Method for Effect Evaluation of a Reception System on Sales, Number of Customers, Hourly Productivity and Churn Based on Intervention Analysis

We propose a method of AI-based evaluation of sales, number of customers, and churn before and after the introduction of a hair salon based on intervention time series analysis. We also used the software package of CausalImpact for the intervention time series analysis. The problem with this method is that the prediction accuracy is insufficient, and the estimated results of the intervention effect are not very valid. We thought it was necessary to verify prediction accuracy by using data before the system was introduced, where correct answer data exists, for the counterfactual prediction value after the system was introduced and devised a method to accurately predict the outcome variable before the system was introduced. Specifically, we introduce two learning models as in the development workflow of a general machine learning model, one for learning and the other one for accuracy verification. However, since CausalImpact does not include the function to verify the prediction accuracy, a separate code was prepared for that purpose to improve the prediction accuracy. As a result, we were able to confirm that the prediction accuracy was almost acceptable.

@article{Arai2025,
  title     = {Method for Effect Evaluation of a Reception System on Sales, Number of Customers, Hourly Productivity and Churn Based on Intervention Analysis},
  journal   = {International Journal of Advanced Computer Science and Applications},
  volume    = {16},
  number    = {5},
  year      = {2025},
  publisher = {The Science and Information Organization},
  author    = {Kohei Arai and Ikuya Fujikawa and Sayuri Ogawa},
  doi       = {10.14569/IJACSA.2025.0160533},
  url       = {https://doi.org/10.14569/IJACSA.2025.0160533}
}