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Project title: E-commerce Brand Preference and Inventory Strategy
Context: Analyzed real shopper data provided by e-commerce platform 91APP through a university course partnership. The brief was open, so our team had to find its own insights and decide how to use the data. We examined shopper behavior across three categories (health supplements, snacks, daily necessities) and sales channels to inform category management and inventory planning.
My role: Team project. I led team discussions, helped develop and narrow down our ideas, worked on the analysis, and prepared the final report. My analysis covered brand preference concentration (HHI, the Herfindahl-Hirschman Index), shopper segmentation into stable and diverse groups (K-means), and channel-level behavior comparisons across categories.
Methods and tools: Python, Excel. Regression, HHI, K-means clustering.
Outcome: Brand preference concentration split shoppers into stable (high concentration) and diverse groups, and their behavior varied strongly with product category, which pointed to category-specific marketing and operations strategies.
Customer value differs: stable shoppers bought less often but had a significantly higher average unit price than diverse shoppers (health supplements: NT$459.16 vs NT$367.64; personal care: NT$441.64 vs NT$241.77).
Categories where the two groups behave very differently (e.g., health supplements): recommended introducing new brands to diverse shoppers, and offering repurchase reminders and same-brand extension recommendations to stable shoppers.
Categories where both groups buy across many brands (e.g., snacks): recommended cross-brand recommendations supported by market basket analysis.
Categories dominated by one brand (e.g., oral care, where one brand held about 50% share in the data): recommended shifting the focus to precise inventory management, keeping choice available while lowering overstock cost and avoiding stockouts.
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