a broader scope

Review

This study tackles a highly pertinent and timely subject: the utilization of data science for consumer trend prediction within the rapidly evolving Indonesian e-commerce landscape. The authors effectively employ a qualitative case study design, focusing on market research teams from three major Indonesian e-commerce players – Tokopedia, Bukalapak, and Blibli. This localized and in-depth approach provides valuable insights into real-world applications and challenges, making the research particularly relevant for practitioners and academics keen on understanding digital transformation processes in dynamic emerging markets. The methodology, encompassing interviews, questionnaires, and observations, offers a robust framework for capturing the nuances of data science integration. The research delivers several significant findings that illuminate current practices. It clearly identifies the prevalent analytical tools such as Python, Tableau, and BigQuery, highlighting their role across the data pipeline from cleansing to visualization. The study commendably notes the market research teams' strong conceptual understanding of data science, coupled with the demonstrable positive impact on marketing strategy accuracy and business decision-making efficiency. However, it also critically uncovers operational friction points, including coordination gaps between divisions, limitations in technical human resources, and inadequate standardized documentation. These insights offer a practical contribution by outlining both the successes and persistent hurdles in fostering a data-driven market research ecosystem. While the qualitative case study excels in providing rich, contextualized detail, its inherent scope limits the generalizability of the findings to the broader e-commerce industry or other geographical contexts. Future research would benefit significantly from expanding the scope to include a wider array of companies, potentially incorporating quantitative methods to validate the observed impacts and challenges across a larger sample. Furthermore, exploring practical solutions or best practices to mitigate the identified obstacles – such as developing frameworks for improved cross-functional collaboration or innovative strategies for talent development and retention – would significantly enhance the practical implications of this foundational work. Nevertheless, this paper offers a crucial and insightful snapshot of data science adoption in Indonesian e-commerce, providing a solid basis for continued exploration.

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