Predict Consumer Behavior Research Articles

How artificial intelligence is transforming social media marketing: Analysing its potential and addressing concerns

Artificial intelligence (AI) has revolutionised social media marketing, offering both significant advantages and potential drawbacks. This paper delves into the dual facets of AI-driven social media marketing techniques, investigating both their advantageous and detrimental impacts. It shows that by leveraging AI, businesses can achieve unprecedented levels of personalisation, engagement and efficiency in their marketing strategies, leading to enhanced customer experiences and increased brand loyalty. AI algorithms analyse vast amounts of data to identify trends and predict consumer behaviour, enabling precise targeting and optimised advertising spend. The paper utilises a qualitative research method to explore the AI-driven social media marketing techniques used by digital marketing and social media marketing professionals. The findings suggest that reliance on AI may perpetuate biases embedded in the training data, leading to ethical concerns and a loss of trust among consumers. This paper highlights the need for robust regulatory frameworks and ethical guidelines to mitigate risks while harnessing the full potential of AI in social media marketing. Ultimately, understanding both the bright and dark sides of AI-driven marketing is crucial for developing strategies that are not only effective but also ethical and sustainable.

Psychological Factors Impacting the Consumer Behaviour of Customers

Objectives: The factors influencing consumer purchasing behavior were the main focus of the study. Finding out how these factors affect individual consumers' decisions to buy goods and services was the primary goal. The aim was to gain insights into the various factors that drive consumer behavior and understand their impact on purchase decisions. Methods: The study of customer behaviour is centred around the purchasing behavior of consumers, who play the roles of user, payer, and buyer. Research has demonstrated that predicting consumer behaviour is challenging. The customers themselves play a vital role as they are the ultimate purchasers of the goods and services offered by organizations. Therefore, businesses are continuously seeking ways to persuade them to make purchases, in order to generate revenue. Findings: The results of this study show that social factors are the most important factors influencing consumers' willingness to make a purchase and have the biggest influence on consumer behavior variables. Brands can develop a strategy, marketing message (Unique Value Proposition), and advertising campaigns that are more successful and better suit the needs and thought processes of their target consumers by recognizing and comprehending the factors that affect their customers. Conclusion: This is a significant advantage for brands seeking to better serve their customers and drive sales growth. Ultimately, understanding customer behavior is a valuable asset that can help brands to increase sales.

Implementation of Distributed Machine Learning in Cloud Calculation: Evidence from Consumer Behavior Prediction

Abstract. In the field of machine learning, distributed machine learning (DML) has gained massive popularity in research and appeared in a wide range of applications including transportation, medicine, and business. Distributed machine learning showed huge potential in processing large amounts of data while keeping the data private. In this paper, a distributed implementation of a deep neural network (DNN) with a large dataset from a telecom company, aiming to predict consumer behavior based on usage data is carried out. Various datasets with distinct heterogeneity settings were applied to the network, simulating a real-world cloud application scenario. Comparisons between both the centralized model and the distributed models were made to analyze the impact on model performance with device heterogeneity and data heterogeneity. These result indicate that under certain circumstances, DML models showed better accuracy and lower loss compared to the centralized model. The paper provided some insights and made some assumptions on how the data and the parameters affect the model performance.

AI security in different industries: A comprehensive review of vulnerabilities and mitigation strategies

Artificial intelligence has quickly and gradually spread across all sectors and fields, achieving exceptional performance in operations and decision-making. Yet, as industries process their tasks with the help of AI systems, new threats appear that require proper solutions. In the following section of this paper, the use of security solutions involving AI in various industries, which are finance, health, manufacturing, and government, as well as a discussion of risks involving artificial intelligence in particular fields and the corresponding measures to be taken against them will be discussed. AI is often applied to detect fraud and credit scoring in the financial sector but has potential risks, such as adversarial attacks and data manipulation. Similarly, the healthcare industry uses AI for predictive healthcare diagnostics and patient information protection; threats are data theft and adversarial examples attacking diagnostic models. In manufacturing, AI individuals contain predictive servicing and excellent control, though they are constantly exposed to industrial espionage and the sabotage of AI models. In retail, however, AI is used to improve marketing and predict consumer behavior. While these benefits exist, questions about using Algorithms that could be biased and privacy infringements continue to raise their ugly head. Defensive and governmental organizations that apply AI for surveillance or automatic operating systems are most vulnerable to adversarial intervention with key control systems and innate security systems. To counter such threats, this paper provides details of industry-specific measures, including adversarial training data sanitization, AI model audit, and privacy-preserving approaches. We illustrate examples linked to every one of these methods to show how they can be deployed in a variety of industries and to guard AI systems. To this end, the following review of the relationship between AI and cybersecurity demonstrates that security measures that rely on artificial intelligence must be regularly checked and updated. This reveals the call for proper, protective, and industry-specific measures to manage the risks and protect AI applications to ensure that the world’s modern, interconnected world is safe for AI.

