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Data-Driven Insights with Predictive Marketing Analysis
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Data-Driven Insights with Predictive Marketing Analysis

A special issue of Systems (ISSN 2079-8954). This special issue belongs to the section "Systems Practice in Social Science".

Deadline for manuscript submissions: 31 March 2026 | Viewed by 9020

Special Issue Editors

Dr. Rui Nunes Cruz

E-Mail Website
Guest Editor
Faculdade de Ciencias Sociais e Empresariais, IADE-Universidade Europeia, 1200-649 Lisboa, Portugal
Interests: marketing; knowledge management; intellectual capital
Dr. Albérico Travassos Rosário

E-Mail Website
Guest Editor
The Research Unit on Governance, Competitiveness and Public Policies (GOVCOPP), Universidade Europeia, 1200-649 Lisbon, Portugal
Interests: sustainability; data science; digital transformation; competitive strategy; competitive dynamics; strategic groups; marketing
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

In today's dynamic and highly competitive market environment, organizations are increasingly relying on data-driven decision making to gain actionable insights and optimize their marketing strategies. The integration of advanced analytical techniques, artificial intelligence, and big data technologies has revolutionized the way businesses approach customer segmentation, campaign management, and performance evaluation. This Special Issue aims to explore how systems thinking can enhance the effectiveness of marketing analytics by considering the complex interdependencies between data sources, customer behavior, and business objectives. The Issue welcomes contributions that emphasize the holistic and interconnected nature of marketing systems, addressing challenges such as data integration, real-time decision making, and ethical considerations. By bringing together perspectives from systems theory, decision science, and marketing analytics, this Issue seeks to provide valuable insights for academics and practitioners looking to leverage data-driven approaches in their marketing strategies.

Dr. Rui Nunes Cruz
Dr. Albérico Travassos Rosário
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 250 words) can be sent to the Editorial Office for assessment.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Systems is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2400 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • marketing analytics systems
  • data-driven decision making
  • artificial intelligence in marketing
  • complex adaptive systems in marketing
  • systems thinking in marketing strategy
  • customer data integration
  • predictive analytics for marketing
  • decision support systems for marketing
  • business intelligence and marketing performance
  • ethical considerations in data-driven marketing

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Published Papers (6 papers)

