Designing Customer Analytics Dashboard in Smart Device Retail Using Power BI
The adoption of data analytics has led to a paradigm shift in business decision-making, moving from intuition-based to data-driven strategies. Specifically in customer analytics, metrics such as Net Promoter Score (NPS), Customer Satisfaction Score (CSS), and Repeat Purchase Rate (RPR) are widely us...
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| Format: | Article |
| Language: | English |
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Informatics Department, Faculty of Computer Science Bina Darma University
2025-06-01
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| Series: | Journal of Information Systems and Informatics |
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| Online Access: | https://journal-isi.org/index.php/isi/article/view/1114 |
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| _version_ | 1849392940735004672 |
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| author | Reza Rahutomo |
| author_facet | Reza Rahutomo |
| author_sort | Reza Rahutomo |
| collection | DOAJ |
| description | The adoption of data analytics has led to a paradigm shift in business decision-making, moving from intuition-based to data-driven strategies. Specifically in customer analytics, metrics such as Net Promoter Score (NPS), Customer Satisfaction Score (CSS), and Repeat Purchase Rate (RPR) are widely used to formulate customer retention strategies. Although dashboard applications like Microsoft Power BI support the visualization of these metrics, existing designs lack integrated filtering capabilities based on demographic characteristics such as gender and age group. This study aims to propose a Power BI dashboard application design that integrates NPS, CSS, and RPR with demographic filters to effectively convey customer loyalty, satisfaction, and advocacy. The research methodology includes four stages which are Power BI understanding, data acquisition, data pre-processing, and metric modeling. The dataset was collected by using an online questionnaire in January 2025 (N = 542). It must be validated and transformed before being modeled by using DAX. The proposed dashboard design offers an interactive interface, allowing users to explore insights through chart elements such as bars and pie slices. This design enhances user experience and supports intuitive analysis, making it a valuable tool for smart device retailers and manufacturers to make data-driven decisions. Additionally, the dashboard is adaptable to other business contexts with similar analytical needs. For real-world implementation, the inclusion of Key Performance Indicators (KPIs) for each metric is recommended to ensure that insights are actionable and aligned with business objectives. |
| format | Article |
| id | doaj-art-a6c07e32b43241bbb31056da83a7d618 |
| institution | Kabale University |
| issn | 2656-5935 2656-4882 |
| language | English |
| publishDate | 2025-06-01 |
| publisher | Informatics Department, Faculty of Computer Science Bina Darma University |
| record_format | Article |
| series | Journal of Information Systems and Informatics |
| spelling | doaj-art-a6c07e32b43241bbb31056da83a7d6182025-08-20T03:40:37ZengInformatics Department, Faculty of Computer Science Bina Darma UniversityJournal of Information Systems and Informatics2656-59352656-48822025-06-01721743175710.51519/journalisi.v7i2.11141114Designing Customer Analytics Dashboard in Smart Device Retail Using Power BIReza Rahutomo0Bina Nusantara UniversityThe adoption of data analytics has led to a paradigm shift in business decision-making, moving from intuition-based to data-driven strategies. Specifically in customer analytics, metrics such as Net Promoter Score (NPS), Customer Satisfaction Score (CSS), and Repeat Purchase Rate (RPR) are widely used to formulate customer retention strategies. Although dashboard applications like Microsoft Power BI support the visualization of these metrics, existing designs lack integrated filtering capabilities based on demographic characteristics such as gender and age group. This study aims to propose a Power BI dashboard application design that integrates NPS, CSS, and RPR with demographic filters to effectively convey customer loyalty, satisfaction, and advocacy. The research methodology includes four stages which are Power BI understanding, data acquisition, data pre-processing, and metric modeling. The dataset was collected by using an online questionnaire in January 2025 (N = 542). It must be validated and transformed before being modeled by using DAX. The proposed dashboard design offers an interactive interface, allowing users to explore insights through chart elements such as bars and pie slices. This design enhances user experience and supports intuitive analysis, making it a valuable tool for smart device retailers and manufacturers to make data-driven decisions. Additionally, the dashboard is adaptable to other business contexts with similar analytical needs. For real-world implementation, the inclusion of Key Performance Indicators (KPIs) for each metric is recommended to ensure that insights are actionable and aligned with business objectives.https://journal-isi.org/index.php/isi/article/view/1114business analyticscustomer analyticspower bidashboarddata analysis |
| spellingShingle | Reza Rahutomo Designing Customer Analytics Dashboard in Smart Device Retail Using Power BI Journal of Information Systems and Informatics business analytics customer analytics power bi dashboard data analysis |
| title | Designing Customer Analytics Dashboard in Smart Device Retail Using Power BI |
| title_full | Designing Customer Analytics Dashboard in Smart Device Retail Using Power BI |
| title_fullStr | Designing Customer Analytics Dashboard in Smart Device Retail Using Power BI |
| title_full_unstemmed | Designing Customer Analytics Dashboard in Smart Device Retail Using Power BI |
| title_short | Designing Customer Analytics Dashboard in Smart Device Retail Using Power BI |
| title_sort | designing customer analytics dashboard in smart device retail using power bi |
| topic | business analytics customer analytics power bi dashboard data analysis |
| url | https://journal-isi.org/index.php/isi/article/view/1114 |
| work_keys_str_mv | AT rezarahutomo designingcustomeranalyticsdashboardinsmartdeviceretailusingpowerbi |