Changes In Customer Behavior Research Articles

Hypothetical e-liquid flavor ban and opinions among vape shop retailers in the Greater Los Angeles Area.

Evaluating anticipated responses to flavor bans in the context of vape shops is needed to inform legislation and enforcement. This cross-sectional study examined vape shop retailers' opinions about the potential impacts of an e-liquid flavor ban on shop sales and customer behavior-change intentions. From December 2019 to October 2020 we conducted structured interviews over the phone with 46 brick-and-mortar vape shop retailers in the Greater Los Angeles Area. Most participants were managers (43.5%), followed by owners (26.1%) and clerks (26.1%). More than half (52.2%) reported that sales would drop a lot if flavored e-liquids were banned in all vape shops. Controlling for store position, multivariable linear regression showed that opposition to a hypothetical ban on non-tobacco flavored e-liquids was associated with participants' opinions that customers would likely not purchase tobacco flavored e-liquids (b= -0.44, p<0.01), and would likely use combustible tobacco products (b=0.47, p<0.05). In this cross-sectional study, vape shop retailers in the Greater Los Angeles Area reported that if a ban on non-tobacco e-liquid flavors occurred, they would oppose strongly, and that a ban would have a negative impact on their shop (e.g. loss in sales) and customer behavior (e.g. would replace vaping with smoking combustible tobacco products). Implications for research and practice are discussed.

Comparative Analysis of General, Insurance Markets: Global Perspectives and Regional Trends

The global general insurance market has experienced robust growth in the past decade, driven by factors such as increasing awareness of insurance, economic growth, and regulatory reforms.The history of general insurance dates back to the Industrial Revolution in the west and the consequent growth of sea-faring trade and commerce in the 17th century.The general insurance market can be segmented into various categories based on product type, distribution channel, and geography.The global general insurance market is highly competitive, with several key players dominating the industry. The general insurance markets vary significantly across different regions, driven by factors such as economic development, regulatory frameworks, and cultural preferences.The global general insurance market is expected to witness steady growth in the coming years, driven by several factors like economic growth, regulatory reforms, technological advancement,changing customer behaviour,increasing risk. This article aims to provide a comprehensive analysis of the general insurance markets from a global perspective, highlighting regional trends, key players, market size, and growth drivers.

The Future of Insurance Intermediation in the Age of the Digital Platform Economy

Today most insurance is sold by over a million brokers and independent agents acting as intermediaries between the insurance companies and their customers. Digitalization and changing customer behavior have fostered the development of insurtech businesses, and, more recently, multi-sided platforms are emerging as new market forms for insurance intermediation. This paper aims to provide a better understanding of how the emergence of the platform economy, with a market dominated by multi-sided platforms, will potentially impact insurance intermediation in the future. Using inductive content analysis on the results of a systematic literature review of the body of research on insurance intermediation, we identify the key functional roles fulfilled by insurance intermediaries. Applying these roles to a literature review on multi-sided platforms allows us to compare how different market forms and players embody the functional roles of intermediaries. Our findings suggest that multi-sided platforms are better able to perform certain roles in terms of agility, scale and scope, and we discuss the future role of platforms in insurance intermediation.

Features of strategic management in the HoReCa sector under martial law

Introduction. Strategic management in the HoReCa industry is essential for success, but it becomes even more challenging during times of war. The state of war affects the socio-economic situation and poses new challenges for hotels and restaurants. Recent research and publications highlight the interest in understanding the impact of war on HoReCa's strategic management. Addressing this issue requires developing adaptive strategies, ensuring safety for staff and guests, and maintaining business stability and productivity. The purpose of the paper is to define the concept of "HoReCa sphere", to identify the features of strategic management in the HoReCa sphere and to study the impact of martial law on strategic management in the hotel and restaurant business. Results. The term "HoReCa" has gained wide acceptance in the commercial hospitality industry. It encompasses a variety of establishments engaged in the production and sale of food for immediate consumption away from home, primarily hotels, restaurants and catering services. Strategic management in the HoReCa sector plays a crucial role in achieving competitive advantage and ensuring the sustainable development of enterprises in this industry. The strategic management cycle consists of four stages, including analysis of the external environment, formulation of the mission and strategic goals, selection of strategies and their implementation with control. The success of HoReCa businesses depends on flexibility, responsiveness to market changes, a customer-oriented approach and the development of competitive advantages, such as quality service, professionalism and positive community relations. Strategic management in the HoReCa sector is essential for navigating the dynamic and competitive environment of the hospitality industry. In times of military conflict, strategic analysis and planning must take into account unique circumstances, including risk assessment, conservation of resources, adaptation to changing customer behaviour and expectations, and adjustments to supply chains due to the constraints and challenges of the conflict situation. Conclusion. Overall, strategic management plays a vital role in the success and resilience of HoReCa businesses, especially in times of war, enabling them to respond effectively to market demands and maintain a positive reputation in the community.

