Measuring Consumer Interactivity in Response to

Measuring Consumer Interactivity in Response to Campaigns Coupiing iVIobiie and Teievision Media ROBERT DAVIS

Consumers are increasingly using the mobiie channel to be interactive with television

Manukau Institute

programming and advertisements. To understand this emerging phenomena, we

of Technology, New Zealand robert.davis@ manukau.ac.nz

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

LASZLO SAJTOS

interactive television content. We define the consumer's interactivity in terms of four

University of Auckland

characteristics: synchronicity, two-way dialogue, contingency, and user control. Based

Business School, New Zealand [email protected],nz

upon these characteristics, we use New Zealand and U.S. interactive television content related campaign data to develop five measures of campaign response effectiveness—Potential Audience Dialogue {PAD), Active Audience Dialogue (AAD), Interactive Audience Dialogue (IAD), Contingent Audience Loyalty (CAL), and Contingent Audience Wearout (CAW) We found simiiar response patterns across the tested New Zealand and U.S, campaigns, with more significant relationships emerging from interactive consumers who are loyal across campaigns.

INTRODUCTION

T/ii' ciuthors would like to acknoivledgc the contribution of Ihe anonymous rcvieiocrs of this article and the supporting role played by Andrew Balemi, Margaret Fitzsiminons, and Brad Robinson.

The use of the mobile channel for advertising is expected to increase at significant rates (Bamett and Hodges, 2000; Dholakia and Dholakia, 2004; Economist, 2007; Haig, 2002; Ingram, 2001; Trappey and Woodside, 2005). Projections are based on the proposition that the mobile channel creates opportunities for building a process of two-way continuous interactivity with consumers (Balasubramanian, Peterson, and Jarvenpaa, 2002; Magura, 2003; Nysveen, Pedersen, and Thorbjarnsen, 2005; Nysveen, Pedersen, Thorbjomsen, and Berthon, 2005; Watson, Pitt, Berthon, and Zinkhan, 2002), Xbox and 20th Century Fox have started to use the mobile channel in synergy with television advertisements for interactive marketing (Pantzar, 2003; Trappey and Woodside, 2005), We call this type of campaign the LOOP. In this type of campaign, the audience is encouraged by the advertisement to interact with the brand by sending a message response in the form of SMS (text message), MMS (multimedia mes-

DOI: 10.2501/S0021849908080409

sage), or VMS (video message) (Barnes, 2002; Barwise and Strong, 2002; Paavilainen, 2001). A U.S. example in 2004 used the mobile channel to allow viewers to react directly to the presenter's questions through their mobile phones' messaging applications. Viewers were able to vote for their traditional most valuable players (MVPs) and the pitchers they would least like to play. Screening the audience responses in real time throughout the game encouraged greater viewer control of the level of interactivity and increased loyalty and trust (Okazaki, Katsukura, and Nishiyama, 2007). At the same time, the distraction of other activities (e.g., making a coffee in the advertising break) was reduced. Beyond the interactive benefits to marketers (Duncan and Moriarty, 1998; Rask and Dholakia, 2001), this approach also has revenue implications because the consumer sending a message pays per message. Despite these developments in marketing practice, little is known in the literature about how to

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conceptualize or measure consumer interactivity in response to LOOP campaigns coupling the mobile and television media (Ferris, 2007; Nysveen, Pedersen, Thorbjornsen, and Berthon, 2005), The most significant research to date in advertising has been by Trappey and Woodside (2005) who focus on the consumers' perceptions of the effectiveness of LOOP-type campaigns. Like this research, they ask why consumers engage in these processes of interactivity. However, Trappey and Woodside (2005) highlight two significant gaps in our knowledge. First, understanding is required of "the Impact of such campaigns" on the consumers' perceptions of the interactive experience (p. 400). Second, research is required that goes beyond consumer self-reports, that is, analysis based on real campaign data across alternative campaign approaches. Lipner (2007, pp. 145-46) also argues that new research technologies such as the mobile phone open up new opportunities and that these technologies help researchers "build for tomorrow's emerging media research and measurement requirements," and detennine "what is the best way to measure online and offline media channels and understand their Individual and combined effects for true holistic assessment?" Therefore, this research seeks to overcome this gap by conceptualizing the core characteristics of consumer interactivity with the mobile and television channels and, based upon this, use real consumer behavior data across alternative campaigns in two countries to quantitatively estimate the effectiveness of the LOOP (Ferris, 2007). Therefore, our research questiorxs are as follows: RQl: What are the characteristics of consumer interactivity while engaging in LOOP campaigns coupling the mobile and television media? 376

