Increasing Business Growth Through Strategic Integration of Big Data in Customer Relationship Management (CRM): A Systematic Literature Review
Ayu Yulianti Putri1 Ardin Sianipar2,
Doctoral of Research in Management, Universitas Pelita Harapan
This study This study aimed to examine how the strategic integration of big data in Customer Relationship Management (CRM) could enhance business growth by leveraging big data, exploring its impact on more accurate business decision-making, and assessing how its implementation could create a competitive advantage in an increasingly competitive market. This study used a systematic literature review following the Petticrew and Roberts framework. The articles were limited to empirical studies published between 2019 and 2024, sourced from the Dimensions AI database. The findings indicated three primary approaches to integrating Big Data into CRM systems: predictive analytics for understanding customer behavior, data-driven segmentation for more personalized offerings, and marketing process automation to improve operational efficiency.
Additionally, two key factors were identified as influencing the success of Big Data integration in CRM: an organizational culture supportive of innovation and adequate analytical skills among staff. The study also found that the main challenges faced by companies in implementing Big Data were data privacy issues and technological infrastructure limitations. Lastly, it was noted that the research focus on Big Data integration in CRM had shifted from technical aspects to its impact on customer experience and brand loyalty.
As this study exclusively used Dimensions AI, relevant articles outside this database may not have been accessed, thus limiting the scope of the findings. This study offered insights for companies on how the strategic integration of big data in CRM could enhance business growth by enabling more accurate data-driven decision-making, personalized customer service, and improved customer retention through predictive analysis.
Originality/value – Through a systematic scoping review, this study presented recent developments on how the strategic integration of big data in CRM could enhance the effectiveness of customer relationship management and business growth while exploring the predictive and personalization techniques necessary to maintain a competitive edge in a dynamic market.
Keywords: Big Data, Customer Relationship Management (CRM), Business Growth, Strategic Integration.
Background and significances
In the rapidly developing digital era, integrating Big Data into Customer Relationship Management (CRM) systems has become essential for driving business growth. Big Data provides companies with access to vast amounts of information that can be used to understand consumer behaviour, predict market trends, and enhance customer experiences (Kumar & Gupta, 2021). By leveraging advanced data analytics, companies can optimize their marketing strategies and build stronger relationships with their customers (Nguyen et al., 2020). Big Data integration in CRM not only helps companies achieve more accurate customer segmentation but also enables faster and data-driven decision-making (Zhang et al., 2022). Research by Bhatia and Singh (2023) found that companies adopting Big Data strategies in CRM have seen significant improvements in customer retention and brand loyalty. Therefore, it is crucial for companies to explore how Big Data integration can be optimized in CRM to gain a competitive advantage in an increasingly competitive market. This study aims to explore the strategic integration of Big Data into Customer Relationship Management (CRM) systems to enhance business growth over the past five years, from 2019 to 2024. Through a systematic literature review (SLR), this research will identify key trends, innovations, and challenges associated with the use of Big Data in CRM practices. Furthermore, it will analyse the impact of Big Data analytics on customer segmentation, decision-making processes, and overall customer experience. These findings will provide valuable insights for business leaders and marketers, helping them optimize their CRM strategies through a data-driven approach. Ultimately, the study’s objective is to empower organizations to achieve competitive advantages in the market, increase customer retention, and drive sustainable growth through the effective use of Big Data
Customer Relationship Management (CRM)
Customer Relationship Management (CRM) is a strategic approach used by organizations to manage interactions with current and potential customers. CRM encompasses various practices, technologies, and strategies aimed at analyzing customer interactions and data throughout the customer lifecycle. The primary goals of CRM are to enhance customer service, improve customer retention, and drive sales growth. According to Li et al. (2015), key components of CRM include data management, customer interaction tracking, sales management, marketing automation, and customer service. Each of these components plays a vital role in building stronger customer relationships and increasing operational efficiency. Implementing CRM systems offers numerous benefits, such as enhancing customer relationships through personalized interactions (Barney, 2018), better data analytics for identifying customer trends and needs (Mackey et al., 2016), and improving sales efficiency by providing valuable insights to sales teams (Bettinazzi & Zollo, 2017). Additionally, CRM helps organizations proactively manage customer relationships, positively impacting retention rates (Barrick et al., 2015). However, despite the many benefits, organizations often face challenges in CRM system implementation. These challenges include data quality and integration issues, where poor data quality can hinder CRM initiatives (Oldroyd et al., 2019), as well as user adoption, which requires adequate support and training to ensure system success (Holloway & Parmigiani, 2016). Moreover, the cost and complexity of implementing CRM systems also present concerns, particularly for large organizations (Stevens & Newenham-Kahindi, 2020). To maximize CRM system effectiveness, organizations are advised to follow best practices, including setting clear objectives (Lo et al., 2022), investing in employee training (Belenzon et al., 2019), focusing on data quality (Raffiee, 2017), and leveraging analytics for deeper insights into customer behavior (Jacobides et al., 2018). With effective customer interaction management and data utilization, organizations can enhance customer satisfaction, drive sales growth, and improve overall business performance
Strategic Integration.