Use of Artificial Intelligence for Marketing

The aim of the article is to investigate the opportunities of artificial intelligence in planning business processes in marketing and assess the impact of artificial intelligence in forecasting real marketing situations. Results analysis. The use of artificial intelligence in the marketing activities of trade enterprises is an effective tool and demand of the time. In the conditions of uncertainty and social challenges for business, it is the digitalization and technologization of product promotion processes that give an opportunity to preserve and develop it. The article examines the features of using artificial intelligence technology in marketing activities. The development of artificial intelligence technology is changing traditional marketing methods, increasing the effectiveness of modern marketing and reducing marketing costs. The study of the use of artificial intelligence technology to build a new marketing model of influence on the consumer is an important vector of marketing development, which will allow marketers to attract customers more effectively and determine their needs more accurately. Artificial intelligence makes it easier to work with databases to find potential customers, satisfy their needs and establish a good relationship between marketers and consumers. For example, the system can predict how certain changes in marketing campaigns might affect sales and profits. Other useful functions of artificial intelligence systems for marketing include the analysis of the effectiveness of marketing campaigns, the identification of new potential markets and the analysis of the competitive situation. In the course of the study, the stages of introducing artificial intelligence into the activities of trade enterprises, its advantages and efficiency in making management decisions are determined. The advantages of using artificial intelligence in SEO and SMM are determined. The significance of the synthesis of artificial intelligence and cognitive marketing is substantiated. Conclusions and directions for further research. Research results indicate that the introduction of artificial intelligence is an effective tool for the enterprise marketing activities, which expands the possibilities of demand forecasting, inventory management, and increases business profitability. Predicting changes in customer behavior is an indispensable function of artificial intelligence. Systems can analyze data about customers and their buying habits and predict how they will respond to certain marketing efforts. The application of artificial intelligence and cognitive marketing can help to understand and satisfy the needs of customers, allowing retailers to increase the effectiveness of marketing campaigns and make them more successful. Based on the conclusions about the effectiveness of the use of artificial intelligence in marketing activities of trade enterprises, the prospects for further scientific research will be the study of the relationship between cognitive marketing and artificial intelligence, which can help understand and satisfy the needs of buyers, and make marketing campaigns more successful.

Assessment and classification of grid stability with cost-sensitive stacked ensemble classifier

Smart Grid is an intelligent power grid with a bidirectional flow of electricity and information, that applies intelligent techniques to operate the grid autonomously near the stability limit. An intelligent technique is developed to identify and predict the abnormalities due to changes in customer behaviour and the unexpected disruption in the grid. A cost-sensitive stacked ensemble classifier (CS-SEC) is proposed for predicting the operations in smart grid that combines four cost-sensitive base classifiers, namely Extreme gradient boosting, Naive Bayes, Nu-support vector machine and Random forest at level-1 and the support vector machine as the meta classifier in level-2. The meta classifier uses the probability of prediction of the first-level classifiers with stratified 5-fold cross-validation to predict the decentralized smart grid stability. The proposed stacked ensemble classifier achieved an accuracy of 98.6% with specificity, recall and precision of 98.34%, 99.0% and 99.06%, respectively. Extensive experimental evaluation and results show that the proposed CS-SEC provides an accurate prediction of grid stability compared with other state-of-the-art models. The results reveal the robustness and competency of the proposed CS-SECs with optimized parameters.

