Abstract

Supermarkets must improve their marketing tactics at a time of changing consumer behavior and increasing competition in the retail industry. Engaging the wide and sophisticated consumer base of today's supermarkets is difficult using traditional methods. This proposal presents a data-driven approach using cutting-edge machine learning methods to enhance supermarket marketing strategies. The main goal is to increase the response rate of marketing initiatives, which will improve consumer engagement and ultimately increase revenue. Additional primary objectives are customer segmentation and targeting, predictive modeling, personalization, ongoing performance monitoring, and ROI evaluation. The current problem centers around personalization and accuracy. Due to the complexity of marketing channels and the demands of modern customers, flexible techniques are needed. The proposal offers a thorough process that includes data gathering, analysis, modeling using machine learning, personalization, and continual development. The idea attempts to create marketing efforts that connect with customers by using prediction models and recommendation systems. The results that are anticipated include a rise in supermarket sales and profitability as well as an improvement in consumer involvement and satisfaction. Additionally, this concept offers a chance to gather crucial information about consumer behavior for the next marketing initiatives. The strategy proposed in this proposal has the potential to change the marketing environment for supermarkets, enabling them to become more adaptable, receptive, and customer-focused in their operations. Predictive models are informed by the technique by utilizing a variety of data sources, including consumer profiles, purchasing history, demographics, and real-time information. Marketing campaigns may be customized due to the integration of regression, classification, and clustering algorithms in these models. Effective consumer segmentation and targeting guarantees that everyone receives customized advertisements and incentives, increasing response rates and engagement. It should be noted that Tableau will be used to extract visually appealing insights from the dataset, SPSS will be used to examine and prepare the data, and Kaggle will serve as the major source for data.

Publication Date

1-2026

Document Type

Thesis

Student Type

Graduate

Degree Name

Professional Studies (MS)

Department, Program, or Center

Graduate Programs & Research

Advisor

Sanjay Modak

Advisor/Committee Member

Hammou Messatfa

Campus

RIT Dubai

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