Retail Analytics and Evaluation Metrics: Data-Informed Decision-Making for Retail Innovation in West Africa

This study examined how retail analytics and evaluation metrics drive data-informed decision-making and performance outcomes in West African retail enterprises. Guided by the technology–organisation–environment framework and a positivist paradigm, the study employed a mixed-methods descriptive survey design. A purposive sample of 286 retail managers and analytics officers was drawn from 48 supermarkets and chain outlets in Lagos, Nigeria, and Accra, Ghana. Data were gathered through the Retail Analytics Adoption Questionnaire (r = .81) and 12 in-depth interviews, and analysed using multiple regression, Pearson’s r, and thematic coding. Findings revealed that analytics capability significantly predicted data-informed decision-making, β = .47, t = 6.32, p < .001, R² = .38, and that dashboards and sales-forecast metrics accounted for 41.6% of the variance in stock-turnover efficiency, F(3, 282) = 66.96, p < .001. However, only 34.3% of outlets used predictive analytics routinely, with cost and skills gaps as leading barriers. Interview data linked metric use to faster markdown decisions and reduced stock-outs. The study concluded that analytics maturity, not mere data availability, differentiates high-performing retailers. It was recommended that retail firms in West Africa invest in analytics training, standardised KPI frameworks, and low-cost dashboard tools.