Academy of Strategic Management Journal (Print ISSN: 1544-1458; Online ISSN: 1939-6104)


Big Data and TV Media: Going Beyond Traditional Segmentation

Author(s): Rafael Fernandez-Alava, Diana Gavilan, Susana Fernandez-Lores

The aim is to show the benefits of a model of advanced segmentation based on big data, that improves results for advertisers and brings new relevance to linear and video-on-demand TV operators. The paper also assesses the subjacent importance of three main Dynamic Capabilities (i.e. sensing, seizing, transforming) to the described process.

Results suggest that the advanced segmentation model is able to impact marketing performance, by doubling the percentage of consumers that visited the advertiser’s website in the first 24 hours. The advertising pressure, below which consumers would not react, is also established. These results constitute a proof that new segmentation models based on big data are able to improve marketing campaign results, as well as able to address the loss of relevance of TV to advertisers.

The business case analyzed proves that a TV campaign result can be optimized and better measured, thanks to the use of big data on a new segmentation model. It also brings new relevance to TV as a media that can compete with digital investments.

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