Targeted Marketing Strategies Research Articles

Social Media Analytics and Business Performance

Social Media Analytics and Business Performance

Data to Decisions: Optimizing E-commerce Sales Potential with Analytics

Descriptive, diagnostic, predictive, and prescriptive analytics play crucial roles in optimizing the performance and user experience of an e-commerce site. Descriptive analytics involves examining historical data to gain insights into past performance, enabling businesses to identify trends, patterns, and anomalies. This analysis helps in understanding what has happened, such as identifying popular products or peak sales periods, and provides a foundation for further analysis. Diagnostic analytics goes beyond descriptive analytics by examining why certain events occurred, identifying factors that influenced outcomes, and uncovering strengths, weaknesses, and areas for improvement within an e-commerce platform. Predictive analytics utilizes statistical algorithms and machine learning techniques to forecast future trends and outcomes, enabling businesses to anticipate customer preferences, demand for specific products, and potential sales opportunities. By leveraging predictive insights, e-commerce sites can adjust strategies, inventory levels, and marketing campaigns proactively to stay ahead of the competition and meet evolving customer needs. Prescriptive analytics takes predictive insights to the next level by recommending specific actions or strategies to optimize business processes and achieve desired outcomes. This could involve personalized product recommendations, targeted marketing strategies, or dynamic pricing adjustments based on real-time data analysis. By harnessing these four types of analytics, e-commerce businesses can make informed, data-driven decisions, enhance customer experiences, drive sales growth, and maximize profitability.

IOT Based Cashless Petrol Pump

The integration of IoT technology with petrol pumps has revolutionized the fueling industry, introducing efficient, secure, and convenient payment methods. This abstract presents an IoT-based cashless petrol pump system utilizing RFID cards for streamlined transactions. Customers authenticate themselves by tapping their RFID cards on readers connected to a central IoT platform, which verifies and authorizes transactions in real-time. The system supports multiple payment methods linked to RFID cards, ensuring flexibility for users. Security measures like data encryption and robust authentication protocols safeguard transactions. The IoT infrastructure enables remote monitoring and management of petrol pumps, facilitating predictive maintenance and targeted marketing strategies. Overall, this system offers a seamless and secure fueling experience, aligning with digital transformation trends in the automotive industry.

Does Experience Matter? Unraveling the Drivers of Expert and Non-Expert Mobile Consumers

The surge in mobile shopping faces a challenge as not all potential consumers are comfortable with this mode. Retailers need a deeper understanding of factors influencing user experience to enhance marketing strategies. Despite extensive research, a gap remains in comprehending this aspect. Using a statistical PLS-SEM-ANN approach, this research aims to explore the psychological dimensions of expert and non-expert mobile shoppers for establishing better targeted marketing strategies in m-commerce settings. Analyzing experience levels in mobile commerce (m-commerce), key drivers like enjoyment, usefulness, subjective norms, and trust were scrutinized as interaction settings for consumers using mobile technologies. The findings reveal that, for less experienced m-shoppers, trust is the most significant driver of attitude and satisfaction, while, for experienced users, trust and usefulness are the primary antecedents. This research provides novel insights, aiding mobile marketers in refining targeting strategies based on consumer experience levels, emphasizing the importance of usefulness and trustworthiness for a seamless m-shopping experience.

Development of marketing management for pomelo products in Samut Songkhram province, Thailand

The study presents a comprehensive examination of the market potential for Pomelo products in Samut Songkhram Province, Thailand, aiming to enhance marketing management strategies. It delves into consumer motivations, attitudes, and purchasing intentions towards Pomelo products, utilizing structural equation modeling to assess influencing factors. Samut Songkhram is identified as a prime region for Pomelo cultivation due to its unique river estuary soil enriched with minerals, creating a distinct taste profile influenced by a mix of saltwater, brackish water, and freshwater, dubbed the three-water area. This geographical and environmental advantage contributes to the high quality and consumer preference for the large white Pomelo variety indigenous to the area. The study explores two primary agricultural practices for growing large white Pomelos: self-cultivation by landowners and orchard leasing by other farmers. Additionally, it investigates two main distribution methods: direct sales at the farm and self-managed sales by farmers or orchard owners. A quantitative analysis involving 400 potential customers, including tourists and out-of-area customers, was conducted to gauge purchase intentions. Results from structural equation modeling indicate a significant and positive relationship between consumer motivations, attitudes, and purchase intentions, underlining the integrated influence of these factors on consumer behavior towards Pomelo products. These findings highlight the critical role of understanding consumer dynamics and the environmental uniqueness of Samut Songkhram’s Pomelos in developing targeted marketing strategies. The research underscores the significance of leveraging the unique characteristics and cultivation heritage of Samut Songkhram Pomelos to foster product differentiation and appeal, suggesting a need for innovative marketing approaches that resonate with the distinct quality and taste profiles of these local fruits.