RQ2:

How can we measure the effectiveness of LOOP campaigns coupling the mobile and television media?

The article takes the following form. We begin with a discussion of the characteristics of the consumers' interactivity. Following our method, we then measure the effectiveness of the LOOP in three real marketing campaigns based upon New Zealand and U.S, data from 2004, with the aim of analyzing the nature of the actual consumers' interactive response. The article concludes with a discussion of the results, and the implications for marketers. UNDERSTANDING INTERACTIVITY

The conceptual foundation focuses on defining the core characteristics of the consumers' interactivity. Working toward this conceptualization, the concept of interactivity is introduced by looking at various marketing, advertising, web-based, and communications perspectives, where an argument for a multidimensional conceptualization of interactivity is formed. Our research propositions are highlighted, linking our conceptual argument with the proposed model of campaign effectiveness. At first sight there seems to be a clear distinction between communication and interactivity in which ".. . it is possible to have communication without interaction (for example listening to the radio or watching TV), but not interaction without communication" (Jensen, 1998, p, 188). Distinguishing one from the other might bring us closer to a definition of interactivity. So in the following we look closely at the domains of communication and interactivity from the perspective of both the marketer and the consumer. In the domain of communication, various pattern models exist that aim to explain the base structure of communication between two actors or mediums (Bor-

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dewijk and van Kaam, 1986; Hoffman and Novak, 1996; McMillan, 2002), Basic communication is usually referred to as oneway communication, where the sender has a strong influence over the channel and the message. The receiver receives this message on an ad hoc or continuous basis. This definition at one extreme represents the traditional "flat world" way of how companies communicate with their consumers and highlights some components of the communication process. We build from these perspectives to underscore why the LOOP'S communication process is driven by the consumers' desire for interactivity. In fact, rather than being flat, this communication process is asymmetrical because the receiver has a strong influence over the channel, message, and timing. McMillan and Hwang (2002) outlined the feature perspective, which seeks to identify the general characteristics of communication. The definition cited above represents a monologue in which a sender disseminates information content to attract an audience or to perform some other persuasive communication function (Hoffman and Novak, 1996). This communication is transmissional because information is produced by the central provider (TV) and its distribution is controlled by the central provider (Bordewijk and van Kaam, 1986). This definition, in conjunction with the TV environment, represents two important features, the direction and the control of communication, where the direction of the communication is set (one-way), and where the marketer retains control of the process (McMillan, 2002). In the LOOP, where the communication is between the consumer and marketer, the mobile phone is a one-to-one device by which an individual consumer responds to an individual firm/marketer offer. In this process the mobile phone fulfills a feedback function, which allows

CONSUMER INTERACTIVITY WITH MOBILE AND TV MEDIA

In the LOOP, where the communication is between the consumer and marketer, the mohiie phone is a one-to-one device by which an individual consumer responds to an individual firm/marketer offer.

one-way communication, but enables receivers (consumers) to participate in the communication process. However, there nre no guarantees that the sender (marketer) will respond individually. Therefore, interactivity has to go beyond what this environment (channel) can offer, and by definition it should start with the basic assumption that it is a two-way communication. However, Newell and NewellLemon (2001) ask whether the customer is interacting or simply reacting. Because of this, a monologue and feedback communication should be classified as being interactive, even if it is, to a lesser extent, by definition a two-way communication. This research proposes that in the LOOP there are two one-way communication models, which, although fundamentally different, converge to make a two-way responsive dialogue model (McMillan, 2002). The existence of two-way communication means by definition that the marketer/firm sends a message to the consumer, which generates a response. It might happen that the marketer and consumer only interact once during a particular campaign, but the essential element is that the actual interaction has happened. It is the basic level of interactivity. Therefore, to measure interactivity we need to count the number of actual transactions by each consumer over time who engage in two-way communications. Therefore, we propose that: PI : Consumer interactivity is characterized by two-way communica-

