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

Competitive 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.