Data-driven Marketing Research Articles

Leveraging the RFM Model for Customer Segmentation in a Software-as-a-Service (SaaS) Business Using Python

Customer segmentation plays a pivotal role in driving marketing strategies and improving customer retention across various industries. This study explores the application of the RFM (Recency, Frequency, Monetary) model for customer segmentation in a Software-as-a-Service (SaaS) business, using Python for efficient data processing and analysis. By analyzing one year of customer purchase data, we segmented customers into key groups such as "Champions," "Loyal Customers," and "At Risk." The results highlight that targeted discount strategies significantly affect profitability, especially for high-value customer segments. Furthermore, the research builds upon existing methodologies, demonstrating how Python-based implementations streamline RFM analysis and allow for scalable solutions in business contexts, as illustrated in prior works by Hermawan et al. (2024). This study offers actionable recommendations, including tailored discounting, loyalty programs, and personalized engagement strategies, to enhance customer retention and business profitability. The findings underscore the importance of data-driven marketing approaches for customer segmentation and engagement, reinforcing the relevance of the RFM model in modern business environments.

Predictive Modeling of Customer Response to Marketing Campaigns

In today’s data-driven marketing landscape, predicting customer responses to marketing campaigns is essential for optimizing both engagement and Return On Investment (ROI). This study aims to develop a predictive model using a Decision Tree (DT) to identify key factors influencing customer behavior and improve campaign targeting. The methodology involves building the DT model, initially achieving an accuracy of 87.3%. However, the model faced challenges with precision and recall due to class imbalance. To address this, a resampling technique was applied, which significantly improved model performance, increasing recall from 44% to 83.1% and the F1-score from 49% to 74.2%. Key influential features identified include the recency of a customer’s purchase, their duration as a customer, and their response history to previous campaigns. This study demonstrates the practicality and interpretability of the DT model, offering actionable insights for marketing professionals seeking to enhance campaign effectiveness and customer targeting.

Bibliometric Assessment of Data-Driven Marketing Research Trends in the Last Two Decades

This study presents a bibliometric analysis of data-driven marketing research trends over the last two decades, with a focus on its intersection with innovation, decision-making, and related topics. Using VOSviewer for network visualization, we analyze co-authorship, keyword co-occurrence, publication frequency, and country collaboration to uncover key themes and research developments. The findings indicate a significant rise in data-driven marketing research, particularly from 2015 onwards, driven by advancements in big data, machine learning, and artificial intelligence. Co-authorship networks reveal strong interdisciplinary collaboration, while keyword co-occurrence maps highlight the growing role of innovation, decision-making, and machine learning in data-driven marketing. Additionally, country collaboration networks show the United States, China, the United Kingdom, and India as central contributors to global research. The keyword density heatmap emphasizes the increasing focus on data-driven innovation and product development. These insights offer valuable implications for academics and practitioners seeking to understand and apply data-driven marketing in a rapidly evolving digital landscape.

Exploring Customer Segmentation in E-Commerce using RFM Analysis with Clustering Techniques

The proliferation of big data and the growth of e-commerce have intensified the challenges associated with extracting actionable data for personalised recommendations and decision-making. With data-driven marketing strategies, understanding and predicting customer behaviour has become paramount for maintaining competitive advantage. This study leverages business analytics tools, focusing on Recency, Frequency, and Monetary (RFM) Analysis, alongside K-Means and Hierarchical (Agglomerative) Clustering algorithms, to segment customer transactional data. Data normalisation, a critical step for accurate clustering, was performed using log transformation and the Power Transformer technique with the Yeo-Johnson parameter, the latter proving more effective for handling both positively and negatively skewed data, enhancing data normalisation and suitability for analysis. This study reveals that RFM Analysis with Hierarchical Clustering outperforms K-Means Clustering, achieving a Silhouette Score of 0.47 and a Calinski–Harabasz Index of 3787.1, indicating a more accurate identification of customer segments. RFM Analysis alone generated eight clusters, while integrating RFM Analysis with both Hierarchical Clustering and K-Means generated three similar-sized clusters with interchanged labels. These metrics highlight the proficiency of Hierarchical Clustering in identifying unique customer segments and customising marketing strategies. The findings indicate that the RFM-Hierarchical Clustering approach enhances segmentation precision and facilitates more refined and effective marketing strategies.

