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Top1. Introduction
In the realm of product advertising and promotion, there exist two distinct approaches, namely, mass marketing and direct marketing (Roddy, 2002). Mass marketing utilizes various mass media channels to disseminate product-related information to both current and potential customers. These channels typically include television, radio, magazines, and newspapers, and the marketing messages are targeted toward large customer segments. Mass marketing does not discriminate against individual customers within the group, and the information delivered remains consistent for all. On the other hand, direct marketing focuses on targeting specific individuals or households with tailored marketing messages.
Direct marketing has become an increasingly important strategy for businesses seeking to reach their target customers (Poon et al., 2017). However, the success of direct marketing campaigns depends on the ability to accurately predict customer behavior and preferences. Inaccurate predictions can lead to wasted resources, ineffective campaigns, and a poor return on investment. Therefore, predicting direct marketing outcomes has become a critical area of research in marketing. Accurate predictions can help businesses optimize their campaigns, targeting the right customers at the right time with the right message. By using advanced data analytics and machine learning techniques, businesses can identify patterns in customer behavior and tailor their marketing strategies accordingly. Furthermore, predicting direct marketing outcomes can also help businesses stay ahead of the competition. As the marketing landscape becomes more competitive, businesses need to find ways to differentiate themselves and provide more personalized experiences to their customers. Predictive analytics can help businesses gain a deeper understanding of their customers, allowing them to deliver more targeted and effective marketing campaigns.
Direct marketing relies on the creation of precise predictive models within databases and is an area that has the potential to benefit significantly from such models (Berger & Magliozi 1992). As an increasing number of companies adopt direct marketing as a distribution strategy, there has been a surge in spending on these channels, with direct marketing staff prioritizing consumer response modeling to enhance revenue, minimize costs, and improve overall profitability. In addition to the traditional statistical approach to forecasting consumer purchases, researchers have recently applied data mining techniques to large, noisy databases, which offer several unique advantages.
Especially, there are several reasons why a new data analysis method is needed for predicting direct marketing (Sharma et al., 2022). First, traditional methods such as logistic regression have limitations in handling large and complex datasets. With the increasing amount of data being generated in today's digital age, there is a need for more powerful and scalable algorithms. Second, consumer behavior and preferences are constantly evolving, making it challenging to build accurate predictive models that can adapt to these changes. Third, the rise of new technologies such as artificial intelligence and machine learning has opened up new possibilities for predictive modeling, and there is a need to explore these methods to improve the accuracy and efficiency of direct marketing predictions. Overall, a new data analysis method for direct marketing prediction is needed to keep up with the changing landscape of consumer behavior and technology, and to improve the effectiveness of marketing campaigns.