Predicting Consumer Behavior in E-Commerce Using Recommendation Systems

As e-commerce platforms continue to expand, understanding consumer behavior has become crucial for enhancing customer satisfaction and driving business success. Recommendation systems play a pivotal role in predicting consumer preferences and delivering personalized product suggestions. This paper presents an extensive literature review on recommendation techniques, including collaborative filtering, content-based approaches, and hybrid models. Notable advancements, such as the use of deep learning, trust-based filtering, and context-aware models, are highlighted. Building on these foundations, we propose a novel model that integrates advanced machine learning algorithms with consumer behavior analysis to predict preferences more accurately. The expected results suggest that this model will improve the precision of recommendations, effectively addressing challenges like data sparsity and evolving user preferences and enhancing overall customer engagement in e- commerce environments.

Consumer Behavior Analysis through Advanced-Data Techniques: A Review for Utilities in Retail Marketing

This review offers a comprehensive analysis of the use of cutting-edge data strategies, including Big Data analytics, Machine Learning (ML), and Artificial Intelligence (AI), etc. in understanding and predicting consumer behavior. The convergence of these technologies has enabled marketers to uncover more detailed knowledge about consumer preferences and enhance customer engagement & satisfaction. By reviewing various methodologies, this study showcases the ways in which these tools are leveraged for segment consumers, predict consumer buying habits, and optimize marketing strategies. Additionally, the paper explores the Social media data utilization in real-time consumer analytics, demonstrating their effectiveness in personalizing marketing efforts and improving customer experience. The challenges of data privacy, ethical considerations, and the necessity for more robust analytical tools are also discussed. The review concludes by identifying potential research paths that could drive forward the field of consumer behavior analysis in retail marketing. Key Words: Artificial Intelligence, Big Data, Consumer Behavior, Machine Learning, Retail Marketing.

Machine Learning in Predicting Consumer Behavior in Online Retail

Machine Learning in Predicting Consumer Behavior in Online Retail

Predicting Consumer Behaviour from E-Commerce Platform Product Reviews: A Machine Learning Approach to Sentiment Analysis

Given the recent advancements in web technology, users make use of the online services and offer their feedback thereby creating a vast amount of data available on the internet. To enable improved decision-making, it is essential to study, analyze, and organize this vast amount of user opinion, perspectives, feedback, and suggestions available through online resources. Nevertheless, the majority of e-commerce websites encounter challenges in comprehensively analyzing each individual review owing to the sheer volume of user feedback they receive. The aim of this study is to conduct sentiment analysis of product review from e-commerce platforms using machine learning to predict consumer behaviour so as to make informed decision. The methodology used was a sentiment analysis using models developed in Python with machine learning algorithms (Support Vector Machine and Naive Bayes) in order to compare the performance accuracy. The model was trained and tested on a dataset, and the performance of the two supervised machine learning algorithms for sentiment classification was compared. Accordingly, the results of the experiment showed that the Support Vector Machine technique outperformed Naive Bayes, obtaining accuracy levels of 97%. The study also demonstrates the significance of carrying out further research by exploring different machine-learning techniques and evaluating their performance on local market datasets. Additionally, it was observed that utilizing more data might improve the model's classification and prediction accuracy.

Predicting Consumer Behavior in E-Commerce Using Decision Tree: A Case Study in Malaysia

Understanding and predicting consumer behavior will help e-commerce businesses improve customer satisfaction and devise better marketing strategies. This study is intended to explore the use of decision tree algorithms in predictions of consumer purchase behavior in the e-commerce platform in Malaysia. Comparing the performances of J48, Random Tree, and REPTree decision tree models using an online shopper dataset collected by surveying 560 Malaysians, on various aspects like accuracy, precision, recall, and F1 score. Results indicate that the highest accuracy has been achieved with the Random Tree algorithm, outperforming J48 and REPTree. The results will, therefore, form the basis upon which e-commerce can restrategize its marketing programs for better customer engagement. This is an important study in that it shows the efficacy of applying a decision tree algorithm to understand customer behavior in the context of Malaysia and adds to the growing body of knowledge in predictive analytics in e-commerce.

