Marketing Resource Allocation in Automotive Enterprises Based on Hermit Crab Optimization

In recent years, the efficient marketing resource allocation in automative enterprises is crucial to maximize the customer engagement. However, the traditional allocation methods faced significant challenges in adapting dynamic customer behavior, which led to inefficient distribution of budget across multiple promotional channels. To overcome these difficulties, Hermit Crab Optimization (HCO) is proposed for optimal marketing resource allocation of automotive enterprises. Initially, the data is collected from Consumer Behavior and Shopping Habits (CBSH) Dataset. After that, the collected data is preprocessed using Minimum-Maximum normalization to normalize the data in the rage of 0 to 1. Furthermore, the normalized data is fed into the improved Support Vector Machine (SVM) model, to classify the customers based on their preferred marketing channels. Lastly, HCO method is employed for allocating the resources dynamically by adjusting marketing budgets based on customer engagement. The proposed HCO obtained better results in terms of accuracy with 99.90% than the existing SVM.