Key Event Analysis of Customer Behaviour Using Clickstream Data in Airline Market

COVID-19 travel restrictions have significantly affected airline markets. A behavioural analysis is presented of 1 million customers of a major airline company over 3 years, using clickstream data, combined with COVID-19 Government Response Tracker (OxCGRT) data. The main results are that pre-pandemic, the variations in traffic volumes were seasonal and there was a consistent ratio of search (76%), buy (5%) and e-service (19%) activities across the year. The start of the pandemic caused a sharp drop in website traffic, which minimised at 1/3 of normal levels and then gradually increased so that post-covid volumes are now 20% higher, which reflects pent-up demand and consumer confidence. At the start of the pandemic, e-service increased sharply and then dropped to below pre-pandemic levels. The proportion of buyers decreased significantly at the start of the pandemic and then returned to just below pre-pandemic levels. The proportion of searchers remained high throughout the pandemic. Comparing post-pandemic with pre-pandemic, the proportion of searchers remains higher, buyers has dropped, and e-service remains the same. These results show the importance of distinguishing between different types of online activity, defined by three broad phases in the customer lifecycle: search, buy and e-service, which can only be achieved by using raw, clickstream data. Typically, web server analytics software employ aggregate data and it is much more difficult to uncover nuanced changes that are apparent at the granular level of individual clickstream data. The contribution of the research is to demonstrate a novel analytical methodology that exploits detailed clickstream data to characterize the dynamic changes in customer behaviour, which gives important insights into how online markets change in response to exogenous shocks by enabling key event analysis. Using the approaches outlined in this paper, airlines and e-commerce companies can anticipate, measure behavioural changes and be pro-active in their strategic responses to significant external events in the competitive environment that are likely to affect customer behaviour.

Predicting customer churn based on changes in their behavior patterns

Customer retention is one of the most important tasks of a business, and it is extremely important to allocate retention resources according to the potential profitability of the customer. Most often the problem of predicting customer churn is solved based on the RFM (Recency, Frequency, Monetary) model. This paper proposes a way to extend the RFM model with estimates of the probability of changes in customer behavior. Based on an analysis of data relating to 33 918 clients of a large Russian retailer for 2019–2020, it is shown that there are recurring patterns of change in their behavior over a single year. Information about these patterns is used to calculate the necessary probability estimates. Incorporating these data into a predictive model based on logistic regression increases prediction accuracy by more than 10% on the metrics AUC and geometric mean. It is also shown that this approach has limitations related to the disruption of behavioral patterns by external shocks, such as the lockdown due to the COVID-19 pandemic in April 2020. The paper also proposes a way to identify these shocks, making it possible to forecast degradation in the predictive ability of the model.

Marketing Strategies During the Time of Covid-19--What Role does Customer Behaviour Changes Play?

This paper discusses both the psychological and physical effects of COVID-19 on customer behavior changes and elaborates on companies approaches to address these changes through marketing strategies and marketing policies. By comparing with the Global Financial Crisis (GFC), the paper analyzes the similar change patterns in customer behavior that COVID-19 shares with GFC and what marketers can leverage from historical experiences. However, the public-health concerns have been playing significant part during this crisis when it comes to customer behavior changes, such as restricted mobilities have been incentivizing customers to shop in different categories and to rely on online services more than ever. In this paper, cases covering different industries have been studied and presented to illustrate how marketers can utilize their technical skills to make the unique charlatanistic of this crisis an opportunity to help the organization to grow.

NFA: A neural factorization autoencoder based online telephony fraud detection

The proliferation of internet communication channels has increased telecom fraud, causing billions of euros in losses for customers and the industry each year. Fraudsters constantly find new ways to engage in illegal activity on the network. To reduce these losses, a new fraud detection approach is required. Telecom fraud detection involves identifying a small number of fraudulent calls from a vast amount of call traffic. Developing an effective strategy to combat fraud has become challenging. Although much effort has been made to detect fraud, most existing methods are designed for batch processing, not real-time detection. To solve this problem, we propose an online fraud detection model using a Neural Factorization Autoencoder (NFA), which analyzes customer calling patterns to detect fraudulent calls. The model employs Neural Factorization Machines (NFM) and an Autoencoder (AE) to model calling patterns and a memory module to adapt to changing customer behaviour. We evaluate our approach on a large dataset of real-world call detail records and compare it with several state-of-the-art methods. Our results show that our approach outperforms the baselines, with an AUC of 91.06%, a TPR of 91.89%, an FPR of 14.76%, and an F1-score of 95.45%. These results demonstrate the effectiveness of our approach in detecting fraud in real-time and suggest that it can be a valuable tool for preventing fraud in telecommunications networks.