Analysis of sales techniques from the perspective of psychology in MIPYMES (MSMEs)

The study analyzes how MSMEs can improve their business strategies using psychological principles such as scarcity, urgency, personalization, and the anchoring effect. These principles help understand and predict consumer behaviors, allowing sales strategies to be adapted more effectively. Additionally, the document highlights the importance of segmenting the market and building long-term relationships with customers to increase loyalty and satisfaction. In terms of customer satisfaction, the importance of exceeding customer expectations and ensuring fair and equitable treatment is highlighted. Strategies such as creating memorable experiences, implementing loyalty programs, and using customer feedback are key to improving satisfaction. Professional development and training are essential for the growth of MSMEs. Continuous training in soft skills such as emotional intelligence and effective communication improves employee performance and job satisfaction. Social learning theory and the 70-20- 10 model are proposed as effective approaches to competency development. Finally, the integration of psychology in business management contributes to organizational knowledge, promoting innovation and transformational leadership. MSMEs that apply these psychological principles can improve their performance and achieve sustainable success. The study concludes with recommendations to develop internal training programs, build trust with customers, and conduct regular market research to adapt sales strategies.

Understanding Consumer Decision-Making in Real Estate: An Integrative Analysis Using the Theory of Planned Behavior

This paper explores consumer decision-making in the real estate market through the lens of the Theory of Planned Behavior (TPB), providing an integrative analysis that enhances our understanding of the psychological and social factors influencing property purchases. By examining the roles of attitudes, subjective norms, and perceived behavioral control, the study demonstrates how these components interact to shape consumer intentions and behaviors in real estate transactions. The findings suggest that TPB offers a robust framework for predicting consumer behavior and designing effective marketing strategies that align with consumer needs. The study also highlights the broader implications for real estate professionals and policymakers, advocating for the adoption of TPB as a guiding model to improve customer engagement and decision-making processes in the evolving real estate industry.

Behavioral Economics: Understanding the Psychological Factors Driving Consumer Decisions

Behavioral economics is an interdisciplinary field that combines insights from psychology and economics to explain and predict consumer behavior in economic decision-making. This article provides a concise overview of the definition and importance of behavioral economics, exploring its core concepts such as bounded rationality, heuristics and biases, and prospect theory. It also examines the impact of emotional and social factors on consumer decisions and discusses practical applications of behavioral economics in marketing and public policy. Finally, the article summarizes key points and looks ahead to future directions in the field.

Exploring the Role of AI for Enhancement of Social Media Marketing

In recent years, the application of artificial intelligence (AI) in social media marketing has gained significant traction. AI has revolutionized the way businesses interact with their audiences on platforms like Facebook, Instagram, Twitter, and LinkedIn. With its ability to analyse vast amounts of data, predict consumer behaviour, and automate tasks, AI has become an essential tool for marketers looking to maximize their social media efforts. AI has transformed content curation, making it easier for marketers to find and share relevant content with their target audience. With AI algorithms, social media platforms can analyse user preferences and behaviour to deliver personalized content recommendations. AI can analyse the interests and browsing patterns of social media users to provide them with content that aligns with their preferences. This personalized content curation helps marketers increase engagement and build stronger connections with their audience. AIpowered chatbots have become an integral part of social media marketing strategies. These intelligent virtual assistants can provide immediate and personalized responses to customer inquiries, improving customer satisfaction and reducing response time (Conick, 2017). Chatbots can handle common queries, provide product recommendations, and even process transactions, saving businesses time and resources. By harnessing the power of AI, social media marketers can leverage predictive analytics to make data-driven decisions.

Advertising in the Era of Artificial Intelligence

This article explores the impact of artificial intelligence (AI) on the advertising industry, detailing its evolution from traditional to AI-driven methods. Advertising has historically been a crucial medium for promoting messages, with its impact magnified during public health crises. The integration of AI has revolutionized advertising by automating functions such as ad optimization, media buying, and personalized ad creation. AI's ability to analyze large data sets and predict consumer behavior has enhanced the efficiency and effectiveness of advertising campaigns, leading to higher returns on investment (ROI). Using a case study methodology, the article examines the social and demographic impacts of AI-driven advertising, particularly on youth and the elderly, and addresses the ethical implications and challenges associated with AI's pervasive use in advertising. Additionally, the case study of Being Patient, an online platform for Alzheimer's disease, illustrates the significant impact of well-crafted AI advertising on business success and community engagement. The article concludes with a call for further research to evaluate AI advertising's reception across different age groups and to develop strategies for its broader acceptance and integration into mainstream practices.

Application of Artificial Intelligence in Neuromarketing to Predict Consumer Behaviour Towards Brand Stimuli

This research aims to analyse the current level of accuracy in predicting consumer behaviour in response to brand stimuli using artificial intelligence, comparing the results with an analysis conducted using neuromarketing biometrics. The study aims to determine the accuracy achieved in predicting consumer levels of attention and visual attraction towards visual stimuli, compared to the results recorded in a neuromarketing investigation with real users, through eye tracking. The implications of these comparative analyses are discussed in the final part of the article, concluding that the emotional intelligence tool provides very accurate predictions of consumer behaviour in response to visual stimuli. The results of this study revealed that the prediction of the percentage of users who would view each area of interest is very good, and regarding visual attraction (time until the first viewing of each area of interest), it is quite similar to the order observed by the consumer group; consequently, the level of approximation to reality of AI is very good.

