Using FPN to optimize automated sales strategies in multi-channel environments

With the spread of digital transformation and the advancement of new technologies, interactions between companies and customers across different platforms have changed drastically. In today’s world, platforms such as websites, mobile applications, and social networks have provided an opportunity to improve customer experience and enhance business performance. However, these multi-channel environments bring complexities such as rapid changes in consumer behavior and intense global competition, which require innovative solutions for data-driven strategic decision-making. Traditional methods, including statistical analysis and static machine learning models, are ineffective in managing these complexities due to their dependence on past data and lack of adaptation to real-time changes. This research attempts to effectively exploit multi-channel data and interactions between them by introducing a new model based on the FPN algorithm, or deep neural network. This deep neural network is written in Python with TensorFlow and studies model-based data in the form of standard customer interaction datasets from four different communication channels. Experimental results showed that the proposed model can significantly improve key performance indicators such as sales growth, return on investment, and customer satisfaction compared to traditional methods. These findings demonstrate the superiority of the proposed model in improving decision-making in complex and multi-channel environments. By providing an innovative framework, this research, in addition to overcoming the limitations of previous models, provides new directions for future research and practical applications in sales strategies.

کلیدواژه ها:

Automated sales strategies ، Multi-channel environments ، FPN algorithm ، Deep neural network ، Customer interaction datasets ، Sales growth ، Return on investment ، Customer satisfaction ، Data-driven decision-making