Transformation of last-mile delivery as a method of improving customer experience in the Russian Federation

Aim. The presented study aims to analyze transformations that last-mile delivery has undergone during the years of the COVID-19 coronavirus pandemic in the Russian Federation, considering that, on the one hand, the final stage of delivery is considered the most problematic part of the delivery process, and on the other — the last mile is often a factor determining the customer’s choice and satisfaction when interacting with the company.Tasks. The authors identify problems that companies face due to changes in customer behavior and experience; search for possible solutions to these problems; analyze how satisfaction with the quality of last-mile delivery affects the customer’s decision to order goods from a particular supplier.Methods. This study uses the method of descriptive literature review, as well as methods of analysis, synthesis, comparison, and generalization.Results. Today, customer experience is as important an element of brand perception as product offering. Customers constantly seek more personalized, convenient, and secure ways to receive delivery services. Research shows that many buyers consider efficient and fast delivery a priority when making a purchase, not the price of goods. The increasing customer demand for quality of service and the boom in e-commerce have led to the development of new last-mile delivery methods that optimize the quality of customer service and contribute to building a strong relationship between the customer and the brand.Conclusions. Improving the quality of customer service through last-mile logistics can be a powerful tool in the competition, as delivery often turns out to be a key factor influencing the consumer’s purchase decision. The result of this study is the formation of an idea about the possible future development and prospects of last-mile delivery.

Demonstration of Bigdata Analysis Implementation in Predicting Consumer Behavior

The rise of personal computers, cell phones, and other smart gadgets has ushered in a new era of enormous data generation, sharing, and use. In the big data age, there has been a significant shift in consumer purchasing behavior due to the high transparency and quick flow of information. Consumers are becoming more logical in their purchasing decisions, paying more attention to the observable advantages that brands offer, and placing greater importance on the individualized services that businesses offer. With this in mind, this article explains some of the functions and analytical techniques of big data analysis in forecasting customer behavior. To be specific, the basic concepts and models of bigdata are introduced. Subsequently, the corresponding applications based on the state-of-art big data analysis techniques are demonstrated. The research of changes in customer behavior will assist businesses in promptly adjusting their marketing plans and winning the first chance in the harsh market rivalry. These results shed light on guiding further exploration of consumer behavior analysis in the new era.

Social media engagement and impacts on post-COVID-19 travel intention for adventure tourism in New Zealand

This study examined the online search behavior of international students and the impacts of social media content on their intention to purchase adventure tourism products during the COVID-19 pandemic. Semi-structured in-depth interviews were conducted with 12 international students in Auckland, New Zealand. The data collected were analysed using a qualitative thematic analysis approach. The findings demonstrate the importance of adventure tourism organizations having a comprehensive understanding of tourist perceptions and search behaviors on social media for information related to purchase intention. Both Firm-Generated Content and User-Generated Content were found to be effective and convincing with popular sources of both types of content on Instagram, Facebook, YouTube and TripAdvisor, as well as other more specific content and platforms directly targeting Chinese tourists. The study demonstrates how changes in customer behavior including how international tourists search for information on social media affected their intent to purchase. This highlights the importance of social media when marketing adventure tourism destinations including destinations in New Zealand. Management implicationsThe research findings further highlight the importance of social media in the era of COVID-19 with increased use and reliance of content on social media platforms produced by firms and users. Therefore, adventure tourism marketers need to formulate new social media marketing strategies to trigger purchase intentions in accordance with the preferences of the target audiences and their own business objectives.

