Generating Competitive Intelligence with Limited Information: A Case of the Multimedia Industry

Abstract

C ompetitive intelligence is a critical component of developing and implementing organizational strategies. Although firms may obtain aggregate market-level competitive information, resource allocation decisions such as inventory management or capacity planning are made at the individual product-firm-market level. Acquiring such disaggregated information about competitors across various products and markets poses significant challenges, including integrating data from different (and conflicting) information sources and updating the same continuously to reflect the changes in the market environment. To address such issues, we build on the literature on goal programming and frame the problem of generating competitive intelligence at the product-market level as a matrix-balancing problem, where products, firms, and markets represent the dimensions of the market-sensing matrix. We develop a decision support system for firms to generate and update the market-sensing matrix over time using weighted integer goal programming. Utilizing data from multiple sources (internal firm data, commercial market data, and secondary data), we create a set of linear restrictions and use goal programming approach to update the market-sensing matrix. We demonstrate-(i) the proposed approach using data from a large multimedia firm that offers multiple products in various markets with many competitors, and (ii) benefits of implementing our approach. We find that timely recovery of disaggregated information at product-firm-market level assists the firm in superior resource allocation.

35 years of research on business intelligence process: a synthesis of a fragmented literature

“…Unfortunately, the literature within this stream was considerably explorative of the BI collection activities and practices (Taylor, 1992; Vedder et al , 1999; Dishman and Calof, 2008; Wright et al , 2009). While some marketing scholars emphasized the use of Bayes’ theorem to determine when more collection becomes cost (Michaeli and Simon, 2008), other explored information sources companies use (Fleisher et al , 2008; Lasserre, 1993; Peyrot et al , 1996) or developed indices to evaluate the adaptability of firm capabilities to BI collection of boundary spanners (Hallin et al , 2017) or to collect BI from disaggregated data (Kumar et al , 2020). While a stream of scholars examined trust in BI collection quality (Robinson and Simmons, 2017), others investigated the type and source of the collected intelligence (Peyrot et al , 1996) or the capabilities to decode each type of intelligence be it soft (Lasserre, 1993) or web-based (Fleisher, 2008; Pawar and Sharda, 1997).…”

Predictive competitive intelligence with prerelease online search traffic

“…While our research suggests PROST models are useful over naïve ones, it remains an open question how PRB compares to commercially available market data. As Kumar et al (2020) argue, such data sources quickly become costly, especially if tracked over time. Therefore, many companies heavily rely on human judgment for their models, remaining relatively immature in their competitor analysis (see Crayon SCIP, 2022;Ranjan & Foropon, 2021).…”

Of BI research: a tale of two communities

“…Two research stands within the MI cluster exhibited an interest in the organizational and individual levels of intelligence. The first stream explored the dissemination and exploitation of gained intelligence relying on social exchange theory (Homans, 1961), the role of hierarchical relationships (Huber and McDaniel, 1986), power and politics in the relationships between the intelligence sender and receiver (Maltz and Kohli, 1996) and disaggregated product-firm-market-level intelligence to yield firms better resource allocation (Kumar et al, 2020). The second stream's attention was directed to boundary spanners' activities vis-à-vis the collection and usage of intelligence and drew from both the cognitive selling paradigm (Kahaner, 1997;Rothberg and Erickson, 2005;Fleisher et al, 2008;Rapp et al, 2011;Mariadoss et al, 2014) and expectancy theory (Tyagi, 1985;Sujan, 1986;Le Bon and Merunka, 2006).…”