The Role of AI-Driven Customer Analytics in Marketing Decision-Making

Background to the Study

Marketing decision-making — deciding whom to target, what to offer, how to price, and where to allocate budget — has always been constrained by the decision-maker's capacity to process available information. As customer data has grown in volume and complexity, this constraint has become increasingly binding: no marketing manager can manually review millions of browsing sessions, thousands of customer reviews, or continuously updating transaction streams. AI-driven customer analytics has emerged as a response to this constraint, using machine learning and natural language processing techniques to convert raw customer data into structured, actionable insight at a scale and speed beyond human capability.

The promise of AI-driven customer analytics is not simply faster reporting, but genuinely improved decision-making: better-segmented markets, more accurate reading of customer sentiment, and earlier identification of at-risk customers, all of which should, in principle, lead to more effective marketing decisions. Yet the relationship between analytics adoption and improved decision-making is not mechanical. Analytics tools generate insight; insight must still be correctly interpreted and incorporated into actual decisions by marketing teams, a translation step that depends on the depth and actionability of the insight produced and on decision-makers' own capacity to understand and apply AI-generated outputs — a dynamic our related work on marketing analytics capability and competitive advantage of businesses documents from a broader capability perspective.

This suggests two distinct empirical questions that are often conflated in practitioner discourse around AI in marketing: first, whether AI-driven customer analytics actually produces richer, more useful customer insight; and second, whether that improved insight, even where produced, translates into better marketing decisions — a translation likely to depend on the AI literacy of the marketing team using it. In Nigeria, where adoption of AI-driven customer analytics is growing rapidly among larger financial, retail, and telecom firms but remains uneven among small and medium enterprises, and where formal AI/data literacy training is not yet standard within marketing education or practice, both questions carry particular practical significance. Institutions such as the Enugu State SME Centre play a direct role in supporting exactly this population of registered businesses as they navigate technology adoption decisions.

Statement of the Problem

Nigerian businesses are increasingly encouraged, by technology vendors and industry commentary alike, to adopt AI-driven customer analytics tools as a route to better marketing decisions. However, many firms adopt such tools without a clear understanding of the mechanism through which they are expected to improve decision-making, and without corresponding investment in building their marketing teams' capacity to interpret AI-generated insight. This creates a risk that firms invest in analytics technology and see disappointing returns, not because the technology fails to generate useful insight, but because that insight is not correctly understood or acted upon by decision-makers lacking sufficient AI literacy — a risk that remains largely unquantified within the Nigerian business context.

At the same time, existing academic literature on AI-driven customer analytics in marketing is dominated by conceptual and technical studies from developed markets, offering limited empirical evidence on the specific mechanism, insight quality, linking analytics adoption to decision outcomes, and even less evidence on the moderating role of marketing team AI literacy in an emerging market context such as Nigeria. This creates a gap between the confident promotional claims made about AI-driven customer analytics and the empirical evidence available to Nigerian marketing managers making real adoption and investment decisions. This study addresses this gap by empirically examining the role of AI-driven customer analytics in marketing decision-making among registered businesses in Enugu State, Nigeria.

Aim and Objectives

The aim of this study is to examine the role of AI-driven customer analytics in marketing decision-making among registered businesses in Enugu State, Nigeria. The specific objectives are to:

1. Examine the effect of AI-driven customer analytics adoption on customer insight quality.

2. Assess the effect of customer insight quality on marketing decision-making quality.

3. Determine the direct effect of AI-driven customer analytics adoption on marketing decision-making quality.

4. Evaluate the moderating effect of marketing team AI literacy on the relationship between customer insight quality and marketing decision-making quality.

5. Determine the combined predictive effect of AI-driven customer analytics adoption, customer insight quality, and AI literacy on marketing decision-making quality.

Research Questions

1. What is the effect of AI-driven customer analytics adoption on customer insight quality?

2. What is the effect of customer insight quality on marketing decision-making quality?

3. What is the direct effect of AI-driven customer analytics adoption on marketing decision-making quality?

4. What is the moderating effect of marketing team AI literacy on the relationship between customer insight quality and marketing decision-making quality?

5. What is the combined predictive effect of AI-driven customer analytics adoption, customer insight quality, and AI literacy on marketing decision-making quality?

Significance of the Study

This study is significant to several categories of stakeholders. To marketing managers and business owners, the findings clarify the actual mechanism through which AI-driven customer analytics is expected to improve decision-making, and highlight the specific role of marketing team AI literacy in realizing this benefit, informing more effective adoption and training investment decisions. To providers of AI-driven analytics and CRM platforms serving the Nigerian market, the study offers evidence on the conditions necessary for customers to realize value from their tools, relevant to onboarding and training product design.

