Big Data and Marketing Strategies in Banking: An SPSS Statistical Examination of Customer Engagement and Satisfaction

Authors

• Praveen Kumar Kanumarlapudi Induvidual

Abstract

Introduction:  Big data and sophisticated marketing communication techniques, the banking sector has changed. Banks are now information aggregators that use big data to improve client interactions rather than merely offering financial services. Banks can develop enduring relationships with customers, enhance service quality, and obtain a competitive advantage by incorporating cutting-edge communication technologies. In an increasingly digitized and competitive environment, banks must comprehend consumer behavior and marketing communication tactics in order to increase client loyalty and boost profitability.

Research Significance: By examining how big data and relationship marketing might promote client loyalty, this study adds to the expanding corpus of knowledge on marketing communication in the banking industry. It draws attention to how client retention is affected by marketing communication tactics like direct marketing, public relations, and internet interaction. The results give bank managers important information for creating communication plans that will improve client happiness and long-term company success. This study also emphasizes how crucial technology developments are to updating banking channels of communication and enhancing service quality.

SPSS statistics: SPSS (Statistical Package for the Social Sciences) is a widely used statistical software for data analysis. It is designed for researchers, analysts, and businesses to analyze data, visualize results, and perform statistical tests efficiently.

Input Parameters: Bank Type (Public, Private, Cooperative), Marketing Channel (Social Media, TV, Newspaper, Email, SMS, Events), Customer Segment (Retail, Corporate, SME, Wealth Management), Campaign Type (Promotional Offer, Brand Awareness, Customer Education, CSR), Communication Style (Formal, Casual, Personalized, Informative), Preferred Language (English, Spanish, French, German, Hindi, Chinese).

Evaluation Parameters: Customer Engagement, Brand Awareness Impact, Conversion Rate Effectiveness, Customer Satisfaction, Message Clarity.

Genetic algorithms (GAs) are computational methods that draw inspiration from the fundamental principles of natural evolution. of natural evolution. They use simplified, chromosome-like representations to encode possible solutions and employ refinement strategies to preserve important information within these structures. Although GAs is primarily used for functional optimization, they have also proven useful in a variety of problem domain.[1]Genetic algorithms (GAs) are optimization and search techniques the genetic algorithm, rooted in the Based on the principles of natural selection and evolutionary genetics, the genetic algorithm was first introduced by Holland in the 1970s.They were developed to model evolutionary processes observed in nature. GAs treats potential solutions as populations of individuals and attempt to find the best solution by evolving this population through successive generations. [2] Genetic algorithm is a stochastic optimization method based on the principles of natural selection and genetics. It starts with an initial population of possible solutions, each encoded as a chromosome. [3]This theory demonstrates robust periodic behaviour in a variety of scaled genetic algorithms that use fixed fitness selection methods. When the mutation rate stabilizes at a positive value and other genetic algorithm operators also reach convergence, the probability distribution over the population becomes completely positive.

This occurs even though the initially generated population, although uniformly generated, does not necessarily contain individuals with optimal fitness.[4] Genetic algorithms are a type of optimization technique used to identify the maximum or minimum of a given function. This research paper serves as an introduction to genetic algorithms aimed at beginners. It explains the key elements that make up genetic algorithms and provides guidance on how to implement them. Several examples, including a genetic algorithm designed to solve the well-known travelling salesman problem, are programmed using MATLAB. [5] Over the past forty years, biologically inspired computational approaches have experienced both periods of decline and revival. However, in the past decade, these methods have seen a significant resurgence within the theory demonstrates robust periodic behaviour in a variety of scaled genetic algorithms that use fixed fitness selection methods. When the mutation rate stabilizes at a positive value and other genetic algorithm operators also reach convergence, the probability distribution over the population becomes completely positive. This occurs even though the initially generated population, although uniformly generated, does not necessarily contain individuals with optimal fitness.[6] When Holland introduced genetic algorithms in 1975, he envisioned them as efficient Accessible and versatile techniques It can be used for a variety of problems.

