Abstract
This paper utilizes market-level data to explore the relative performance of individual companies amongst defined competitors. We show the potential of using consumer clickstream data, an important type of big data, to create a new set of B2B analytical frameworks. In the markets where complex interactions between competitors, search intermediaries and consumers create a network, B2B relationships can be inferred from consumer search patterns, and can then be modeled to gauge the online performance. A commercial dataset from ComScore’s US panel of one million users is used to illustrate a new approach to measure and evaluate the online performance of competitors in the US airline market. The methodology and associated performance framework demonstrate the potential for new forms of market intelligence based on the visualization of market networks, online performance calculated from matrix algorithms, the measurement of the impact of search intermediaries, and the identification of latent relationships. This research makes theoretical and empirical contributions to the debate on the use of big data for B2B market analytics. B2B managers can use this approach to extend their network horizon from an egocentric to a network view of competition and map out their competitive landscape from the perspective of the customer.
Highlights
Business-to-business (B2B) analytics is relatively undeveloped compared to business-to-consumer (B2C) analytics (Wedel & Kannan, 2016)
We show the potential of using consumer clickstream data, an important type of big data, to create a new set of B2B analytical frameworks
The methodology and associated performance framework demonstrate the potential for new forms of market intelligence based on the visualization of market networks, online performance calculated from matrix algorithms, the measurement of the impact of search intermediaries, and the identification of latent relationships
Summary
Business-to-business (B2B) analytics is relatively undeveloped compared to business-to-consumer (B2C) analytics (Wedel & Kannan, 2016). It answers the call to measure online performance in order to inform online marketing strategies (Wind, 2008) by developing a new, externally focused analytical approach that is complementary to existing web analytics This includes identifying the scope and the boundary of the network that shows the positions of the key competitors within the competitive landscape (Koka & Prescott, 2002); the ability of an organization to attract visitors from its competitors and search intermediaries; and to use this data to inform the discussion regarding the performance of an organization relative to its competitors. The commercial and managerial implications of the results are described, the synthesized framework is presented, and future research opportunities and limitations based on this new approach are outlined
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