Analyzing Customer-Facing Vendor Experiences with Time Series Forecasting and Monte Carlo Techniques

TL;DR

This paper presents a data-driven approach combining time series forecasting and Monte Carlo simulations to determine the optimal timing for disabling problematic vendors on eBay, aiming to balance customer retention and vendor management.

Contribution

It introduces a novel method integrating seasonality models, Monte Carlo simulations, and linear models to optimize vendor disabling decisions based on customer behavior forecasts.

Findings

Effective identification of vendor disablement timing

Improved customer retention strategies

Quantitative framework for vendor management

Abstract

eBay partners with external vendors, which allows customers to freely select a vendor to complete their eBay experiences. However, vendor outages can hinder customer experiences. Consequently, eBay can disable a problematic vendor to prevent customer loss. Disabling the vendor too late risks losing customers willing to switch to other vendors, while disabling it too early risks losing those unwilling to switch. In this paper, we propose a data-driven solution to answer whether eBay should disable a problematic vendor and when to disable it. Our solution involves forecasting customer behavior. First, we use a multiplicative seasonality model to represent behavior if all vendors are fully functioning. Next, we use a Monte Carlo simulation to represent behavior if the problematic vendor remains enabled. Finally, we use a linear model to represent behavior if the vendor is disabled. By comparing these forecasts, we determine the optimal time for eBay to disable the problematic vendor.