Big Data Analytics in Marketing Decision-Making
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Abstract
About This Research Topic
The volume, velocity and variety of data from digital marketing, transactions, CRM and social media have exploded, making big data analytics central to modern marketing. Businesses that systematically examine large datasets to uncover patterns should make faster, more accurate and more defensible decisions than those relying on intuition alone.
At SCHOLARNESTHUB, we transform survey-based projects into publication-ready SEO articles. This study on big data analytics in marketing decision-making is tailored for students searching for marketing project topics and business administration project topics in Nigerian secondary cities. While global surveys by McKinsey report growing analytics investment, adoption is uneven — large firms with data science teams progress faster than resource-constrained SMEs. Whether Enugu metropolis businesses have meaningfully integrated analytics or still rely on managerial intuition remains empirically underexplored. This article presents a fully verified guide with descriptive and inferential evidence from 240 marketing managers in Enugu East, North and South.
Main Abstract
This study examined the role of big data analytics in marketing decision-making among businesses in Enugu metropolis. Guided by four objectives, it examined extent of utilisation, effect on quality/accuracy, effect on speed, and relationship with overall effectiveness, plus comparison between firms with and without dedicated analytics tools/teams.
A descriptive survey design was adopted. Data were collected from 240 marketing managers and business decision-makers in Enugu metropolis, determined using Taro Yamane formula from estimated population of 600 businesses with formal marketing decision-making function, selected through multi-stage sampling, using structured 24-item 5-point Likert-scale questionnaire. Analysis used descriptive statistics (frequencies, percentages, mean scores) and inferential statistics (simple linear regression, Pearson Product Moment Correlation, independent samples t-test) with SPSS version 26.
Findings revealed: (1) big data analytics utilisation significantly and positively predicts quality and accuracy of marketing decisions (β = 0.556, p < 0.05); (2) utilisation significantly and positively predicts speed of decision-making (β = 0.487, p < 0.05); (3) strong positive relationship between utilisation and overall marketing decision-making effectiveness (r = 0.634, p < 0.05); and (4) businesses with dedicated analytics tools or teams reported significantly higher effectiveness than those without, t = 6.93, p < 0.05.
The study concluded big data analytics is statistically significant and substantial driver of both quality and speed, and that formal dedicated investment, rather than ad hoc data use, most strongly distinguishes higher-performing decision-makers. It recommends formalising analytics capability through tools, personnel or training, equipping managers with data literacy, and exploring affordable outsourced/platform-based options for smaller businesses to close the effectiveness gap.
Chapter One Preview
Background to the Study
The volume, velocity and variety of data generated through digital marketing channels, transaction systems, CRM platforms and social media have grown exponentially, giving rise to big data. Big data analytics — systematic examination of large, varied and often unstructured datasets to uncover actionable insights — has moved from periphery into operational core, informing customer segmentation, campaign targeting, pricing and product development (Wedel & Kannan, 2016).
The appeal rests on decision science: decisions grounded in systematic analysis of large relevant datasets should be more accurate than intuition. This builds on Herbert Simon's theory of bounded rationality (1955), that decision-makers operate under cognitive and informational limits; analytics extends boundaries of rational decision-making by processing far more information systematically (Provost & Fawcett, 2013). Global surveys consistently report growing investment, with data-driven organisations reporting advantages in confidence, speed and performance (McKinsey & Company, 2023).
However, transformation is uneven. Large well-resourced organisations with dedicated data science teams progress further than smaller resource-constrained firms for which cost, skill requirements and infrastructure remain barriers, as documented by research on data-driven decision-making. Within Nigeria, extent to which Enugu metropolis businesses have meaningfully integrated big data analytics into marketing — as opposed to superficial use or continued reliance on intuition and informal observation — remains open empirical question. This study examines role of big data analytics in Enugu with attention to decision quality/accuracy, speed, and overall effectiveness.
Statement of the Problem
Despite global evidence linking big data analytics to improved marketing decisions, many businesses in Nigerian secondary cities such as Enugu continue to rely substantially on managerial intuition, informal market observation and limited historical sales data. This raises questions about foregoing measurable benefits.
Three unresolved concerns: (1) proportion of Enugu businesses that have meaningfully integrated big data analytics and form this integration takes, given variation in infrastructure and resources; (2) while global literature links analytics to quality and speed, empirical evidence specific to secondary-city contexts where infrastructure, personnel and budgets are more limited than Lagos/multinational firms remains limited; (3) whether presence of dedicated analytics tools/personnel, as opposed to informal ad hoc use, is what most meaningfully distinguishes higher-performing decision-makers in Enugu. This study generates empirically grounded evidence to guide analytics investment.
Aim and Objectives of the Study
The aim is to examine the role of big data analytics in marketing decision-making among businesses in Enugu metropolis.
· Examine the extent of big data analytics utilisation in marketing decision-making among businesses in Enugu metropolis
· Assess the effect of big data analytics on the quality and accuracy of marketing decisions
· Evaluate the effect of big data analytics on the speed of marketing decision-making
· Determine the relationship between big data analytics utilisation and overall marketing decision-making effectiveness
· Compare marketing decision-making effectiveness between businesses with dedicated analytics tools/teams and those without
· Identify the challenges militating against big data analytics utilisation among businesses in Enugu metropolis
Research Questions
· To what extent do businesses in Enugu metropolis utilise big data analytics in marketing decision-making?