tions, measured by the number of consumers who are interactive once. At this stage the questions arises as to what else is needed to identify and define interactivity in the communication process. One would expect that the real interaction would involve more LOOPs to be completed between consumer and marketer, which implies that we would expect them to interact simply more and continuously, as well as more quickly. This means that marketers and audience have to act and react to each other in a continuous manner, which brings a new feature into the interactivity equation, the temporal dimension. This temporal dimension focuses on responsiveness (Rafaeli, 1988): for a conversation to evolve, feedback should be immediate (Shih, 1998). They assert that the immediate succession of action and reaction reinforces interactivity, which Alba et al. (1997) refers to as response within seconds. Therefore, on the one hand we are interested in the continuous nature of this relationship, which we still can consider as counting the transactions, but on the other hand we have to focus on how many consumers respond continuously and how many of them exit the LOOP (Lee and Park, 2007). We also have to take into account how quickly they interact. This feature is referred to as synchronicity. It should be pointed out that the synchronous nature of interactivity is not only driven by consumers who have a choice to be engaged

or not, and how quickly they will do it. Synchronicity is enhanced when marketers implement flexible programming that encourages interaction with consumers that is like a "conversation" (Trappey and Woodside, 2005). From a temporal perspective, measurement should then focus on consumers who engage in two-way communication more than once. Therefore, we propose: P2:

Consumer interactivity is characterized by synchronous communications, measured by the number of times a consumer is interactive more than once.

In the context of the LOOP, we argue that synchronous communication is an important component of interactivity especially because viewers who have interacted for the first time with the television program or advertisement may easily become distracted by other activities or get bored and switch between channels. Therefore, flexible programming is a vital element to maintain the sense of interactivity of an otherwise passive audience (Hoffman and Novak, 1996). The TV medium has traditionally been considered one of low interactivity and passive audience reception. We referred to TV communication as transmissional because consumer activity in this environment is pure reception. There is no interaction required from the viewers. However, some authors stress that marketers have to make consumers active (Liu and Shrum, 2002: van Dijk and de Vos, 2001), which brings us to the concept of control. Control can be considered from the perspective of the marketer as well as the consumer. Even though it Is the same phenomenon, the two actors see different sides of it. Control is a basic requirement and a desirable outcome from the side of the marketer, who controls what

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The existence of two-way communication means by definition that the marketer/firm sends a message to the consumer, which generates a response. It might happen that the marketer and consumer only interact once during a particular campaign, but the essential element is that the actual interaction has happened.

information is requested and the choices that can be made by the consumer. It is a way of influencing the communication content. Increased interactivity is the unique characteristic and primary distinction of new technologies compared with other traditional media (Bezjian-Avery and Calder, 1998; Hetter, 1989). Steuer defines interactivity as "the extent to which users can participate in modifying the form and content of a mediated environment in real time" (Steuer, 1992, p. 41). On this continuum some communication technologies are relatively low in their degree of interactivity, such as newspapers, radio, and broadcast TV, while others, such as computer bulletin boards, video games, the internet, and other multiuser domains are more highly interactive. Steuer's definition focuses on the user's ability to input (primarily the conversation pattern); interactivity is only driven by customers' choice, while the medium simply serves to facilitate this interaction (Schumann, Artis, and Rivera, 2Ü01). However, our research proposes that control is quite heavily mediated by the channel (Trappey and Woodside, 2005). In traditional media, users have many choices, but no control over the messages. The only thing they can do is change the channels to look for the messages that match with their own ex378