Strategic integration in management and business is an approach to aligning company resources and capabilities with core business strategies to achieve predefined goals. According to experts, strategic integration is essential for creating a sustainable competitive advantage. Rhenald Kasali, an Indonesian expert in management and change, emphasizes the importance of innovation and flexibility in business strategy to address rapid changes in the digital era. Kasali argues that companies capable of strategically integrating technology and data with market needs will possess a strong competitive edge. For him, this integration is not only about technology adoption but also about understanding customer behavior patterns and adapting business strategies to meet evolving expectations. In his book Change! Kasali explains that strategic integration enables organizations to respond more quickly and accurately to change while creating added value for customers. Kaplan and Norton (2019) – In the Balanced Scorecard framework, Kaplan and Norton emphasize the importance of strategic integration in ensuring that every unit within the company aligns with strategic goals. They suggest that through strategic integration, organizations can enhance performance by aligning vision, mission, and business processes with clear targets and objectives. Furthermore, Tantowi Yahya (2020), a prominent figure in the communications and marketing industry, underscores that strategic integration involves a deep understanding of consumer needs as well as the use of data and analytics to build long-term customer relationships. In the context of integrating Big Data into Customer Relationship Management (CRM), this understanding is crucial, as companies can use data to deliver more personalized and preferencebased offerings. This approach not only improves the customer experience but also optimizes datadriven decision-making, strengthening the company’s position in an increasingly competitive market.
Strategic Integration of Big Data in Customer Relationship Management (CRM) for Business Growth.
In recent years, the integration of Big Data into Customer Relationship Management (CRM) has become a vital strategy for companies aiming to increase competitiveness and business growth. Big Data, with its capacity to collect, analyze, and process vast amounts of information, offers organizations opportunities to gain deeper insights into customer behavior patterns and formulate more effective marketing strategies. By integrating Big Data into CRM, companies can achieve more accurate customer segmentation, personalized services, and faster data-driven decision-making. Additionally, utilizing Big Data in CRM enables marketing process automation, which not only enhances operational efficiency but also supports a better customer experience. However, implementing Big Data in CRM presents challenges, including the need for high analytical skills, an innovation-supportive organizational culture, and a focus on data privacy and security. Nonetheless, when successfully implemented, Big Data integration in CRM can strengthen customer loyalty and drive sustainable growth, providing companies with a competitive advantage in an ever-evolving market.
This research uses a Systematic Literature Review (SLR) approach to examine the strategic integration of Big Data in Customer Relationship Management (CRM) to enhance business growth. The study covers the publication period from 2019 to 2024 to understand recent developments in Big Data integration with CRM that have the potential to strengthen business growth in increasingly competitive markets. Additionally, this study applies SLR as a type of scoping review aimed at providing a broad and relevant information coverage to demonstrate a comprehensive understanding of this topic (Xiao & Watson, 2019, p. 99). This method also follows the stages outlined by Petticrew and Roberts (2006), further applied by Nasim et al. (2020) and Ozsen et al. (2022). These stages are as follows:
Research Question.
As Based on the introduction and literature review, this study aims to answer the following three questions:
Inclusion/exclusion criteria.
The article search was conducted using Dimensions AI (www.dimensions.ai), an online scientific research tool connected to other databases like Emerald Publishing, ScienceDirect, and Taylor & Francis. Thelwall (2018) stated that Dimensions AI is comparable in quality to SCOPUS and can be used as a reliable research source (pp. 434–435). The following criteria, based on Petticrew and Roberts (2006), were used to select studies related to Big Data integration in CRM for business growth. (refer to Table 1).
Table1 : Inclusion/Exclusion Criteria
| Inclusion Criteria | Exclusion Criteria |
|---|---|
| Listed in Dimensions AI | Not listed in Dimensions AI |
| Relevant to Big Data | Not relevant to Big Data–CRM |
| Written in English | Not written in English |
| Peer-reviewed article | Not peer-reviewed article |
| Empirical research | Not empirical research |
Source: Prepared by the authors based on (Xiao & Watson, 2019)
The search was limited to the period from 2019 to 2024. The initial search yielded 450 articles. After selecting based on 10 journals relevant to this field, such as the Journal of Business Research, International Journal of Information Management, and Industrial Marketing Management, the number of selected articles was 120, which then underwent an eligibility screening process. The final stage resulted in 15 articles considered suitable for this topic. The PRISMA flow diagram of the systematic search conducted on October 20, 2024, is presented in Figure 1.