However, the current literature indicates that genetic algorithms often require significant expertise to be used effectively. This is because users typically struggle to select appropriate encodings, select appropriate operators, and determine optimal parameter values ​​for the algorithm to perform well in a particular application. [7]The genetic algorithm (GA), a form of evolutionary algorithm, is a heuristic approach designed to address optimization and search problems by following the process of natural evolution. Method used for optimization and problem-solving. and probabilistic approach used to solve complex optimization problems. method used for optimization. problem solving and optimization. method. approach used for search and optimization tasks. Its powerful global search capability and ability to find nearly optimal solutions without requiring gradient information from error functions have made GA an essential tool in optimization, search applications, and machine learning (Yao, 2004). [8] Within the evolutionary computing community, there is often a reverent attitude towards populations, and rightly so. Populations play a crucial role in the success or failure of a genetic algorithm (GA) because they support important processes such as function evaluation, project analysis, and partition recognition.[9] Genetic algorithms tackle optimization problems by following natural evolutionary processes such as genetic mutation, selection, and crossoverthese algorithms, which draw inspiration from biological evolution and natural selection, offer greater robustness compared to conventional AI approaches. They are particularly useful in situations where the input data changes little or contains significant amounts of noise. [10] Genetic algorithms (GAs) are probabilistic search and optimization techniques rooted in the principles of natural selection, evolutionary biology, and genetics. They draw inspiration from evolutionary theory and natural genetics and use processes such as selection, crossover, and mutation to explore and develop solutions over many generations.

They have inherent parallelism, which makes them very useful for exploring complex, large-scale, and multimodal solution spaces. GAs is particularly useful for finding near-optimal solutions to objective or goal functions in various optimization problems. [11]Arabia and Hubert highlight three primary fields in which clustering techniques are commonly used: sociology and social psychology, market structure analysis, and the construction of evolutionary trees. They propose a new clustering approach based on genetic algorithms that have significant potential for data mining applications. [12] Genetic Algorithm (GA) is a met heuristic approach inspired by genetic principles and aimed at finding efficient solutions to complex problems. In this process, a set of random solutions (individuals) is initially generated, each solution having different characteristics (chromosomes). [13] Many claims have been made about the Performance of various selection methods in genetic algorithms (GAs) but these are often supported by limited and unconstrained simulations. Notably, there is a lack of in-depth analysis of comparative expected fitness gains, accumulation times, or distinct patterns of selective accumulation. [14]Genetic algorithms (GAs) are gaining widespread use in both engineering and academic fields as adaptive strategies for solving complex problems. As a meta-heuristic approach, GAs is particularly useful for solving hybrid computational tasks.

They efficiently guide the search process using evolutionary this process is driven by mechanisms such as selection, crossover, and mutation;they are based on the principles of natural selection and genetic evolution. [15]Genetic algorithms (GAs) are random search and optimization techniques founded on the principles of natural selection, evolutionary biology, and genetics. Inspired by evolutionary theory and natural genetics, they use mechanisms such as selection, crossover, and mutation to generate and refine solutions over successive generations.

Alternative:

Medical service:Medical services refer to any care that maintains or prevents the human body, as well as any care that includes treatment, services, or interventions for disease, dysfunction, or injury affecting the human body.

Service responsiveness: Service responsiveness describes a company's ability to promptly and efficiently address customer questions, needs, and concerns. It is a key aspect of customer service that impacts satisfaction, loyalty, and overall business performance. Companies that prioritize responsiveness are better prepared to build strong customer relationships and achieve a competitive advantage.

Discharge:Discharge has various meanings, including releasing a patient from a hospital, releasing an object, or carrying out a sentence ordered by a court.

Admission:Entry refers to coming in, entering, or entering. It also refers to the right to enter. Synonyms include access, admission, entry, and entry.

Hygiene:Hygiene refers to a series of practices aimed at preventing the spread of disease and maintaining health. This is usually achieved by keeping the body and the surrounding environment clean. Good hygiene is crucial for staying healthy because it reduces the risk of disease.

Evolution:

Scale Construction:Scale construction is the process of creating measurement scales by grouping items together based on their internal consistency or homogeneous scaling, involving strong correlations between selected items.

Scale Validation:In psychological testing, a validity scale is used to assess the reliability of responses, with the aim of identifying problems such as defensiveness, misunderstanding, or careless or inconsistent responding.

Nurses:Nurses are dedicated to promoting health, preventing illness, and helping patients with medical conditions. In providing care, they monitor, evaluate, and record symptoms, reactions, and progress. Nurses work with physicians to develop treatment plans, perform tests, administer medications, and monitor patient recovery.

Staff members:A staff member is an employee working in an organization who performs specific tasks to ensure the smooth functioning of that organization. 