Transforming marketing strategies with data analytics: A study on customer behavior and personalization

In the rapidly evolving digital landscape, data-driven marketing has emerged as a pivotal strategy for businesses seeking to enhance customer engagement and optimize marketing outcomes. This review paper delves into the transformation from traditional to data-centric marketing, highlighting the historical evolution and technological advancements that have propelled this shift. It examines the critical role of data analytics in understanding customer behavior, detailing the tools and techniques employed to interpret vast amounts of customer data. Furthermore, the paper explores personalization as a key marketing strategy, discussing methods to achieve it through data analytics and emphasizing the importance of customer segmentation. Ethical considerations and challenges in personalized marketing are also addressed. The impact of data-driven marketing is evaluated through successful case studies, and the future trajectory of marketing in the age of analytics is predicted. The review summarizes key insights and their strategic implications for marketers, underscoring the necessity of balancing innovation with ethical responsibility.

The Applicability of Artificial Intelligence Marketing for Creating Data-driven Marketing Strategies

The purpose of this paper is to explore the applicability of AI marketing for creating data-driven marketing strategies. Notably, the paper illustrates the existing circumstances of artificial intelligence in marketing practice. Besides, this paper argues for awareness of AI for customer satisfaction, employing AI to improve positioning, applying AI for accurate decision-making, and utilizing AI for sales, cost, and risk reductions. Lastly, this paper compares the applicability of AI marketing within two major regions from four regions identified in the study. A two-step approach was used to address the research question. First, a systematic literature review was conducted to identify the knowledge gap. Second, primary research through a survey study was conducted. Respondents of the primary study were represented by 367 marketing practitioners with 22 different marketing professions, representing 11 countries from 18 different industries, mainly from the Baltic and Caucasus regions. Based on findings and analysis, conclusions, limitations, and concepts for the future study were highlighted. The findings synthesized AI drivers, barriers, and outcomes in marketing. Further, outcomes confirmed a positive relationship with unitizing AI marketing in long-term strategic marketing planning. The paper offers practical guidance to the companies or inspires marketing leaders to use AI in data-driven marketing strategies. It has a significant value due to the complexity of the current marketing environment, whether micro or macro. Marketing Practitioners are searching for added value to prove the applicability of AI marketing in everyday strategies for decision-makers.

Balancing Innovation and Ethics in AI- and Big Data-Driven Marketing

Balancing Innovation and Ethics in AI- and Big Data-Driven Marketing

The future of events in the tourism industry: the case of the “Best of the Best Things to Do 2023” on TripAdvisor

PurposeThis study aims to use TripAdvisor data to identify which themes and events will attract more interest in the future of tourism activities and to provide guidance to the industry accordingly.Design/methodology/approachThis study used qualitative case study approach. To identify key themes in the top-rated activities, thematic analysis of user-generated content was conducted through MAXQDA 2020 Analytics Pro software.FindingsThe findings of this study indicate a growing trend in tourist preferences, with a notable increase in the popularity of wellness activities such as spa treatments and yoga retreats. Tourists also highly prefer cultural experiences like local festivals and historical tours, adventure activities such as hiking and water sports, and food-related activities like cooking classes and wine tastings. There is a significant trend towards sustainable and environmentally friendly tourism activities. These results underscore the need to cater to diverse tourist interests in developing future tourism offerings.Practical implicationsDeveloping innovative events, using data-driven marketing strategies, involving local communities, and catering to diverse preferences can enhance the sustainability and competitiveness of the tourism sector.Social implicationsUnderstanding tourists' tendencies towards cultural, adventurous and sustainable experiences helps tourism operators and managers to create offers that satisfy tourists while promoting environmentally friendly practices. This shift promotes cultural preservation, environmental protection and community involvement.Originality/valueThis study uniquely analyzes top-rated TripAdvisor activities to identify key trends in tourist preferences, highlighting the growing demand for cultural, adventurous, gastronomic, and sustainable tourism.