Exploring user satisfaction and improvement opportunities in public charging pile resources

The existing market of public charging pile services for electric vehicle (EV) users has occupied a particular market share. However, instead of solely focusing on pre-planning the construction of charging piles, it is crucial to address the shortcomings of the existing charging pile service and develop effective marketing strategies. This approach can help optimize the utilization of charging pile resources and minimize wastage. In this study, we explore EV users’ comments on the public charging pile service and adopt a natural language pre-training model to classify comments for extracting positive and negative comments. For these two types of comments respectively, we construct the text-to-knowledge to mine the keywords from multiple dimensions. We further excavate the words correlated with the keywords by utilizing dependency parsing to create relational dependency graphs. Taken together, we identify key factors influencing EV user satisfaction or dissatisfaction and uncover the relationships among these factors. These insights provide valuable information for charging pile operators to develop targeted marketing strategies and improvement plans for the existing public charging pile resources, ultimately enhancing the overall user experience.

Smart Shopping Trolley Using Internet of Thing (IOT)

Abstract: Currently, billions of consumers shop at large in shopping mall stores for their everyday necessities. In today's life, purchasing has become a part of everyday life. On holidays and weekends, malls experience massive crowds .The integration of Internet of Things (IoT) technology into shopping trolleys presents a significant advancement in the retail industry, revolutionizing the shopping experience for consumers. The proposed SST utilizes a network of sensors and connectivity solutions to enable real-time monitoring, inventory management, and personalized services. Through IoT integration, the SST offers features such as automatic item detection, seamless checkout processes, and personalized recommendations based on individual shopping habits. Moreover, the system empowers retailers with tools for efficient inventory management, reducing stock outs, and analysing consumer behaviour for targeted marketing strategies. Intelligent Carts and automated recognition systems are being utilized by merchants to enhance the efficiency of their offerings and to boost market demand, enabling them to save time and money.

The impact of digital transformation on business development strategies: Trends, challenges, and opportunities analyzed

Digital transformation has become a crucial driver of business development, reshaping industries and redefining how companies operate. This review analyzes the trends, challenges, and opportunities arising from this transformative process. Companies are increasingly adopting digital technologies such as cloud computing, AI, IoT, and big data analytics to streamline operations, improve customer experiences, and drive innovation. Digital transformation has led to a focus on understanding and meeting customer needs through personalized experiences and targeted marketing strategies. COVID-19 pandemic accelerated the adoption of remote work and digital collaboration tools, leading to new ways of working and collaborating across teams and organizations. Many organizations struggle with outdated legacy systems and infrastructure, making it challenging to integrate new digital technologies. As companies collect and analyze more data, ensuring data privacy and security has become a major concern. There is a growing demand for professionals with digital skills, but many organizations face challenges in finding and retaining talent. Digital transformation enables companies to collect and analyze vast amounts of data, providing valuable insights into customer behavior and preferences. Automation and digital technologies can streamline processes, reduce costs, and improve efficiency. Digital transformation opens up new opportunities for innovation and the development of new business models, enabling companies to stay competitive in a rapidly changing market. Digital transformation is reshaping business development strategies, driving innovation, and creating new opportunities for growth. However, it also presents challenges, such as legacy systems, data security, and skills gaps, that organizations must address to fully realize the benefits of digital transformation.

Used Car Customer Segmentation Using K-Means Clustering Model With SPSS Program: Case Study Caroline.Id

This study discusses the use of K-Means clustering algorithm to determine market segmentation and prepare targeted marketing strategies. The process involves grouping customer data based on various factors such as transmission type, customer satisfaction, payment method, and branches. After grouping the data, the initialization stage is carried out by providing an initial number, and then the clustering process is carried out. The resulting clusters are analyzed to identify different customer profiles and needs. With an in-depth understanding of each segmentation, companies can develop specific and targeted marketing strategies for each customer group. Additionally, this study discusses the construction of a brand persona by identifying the target audience, understanding their needs and wants, creating a character profile, and compiling a brand persona document that includes all the important information. The customized brand persona can then be used in the development of online value proposition and marketing strategies.