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29 pages, 1118 KB  
Article
The Ecological Delivery Paradox in the Programmatic Advertising System Under Predictive Marketing
by İbrahim Kırcova, Munise Hayrun Sağlam and Ebru Enginkaya
Systems 2025, 13(12), 1059; https://doi.org/10.3390/systems13121059 - 23 Nov 2025
Viewed by 529
Abstract
Data-driven marketing analytics has advanced targeting and optimization, yet its underlying infrastructure now functions as a complex sociotechnical system with overlooked ecological costs. This study conceptualizes programmatic advertising through a systems lens. It introduces the Ecological Delivery Paradox, a structural incongruity where environmentally [...] Read more.
Data-driven marketing analytics has advanced targeting and optimization, yet its underlying infrastructure now functions as a complex sociotechnical system with overlooked ecological costs. This study conceptualizes programmatic advertising through a systems lens. It introduces the Ecological Delivery Paradox, a structural incongruity where environmentally friendly advertising messages are transmitted via energy-intensive delivery pipelines. Using an interpretivist–abductive design, we conducted 38 in-depth interviews with consumers and professionals, which were analyzed using reflexive thematic analysis in MAXQDA. Results show that awareness of hidden delivery costs emerges through a concretization threshold and crystallizes into metaphors such as “clean message, dirty conduit,” which trigger differentiated cognitive–affective pathways. These pathways shape trust trajectories across four profiles: cliff erosion, slow seep, suspended risk, and resilient cores. System-level moderators, including rationalization buffers, efficiency beliefs, and the visibility of low-data alternatives, determine outcomes. The findings extend marketing systems theory by reframing greenwashing as message–infrastructure misalignment and by integrating delivery congruence into advertising trust models. We propose a data-driven control architecture that aligns predictive analytics with ecological proportionality through mechanisms such as lightweight creatives, carbon-aware bidding coefficients, frequency–data quotas, and ad-level transparency labels. This systemic approach advances legitimacy, audience trust, and sustainability as joint objectives in programmatic advertising. Full article
(This article belongs to the Special Issue Data-Driven Insights with Predictive Marketing Analysis)
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19 pages, 754 KB  
Article
From Roadmap to Ecosystem: A Comprehensive Framework for Implementing Business Intelligence in Higher Education Institutions
by Romeu Sequeira, Arsénio Reis, Frederico Branco and Paulo Alves
Systems 2025, 13(11), 1032; https://doi.org/10.3390/systems13111032 - 18 Nov 2025
Viewed by 395
Abstract
Higher Education Institutions (HEIs) face increasing pressure to transform fragmented information environments into cohesive, data-driven ecosystems that support strategic and operational decision-making. This study proposes a comprehensive framework for implementing Business Intelligence (BI) in HEIs, evolving from a validated roadmap to an integrated [...] Read more.
Higher Education Institutions (HEIs) face increasing pressure to transform fragmented information environments into cohesive, data-driven ecosystems that support strategic and operational decision-making. This study proposes a comprehensive framework for implementing Business Intelligence (BI) in HEIs, evolving from a validated roadmap to an integrated ecosystem perspective. Grounded in the Design Science Research methodology, the work combines a systematic literature review, the design of a flexible BI architecture, and an in-depth case study at the University of Trás-os-Montes and Alto Douro (UTAD). The framework addresses critical factors such as strategic alignment, data governance, and system interoperability, and demonstrates how dashboards and analytics can enhance institutional intelligence and evidence-based management. Results from the UTAD case confirm the framework’s capacity to overcome technical and organisational barriers, enabling the transition from isolated systems to intelligent, interconnected data infrastructures. This research contributes to the literature by bridging theoretical guidelines and practical implementation, providing a scalable reference model to guide BI-driven digital transformation in higher education. It also demonstrates the tangible institutional value of integrated BI ecosystems in supporting more informed, timely, and efficient decision-making. Full article
(This article belongs to the Special Issue Data-Driven Insights with Predictive Marketing Analysis)
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19 pages, 134793 KB  
Article
A BERT–LSTM–Attention Framework for Robust Multi-Class Sentiment Analysis on Twitter Data
by Xinyu Zhang, Yang Liu, Tianhui Zhang, Lingmin Hou, Xianchen Liu, Zhen Guo and Aliya Mulati
Systems 2025, 13(11), 964; https://doi.org/10.3390/systems13110964 - 30 Oct 2025
Viewed by 998
Abstract
This paper proposes a hybrid deep learning model for robust and interpretable sentiment classification of Twitter data. The model integrates Bidirectional Encoder Representations from Transformers (BERT)-based contextual embeddings, a Bidirectional Long Short-Term Memory (BiLSTM) network, and a custom attention mechanism to classify tweets [...] Read more.
This paper proposes a hybrid deep learning model for robust and interpretable sentiment classification of Twitter data. The model integrates Bidirectional Encoder Representations from Transformers (BERT)-based contextual embeddings, a Bidirectional Long Short-Term Memory (BiLSTM) network, and a custom attention mechanism to classify tweets into four sentiment categories: Positive, Negative, Neutral, and Irrelevant. Addressing the challenges of noisy and multilingual social media content, the model incorporates a comprehensive preprocessing pipeline and data augmentation strategies including back-translation and synonym replacement. An ablation study demonstrates that combining BERT with BiLSTM improves the model’s sensitivity to sequence dependencies, while the attention mechanism enhances both classification accuracy and interpretability. Empirical results show that the proposed model outperforms BERT-only and BERT+BiLSTM baselines, achieving F1-scores (F1) above 0.94 across all sentiment classes. Attention weight visualizations further reveal the model’s ability to focus on sentiment-bearing tokens, providing transparency in decision-making. The proposed framework is well-suited for deployment in real-time sentiment monitoring systems and offers a scalable solution for multilingual and multi-class sentiment analysis in dynamic social media environments. We also include a focused characterization of the dataset via an Exploratory Data Analysis in the Methods section. Full article
(This article belongs to the Special Issue Data-Driven Insights with Predictive Marketing Analysis)
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18 pages, 898 KB  
Article
TimeWeaver: Time-Aware Sequential Recommender System via Dual-Stream Temporal Network
by Yang Liu, Tao Wang and Yan Ma
Systems 2025, 13(10), 857; https://doi.org/10.3390/systems13100857 - 29 Sep 2025
Viewed by 1363
Abstract
Recommender systems are data-driven tools designed to assist or automate users’ decision-making. With the growing demand of personalized sequential recommendations in business intelligence or e-commerce, effectively capturing temporal information from massive user-sequence data has become a crucial challenge. State-of-the-art attention-based models often struggle [...] Read more.
Recommender systems are data-driven tools designed to assist or automate users’ decision-making. With the growing demand of personalized sequential recommendations in business intelligence or e-commerce, effectively capturing temporal information from massive user-sequence data has become a crucial challenge. State-of-the-art attention-based models often struggle to balance performance with computational cost, while traditional convolutional neural networks suffer from limited receptive fields and rigid architectures that inadequately model dynamic user interests. To address these limitations, this paper proposes TimeWeaver, a time-aware dual-stream network for sequential recommendation, whose core innovations comprise three key components. First, it employs a re-parameterized large-kernel convolution to expand the effective receptive field. Second, we design a Time-Aware Augmentation mechanism that integrates inter-event time-interval information into positional encodings of items. This allows it to perceive the temporal dynamics of user behavior. Finally, we propose a dual-stream architecture to jointly capture dependencies across different time scales. The context stream employs a modern Temporal Convolutional Network (TCN) structure to strengthen the memorization of users’ medium- and long-term interests. In parallel, the dynamic stream leverages an Exponential Moving Average (EMA) mechanism to weight recent behaviors for sensitively capturing users’ immediate interests. This dual-stream design allows TimeWeaver to comprehensively extract both long- and short-term sequential features. Extensive experiments on three public e-commerce datasets demonstrate TimeWeaver’s superiority. Compared to the strongest baseline model, TimeWeaver achieves average relative improvements of 4.62%, 9.59%, and 4.59% across all metrics on the Beauty, Sports, and Toys datasets, respectively. Full article
(This article belongs to the Special Issue Data-Driven Insights with Predictive Marketing Analysis)
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46 pages, 6193 KB  
Article
E-Commerce Revolution: How the Pandemic Reshaped the US Consumer Shopping Habits: A PACF and ARIMA Approach
by Catalin Popescu, Manuela Rozalia Gabor and Adrian Stancu
Systems 2025, 13(9), 802; https://doi.org/10.3390/systems13090802 - 13 Sep 2025
Cited by 1 | Viewed by 4065
Abstract
Accelerated digital transformations and the evolution of consumer behavior in recent years underscore the need for a systemic perspective in marketing analytics to better comprehend the complex interplay between technology, data, and the profound changes triggered by global events, such as the COVID-19 [...] Read more.
Accelerated digital transformations and the evolution of consumer behavior in recent years underscore the need for a systemic perspective in marketing analytics to better comprehend the complex interplay between technology, data, and the profound changes triggered by global events, such as the COVID-19 pandemic. The COVID-19 pandemic has catalyzed a massive shift toward digitalization and transformed e-commerce from an option to a necessity for both businesses and consumers. This paper analyzes the total store and non-store sales, as well as total e-commerce sales, of the US retail trade across six main business categories and nine subcategories from the first quarter of 2018 to the first quarter of 2024. The data was divided into three time spans, corresponding to pre-, during, and post-COVID-19 pandemic periods, to examine the changing behavior of US consumers over time for different business categories. The statistical and econometric methods employed are the partial autocorrelation function (PACF), autocorrelation function, autoregressive integrated moving average model, inferential statistics, and regression model. The results indicate that the pandemic significantly increased non-store retailer sales compared to the pre-pandemic period, underscoring the importance of e-commerce. When physical stores reopened, e-commerce sales did not decline to pre-pandemic levels. The PACF analysis showed seasonality and lagged correlations. Thus, the pandemic-induced buying behaviors of US consumers continue to influence current sales patterns. The pandemic was more than just a temporary disruption, which permanently changed the retail sector. Retailers that quickly adapted to online models gained a competitive edge, whereas US consumers became accustomed to the convenience and flexibility of e-commerce. The behavior of US consumers adapted not only in response to immediate needs during the pandemic but also led to longer-term shifts in spending patterns, with each category reacting uniquely based on product type and perceived necessity. The analysis of how the COVID-19 pandemic transformed consumer behavior in the US reveals several important implications for both consumers and trade policymakers. First, the long-lasting and structural shift toward e-commerce is confirmed, representing a fundamental change in the dynamics of demand and supply. For consumers, the convenience, flexibility, and accessibility of digital channels have moved beyond mere situational advantages to become a behavioral norm. This shift has empowered consumers by giving them greater access to price comparisons, more diverse options, and increased informational transparency. Additionally, the data shows the emergence of hybrid consumption models: essential goods are mainly purchased online, while purchases of branded clothing, electronics, furniture, luxury items, and similar products continue to favor the traditional retail experience. Full article
(This article belongs to the Special Issue Data-Driven Insights with Predictive Marketing Analysis)
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Other