Consumer Behavior Analysis in the Age of Big Data for Effective Marketing Strategies

Purpose: The aim of the study was to examine the consumer behavior analysis in the age of big data for effective marketing strategies. Methodology: This study adopted a desk methodology. A desk study research design is commonly known as secondary data collection. This is basically collecting data from existing resources preferably because of its low cost advantage as compared to a field research. Our current study looked into already published studies and reports as the data was easily accessed through online journals and libraries. Findings: The study found that consumer behavior analysis in the age of big data presents a transformative opportunity for businesses to enhance their marketing strategies and better understand their target audiences. The studies discussed demonstrate the effectiveness of leveraging Big Data analytics in various domains such as social media sentiment analysis, personalized marketing, predictive analytics, customer segmentation, location-based marketing, and real-time personalization. These strategies enable businesses to tailor their marketing efforts more precisely, leading to increased consumer interest, improved engagement, and higher conversion rates. Unique Contribution to Theory, Practice and Policy: Social Identity Theory, Information Processing Theory & Technology Acceptance Model (TAM) may be used to anchor future studies on Consumer Behavior Analysis in the Age of Big Data for Effective Marketing Strategies. Promote the adoption of advanced data analytics tools and techniques by businesses to enhance their marketing strategies. Companies should invest in robust data infrastructure, analytics platforms, and talent to effectively collect, analyze, and interpret consumer data. By leveraging predictive analytics, sentiment analysis, customer segmentation, and real-time personalization, businesses can tailor their marketing efforts to individual consumer preferences and behaviors, leading to improved engagement, customer satisfaction, and ultimately, increased sales and profitability. Advocate for the development of regulatory frameworks that balance consumer privacy rights with the benefits of data-driven marketing. Policymakers should collaborate with industry stakeholders to establish guidelines and standards for ethical data collection, usage, and storage.

Impact of Digital Transformation on Strategic Marketing Practices in Germany

Purpose: The aim of the study was to examine the impact of digital transformation on strategic marketing practices in Germany. Methodology: This study adopted a desk methodology. A desk study research design is commonly known as secondary data collection. This is basically collecting data from existing resources preferably because of its low cost advantage as compared to a field research. Our current study looked into already published studies and reports as the data was easily accessed through online journals and libraries. Findings: The impact of digital transformation on strategic marketing practices in Germany has been profound and multifaceted, driving significant changes in how businesses engage with their customers and operate in the market. The adoption of advanced digital technologies such as artificial intelligence, big data analytics, and social media platforms has enabled companies to develop more personalized, efficient, and data-driven marketing strategies. This transformation has facilitated improved customer targeting, enhanced engagement, and greater insights into consumer behavior, leading to more effective and responsive marketing campaigns. Moreover, digital transformation has encouraged a shift from traditional marketing methods to innovative, omnichannel approaches, allowing German businesses to reach a broader audience and provide a seamless customer experience. The integration of digital tools has also streamlined marketing operations, reduced costs, and increased the agility and adaptability of marketing teams in responding to market changes and consumer demands. Unique Contribution to Theory, Practice and Policy: Resource-Based View Theory, Diffusion of Innovations Theory & Dynamic Capability Theory may be used to anchor future studies on Impact of Digital Transformation on Strategic Marketing Practices in Germany. Organizations are encouraged to invest in continuous learning and development programs aimed at enhancing digital marketing capabilities among employees. This entails providing training, workshops, and access to resources that enable marketers to stay abreast of the latest digital trends, tools, and techniques. There is a need to advocate for the development of policies and regulations that foster innovation, competition, and consumer protection in digital marketing environments. This involves engaging policymakers and stakeholders to create an enabling regulatory framework that encourages digital innovation while addressing concerns related to data privacy, cybersecurity, and ethical marketing practices.

Meningkatkan Daya Saing UMKM Lokal melalui Strategi Pemasaran Digital di Era Digital

This research explores the implementation of digital marketing strategies for Micro, Small, and Medium Enterprises (MSMEs) in an increasingly competitive digital era. Through a descriptive approach, practical steps in adopting digital marketing strategies are identified, including creating responsive websites, strategically using social media, developing quality content, SEO optimization, and data-driven marketing. The challenges faced by MSMEs in implementing these strategies are also analyzed, along with tips and tricks to overcome them. Additionally, recommendations for the government and stakeholders are presented, including policies and programs that support MSMEs in digital marketing, as well as collaboration with digital platforms and service providers. In conclusion, with the right combination of strategies and continuous support, MSMEs can leverage the full potential of digital marketing to enhance their competitiveness and growth in the digital era.