Dynamic customer journey analysis and its advertising impact

ABSTRACT Advertisers apply customer journey analyses to gain insights into customer behavior at various touchpoints and measure their advertising impact using attribution models. Along the customer journey, customers constantly adjust goals and expectations, generating new data that can influence attribution results. However, extant attribution models do not capture changes in customer behavior over time, which limits their results’ meaningfulness. In response, the authors present a dynamic approach to customer journey analysis based on Markov chains that updates attribution results on a rolling basis by sequentially considering new data. Applying this approach to a nine-year data set of 45,694 customer journeys leads to empirical generalizations and channel-specific insights. A model comparison shows that rolling determination increases the attribution accuracy and extends previous research. Furthermore, data collection periods influence advertising impact and should be considered in attribution modeling. Thus, the study enables advertisers to interactively manage their marketing strategy and improve budget allocation.

The implementation of purchasing omnichannel marketing based through the expansion of the UTAUT 2 model ,

This study studies the influence of omnichannel service benefits in customer product purchasing decisions and investigates the omnichannel adoption behavior of customers in fashion apparel retail in Bali. Data collection is completed twice in stages I and II, covering fifteen to thirty days (15-30). The population in this study is a group of Balinese customers with high adaptation to technology. The maximum sample in this study is 210 respondents. The study findings reveal that their path coefficient values supported some hypotheses while some others were not supported. The research findings indicate that the variables of service integration and perceived effectiveness cannot be proposed to expand the UTAUT 2 model. The technology familiarity variable can be the variable proposed to expand the UTAUT 2 model. This study’s findings confirm the inconsistent relationship between the effect of intention to purchase apparel products on product purchase behavior that could be overcome by studying behavioral intention to use behavior using a longitudinal method. Implications of research through the data collection method involves two stages of research by collecting the same sample group, which is declared to be able to observe changes in customer behavior.

The Mediating Role of Firm Prestige in the Relationship between Perceived Quality and Behaviour Intention in Customer Cafeteria Experiences

Customer experience is viewed from different perspectives as an economic, marketing, management, and competitive strategy. Regardless of the perspective, customer experience is a business strategy and a critical concept that plays a key role in firm success in the 21st century. In recent years, there has been an increasing focus on customer experience as a way for companies to differentiate themselves and build strong relationships with their customers. This approach is based on the idea that companies can build emotional bonds with their customers by providing memorable and positive experiences, leading to greater loyalty, repeat business and positive word-of-mouth. To create these experiences, companies adopt a customer-centric approach that involves understanding their customers’ needs, preferences and behaviours and using this information to design and deliver experiences that exceed their expectations. This approach requires moving away from traditional product-centric or sales-oriented strategies and instead places the customer at the centre of the company’s operations and decision-making processes. From a customer-centric perspective, meeting customer expectations in customer experiences is very important in shaping customer perceptions, attitudes and behaviours. From an experience-based perspective, a study was designed by taking into account the expectancy-affirmation theory, which is a psychological model that explains how customers evaluate their satisfaction with a product or service based on their expectations and perceptions of the experience, and a psychological theory that predicts that meeting customers’ expectations from their experiences may lead to a change in customer behaviour. The study examined the mediating role of firm prestige in the relationship between the perceived quality of customer experiences and customer behaviour intention. For this purpose, data were collected from 230 participants who had cafeteria experience through a questionnaire. The collected data were increased to 5000 by bootstrapping (derivative sampling) through the PLS-SEM algorithm. The study found that store prestige partially mediated the relationship between perceived experience quality and behavioural intention (customer satisfaction/loyalty) but did not mediate the relationship between food quality and behavioural intention (customer satisfaction/loyalty). The study also found that store prestige mediated the relationship between atmosphere quality and service quality, and behavioural intention (customer satisfaction/loyalty). Subsequently, cafeteria management policies should prioritise atmosphere and service quality. These factors directly impact the store’s prestige, leading to customer satisfaction and loyalty.

