A Data-Driven Research of Sales and Delivery on JD.Com Platform: Based on DID Model
Bibliographic record
Abstract
E-commerce has become an integral part of people's lives and its ease and breadth have given great convenience.Price changes and platform activity are always points of concern in people's online purchases.This paper takes the JD.com platform as the research object to explore the impact of the Butterfly Festival on JD.com sales and delivery speed.Data from the database provided by JD.com are used for the analysis.Two time periods during and after the Butterfly Festival were selected, the parameters were estimated by linear regression based on the DID model, and the results obtained were representative and convincing because of the large data sample.By comparing the data during and after the Butterfly Festival, it was found that there was a decrease in the average sales per item and no significant impact on the delivery speed.For these phenomena, reasonable explanations and future predictions were made based on the characteristics of the JD.com platform and the industry.In conclusion, the Butterfly Festival must have been a success and made a significant contribution to JD.com's first-quarter sales in 2018, while it can be seen that JD.com is a mature platform in terms of logistics management.In the future, JD.com can launch strategies for different customer populations on a large scale to make JD.com a larger and more mature integrated e-commerce platform.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".