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38 pages, 3361 KB  
Systematic Review
Data-Driven Decision-Making in Marketing: A Systematic Literature Review of Emerging Themes and Research Gaps
by Rui Nunes Cruz and Albérico Travassos Rosário
Systems 2025, 13(12), 1114; https://doi.org/10.3390/systems13121114 - 10 Dec 2025
Viewed by 226
Abstract
This study assesses how Data-Driven Decision-Making (DDDM) impacts marketing practices and research. Using the PRISMA 2020 protocol, this research conducted systematic reviews of 94 peer-reviewed articles and utilized bibliometric and thematic analyses. From this, four major themes emerged: improvement in the customer experience [...] Read more.
This study assesses how Data-Driven Decision-Making (DDDM) impacts marketing practices and research. Using the PRISMA 2020 protocol, this research conducted systematic reviews of 94 peer-reviewed articles and utilized bibliometric and thematic analyses. From this, four major themes emerged: improvement in the customer experience via the personalization of marketing; marketing driven by innovation through data resource versatility, Machine Learning, analytics, and Artificial Intelligence; performance enhancement through the optimal allocation of resources; and the data governance and ethical use of such resources, and the use of such data resources. This study illustrates how the combination of multi-level theory and methodical stricture accounts for the systemic influence of DDDM in marketing. This study adds to these theories by proposing a cohesive and synthesized understanding of the interplay of the technological, organizational, and governance elements in data-driven marketing. This research provides organizations with actionable guidance aimed at increasing effective analytics-driven decision-making, while also ensuring the responsible use of data. Full article
(This article belongs to the Special Issue Data-Driven Insights with Predictive Marketing Analysis)
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