Enhancing Strategic Decision Quality through Market and Competitive Intelligence

The value of competitive intelligence can be assessed by examining how its findings inform strategic choices. Which assumptions did the analysis lead decision-makers to reconsider, and how did it shape their evaluation of strategic alternatives? These questions become increasingly important as digital platforms and artificial intelligence expand organizations’ capacity to process information. Assessing the value of this expanded capacity requires understanding how managers interpret analytical findings and whether their use improves the quality of strategic decisions. Integrating market, competitor, and customer information is an established approach within competitive intelligence. Earlier research links this integration to understanding the external environment and supporting strategic decisions (Aspinall, 2011; Santos & Correia, 2010). The contribution of contemporary research therefore lies in explaining the mechanisms through which organizations identify relevant signals, assess their interpretations, and apply the resulting insights. Recent scholarship examines competitive intelligence in relation to data analytics capabilities and conceptually connects it to sensing external change, taking strategic action, and organizational adaptation (e.g., Černý et al., 2026; Ranjan & Foropon, 2021). The research challenge is therefore to explain the mechanisms through which integrated information contributes to strategic decision quality. Evidence from practice highlights this challenge. A Forrester survey reports that intelligence teams integrate diverse information sources to support decisions across strategy, product development, and sales. Their performance is often assessed through the number of analyses produced and users reached, while their contribution to business outcomes remains difficult to establish (Caplow, 2026). Although limited in scope, the survey highlights a measurement problem: assessing the strategic value of intelligence requires understanding how its findings inform decisions. Competitive intelligence research should therefore examine the conditions under which high-quality information supports better-informed strategic choices. Research on business intelligence offers relevant evidence on these mechanisms, although its findings require careful interpretation in the competitive intelligence context. Data quality, visualization, and analytics management have been positively associated with decision quality (Abu-AlSondos, 2023). A subsequent banking-sector study identifies organizational AI capabilities as moderators of the relationships between data quality, system quality, visualization, and decision quality (Abu-AlSondos, 2026). These findings direct attention to the capabilities through which organizations put analytical resources to use. AI further sharpens the question of how analytical outputs inform reasoned choices. A framework tested using customer interaction data demonstrates how customer segmentation and explainable AI can be combined with the prioritization of decision criteria to support resource allocation (Marín Díaz et al., 2025). Although the study does not examine the joint integration of market, competitor, and customer information, it offers an approach to explaining analytical outputs and connecting them to explicit choice criteria. Taken together, these findings suggest that the contribution of analytics should be assessed in relation to organizational technological capabilities and the information requirements of the decision at hand. Meeting these requirements also depends on information granularity. Generating competitive intelligence at the product, firm, and market levels can support resource planning even when information is limited (Kumar et al., 2020). This highlights the importance of matching analytical detail to the decision task: understanding the broader market and allocating specific resources require different levels of information. Research should therefore establish which levels of granularity improve the evaluation of strategic alternatives and when the costs of acquiring and analyzing additional information exceed its contribution to the decision. The breadth of analysis likewise needs to reflect the decision context. Research on new product development shows that the relationships between incremental and comprehensive information processing and strategy outcomes vary with the time sensitivity and analyzability of market information (Durmusoglu et al., 2023). For competitive intelligence, this invites closer examination of which analytical approaches suit the available information and the time available for a decision. More extensive analysis should not be assumed to yield a better choice under all conditions. Analytical suitability, however, does not determine how findings will be used. The value of competitive intelligence also depends on how those findings are interpreted and discussed within the organization. Managers’ attitudes and beliefs shape competitive information search and interpretation (Qiu, 2008), while collaboration procedures between analysts and decision-makers, together with internal political behavior, influence the conditions of its use (Kowalczyk & Buxmann, 2015). Alongside analytical quality, research and practice must therefore consider an organization’s willingness to test its assumptions, examine alternative explanations, and engage with contradictory evidence. A substantive test of competitive intelligence is whether its findings enable decision-makers to justify, revise, or reject strategic choices on the basis of evidence.