The Psychology in Tech Tactics: Basis for E-Commerce Website Development

This study investigates e-commerce user preferences, revealing key insights that can inform website development and strategy. The findings indicate a predominant user base in the 18-34 age range, with a slightly higher representation of females. Understanding these socio-demographic characteristics is crucial for targeted marketing and design. The psychological impact on user preferences underscores the significance of aesthetics. Users value intuitive layouts, appealing color schemes, and well-chosen fonts. Mobile optimization and fast website performance are essential for user satisfaction, highlighting the importance of responsive design and quick loading times in the mobile era. The study also delves into the profitability of e-commerce, emphasizing the role of data-driven marketing, pricing optimization, and customer feedback management. These factors can significantly impact a business's bottom line. This research provides actionable recommendations for e-commerce businesses. By focusing on intuitive design, mobile optimization, and data-driven strategies, businesses can create more appealing and profitable online shopping experiences.

Impact of Ramadan on Halal Food Marketing Strategies in the Chinese Market: A Data Analytics Approach

This study investigates the integration of halal food into Chinese multinational culture amid the Coronavirus epidemic. Applying a thorough data analytics procedure, the research focuses on the evolving e-commerce landscape within China. The adaptation under scrutiny includes a wide range of sociocultural transformations, including linguistic proficiency, social norms, dietary preferences, and e-commerce consumption habits from various cultural domains. A data-driven marketing strategy's analytical structure, supported by an updated Segmentation, Targeting, and Positioning (STP) model, carefully examines product differentiation criteria. Furthermore, the investigation includes the implementation of a prediction model that uses complex algorithms to estimate market trends and consumer preferences. The term "halal" comes from Arabic and refers to food that is considered permissible for consumption excluding porcine products, carrion, blood, and alcohol. Halal cuisine, which adheres to Islamic dietary regulations, is critical to Muslims' religious devotion around the globe. Halal dietary habits are integrated into Chinese cuisine through major e-commerce platforms. This study utilizes a data-analytics marketing model to analyze transactional data from Taobao.com and Tmall.com, aiming to understand purchasing patterns of halal food products in China. The analysis highlights significant seasonal fluctuations, particularly during major festivals such as Ramadan, Chinese New Year, and Eid, which heavily influence sales.

UNLOCKING THE POWER OF DATA: EFFECTIVE DATA-DRIVEN MARKETING STRATEGIES TO ENGAGE MILLENNIAL CONSUMERS

As the marketing landscape continues to evolve, data-driven strategies have emerged as a critical component in effectively reaching and engaging with the millennial consumer demographic. In the age of big data, data driven marketing has become a powerful tool for businesses seeking to gain a competitive edge and optimise customer engagement. This paper provides a comprehensive literature review on effective data-driven marketing strategies to reach millennial consumers. It explores the use of big data and social relationship maps to identify key influencers and leverage their reach for exponential information dissemination. Additionally, the review examines various approaches and tactics for engaging with millennial consumers using data-driven techniques. Key findings suggest that effective data-driven marketing strategies can significantly enhance consumer engagement, brand loyalty, and overall marketing effectiveness. The paper aims to provide insights and understanding of the current research and best practices in data-driven marketing strategies for targeting the millennial demographic.

Customising generative AI: Harnessing document retrieval and fine-tuning alternatives for dynamic marketing insights

This study delves into the transformative impact of leveraging large language models (LLMs) in marketing analytics, particularly emphasising a paradigm shift from fine-tuning models to the strategic application of document retrieval techniques and more. Focusing on innovative methods, such as retrieval augmented generation and low-rank adaptation, the paper explores how marketers can now activate against vast and unstructured datasets, such as call centre transcripts, unlocking valuable insights that were previously overlooked. By harnessing the power of document retrieval and adaptation, marketers can bring their data to life, enabling a more nuanced and adaptive approach to understanding consumer behaviour and preferences. This research contributes to the evolving landscape of applied marketing analytics by demonstrating the efficacy of document retrieval in enhancing the utilisation of LLMs for dynamic and data-driven marketing strategies.

E-Commerce Website

Abstract: "Examining e-commerce websites' evolution, this review explores user experience, security, technology, and marketing strategies. Analyzing diverse methodologies and trends, it identifies key factors shaping their success. The abstract highlights the significance of user-centric design, cybersecurity, and data-driven marketing while suggesting future research directions in this dynamic landscape."