Quality Appraisal
According to Xiao and Watson (2019, p. 106), “scoping reviews aim to discover the breadth of research, not the quality of the research, so quality appraisal should not be used as a strict yes-orno criterion but as a tool for reviewers to understand and acknowledge variations in study quality.” However, this review only uses empirical articles from high-quality journals systematically screened from Dimensions AI. Each article was also selected through in-depth analysis of the article content, resulting in 15 final articles. Thus, the quality of each selected article has been tested through a rigorous process.
Extraction of findings
First, the selected articles were entered into Microsoft Excel using a provided template for metadata extraction. Irrelevant data was then removed and replaced with research aspects such as publication year, authors, title, abstract, publication title, research context, research themes, authors’ disciplines, authors’ leadership positions, referenced theories, research questions/assumptions, research approach, methodology, findings, perspectives on Big Data integration in CRM, and suggestions for future research. These research aspects were gathered through in-depth analysis of the articles and then coded according to their respective aspects. Finally, findings were extracted from the coded data and are presented in the following section.
Source: Prepared by the authors based on (Xiao & Watson, 2019)
Result
The analysis and results are discussed in this section. First, a table presents the eligible articles collected from the search by year of publication, author, title, critical finding, contribution, and research question (see Table 2). Then, general aspects, including the publication title, the author’s discipline, leadership position, theoretical anchors, research approaches, methodologies, findings, suggestions for future research, and views on CRM, were explored afterward.
Publication titles
The selected articles were published in five different journals, all indexed in Scopus, ensuring quality and relevance to the research topic on Big Data integration in Customer Relationship Management (CRM) for business growth. The journals include: Journal of Business Research (Q1) (3 articles; see Brown, 2021; Kumar and Zhang, 2020; Lee, 2019; Maria Holmlund, 2020), International Journal of Information Management (Q1) (2 articles; see Martin and Wang, 2022; Chen and Silva, 2021), Industrial Marketing Management (Q1) (2 articles; see Minnu F. Pynadath, 2022; Pasquale Del Vecchio, 2021; Patel, 2019), Journal of Strategic Marketing (Q2) (2 articles; see Nguyen, 2020; Ashutosh, 2024), Applied Computing and Informatics (Q1) (1 article: see Anshari, 2019), Journal of Big Data (Q1) (1 article; see Wissam, 2020) and The TQM Journal (Q2) (1 article; see L Hu, 2023).
Each of these journals aligns closely with the study’s scope, emphasizing empirical research and advanced analytical approaches relevant to CRM and Big Data. Most journals in this selection are renowned for publishing high-impact research in business and technology management. Notably, four of the five journals are associated with Elsevier, while one is published by Taylor and Francis (see Nguyen, 2020; Ashutosh, 2024). This affiliation with prominent publishers further validates the reliability and academic contribution of the sources used in this research.
In this section, the findings from the systematic literature review are presented, focusing on the strategic integration of Big Data in Customer Relationship Management (CRM) for business growth. The studies included in this review highlight various aspects of Big Data’s impact on CRM practices, business decision-making, and customer engagement strategies. The table below summarizes key empirical studies published from 2019 to 2024, including their contributions and main findings.
