The LOOP Model: Conceptualizing and Measuring Consumer Interactivity in Response to Campaigns

I would like to showcase some research I have been doing since 2003 on the interactivity between the mobile or ubiquitous channel and other channels of communication. The below summary of the paper is due to be published in the Journal of Advertising Research in the 2008 September 48 (3) issue. 

The paper arose out of some qualitative work with executives in Thailand. One executive from TV3 cited how the use of mobile server data could be used to measure the audience response. Great idea. TV3 like all the rest of the world had been ‘booing’ traditional ratings models. They were looking for alternatives. That set me going to conceptualize and develop an alternative approach. Along the way a qualitative version of the paper was developed for the Communications of the ACM (2005 48-7) and researchers like Brad Robinson, David Yung, Andrew Balemi and Laszlo Sajtos helped to develop momentum for the paper.      

The work has been important because consumers are increasingly using the mobile channel to be interactive with television programming and advertisements. To understand this emerging phenomena, we develop a model, (the LOOP), conceptualizing the consumers interactivity when using their mobile phone to interact with television content. This model proposes new thinking regarding the role of the mobile channel in the consumer’s experience of the interactive television content.

We define the consumer’s interactivity in terms of four characteristics: synchronicity, two-way dialogue, contingency and user control. Based upon these characteristics, we use New Zealand and USA interactive television content related campaign data to develop five measures of campaign response effectiveness. First, we measure Potential Audience Dialogue (PAD), defined as the amount of the audience that could potentially be interactive with the campaign. Second, we measure the Active Audience Dialogue (AAD) which is the number (frequency) of unique audience viewers who become actively interactive with the campaign.  To assess the strength of interactivity, we correlate the measure of the potential audience (PAD) with interactive audience (AAD). We call this rating Interactive Audience Dialogue (IAD). Third, we measure Contingent Audience Loyalty (CAL), that is, a count of the number of people who continuously interact with the program over time. Finally, we measure the Contingent Audience Wearout (CAW) representing the response decay of unique interactive viewers. We found similar response patterns across the tested New Zealand and U.S. campaigns, with more significant relationships emerging from interactive consumers who are loyal across campaigns.

The research implications are simple. An alternative, simple and cost effective model for measuring interactive communications. 

The next stage of the research is to look at radio:mobile data from a qualitative and quantitative perspective. I also have some other data from a social marketing campaign called CMON GT^ that I ran in NZ with high school students. Work in progress….

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