A systematic data-driven approach for targeted marketing in enterprise information system

ABSTRACT In the rapidly evolving landscape of digital marketing, leveraging data analytics within Enterprise Information Systems (EIS) has become crucial for businesses aiming to understand and engage their customers more effectively. This study presents a comprehensive analysis of data-driven marketing strategies, emphasising the critical role of diverse data sources in developing targeted, EIS-specific campaigns. Central to our investigation is a comparative analysis of various machine learning algorithms, including Random Forest, CART, Support Vector Machine, Linear Discriminant Analysis, Logistic Regression, K-Nearest Neighbours, and Naïve Bayes. Our selection and evaluation of these models are grounded in their ability to address the complex dynamics of customer behaviour and marketing data, demonstrating the superior efficacy of Random Forest and CART in this context.

ANALYSING THE CUSTOMER CENTRIC MARKETING STRATEGIES IN ATTAINING COMPETITIVE ADVANTAGE FOR THE FIRM AND SUSTAINING BUSINESS SUCCESS

This paper talks about how putting customers at the center of marketing helps businesses stay ahead. It looks at ways companies can use what they know about customers to stand out and do well. The paper examines different customer-focused tactics, like tailored marketing, managing customer experiences, and using data to make choices, and how they affect how well a company does. By studying real examples and proof, the paper shows why it's crucial to get how customers act and what they need in today's fast business world. It ends by giving advice on how companies can go about being more customer-focused, stressing the importance of always coming up with new ideas, being able to change, and really caring about making customers happy. This work adds to what's already known about marketing and staying on top by giving a detailed view of how focusing on customers can help companies succeed in the digital age. KEYWORDS: Research Paper, Customer-Centric Marketing, Customer Experience, Data-Driven Marketing,

A STUDY OF DIGITAL MARKETING TRENDS IN INDIA

In order to better understand the shifting landscape of digital marketing trends in India, the goal of this study is to conduct an analysis. Additionally, it provides insights on key changes and new practices that are having an impact on the industry. The purpose of this study is to analyze and evaluate significant trends in digital marketing strategies, platforms, and technologies that have been utilized by businesses in India. For the purpose of carrying out this evaluation, a comprehensive analysis of industry reports, case studies, and the opinions of industry experts is carried out. A number of the most important areas of attention that are now being addressed include the growing significance of mobile marketing strategies, the adoption of data-driven marketing approaches, the advent of influencer marketing, and the expanding value of social media marketing. In addition, the purpose of this study is to analyze the impact that alterations in rules, advancements in technology, and shifting consumer behaviors have had on digital marketing techniques in the Indian market. These findings not only contribute to a more in-depth understanding of digital marketing trends in India, but they also give major insights that can be exploited by firms, governments, and marketers as they navigate the rapidly changing digital world. In other words, the findings of this study offer substantial insights. Keywords: digital marketing, trends, India, social media marketing, influencer marketing, data-driven marketing, mobile marketing, regulatory changes, consumer behavior.

Analysis of Business Intelligence Technology in the Big Data Era A Case Study of Alibaba Group

In the age of big data, business intelligence technology is pivotal in enhancing user experiences and driving innovation across industries. This paper focuses on Alibaba Group, a trailblazer in e-commerce, to examine the transformative role of business intelligence. This paper investigates Alibabas cutting-edge application of business intelligence technology, focusing on intelligent recommendation systems, personalized marketing strategies, and efficient supply chain management. The recommendation system harnesses data analysis to provide tailored product suggestions, boosting user satisfaction and sales. Data-driven marketing strategies enable Alibaba to create personalized promotions and coupons, enhancing user experiences and building loyalty. Intelligent supply chain management employs real-time monitoring, optimized transportation, and data-driven decisions to ensure timely deliveries and cost efficiency. A case study of Alibaba's "Singles Day Global Shopping Festival" illustrates how business intelligence technology creates a dynamic, data-powered shopping event. Every decision during this event is informed by real-time analysis and AI insights, enabling swift responses to evolving consumer needs. In summary, business intelligence, a driving force in the age of big data, is at the heart of Alibabas success. Alibaba has tapped the potential of business intelligence by enhancing the user experience and facilitating data-driven decision-making.