An explicit Nash equilibrium for a market share attraction game

In competitive marketing, the speed of generating the best price has become as critical as its reliability. In this study, we aim to design a practical marketing management tool. We consider a non-cooperative marketing environment with multiple substitute products, where total market size is moderately price-sensitive. The price-demand relations are determined by a market share attraction model, where the attraction of each product is a linear function of its price. The product's brand image is reflected in the parameters of this linear function. For the general case of multiple substitute products, we derive explicit expressions for the best-response functions. For the specific case of two substitute products, we derive closed form expressions for the prices at Nash equilibrium. These expressions help managers in changing their marketing instruments other than price, so as to obtain substantial individual profits. We show how our closed form Nash equilibrium enables the examination of the profit loss due to competition. Relevant for practice is the fact that our model can be easily calibrated. We provide a simple procedure for estimating the model parameters

Datasets for Crime Hotspot Prediction Project

Datasets: This repository includes a folder titled Datasets that contains all crime and park event tables. These tables consist of event observations across street segments (columns) with time steps (rows). This is a crucial aspect of the project. Additionally, the folder contains Python pickles that store network information, street segment details, and crime location information. Each file in this folder is required by the script to generate predictions.Predictions: The folder also contains predictions across all the models (e.g., theft daily, robbery shift). These files have two columns: actual and predicted values, respectively. The structure is 2459 rows by test days. This means that the first 2459 rows represent predictions for each segment on the first day, the next 2459 rows represent predictions for the second day, and so on.</p

A New 360° Framework to Predict Customer Lifetime Value for Multi-Category E-Commerce Companies Using a Multi-Output Deep Neural Network and Explainable Artificial Intelligence

Online purchasing has developed rapidly in recent years due to its efficiency, convenience, low cost, and product variety. This has increased the number of online multi-category e-commerce retailers that sell a variety of product categories. Due to the growth in the number of players, each company needs to optimize its own business strategy in order to compete. Customer lifetime value (CLV) is a common metric that multi-category e-commerce retailers usually consider for competition because it helps determine the most valuable customers for the retailers. However, in this paper, we introduce two additional novel factors in addition to CLV to determine which customers will bring in the highest revenue in the future: distinct product category (DPC) and trend in amount spent (TAS). Then, we propose a new framework. We utilized, for the first time in the relevant literature, a multi-output deep neural network (DNN) model to test our proposed framework while forecasting CLV, DPC, and TAS together. To make this outcome applicable in real life, we constructed customer clusters that allow the management of multi-category e-commerce companies to segment end-users based on the three variables. We compared the proposed framework (constructed with multiple outputs: CLV, DPC, and TAS) against a baseline single-output model to determine the combined effect of the multi-output model. In addition, we also compared the proposed model with multi-output Decision Tree (DT) and multi-output Random Forest (RF) algorithms on the same dataset. The results indicate that the multi-output DNN model outperforms the single-output DNN model, multi-output DT, and multi-output RF across all assessment measures, proving that the multi-output DNN model is more suitable for multi-category e-commerce retailers’ usage. Furthermore, Shapley values derived through the explainable artificial intelligence method are used to interpret the decisions of the DNN. This practice demonstrates which inputs contribute more to the outcomes (a significant novelty in interpreting the DNN model for the CLV)