isting attitudes and interests. This means that the choice of the channel (s) will have an impact on the level of interactivity, as an outcome of the process, which is influenced by the users' perceived experience with the channel. If the consumer does not experience this interactive environment with regard to that particular medium or device, the "sense of interactivity can greatly diminish" as they will no longer feel engaged in the conversation (Shih, 1998, p. 656). Communication will ruphjre (Haeckel, 1998; Liu and Shrum, 2002). To encourage consumers into synchronistic communication, marketers employing the LOOP in strategic communications should be adept at allowing the consumer to control the nature and outcome of interactivity (Bordewijk and van Kaam, 1986; van Dijk and de Vos, 2001). By allowing the consumer to control interactivity, the traditionally passive audience is transformed into one that has active control and participation. As a result of the conversation with the consumer, the marketer can develop greater knowledge about the consumer. Furthermore, over time the consumer can be more involved in product/service and brand (Trappey and Woodside, 2005). The extent to which consumers chose to disengage in interactivity represents a measure of the consumer's level of control over the campaign

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communication process. In other words, the campaign's response wears out over time. Therefore, we propose that: P3:

Consumer interactivity is characterized by controlled communications, measured by the interactive response decay as consumers choose to end being interactive.

In mutual discourse or interpersonal communication (Hoffman and Novak, 1996), the sender and receiver roles are clearly distinguished (McMillan, 2002) and this was our approach in defining the concept of interactivity. However, with regard to the control feature, it should be pointed out that this distinction has a detrimental impact on the interactivity process. This means that the consumer should not be onJy considered as the receiver of the message, but as a cocreator of the content. Therefore, this research proposes that control can be maintained in the LOOP if the customer is perceived to have control without being conscious of it. In reality, the value of this interactivity for the consumer is "the extent to which users can participate in modifying the form and content of a mediated environment in real time" (Hoffman and Novak, 1996, p. 84). This characteristic is called contingency, that is, tbe extent to which messages in a sequence relate to each other, and especially the extent to which later messages recount the relatedness of earlier messages (Rafaeli, 1988). In a contingent interactive process, both actors seek a certain benefit; the marketer stimulates the consumer to interact and seeks economic and informational benefits, while the consumer chooses to interact and seeks involvement and/or entertainment benefits (Trappey and Woodside, 2005). Like synchronicity, contingency can be measured by counting the number of consumers who engage in two-way communication

OONSUMER INTERACTIVITY WITH MOBILE AND TV MEDIA

By allowing the consumer to control interactivity, the traditionally passive audience is transformed into one that has active control and participation. As a result of the conversation with the consumer, the marketer can develop greater knowledge about the consumer.

more than once. Therefore, we propose that: P4: Consumer is characterized by contingent communications, measured by the number of tLmes a consumer is interactive more than once. METHOD

Building long-term relationships with customers is one of the most important factors for companies (Coviello, Brodie, Danaher, and Johnston, 2002). In this longterm relationship, one of the companies' basic aims is to encourage consumers to take an active role in shaping this relationship, that is, encouraging customers to engage in an interactive dialogue, in which technology can be one of the major supports (Coviello, Milley, and Marcolin, 2001; Lee and Park, 2007). In the interactive dialogue of the LOOP, TV advertising plays a major role, where advertisers as well as broadcasting companies are interested in the size and composition of the viewing audience. Advertisers want to know the size and characteristics of the audience they are reaching because they have to allocate money for a particular time and program. Broadcasting companies, on the other hand, have to determine the amount they can charge for commercial time (Belch and Belch, 1998). Therefore, in LOOP-type campaigns all stakeholders are interested in measuring

how many viewers would be willing to interact with the program. In response, this article proposes a combination of traditional (TV) and new (mobile phone) media that could provide a better ground for measuring interactivity, in which people using mobile phone messaging services respond to TV advertisements or programming (Trappey and Woodside, 2005).