Predictive Analytics Techniques in Consumer Behaviour: A Literature Review

Under fierce market competition, there is a need for enterprises to analyze and predict the behavior of consumers in order to improve the market competitiveness. We find that in the previous literature, there is little specific systematic summary and research on the methods to predict customer behavior. Thus, in this paper, we discuss relevant concepts of customer behavior and predict consumer behavior by studying the operation mechanism of Big Data Analysis (BDA), Decision Tree (DT) and Consumer Relationship Management (CRM). We also study the application of each technology to enterprises in different fields and its impact on consumer behavior. BDA can efficiently organize a large amount of data into several variables that is related to the prediction and provide a reference prediction for the business. DT can effectively improve the accuracy of market segmentation, classify customers into sub-customer groups with distinct consumption characteristics, and help enterprises make targeted decisions. CRM can collect customer information through data sampling to build a comprehensive view of customers, and create customer analysis and prediction models according to the different needs of the enterprise. Studying the past consumer behavior can help enterprises to better develop products and make personalized decision plans for specific customer groups.

The Impact of Inflation on Jewelry Consumers in Nepalgunj: An Exploratory Study

This study examines the effects of inflation on jewelry consumers in Nepalgunj, Nepal, using a descriptive research design. The research aims to understand how inflation affects purchasing power, identify coping strategies, assess the impact on demand, and explore mitigation measures. The findings reveal that 70% of respondents believe middle-class individuals are most affected by inflation, leading to reduced jewelry affordability. Additionally, 30% of respondents agree that people buy less jewelry nowadays, and 76% agree that customers opt for substitute products as gifts instead of jewelry. The study suggests that inflation significantly impacts the jewelry industry, reducing consumer purchasing power and altering spending behaviors. To navigate these challenges, businesses in the jewelry sector must adopt strategic measures, such as price adjustments, value emphasis, and efficient sourcing practices. The findings provide valuable insights for policymakers, economists, and businesses in the jewelry industry. The study's implications include the need for policymakers toconsider the impact of inflation on the jewelry industry and its consumers, for economists to develop models that better predict consumer behavior during inflation, and for businesses to adopt strategic measures to mitigate the effects of inflation. Overall, the study highlights the importance of understanding the effects of inflation on jewelry consumers and the need for stakeholders to take proactive measures to address these challenges.

The Use of Artificial Intelligence and Machine Learning in Forecasting the Financial Growth of Automobile Industries

The automotive industry's financial forecasting is vital for growth and competitiveness in today's rapidly evolving market landscape. Accurate and reliable forecasting is essential for strategic planning, resource allocation, and decision-making processes. However, traditional forecasting methods often struggle to adapt to the dynamic nature of the automotive sector, characterized by fluctuating consumer demands, evolving market trends, and complex supply chain dynamics. The integration of Artificial Intelligence (AI) and Machine Learning (ML) technologies has revolutionized financial forecasting in the automotive industry, transforming forecasting accuracy, efficiency, and decision-making processes. By leveraging AI algorithms and ML models, automotive companies can gain deeper insights into market dynamics, predict consumer behavior more accurately, optimize production schedules, and streamline distribution processes. This paper explores the transformative impact of AI and ML technologies on financial forecasting in the automotive industry, delving into key applications, benefits, challenges, and future directions of integrating these advanced technologies into forecasting processes. The adoption of AI and ML empowers automotive companies to make data-driven decisions with greater precision and agility, driving innovation, growth, and competitiveness in this dynamic sector. Key Words: Artificial Intelligence, Machine Learning, Financial Forecasting, Automotive Industry, Predictive Maintenance, Hybrid Decision Support Systems, Production Planning and Control Systems, Intelligent Transport Logistics.

AN ANALYTICAL STUDY THE ROLE OF STATISTICAL TECHNIQUES IN THE CONSUMER BUYING PROCESS: AN EMPIRICAL STUDY

The objective of this article is to analyze different types of decision making and use statistical models to predict consumer behavior in different situations and areas of application. We have examined and classified several case studies on consumer decision making and we have classified them thematically, as well as the underlying quantitative techniques to assess their relevance. We wanted to formulate a model based on real-time data and then the comparison data used in the model, analyze and interpret scientifically.

Decoding Consumer Behavior: Predictive Analytics of Social Media Advertising Effectiveness

Decoding Consumer Behavior: Predictive Analytics of Social Media Advertising Effectiveness