Optimizing E-commerce Performance Through Marketing Analytics: A Longitudinal Analysis of a Fashion Brand (2019-2023 djamaludin, puti renosori, slamet
universitas islam bandung
Abstract
Intense competition in the e-commerce fashion industry requires companies to adopt data-driven strategies to achieve sustainable growth. This research aims to optimize the e-commerce performance of a fashion brand through a marketing analytics approach. This study conducts a longitudinal analysis of five years of sales data (2019-2023) to evaluate multiple performance dimensions. The methodology includes the analysis of annual sales trends, identification of best-selling and underperforming products, mapping of sales channel (marketplace) effectiveness, assessment of promotional impact, and analysis of customer purchasing patterns based on demographics and time.
Key findings reveal performance volatility, with a significant sales decline in 2020 due to the COVID-19 pandemic , followed by a strong recovery in 2021 and reaching peak revenue in 2023. The analysis reveals that product categories such as ^Oversized Tees^ and ^Headwear^ are primary revenue contributors. Shopee consistently dominates as the primary sales channel, holding approximately 60% of the market share. A very strong positive correlation was found between promotional costs and revenue - however, the Return on Promotion Spend (ROPS) exhibited a downward trend in 2023 as promotional budgets were aggressively increased. Furthermore, female customers were identified as the segment contributing to a larger share of total revenue.
This study concludes that the systematic application of marketing analytics provides deep strategic insights for decision-making. The implication is that fashion brands can optimize product portfolios, efficiently allocate resources across sales channels, and design more effective promotional campaigns to drive sales performance and achieve sustainable business growth in a dynamic market.
Keywords: Marketing Analytics, E-commerce, Performance Optimization, Longitudinal Analysis, Fashion Industry
