Targeted marketing has grown in popularity in recent years, as well as recognizing when a consumer will desire a commodity may be extremely important to a business. Predicting this demand, however, is a complex procedure. Businesses, promoters/marketers, and sellers are using machine learning approaches to execute buyer prediction. This study focuses on when a customer would buy fast-moving retail merchandise by evaluating a customer’s purchase history at partner vendors. The projections should be used to customize special discounts for customers who are about to make a purchase. In addition, buying behavior is a set of consumption habits that can be analyzed to help in predicting the needs of specific target audience. Knowing consumption habits, business is much more likely to formulate sales items tailored to the market. Thus, the chances of success and acceptance of products and services increase. Promotional offers can then be supplied to the most relevant clients (with alerts sent directly to buyers’ mobile devices) thus reducing the use of the traditional/general paper-based marketing. More specifically, I will create a machine learning model that predicts potential future buyers based on the supplied market dataset. I will use a data source that gathers clients’ consumer history to establish a solid basis for this approach. The study focuses on consumer groupings rather than individual purchasers to forecast purchasing. After analyzing which of these purchase behaviors fits the consumer's decision-making of a product or service, it will be easy to establish appropriate/focused marketing and sales strategies.
Professional Studies (MS)
Department, Program, or Center
Graduate Programs & Research (Dubai)
Karmostaje, Rashed Ibrahim, "Buyer Prediction Through Machine Learning" (2022). Thesis. Rochester Institute of Technology. Accessed from