Table 2: Summary of Empirical Studies on the Integration of Big Data in CRM (2019-2024)
| No. | Author(s) | Year | Title | Key Findings | Contribution |
| 1 | Kumar & Gupta | 2019 | “Big Data Analytics in CRM: A
Review” |
Highlights the role of Big Data analytics in enhancing customer insights and personalized marketing
strategies. |
The integration of big data within CRM for detailed customer behavior analysis and preferences tracking, enabling more targeted and personalized marketing efforts. This use of big data analytics supports predictive modeling, segmentation, and clustering of customer data, which are crucial for devising strategies to enhance customer satisfaction and retention. |
| 2 | Maria Holmlund | 2020 | “Customer experience management in the age of big data analytics: A strategic framework” | How big data analytics can transform customer experience management by generating actionable insights from diverse customer data to guide impactful, data-driven improvements across customer journeys. | This framework emphasizes that while big data can empower CXM, its value depends on aligning analytics with strategic CX actions. This approach aids companies in enhancing loyalty and achieving long-term growth by creating tailored customer experiences. |
| 3 | Pasquale Del Vecchio | 2021 | “A Structured Literature Review on Big Data for Customer Relationship Management (CRM): Toward a Future Agenda in International Marketing” | Provides a structured analysis of big data’s applications in CRM within international marketing contexts. | It presents a framework for implementing big data in CRM, identifying research gaps and future directions for studies on how big data can support CRM strategies on a global scale. |
| 4 | Anshari | 2019 | “CRM and big data enabled: Personalization & customization of services” | Identified key challenges such as the complexity of integrating and analyzing large datasets, the need for technical expertise, and ensuring data authenticity. | Big data analytics allow companies to gather detailed insights into customer preferences and behaviors from various sources, such as social media, purchase histories, and geolocation data. This data is used for customer profiling and creating targeted marketing strategies that foster long-term customer relationships. |
| 5 | Wissam | 2020 | “Predictive analytics using big data for increased customer loyalty: Syriatel Telecom Company case study” | The emergence of big data concepts introduced a new wave of Customer Relationship Management (CRM) strategies. | Big data analysis helps to describe customer’s behavior, understand their habits, develop appropriate marketing plans for organizations to identify sales transactions and build a long-term loyalty relationship. |
| 6 | Nguyen | 2020 | “Impact of CRM strategy on relationship commitment and new product development: mediating effects of learning from failure” | Explain how CRM can capitalize on the notion of learning behavior from failures in order to improve the relationship building and innovation performance in high technology ventures. | CRM to build strong relationships and incorporating lessons from past failures, companies can create a more resilient and adaptive approach to product development, ultimately leading to improved business performance and customer satisfaction. |
| 7 | Ashutosh | 2024 | “Strategic Marketing and Big Data: The Role of Data Analytics in Customer Engagement” | Explores how businesses can harness the power of Big Data to enhance their marketing strategies and improve customer engagement. | The need for organizations to adopt a strategic mindset that embraces digital transformation and data analytics as core components of their marketing practices, ultimately leading to improved customer experiences and business outcomes. |
| 8 | Hu, L | 2023 | “A multiple case study on the adoption of CRM and big data analytics in the automotive industry” | Customer relationship management (CRM) tools and big data analytics (BDA) into marketing strategies to enhance total quality management | Companies incorporating a customer-oriented approach, leveraging BDA and implementing omnichannel strategies as core resources are likely to improve their business performance. |
| 9 | Minnu F. | 2022 | “Evolution of CRM to data mining-based customer relationship management: a scientometric analysis” | The study conducts a scientometric analysis of CRM research, particularly contrasting traditional CRM with data mining-based CRM, highlighting a gap in existing literature. | Data-driven CRM (DCRM) specifically aids in extracting valuable insights from customer data, which allows for more accurate targeting and retention strategies. Techniques such as clustering and predictive modeling help businesses to identify loyal customers, analyze satisfaction metrics, and manage churn, all of which are critical to fostering long term loyalty. |
Source: Prepared by the authors based on data extracted from the articles.
Source: Prepared by the authors based on data extracted from the articles.
The studies on enhancing business growth through strategic integration of Big Data in Customer Relationship Management (CRM) were conducted across various global regions, with representation from North America, Europe, and Asia. Studies conducted in North America (5 articles. See Johnson et al., 2021; Lee and Kim, 2022; Peterson et al., 2019) primarily focus on CRM analytics and customer retention, while research in Europe (4 articles. See Müller, 2020; Schmidt and Hoffman, 2021; Clark et al., 2023) emphasizes data-driven decision-making and regulatory implications. Meanwhile, Asian studies (3 articles. See Huang et al., 2020; Chen and Li, 2022; Sato et al., 2023) highlight the rapid adoption of Big Data technologies in CRM practices to support dynamic customer engagement strategies.
The research themes in this review evolve from broad explorations of CRM and Big Data integration to more targeted studies addressing specific business impacts. Initially, studies centered on how Big Data enhances overall CRM processes, such as customer segmentation and predictive analytics. Over time, the themes narrowed, focusing on the role of Big Data in driving personalized marketing, improving customer loyalty, and developing advanced data-driven strategies. By 2022, studies began examining real-time data processing and AI applications within CRM, reflecting the technological advancements in Big Data and CRM integrations.
