Summary of Analyzing Customer-facing Vendor Experiences with Time Series Forecasting and Monte Carlo Techniques, by Vivek Kaushik and Jason Tang
Analyzing Customer-Facing Vendor Experiences with Time Series Forecasting and Monte Carlo Techniques
by Vivek Kaushik, Jason Tang
First submitted to arxiv on: 30 Jul 2024
Categories
- Main: Machine Learning (stat.ML)
- Secondary: Machine Learning (cs.LG); Computation (stat.CO)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This paper proposes a data-driven approach to help eBay decide whether to disable a problematic vendor and when to do so. The goal is to prevent customer loss while minimizing disruptions. The authors use three forecasting models: multiplicative seasonality, Monte Carlo simulation, and linear regression. These models predict customer behavior under different scenarios: all vendors functioning normally, the problematic vendor remaining enabled, and the vendor being disabled. By comparing these forecasts, the optimal time for eBay to disable the problematic vendor is determined. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary eBay partners with external vendors, which can sometimes cause problems. When a vendor goes down, it’s important for eBay to decide whether to stop using that vendor and when to do so. The paper suggests using data to make this decision. It uses three different models to predict what will happen if the vendor stays up or gets shut down. By comparing these predictions, the authors figure out the best time for eBay to disable the problematic vendor. |
Keywords
* Artificial intelligence * Linear regression