CLASSIFICATION OF CUSTOMERS’ REPEAT ORDER PROBABILITY USING DECISION TREE, NAÏVE BAYES AND RANDOM FOREST

Limited customer information in sales data on e-commerce in Indonesia hinders companies in determining targeted marketing strategies, especially in targeting groups of potential customers to make repeat purchases. Sales data in the form of customers' names and cellphone numbers has been hidden by e-commerce, and only data is available in the form of products purchased, number of purchases, and customer addresses. So far, the methods used to determine potential customers mostly use more complete data features. Research that uses limited e-commerce data to determine potential customers is scarce. Several algorithms for predicting repeat purchases in e-commerce also have been widely used. However, the comparison of the performance of these methods in the context of e-commerce in Indonesia with limited data has yet to be discovered. In this research, the Decision Tree, Naive Bayes, and Random Forest methods were compared to classify potential customers using Maschere brand sales data from two e-commerce sites, namely Tokopedia and Shopee. The research results show that the Decision Tree algorithm achieved an accuracy of 90.91%, Naive Bayes achieved an accuracy of 37.50%, and Random Forest achieved the best level of accuracy, namely 93.94%. These results show that the Random Forest method is the best method for classifying customers' probability of repeat purchases. In the future, the results of this research can be developed again as a decision-making system to determine potential customers.

Impact of Perceived Benefits and Perceived Risks on Online Shopping Behaiour with Mediating Role of Purchase Intention

Online purchasing has revolutionized consumer market. It allows individuals to access a variety of products with a single click. With evolution of e-commerce understanding the factors influencing online shopping behavior is imperative for firms to remain competitive. This study investigates the relationship between perceived benefits (PB) and perceived risks (PR), mediated by purchase intention (PI) on online buying behavior (OSB). Six key elements—shopping convenience, product variety, price advantage, product risk, privacy risk, and financial risk—were evaluated through data collected from 300 internet shoppers via a structured questionnaire. The data is analyzed with SPSS. Results underline the significance of convenience, product variety, and price advantage in stimulating online purchases, with PB positively impacting customers' online purchasing behavior (OPB). Conversely, perceived risks negatively affect online shopping decisions, necessitating efficient risk management strategies to bolster consumer confidence. The mediation role of PI was examined using the Hayes macro process, revealing its significance in the relationship between PB, PR, and OSB. Notably, PI mediates the impact of both PB and PR on OSB, highlighting its pivotal role in shaping consumers' online purchasing behavior. These findings offer actionable insights for businesses, emphasizing the importance of leveraging PB to encourage beneficial purchasing intentions and mitigate PR to foster online shopping behavior. Ultimately, this study contributes to a deeper understanding of consumer decision-making in online settings, facilitating the development of targeted marketing strategies and enhancing overall online shopping experiences.

Examining link prediction and node connectivity objectives in social networks: Comprehensive review

A social network plays a pivotal role in modern society, facilitating communication, information dissemination and social interactions. As systems such as this one continues to grow in complexity and scope, yet the challenges become identifying the absence of a node as well as ensuring an uninterrupted connection. These comprehensive reviews analyze the objectives of link prediction and node connectivity in the context of social networks. The objective of link prediction and node connectivity in social networks is to pave the way for future analysis in network design and security. This review analyze the node connectivity and link prediction, which is represented as graph G= (s, e) where s denotes the number of vertices and e denotes the number of edges. It highlights the effectiveness of linkages along with node connectivity and forecasting future interactions in enabling the development of targeted marketing strategies and personalized recommendation systems. The significance of node connectivity analysis is to evaluate the resilience and robustness of social networks. It explores the identification of critical nodes and the impact of their removal on network fragmentation, thus emphasizing the importance of developing effective strategies to enhance network resilience and security. Furthermore, the review emphasizes the interplay between link prediction and node connectivity in advancements of link prediction techniques that can booster the accuracy of node connectivity in social networks. It underscores the implications to understand the network dynamics as well as predicting the development of security and efficient social network infrastructures.

Breaking the Taboo: Understanding the Relationship between Perception, Beliefs, Willingness to Eat Insects, and Food Neophobia among Polish Adults.

