درخت حوزه‌های تخصصی

Export is a crucial driver of economic growth in various countries and significantly contributes to a country's entry into global markets and enhances economic success. In developing countries such as Iran, economic and social development programs prioritize the expansion of exports, particularly high value added agricultural products. The growth in non-oil exports, such as dried fruits, and the entry of domestic producers into new global markets have led to an increased demand for Iran's export products. This has also resulted in higher production levels, increased employment opportunities, and higher value added in the related activities. To enhance export performance, which is a crucial measure of a company's success in utilizing its resources and capabilities in the international arena over a specific period of time, it is important to focus on improving marketing strategies and specialized knowledge. Therefore, this research aims to examine the impact of marketing mix and specialized marketing knowledge on the export performance of small and medium-sized enterprises (SMEs) involved in exporting dried fruits in Mashhad, Iran in 2022. A total of 80 questionnaires were distributed among senior managers, board members, and business managers of dried fruits SMEs using the available sampling method. Structural equation modeling was employed for data analysis and test of research hypotheses. The statistical data and structural equation modeling revealed that the joint impact of marketing mix and specialized marketing knowledge has a positive and significant influence on export performance. In order to improve the company's profitability, it is essential for senior managers and sales managers to recognize the significance of these two factors and undergo relevant training to acquire the necessary skills. Moreover, managers should make effective use of appropriate distribution channels to expand their exports. Simultaneously, they should consider adapting product quality and packaging to align with the preferences of foreign buyers.

Businesses are increasingly interested in how big data, artificial intelligence, machine learning, and predictive analytics can be used to increase revenue, lower costs, and improve their business processes. In this paper, we describe how we have developed a data-driven machine learning method to optimize the collection process for a debt collection agency. Precisely speaking, we create a framework for the data-driven scheduling of outbound calls made by debt collectors. These phone calls are used to persuade debtors to settle their debt, or to negotiate payment arrangements in case debtors are willing, but unable to repay. We determine daily which debtors should be called to maximize the amount of delinquent debt recovered in the long term, under the constraint that only a limited number of phone calls can be made each day. Our approach is to formulate a Markov decision process and, given its intractability, approximate the value function based on historical data through the use of state-of-the-art machine learning techniques. Precisely, we predict the likelihood with which a debtor in a particular state is going to settle its debt and use this as a proxy for the value function. Based on this value function approximation, we compute for each debtor the marginal value of making a call. This leads to a particularly straightforward optimization procedure, namely, we prioritize the debtors that have the highest marginal value per phone call. We believe that our optimized policy substantially outperforms the current scheduling policy that has been used in business practice for many years. Most importantly, our policy collects more debt in less time, whilst using substantially fewer resources leading to a large increase in the amount of debt collected per phone call.