Consumer‑to‑consumer (C2C) e-commerce sellers routinely face high exposure but low conversion. Existing research, however, relies largely on qualitative descriptions or small-scale surveys and rarely exploits behavioral logs to pinpoint where buyers drop out. Using a public Taobao user-behavior data set from 2017 as a methodological benchmark, this study constructs a de-duplicated user-item conversion framework and cross-checks it against an event-count metric. Under the de-duplicated metric, only 5.640% of interested user-item pairs culminate in purchase – well below the 36.450% implied by raw event ratios – and 75.600% of completed purchases bypass the shopping cart entirely. Conversion peaks during 10:00-13:00 and troughs during 19:00-23:00; the top 10.000% of categories account for 67.900% of sales. The bottleneck is therefore a mismatch between traffic timing and category structure, not shopping-cart abandonment. The framework itself is portable across periods, although the exact time labels must be recalibrated as the platform environment changes. This study anchors its contribution in reusable structural diagnosis rather than in point predictions for any single period.
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Yang, A. (2026) De-duplicated User-item Metrics for E-commerce Conversion Funnel Diagnosis: Method, Validation, and Operational Audit Cues. Hong Kong Financial Bulletin, 2(3), 1-9. https://doi.org/10.71052/hkfb2025/RLVA3957