Insects are a potential source of protein for direct human consumption or indirectly in processed foods. The research examined consumer perceptions and beliefs regarding the consumption of insects and food products containing insect proteins. The study aimed to identify beliefs about insect consumption, assess the perception of food products containing added insect proteins, and gain a deeper understanding of the role of food neophobia in accepting insects as an alternative source of protein. The data were collected in 2023 through a cross-sectional quantitative online survey of a group that was representative of consumers by age, gender, and region sample (n = 1000) who were responsible for food purchasing decisions in their households. While many respondents viewed foods enriched with insect protein as innovative and environmentally friendly, others found the idea of consuming insects repulsive and expressed concerns about potential allergic reactions. Food neophobia was a determining factor in respondents' beliefs and perceptions regarding the consumption of insects and food products containing insect proteins. Respondents were more inclined to consume products with added insect protein rather than visibly identifiable insects. The results highlight the need for targeted marketing strategies and product development approaches to align with the varied expectations of consumers in the rapidly expanding insect-based food market.

Unravelling the Influential Factors in the Decision-Making Process of Bank Selection Preferences among Atharvians and Their Relatives

This research investigates the complex process of bank selection among individuals and their relatives within the context of Atharva Business College. The choice of a bank is a significant decision influenced by various factors encompassing financial, social, and personal aspects. Understanding these influencing factors can enable banks to tailor their services effectively and enhance customer satisfaction. Through the use of a quantitative research approach, specifically a questionnaire survey, this study thoroughly explores the behaviours and motivations behind bank selection among those affiliated with Atharva Business College. The quantitative analysis of survey data reveals key determinants, emphasizing the relative importance of factors such as interest rates, fees, accessibility, digital banking services, and brand reputation. The findings contribute valuable insights into consumer behaviour within the banking sector, guiding banks in crafting targeted marketing strategies, improving service quality, and innovating digital solutions. Additionally, the study underscores the influence of familial connections on bank selection, highlighting the need for banks to consider the interconnected decisions of family members when developing customer engagement strategies. In conclusion, this study provides a comprehensive understanding of the bank selection process among individuals and their relatives associated with Atharva Business College.

Adventure Tourism in Bilaspur: A Framework for Assessment and Strategic Development

Abstract:- This research paper investigates the state of adventure tourism in Himachal Pradesh’s Bilaspur district, employing the NIRVANA framework for assessment and strategic development. The assessment and planning process within this framework aims to cultivate a state of perfect peace and happiness akin to "Nirvana'' by fostering sustainable development practices. Through a broad analysis of Bilaspur's adventure tourism potential, encompassing Natural Resources and Geography; Infrastructure, Accessibility, and Investment; Risk, Regulation, and Marketing; Visitor Experience Management; Community Engagement; Sustainability and Conservation; collaboration; and monitoring thereof. Utilizing a combination of destination survey, secondary research, data extraction from published sources, and primary data acquisition from authorities, practitioners, and subject experts of the region this study was analyzed within the specified framework. Findings indicate Bilaspur's rich natural landscapes and existing adventure infrastructure, such as paragliding sites and water sports complexes, position it as a promising adventure tourism destination. However, challenges in investment, risk management, market understanding, and marketing persist. Recommendations include enhancing infrastructure, diversifying investment sources, improving risk management practices, and implementing targeted marketing strategies. The study contributes to the understanding of adventure tourism development, emphasizing the need for a balanced approach that respects natural and cultural heritage while promoting sustainable and innovative practices.

Motive based segmentation of the wellness tourism market

ABSTRACT This study aims to segment the wellness tourism market based on travel motivations among international tourists visiting Phuket during the post-COVID-19 from December 2022 to January 2023. A total of 464 usable questionnaires were collected from international tourists who had engaged in wellness activities during their stay in Phuket. The study employs factor analysis to identify six key motivational factors, namely “Healthy Diet, Mindfulness and Learning about Wellness,” “Movement and Fitness,” “Rest & Relaxation,” “Escape and Mental Healing,” “Socialisation,” and “Outdoor Activities.” Subsequently, cluster analysis reveals three distinct clusters: “Serene Retreat Seekers,” “ Wellness Enthusiasts,” and “Connectivity Enthusiasts.” Sociodemographic profiling reveals variations in gender, regions, age, length of stay, travel party, and monthly income among the clusters. These findings not only enrich the theoretical landscape of wellness tourism by offering insights into the diverse motivations and profiles of wellness tourists but also bear practical significance for destination management. They pave the way for targeted marketing strategies, the creation of personalised experiences, and the formulation of sustainable growth plans within the wellness tourism sector.