· What effect does big data analytics have on the quality and accuracy of marketing decisions?
· What effect does big data analytics have on the speed of marketing decision-making?
· What is the relationship between big data analytics utilisation and overall marketing decision-making effectiveness?
· Is there a significant difference in marketing decision-making effectiveness between businesses with dedicated analytics tools/teams and those without?
· What challenges militate against big data analytics utilisation among businesses in Enugu metropolis?
Research Hypotheses
· H01: Big data analytics utilisation does not significantly affect the quality and accuracy of marketing decisions.
· H02: Big data analytics utilisation does not significantly affect the speed of marketing decision-making.
· H03: There is no significant relationship between big data analytics utilisation and overall marketing decision-making effectiveness.
· H04: There is no significant difference in marketing decision-making effectiveness between businesses with dedicated analytics tools/teams and those without.
Significance of the Study
For business owners and marketing managers, findings offer evidence-based insight into whether analytics investment translates into measurable improvements in quality and speed. For analytics and business intelligence providers, findings on adoption patterns and challenges inform design of accessible tools suited to resource realities of secondary-city businesses. For policymakers and business support institutions, study provides context-specific evidence for data-literacy and analytics-capacity-building programmes. Academically, study extends decision-science and marketing analytics literature, particularly Bounded Rationality Theory and DIKW Hierarchy, into Nigerian secondary-city context with growing importance but limited documentation. Serves as methodological reference for students and future researchers.
Scope of the Study
Delimited to examination of role of big data analytics in marketing decision-making among businesses operating in Enugu metropolis (Enugu East, North, South LGAs). Focuses on marketing managers' self-reported extent of utilisation and its relationship with decision quality/accuracy, speed and overall effectiveness, rather than independently audited financial data. Cross-sectional, does not track adoption over extended period.
Operational Definition of Terms
Big Data: Large, high-velocity, highly varied datasets, often unstructured (social media, clickstream, transaction logs), exceeding conventional manual processing.
Big Data Analytics: Systematic application of statistical, computational and visualisation techniques to large varied datasets to uncover patterns and actionable insights.
Marketing Decision-Making: Process by which managers evaluate information and select courses of action related to marketing strategy, campaigns, pricing, segmentation or resource allocation.
Decision Quality/Accuracy: Perceived degree to which marketing decisions prove correct or well-founded in light of subsequent outcomes.
Decision Speed: Perceived time required to gather relevant information and arrive at marketing decision.
Marketing Decision-Making Effectiveness: Overall perceived success of marketing decision-making in achieving desired business outcomes, encompassing quality and speed.
Dedicated Analytics Tools/Team: Formally allocated software, personnel or units responsible for data analysis to support decisions, distinct from informal ad hoc use.
Conclusion
Findings revealed β=0.556 (p<0.05) for quality/accuracy, β=0.487 (p<0.05) for speed, r=0.634 (p<0.05) for overall effectiveness, and t=6.93 (p<0.05) showing dedicated tools/teams significantly outperform ad hoc use. Study concluded big data analytics is statistically significant and substantial driver of both quality and speed among Enugu businesses, and formal dedicated investment most strongly distinguishes higher performers. Recommended formalising analytics capability through dedicated tools, personnel or training, equipping marketing managers with basic data literacy, and exploring affordable outsourced/platform-based analytics for smaller businesses unable to afford dedicated teams to close effectiveness gap.
Frequently Asked Questions (FAQs)
1. What is big data analytics in marketing?
Systematic examination of large varied datasets (social, transactional, CRM) to uncover patterns, correlations and actionable insights that inform segmentation, targeting, pricing and product decisions.
2. Does big data analytics improve decision quality in Enugu?
Yes. Study of 240 Enugu businesses found utilisation significantly predicts quality/accuracy (β=0.556, p<0.05). Systematic analysis outperforms intuition alone, extending bounded rationality.
3. Does it make decisions faster?
Yes. β=0.487, p<0.05 for speed. Analytics reduces information gathering time and provides evidence-based shortcuts.
4. What is relationship with overall effectiveness?
Strong positive correlation r=0.634, p<0.05 between utilisation and overall marketing decision-making effectiveness.
5. Do dedicated analytics teams matter?
Critically. Businesses with dedicated tools/teams reported significantly higher effectiveness than those without (t=6.93, p<0.05). Formal investment matters more than ad hoc use.
6. How was study conducted?
Descriptive survey, population 600 businesses, sample 240 via Taro Yamane, multi-stage sampling, 24-item 5-point Likert questionnaire, SPSS 26 analysis with regression, Pearson r, t-test.
7. What challenges face Enugu businesses?
Cost, technical skill gaps, data infrastructure limits, and limited data literacy – typical for secondary cities vs Lagos/multinationals.
8. What should small businesses do if they cannot afford a team?
Explore affordable outsourced or platform-based analytics options (e.g., Google Analytics, Meta Business Suite, affordable BI tools) and invest in basic data literacy training.
9. Is this relevant beyond Enugu?
Context-specific but applicable to Nigerian secondary cities with similar business and infrastructure profiles. Limited generalisability to Lagos/multinational settings noted as limitation.
10. Where can I download full project?
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