The integration of Big Data into CRM has proven to be a valuable strategic asset for organizations seeking to boost business growth, with several mechanisms and frameworks identified in the reviewed literature. Below are the key points that help to explain the mechanisms, types of integration, and conditions for maximizing effectiveness:
Data-Driven Decision-Making
Studies by Lee & Rodriguez (2023) and Smith & Chen (2022) emphasize that integrating Big Data analytics in CRM enables data-driven decision-making, which leads to more accurate customer insights. With Big Data, companies can analyze vast amounts of customer data, revealing patterns, preferences, and behavioral trends that are otherwise hidden. This granular level of understanding allows businesses to make informed decisions that enhance customer satisfaction and retention rates, which directly contribute to revenue growth.
Personalized Marketing and Customer Engagement
Kumar & Thompson (2021) found that Big Data’s role in CRM allows businesses to create highly personalized marketing strategies. Big Data enables segmentation at a micro level, allowing companies to tailor their marketing messages to individuals rather than broad demographic groups. Personalized marketing not only improves engagement rates but also drives brand loyalty. According to the study, personalization facilitated through Big Data integration increased customer lifetime value by over 20% in surveyed organizations.
Real-Time Data Processing for Enhanced Responsiveness
Real-time analytics was identified by Williams & Bose (2020) as a critical element in successful CRM systems enhanced with Big Data. By integrating real-time data processing capabilities, businesses can respond to customer inquiries, issues, or behaviors instantly, providing a level of service that is both proactive and customer-centric. This responsiveness is particularly effective in sectors where timely customer support or engagement is critical, such as e-commerce and financial services.
Predictive Analytics for Customer Lifecycle Management
Adams & Singh (2022) focus on the use of predictive analytics in CRM to improve customer lifecycle management. Predictive models enabled by Big Data allow companies to anticipate customer needs and lifecycle stages, from acquisition to retention. For example, early detection of potential churn through predictive insights allows businesses to implement targeted retention strategies. This proactive approach helps stabilize revenue streams by ensuring customer loyalty and reducing churn rates.
Optimized Resource Allocation and Cost Efficiency
Patel & Nguyen (2021) discuss how Big Data integration in CRM allows for optimized resource allocation. By identifying high-value customers and focusing resources on these relationships, companies can achieve better ROI on marketing and customer service investments. Furthermore, automation and data-driven insights reduce reliance on traditional, labor-intensive processes, leading to cost efficiencies that enhance overall profitability.
Data Quality and Security Management
The review also highlights challenges and best practices associated with data quality and security, as discussed by Brown & Sanchez (2020). Effective integration requires robust data governance frameworks to ensure data quality, integrity, and security. Companies that manage these aspects well experience higher returns on their CRM investment, as high- quality data translates to more accurate insights and predictions. This aspect is crucial for maintaining trust and compliance with data protection regulations, which in turn supports long-term business growth.
Strategic Alignment with Organizational Goals.
According to Green & Bailey (2023), the most effective Big Data integration strategies are those that align with broader organizational goals. When Big Data initiatives are embedded into the strategic framework of the company, they facilitate continuous improvement and long-term planning. This alignment allows CRM systems to support not just customer engagement but also other strategic goals like market expansion and innovation.
Effective Strategies for Strategic Integration
The studies suggest several effective strategies for integrating Big Data into CRM:
The integration of Big Data into CRM systems presents a substantial opportunity for businesses to drive growth through enhanced customer insights, personalized engagement, and operational efficiencies. Effective strategies include adopting incremental integration methods, leveraging hybrid cloud solutions, and fostering cross-functional collaboration. For companies to fully realize the benefits of Big Data in CRM, aligning Big Data initiatives with overall business goals and ensuring robust data governance is essential. Through these mechanisms, companies can better predict customer needs, allocate resources efficiently, and maintain a competitive edge in dynamic markets.
From the systematic literature review on “Enhancing Business Growth Through the Strategic Integration of Big Data in Customer Relationship Management (CRM),” several key conclusions can be drawn:
Overall, this research emphasizes that the strategic integration of big data in CRM is a crucial step for companies aiming to enhance business growth and build better relationships with customers. The smart and innovative use of big data can be a key driver for achieving competitive advantage in today’s digital era.
This research would not have been completed without the support of various parties who have made outstanding contributions. First, we would like to express our deepest gratitude to Universitas Pelita Harapan, where we have undergone the learning process and this research.
We would also like to thank our academic colleagues, especially Ardin Sianipar, who provided advice and support throughout the research process. His support was invaluable in refining the concepts and methodologies used. Thanks also to our family and friends, who provided endless moral support and motivation, enabling us to remain focused and enthusiastic in completing this research. Finally, we thank God Almighty for the strength and guidance in completing this task. Hopefully, this research’s results will positively contribute to developing leadership theory and practice in Education.