Optimizing Marketing Strategies with RFM Method and K-Means Clustering-Based AI Customer Segmentation Analysis

Retrospectively, an organization’s capacity to comprehend the distinct needs of its clients will undoubtedly provide it with a competitive advantage in terms of delivering targeted client services and tailoring personalized marketing initiatives. This research investigated the efficiency of the k-means clustering algorithm as a technique for efficient consumer segmentation. The k-Means algorithm consolidated with RFM analysis is globally accredited as a profound partitioning clustering technique that has proven to be highly efficient in various business settings. The experimental outcomes provided persuasive evidence of the algorithm's performance in terms of consumer segmentation. The overall cluster purity evaluation was computed to be 0.95. This value demonstrated that the k-Means clustering algorithm incorporated with the RFM analysis attained a relatively high accuracy rate of 95% in terms of precisely and accurately segmenting the consumers based on their shared behaviors and characteristics. The high purity value of 0.95 illustrated the efficiency of the k-Means clustering algorithm in terms of accurately segmenting and categorizing the clients. This showcased that the algorithm efficiently organized and pinpointed consumers into distinct clusters based on their similarities, facilitating targeted marketing strategies and personalized approaches.

Brand Building Triumphs: Le Labo's Inspiring Market Entry

Due to the impact of the epidemic on the economy, an increasing number of consumers have changed their consumption preferences, and the demand for non-essentials has decreased gradually. Nevertheless, the fragrance market has experienced a substantial increase in sales during this era. Le Labo is considered to be one of the representative brands in the fragrance market. Thus, this research investigated the methods utilized by Le Labo in order to stimulate consumer purchase intent through the implementation of effective marketing strategies. The present study examined the impact of brand identity, co-branding strategy, and brand awareness on consumer purchase intention in the context of new market entry. The results of the analysis revealed significant correlations between these three factors and the likelihood of consumers intending to make a purchase. Subsequently, based on the targeted marketing strategies, this research gave some suggestions, which have led to the formulation of practical recommendations aimed at boosting brand identification and brand awareness.

E-Commerce Adoption: The Moderating Role of Commitment between Purchase Intention and Customer Purchase Behavior

This study aims to explore how factors such as attitude, self-efficacy, subject norms, and perceived usefulness influence consumer purchase intention in the context of online shopping. Additionally, it examines the moderating role of commitment in shaping these relationships. Understanding these dynamics is crucial for businesses looking to enhance their online retail strategies and customer engagement. A quantitative approach was employed, using quantitative surveys and data was collected from 251 internet users. The study found that attitude, self-efficacy, subject norms, and perceived usefulness significantly influence online purchase intention. A positive attitude towards online shopping, higher self-efficacy in using online platforms, favorable subject norms, and perceived usefulness of online shopping platforms were all linked to increased purchase intention. Notably, commitment was observed to have a moderating effect, strengthening the relationship between these factors and purchase intention. High commitment levels enhanced the impact of positive attitudes, self-efficacy, and perceived usefulness on the likelihood of making online purchases. This research contributes to the existing literature by integrating various psychological and social factors under a unified framework to understand online consumer behavior. It highlights the importance of commitment as a moderating factor, offering new insights for online retailers. The findings provide valuable implications for developing targeted marketing strategies and improving online customer experiences, ultimately boosting online sales and customer loyalty.

From clicks to cravings: exploring consumer perceptions of online fast food purchasing

PurposeThis study explores consumers' perceptions of purchasing fast food items through online platforms. The central idea of this research is to practically assess the various elements impacting the consumers’ perceptions of online purchasing of fast food items and find out the factors with high importance and performance value.Design/methodology/approachA quantitative research approach was used to collect data from 402 participants in the form of a pen-and-paper-based method using a 5-point Likert scale. The collected data were analyzed using structural equation modeling (SEM) and importance-performance analysis. Theory of planned behavior and technology acceptance model form the basis for this research.FindingsThe findings indicate that constructs such as convenience, perceived quality and perceived healthiness positively influence consumers' perceptions of online purchasing of fast food items. On the other hand, competitive prices, discounts and promotions (CPDP) and online shopping experience have no significant impact on perceived value for money.Research limitations/implicationsA constraint of this study is that it was done in a particular geographical location, which restricts the generalizations of the findings. The study only examined consumers' perceptions of online fast food purchasing, and future research could explore consumers' actual behaviors toward personalized fast food recommendations by online sellers.Originality/valueThe research supports and extends the existing literature by comprehensively understanding consumers' perceptions of purchasing fast food online. These findings can help online fast food sellers improve their services and develop targeted marketing strategies.