GRA metho d

The structure of this paper is as follows: Section II provides a comprehensive review of current network selection decision-making approaches, and a complete description of the GRA-based decision-making algorithm. Section III introduces our proposed strategies aimed at addressing and solving the ranking inversion problem in GRA-based methods. Network selection. [16] Based on the traditional GRA approach, this study primarily aims to determine attribute weights by combining both available spherical linguistic fuzzy information and instances where attribute weight data are completely unknown. Therefore, overcoming this challenge is of considerable importance.[17] The improved GRA approach is developed using intuitive fuzzy numbers (IFNs), with detailed computational procedures outlined in Section 3. To demonstrate the effectiveness of the method, an empirical case study is conducted to assess the development potential of a cultural and creative garden. Furthermore, Section 4 presents a comparative analysis that underlines the strengths and excellent performance of the proposed method.[18 ]The results that demonstrate the strengths of the GRA method are obtained from original data. As a gray-structured technique for addressing multi-attribute decision-making (MADM) problems, GRA provides clear and straightforward calculations.

In business settings, it has established itself as an important tool for supporting managerial decision-making. Establishing itself as one of the most practical and efficient methods. [19] The GRA method offers additional advantages in design validation, as shown by using ANOVA Minitab software to assess the importance of each parameter in the design process. By optimizing the design parameters, a functional shear-mode MR damper was developed; the corresponding force-displacement curve was recorded using experimental equipment, and the measured force values ​​at each level of magnetic field strength showed strong agreement with theoretical calculations.[20]In their study, Cool and Boyer used the Taguchi-GRA method to optimize key input parameters such as peak current, tool polarization, pulse-on time, gap voltage, and tool spindle speed in EDM operations. Their objective was to improve the material removal rate and reduce surface roughness during machining of titanium alloys. Similarly, Thracians et al. used the GRA technique to simultaneously improve the material removal rate and surface finish during EDM machining of Al7075 alloy.[21] The primary objective of this paper is to use the Gray Correlation Analysis (GRA) technique to investigate the effect of three controllable EDM parameters on the surface roughness of machined components, with a particular focus on D3 grade tool steel. Furthermore, this approach helps to identify the optimal set of EDM process parameters to achieve the most favourable effects on the response variables..[22] Gray Relational Analysis (GRA) is a method used to optimize multiple responses and handle complex relationships between various performance characteristics. It begins by combining multiple objectives into a single, unified goal. [23]Traditional GRA methods face difficulties in solving fuzzy MADM problems when the attribute weight information is incomplete. A major area of ​​research based on the fundamental concepts of traditional GRA involves developing techniques for extracting attribute weights using both available fuzzy data and partially known weight information - an endeavour that presents a considerable and complex challenge.[24]The idea behind this method arises from the recognition that in practical situations, it is more useful to reorganize a matrix containing multiple regional data into a higher-dimensional matrix, rather than combining rows or columns and applying constraints as is done in traditional GRA.

This approach applies constraints on all dimensions of the matrix for better results.[25]A GRA approach has been developed to solve MADM problems within the framework of Pythagorean fuzzy sets (PFS). Khan and Abdullah proposed a GRA method specifically designed to handle decision-making problems involving interval-valued Pythagorean fuzzy information. However, both the intuitionist fuzzy GRA method and the Pythagorean fuzzy GRA method face limitations in processing linguistic Pythagorean fuzzy data. [26]Since its inception in 1982, gray system theory has been used to study systems involving uncertainty. and incomplete or ambiguous information. Gray relational analysis (GRA), a central component of this theory, has found widespread application in various research areas for evaluating statistical data. The GRA method works on the basis of the following equations.[27]As indicated in the table, the comparison of the two optimization methods shows that model number '7' ranks highest according to the GRA method. This finding is further verified by the AHP ranking, which also recognizes it as the optimal solution for the previous optimization task. [28] During the experimental test, the pulse-on time is varied from 90 to 250 µs, while the pulse-off time and peak current are adjusted within the ranges of 45 to 150 µs and 14 to 22 A, respectively. Then, the Gray Relational Analysis (GRA) method is used to optimize the EDM process. process for different purposes. [29] Deng first introduced the Gray Relational Analysis (GRA) method to address the challenges of practical multi-attribute group decision making (MAGDM). In contrast to traditional MAGDM approaches, GRA evaluates the degree of similarity between both the Positive Ideal Solution (PIS) and the Negative Ideal Solution (NIS) for each alternative. Tan et al. improved this method by integrating it with the Analytic Hierarchy Process (AHP) to evaluate various design alternatives. Malay et al. introduced an improved hybrid GRA model for use in green supply chain management. Astana et al. made significant contributions to the advancement of this field.[30]

Table1: Healthcare Service

Table 1: presents the results of data analysis using the Gray Relational Analysis (GRA) method focusing on the five health service dimensions. Staff rated discharge (94.22) and hygiene (84.93) as the highest, while nurses emphasized service responsiveness (76.54). Scale validation scores were higher than scale construct scores in all categories, reflecting strong measurement consistency.