Biplot analysis: a study of the change of customer behaviour on e-commerce

COVID-19 pandemic affected many aspects in global life, the one of aspect is e-commerce. The COVID-19 pandemic changes the general society into having a more consumptive behavior. This research is conducted to identify whether there is a difference in mean/average sales between the period before the COVID-19 pandemic and during the COVID-19 pandemic. This paper used T-Test Paired Samples to analyze the difference of behavior's customer of some e-commerce in Indonesia. Biplot analysis is also used to give visualization of the behavior's change caused by the COVID-19 pandemic. Six variables are used in this paper and purposive sampling is selected to get a sample size. We use 63 respondents are eligible to fulfill the questionnaire. The T-Test paired samples resulted that there is a difference in mean sales for the service or online products, food and beverages, electronics, and cosmetics and skincare. Those aspects are evidence that COVID-19 pandemic could change the customer behavior in E-Commerce. We also illustrate the change of customer behavior through biplot

ОСОБЛИВОСТІ ВИКОРИСТАННЯ ЦИФРОВОГО МАРКЕТИНГУ ТА ШТУЧНОГО ІНТЕЛЕКТУ НА РИНКУ СІЛЬСЬКОГОСПОДАРСЬКОЇ ПРОДУКЦІЇ

The article examines the peculiarities of using digital marketing and artificial intelligence in the market of agricultural products. The cost of food products has increased significantly as a result of the destructive factors that have taken place in recent years. This led to a negative impact on the purchasing power of the population in many countries of the world. The influence of globalization and digitalization on the market of agricultural products is studied. The feasibility of developing modern marketing strategies for agricultural companies has been proven, which will allow identifying the target audience and building effective communications in the long term. The need to use digital marketing tools in connection with the reorientation of a significant number of the population to the digital environment is substantiated. For a successful marketing strategy, it is necessary to highlight the special characteristics of agricultural products that make a certain brand distinct for the target audience and encourage them to prefer these products. The peculiarities of artificial intelligence integration into the marketing strategies of agricultural companies in order to achieve optimal results are revealed. The necessity of using large amounts of information in the implementation of artificial intelligence algorithms, which allows to increase the level of conversion, has been established. In today's conditions of active development of artificial intelligence, farmers, agribusinesses and agricultural companies have new opportunities to make effective decisions regarding the promotion of their products, and at the same time they can do this with relatively little expenditure of monetary resources. The main ways of applying artificial intelligence in the digital environment for the implementation of agricultural companies marketing strategies are considered. The implementation of artificial intelligence in chatbots allows to increase the accuracy of answers to user requests and gives the impression of communication with real people. Artificial intelligence provides agricultural companies with the opportunity to analyze the development of relevant markets, predict changes in customer behavior and forecast future demand for products and services of the agro-industrial sector.

Development of MSMEs' potential through digital marketing in Pabean Udik Village, Indramayu Regency

Development of digital marketing techniques is mostly driven by changes in customer behavior and technology advancements. A major element of this transformation is the MSME sector. The Faculty of Computer Science, UPN Veteran Jakarta provides community service by training and implementing digital marketing for MSMEs in Pabean Udik Village. This program was carried out to support MSMEs so they can take advantage of digital technology to promote products, effectively interact and communicate with potential customers wherever they are. The program method involves training participants on how to digitize products using their personal smartphones, which is followed by the development of a company profile that serves as a landing page for customers using the Google My Business program. A technical team and trainers help them develop business profiles and upload images of their products. The training has increased the participants' knowledge of product digitization. Additionally, MSME products are eye-catching and simple to market online using Google My Business applications.

DESIGN THINKING PROCESS IN SME, IS IT STILL POWERFUL IN DEALING WITH THE COVID 19 MARKET TURBULENCE?

Studies on SME new product development utilizing the design thinking methodology during the pandemic have received little attention from academics. This study aims to examine how a firm’s new product and performance are impacted by the design thinking process. The study was carried out at the height of Covid-19’s market dominance. Data was gathered using an online survey of 187 SMEs from diverse industries located throughout Indonesia. Purposeful sampling is conducted to find the chosen companies, particularly those that have altered their business model or added new products. The data is then analysed and modelled using SEM-PLS to determine how the concepts connect. This study discovered that SMEs can still use the design thinking method to assist them in coping with the effects of the pandemic. It has also been demonstrated that market volatility makes the design thinking method less effective. The study highlights the significance of a firm’s absorptive capacity for fostering the design-thinking process. The results of this study demonstrate how design thinking helps a business succeed during a period of significant changes in customer behaviour, bridging a knowledge gap. The study’s conclusions might help SMEs modify their business practices during and after the pandemic.