2692-2967ISSN (Online)
2770-0216ISSN (Print)
The strategic importance of customer retention in small and medium-sized enterprises (SMEs) is due to the fact that the resources are limited, and the indiscriminate customer acquisition and customer retention campaigns are economically inefficient. However, the descriptive reporting used by many SMEs does not have the advantages of transactiondriven analytics that allows differentiating between high-value and low-yield customer relationships. This paper creates a repli-cable customer-analytics pipeline in SME-type retail environments, using publicly available transactional data. In con-trast to the macro-level forecasting research, the paper integrates customer value segmentation with the futureoriented repeat-purchase prediction and translates the results into retention actions explicitly. The customer-level features were based on invoices, quantities, prices, product variety, and return behavior and were derived using the public Online Retail dataset. Observation windows on a monthly were transformed into a repeat-purchase 90-day problem. Three predictive models—logistic regression, random forest, and gradient boosting—were compared after customer segmentation based on recency, frequency, and monetary behavior. The findings indicate that random forest model had the highest discrimination (ROC-AUC = 0.750; PR-AUC = 0.821), followed by logistic regression, which was only slightly less than it and more interpretable. Segment analysis also showed a very concentrated revenue base with Champions having 27.5 percent of the customers but 67.2 percent of recent revenue and 81.0 rate of repeat purchasing. The paper provides a submission-ready, transparently reproducible, and managerially understandable design that is particularly applicable in SMEs that want low-cost retention analytics, customer ranking, and allocation of marketing resources.
Read MoreDoi: https://doi.org/10.54216/AJBOR.140201
Vol. 14 Issue. 2 PP. 01–12, (2026)
This paper evolves a business data analytics approach to capital allocation by exploring how the use of public panel data can aid in estimating, classifying, and profiling strategic firms. The paper examines the claim that lagged market value, capital stock, and growth signals can explain the current investment behavior and hint when the investment activity is unusually high using the public-domain Grunfeld investment data, which has annual observations of major U.S. manufacturing firms. The empirical design is deliberately non-standard as compared to typical forecasting research and it consists of three analytical layers; fixed-effects panel estimation, supervised classification of high-investment periods, and firm-level strategic segmentation. The findings indicate that the growth of lagged investment, lagged capital stock and firm value is highly correlated with the present level of investment, and that machine-learning classifiers offer helpful discrimination of high in-vestment periods. Strategic segmentation exercise also indicates the clear profiles of firms that can be used to prioritize resources and track capital. The value of the paper is two-fold. First, it illustrates how an old, conventional, public data may be re-used as a new business data analytics example to support decision-making. Second, it interprets quantitative results into a managerial advice on capital planning, growth monitoring, and portfolio-style firm evaluation. Accordingly, the paper provides a reproducible submission-ready study that has a different structure than the traditional business intelligence forecasting papers and is more in line with the requirements of strategic financial analysis and data-driven capital allocation.
Read MoreDoi: https://doi.org/10.54216/AJBOR.140202
Vol. 14 Issue. 2 PP. 13–25, (2026)