Figure1 : Healthcare Service

Figure 1: illustrates the health service quality analysis using the GRA method. Service responsiveness and hygiene received high ratings from nurses (76.54, 63.67) and staff (65.98, 84.93), indicating their importance. Discharge received the highest score among staff (94.22). The criterion validation consistently outperformed the criterion construction, highlighting improved rating accuracy across service dimensions.

Table2: performance value

Table 2: It shows the performance results obtained from the GRA method in various dimensions of health service. The medical service achieved the highest performance (1.00000), especially in the first column. The admissions in the nurses category received the highest score (1.00000), while the service response criterion ranked first in the validation (1.00000), highlighting the different priorities in the evaluation perspectives.

Tabl e3: weight

Table 3: shows an equal distribution of weights (0.25) across all health service dimensions using the GRA method. Each factor – clinical service, service response, discharge, admission and hygiene – was assigned the same importance across the four assessment categories. This equal weighting represents a balanced approach to analysing overall health service performance and priorities.

Table: 4 weighted normalized decision matrixes

Table 4:Provides a weighted and normalized result matrix using the GRA method. Performance scores across the health care dimensions. Medical service achieved the highest normalized value (1.00000), while admissions nurses achieved the highest performance (1.00000). Overall, the values ​​suggest consistent service quality, with health and service responsiveness also showing strong ratings.

Figure 2: It illustrates how the decision matrix was weighted and normalized using the GRA method, focusing on clinical factors. Service was identified as the best performing dimension (1.00000). Nurses rated admissions the highest (1.00000), while service response rate led the way in verification (1.00000). Overall, all dimensions showed relatively high scores, indicating a well-balanced healthcare performance across different assessment perspectives.

Table 5: preference score and rank

Table 5 presents the priority scores and rankings using the GRA method. Medical service ranks first (2.27104), indicating the highest overall preference. Service responsiveness ranks second (0.70022), while healthcare ranks third. Discharge and admission occupy lower ranks, indicating relatively low preference in healthcare service evaluation.

Figure3: preference score and rank

Figure 3 shows the priority scores and rankings based on the GRA method. Medical service is ranked highest (2.27104), indicating top priority in the health assessment. Service responsiveness and hygiene are ranked second and third, respectively. Discharge and admission score low, reflecting their relatively low importance in the overall service quality assessment.

1. INTRODUCTION
2. MATERIAL AND METHOD
3. RESULT AND DISCUSSION
4. CONCLUTION

This study used the Gray Relational Analysis (GRA) method to assess various aspects of health service quality. The results provide valuable insights into priorities and performance across various service dimensions and stakeholder perspectives. Medical service ranked highest with a priority score of 2.27104, underscoring its importance in health assessments. Service responsiveness ranked second (0.70022), hygiene (0.65275) ranked third, and discharge processes and admission procedures ranked fourth and fifth, respectively. These rankings reflect the relative value placed on various aspects of health delivery by stakeholders. The analysis revealed significant patterns across stakeholder groups. Staff prioritized discharge processes (94.22) and hygiene (84.93), while nurses focused more on service responsiveness (76.54). In addition, the measurement validation scores consistently outperformed the measurement construct scores across all categories, indicating strong consistency in the assessment framework. The weighted normalized decision matrix showed that clinical service achieved the highest score (1.00000), with nurses rating admissions practices as their top priority. Equal weight distribution (0.25) across all dimensions ensured an unbiased approach, avoiding bias towards any single factor. This GRA-based assessment provides healthcare administrators with important insights for resource allocation and service improvement. The clear priority of clinical service quality indicates that clinical functions are essential for healthcare excellence, while the emphasis on responsiveness highlights the increasing importance of patient-cantered care. Future research could expand on this by examining how these priorities evolve across different healthcare settings or specialties, particularly by incorporating patient perspectives. The performance of the GRA method in this study demonstrates its value for multi-criteria decision-making in complex healthcare environments, where balancing multiple quality dimensions is crucial for overall service improvement.

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