To business support institutions and marketing educators, the study provides evidence to inform the design of AI literacy curricula and capacity-building programmes for marketing professionals. For the academic community, it extends Information Processing Theory and Bounded Rationality Theory into an under-researched emerging market marketing context. Marketing professionals or students working on comparable AI-analytics research may find it worth refining their own methodology with ScholarNestHub's research coaching support.

Scope of the Study

The study is focused on examining the role of AI-driven customer analytics in marketing decision-making. The content scope covers AI-driven customer analytics adoption, customer insight quality, marketing team AI literacy, and marketing decision-making quality. The geographical scope is limited to registered businesses operating in Enugu State, Nigeria, spanning the retail/consumer goods, financial services, media/telecom, and manufacturing/other sectors. The study covers business practices and reported outcomes within the period 2023–2026.

Operational Definition of Terms

AI-Driven Customer Analytics

The use of artificial intelligence and machine learning techniques (segmentation algorithms, sentiment/text analytics, predictive/churn models) to analyse customer data and generate marketing-relevant insight.

Customer Insight Quality

The depth, accuracy, timeliness, and actionability of the customer-related insight generated by a firm's analytics processes.

Marketing Team AI Literacy

The degree to which marketing personnel understand how AI-driven analytics tools generate their outputs and are able to correctly interpret and apply those outputs in decision-making — a capacity gap that agencies such as NITDA increasingly address through broader national AI skills and literacy initiatives.

Marketing Decision-Making Quality

The perceived speed, confidence, and effectiveness of marketing decisions, such as targeting, budget allocation, and campaign design, made within a firm.

Conclusion

The mediation finding here is the one worth sitting with: AI-driven analytics mostly improves marketing decisions indirectly, by improving the quality of customer insight available, not by some direct, automatic effect on decision quality itself. That means insight quality is the real lever, and AI literacy is what determines how much of that lever actually gets pulled. A firm that adopts sophisticated analytics tools but doesn't invest in its marketing team's ability to interpret them is, in effect, generating insight nobody can fully use. For Enugu's registered businesses weighing where to spend next, the finding is fairly direct: analytics tools and AI literacy training aren't separate line items competing for budget, they're two halves of the same investment. Readers researching related marketing, AI, or decision-making questions can find further comparative material in our marketing project topics library.

Frequently Asked Questions

1. Does AI-driven customer analytics directly improve marketing decision-making?

Partly — the study found a smaller but statistically significant direct effect, alongside a stronger indirect effect that works through improved customer insight quality, consistent with partial mediation.

2. What is customer insight quality, and why does it matter so much?

It refers to the depth, accuracy, timeliness, and actionability of customer-related insight, and the study found it to be the primary mechanism through which AI-driven analytics improves marketing decision-making quality.

3. What is marketing team AI literacy?

It is the degree to which marketing personnel understand how AI-driven analytics tools generate their outputs and can correctly interpret and apply those outputs in real decisions.

4. Why does AI literacy matter if the analytics tool already generates good insight?

Because insight only improves decisions once it is correctly understood and acted upon; the study found that marketing team AI literacy significantly and positively moderates how much benefit teams actually realize from high-quality customer insight.

5. What theoretical frameworks guided this study?

The study was anchored on Information Processing Theory, Bounded Rationality Theory, and the Technology-Organization-Environment framework.

6. Which businesses were studied, and why Enugu State specifically?

The study surveyed marketing managers and business owners from registered businesses in Enugu State across retail/consumer goods, financial services, media/telecom, and manufacturing sectors, an emerging-market context underrepresented in existing AI-marketing literature.

7. What research design and sample were used in this study?

A descriptive survey design was used, with a sample of 300 determined from a population of 1,200 registered businesses using the Taro Yamane formula and stratified random sampling, analysed using SPSS version 26.

8. What did the study recommend for businesses adopting AI-driven customer analytics?

It recommended investing jointly in AI-driven analytics tools and in building marketing staff's AI literacy, rather than treating tool adoption alone as sufficient to improve decision-making.

9. Is this pattern specific to large firms, or does it apply to SMEs too?

The study specifically examined registered businesses in an emerging market context where AI analytics adoption remains uneven among SMEs, making the findings particularly relevant to smaller firms weighing initial adoption decisions.

10. What's the biggest risk the study identifies for firms adopting AI analytics tools?

That firms invest in analytics technology and see disappointing returns, not because the technology fails to generate useful insight, but because that insight is not correctly understood or acted upon by decision-makers lacking sufficient AI literacy.