VANY, ANDIKA DIO (2026) PENERAPAN DESCRIPTIVE ANALYTICS UNTUK MONITORING PERFORMA LIVE STREAMING E-COMMERCE PADA LIVE & GO PRODUCTION. S1 thesis, Universitas Mercu Buana Jakarta.
|
Text (HAL COVER)
COVER.pdf Download (924kB) | Preview |
|
![[img]](https://repository.mercubuana.ac.id/style/images/fileicons/text.png) |
Text (BAB I)
BAB 1.pdf Restricted to Registered users only Download (214kB) |
|
|
Text (BAB II)
BAB 2.pdf Restricted to Registered users only Download (403kB) |
|
|
Text (BAB III)
BAB 3.pdf Restricted to Registered users only Download (434kB) |
|
|
Text (BAB IV)
BAB 4.pdf Restricted to Registered users only Download (667kB) |
|
|
Text (BAB IV)
BAB 4.pdf Restricted to Registered users only Download (667kB) |
|
|
Text (BAB V)
BAB 5.pdf Restricted to Registered users only Download (206kB) |
|
|
Text (DAFTAR PUSTAKA)
DAFTAR PUSTAKA.pdf Restricted to Registered users only Download (204kB) |
|
|
Text (LAMPIRAN)
LAMPIRAN.pdf Restricted to Registered users only Download (359kB) |
Abstract
The rapid growth of e-commerce has encouraged companies to adopt live streaming as an effective marketing strategy to increase customer engagement and product sales. Live streaming activities generate a large amount of performance data, including viewers, active viewers, likes, comments, product clicks, orders, buyers, and Gross Merchandise Value (GMV). However, monitoring these data is often conducted manually, making it difficult for companies to evaluate performance and make timely business decisions. This research aims to implement Descriptive Analytics to develop a dashboard for monitoring live streaming performance at Live & Go Production using Microsoft Power BI. The variables analyzed in this study include Traffic (Views, Active Viewers, Peak Viewers), Engagement (Likes, Comments, Shares), Product Interaction (Click Product and Add to Cart), Conversion (Orders and Buyers), Sales Performance (Gross Merchandise Value and Product Sold), Live Performance (Average Watch Duration), and Host Performance. This study employs a descriptive quantitative research method using historical live streaming performance data obtained from the Shopee Live Dashboard. The collected data undergo preprocessing, including data cleaning and data transformation, before being analyzed and visualized through an interactive dashboard. The results indicate that the developed dashboard effectively presents key performance indicators (KPIs), such as GMV, Click Through Rate (CTR), Conversion Rate, Engagement Rate, product performance, audience traffic, and host performance. Furthermore, the dashboard enables users to monitor live streaming performance efficiently, identify sales trends, evaluate promotional effectiveness, determine best-selling products, and support datadriven business decision-making. Therefore, the implementation of Descriptive Analytics provides an effective solution for improving live streaming performance monitoring and enhancing business decision-making at Live & Go Production. Keywords: Descriptive Analytics, Business Intelligence, Dashboard Monitoring, Live Streaming E-commerce, Microsoft Power BI. Perkembangan e-commerce mendorong perusahaan untuk memanfaatkan live streaming sebagai strategi pemasaran yang efektif dalam meningkatkan interaksi dengan pelanggan dan penjualan produk. Aktivitas live streaming menghasilkan data performa dalam jumlah besar, seperti jumlah penonton, tingkat interaksi, klik produk, transaksi, dan nilai penjualan. Namun, proses monitoring terhadap data tersebut masih banyak dilakukan secara manual sehingga menyulitkan perusahaan dalam memperoleh informasi secara cepat dan akurat untuk mendukung pengambilan keputusan. Penelitian ini bertujuan untuk menerapkan Descriptive Analytics dalam membangun dashboard monitoring performa live streaming pada Live & Go Production menggunakan Microsoft Power BI. Variabel yang digunakan dalam penelitian ini meliputi Traffic (Views, Active Viewers, Peak Viewers), Engagement (Likes, Comments, Shares), Product Interaction (Click Product dan Add to Cart), Conversion (Orders dan Buyers), Sales Performance (Gross Merchandise Value/GMV dan Product Sold), Live Performance (Average Watch Duration), serta Host Performance. Penelitian menggunakan metode deskriptif dengan pendekatan kuantitatif. Data yang dianalisis merupakan data historis performa live streaming yang diperoleh dari Shopee Live Dashboard, kemudian melalui tahapan preprocessing yang meliputi data cleaning dan data transformation sebelum divisualisasikan dalam dashboard interaktif menggunakan Microsoft Power BI. Hasil penelitian menunjukkan bahwa dashboard yang dibangun mampu menyajikan informasi performa live streaming secara terintegrasi melalui visualisasi Key Performance Indicator (KPI), seperti GMV, Conversion Rate, Click Through Rate (CTR), Engagement Rate, jumlah penonton, serta performa produk dan host. Dashboard ini memudahkan pengguna dalam memantau kondisi live streaming, mengidentifikasi tren penjualan, mengevaluasi efektivitas promosi, menentukan produk unggulan, serta mendukung proses pengambilan keputusan bisnis secara lebih cepat, akurat, dan berbasis data. Dengan demikian, penerapan Descriptive Analytics memberikan manfaat dalam meningkatkan efektivitas monitoring dan evaluasi performa live streaming pada Live & Go Production. Kata Kunci : Descriptive Analytics, Business Intelligence, Dashboard Monitoring, Live Streaming eCommerce, Microsoft Power BI.
Actions (login required)
 |
View Item |