From old school to new waves

The people-meter of ACNielsen can be considered as a traditional audience measurement tool that aims to determine the size of the audience that is watching a particular program on TV (Rossiter and Danaher, 1998). People-meter uses a panel that tells panelists to use the remote control if they start or stop watching a program or switch to a different channel. The measures of people-meter serve as a base for determining audience size. However, TV programming has become increasingly fragmented (e.g., over 30 and 500 channels in New Zealand and United States, respectively) and individual ratings levels for programs and commercials are all declining and will continue to do so. As a consequence, audience sizes have become very small and, hence, research results more inaccurate and unpredictable. Therefore, like Trappey and Woodside (2005), the advertising industry has been calling for changes to the way TV

viewing audiences are measured (Belch and Belch, 1998). However, there is a clear lack of understanding in current marketing literature of how to quantitatively measure interactivity in LOOP-type campaigns (Belch and Belch, 1998). Marketers have found that they can use computer-server collected data, generated as a means of augmenting program ratings, to develop a greater understanding of the nature of the audience's interaction with programs. Hence, mobile phones can help create a different measurement system, one that is based on viewers' interaction with the program. In our research, we outline in Table 1 and Figure 1 five measures to describe the actual response of an audience that is engaging in the LOOP and answer the central question: was the campaign effective/interactive? First, measuring actual response begins with the Potential Audience Dialogue (PAD), defined as the amount of the audience that could potentially be interactive with the campaign. This is a count of the number of viewers who have access to a mobile phone, and this brings the penetration of mobile phone into the equation. Takijig traditional formulas and penetration rates into consideration, we can get an idea of how many people are potentially interactively capable and actually watching TV (Rossiter and Danaher, 1998). Second, according to our understanding of interactivity in the LOOP, we then need to assess whether more people are interacting and whether the same people are interacting more. We measure the Active Audience Dialogue (AAD), which is the number (frequency) of unique audience viewers who become actively interactive with the campaign. In other words, in response to the television advertisement or program, they choose to send an SMS message, representing

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TABLE 1

Interactive Response Measures Audience Response Measure

Conceptual Definition

Potential Audience Dialogue (PAD)

The total potential estimated audience viewership

Active Audience Dialogue (AAD)

The total number of unique audience viewers who

Proposition

PI

become actively involved in the LOOP Interactive Audience Dialogue (IAD)

An interactive rating—the correlation between the

PI

potential audience {PAD) and the active audience (AAD) Contingent Audience Loyalty (CAL)

The ratio of audience viewers who are converted to

P2 and P4

become "loyal" (i.e., interact on more than one occasion) users of the LOOP Contingent Audience Wearout (CAW)

The response decay of unique viewers over time who

P3

become repeaters

Interactivity Intensity

CAL (Loyal Interactive Audience)

measure represents the ratio of interactive audience viewers (AAD) who respond to the television advertisement or program using SMS more than once. They become more loyal to and engaged with the interactive communication process because they are engaging in synchronous communication (P2), which is contingent (P4). Finally, we measure the Contingent Audience Wearout (CAW) representing the response decay of unique interactive viewers who had responded to the television advertisement or program using SMS more than once. This measure places emphasis on the extent to which the consumer enacts control over the communication process and at what stage they stop being interactive (P3).

Figure 1 Interactive Response Measures

interactive communication. 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). Together these measures help us 380

to assess the level and strength of twoway interactive communication (PI). Third, we measure the Contingent Audience Loyalty (CAL), that is, a count of the number of people who continuously interact with the program over time. This

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These asymmetrical measures place emphasis on the consumer and essentially count the number of times a consumer engages in an interactive transaction. The measures also describe the longitudinal process as a continuum—from the inactive, but potential audience, through to the active audience, toward the "loyal" viewer, and ending with a measure of the

CONSUMER INTERACTIVITY WITH MOBILE AND TV MEDIA

inevitable audience response decay. We argue that these measures are generally consistent with previous media modeling research (Danaher, Hardie, and Putsis, 2001; Ehrenberg, 1988; Fader, Hardie, and Zeithammer, 2003; Kalwani and Silk, 1980; Mahajan, MuUer, and Bass, 1990). Three campaigns, two countries

The data are collected from three 2004 implemented campaigns in two countries. The first two data sets are from two sponsored television shows in New Zealand (show A and show B) that incorporated a promotion as part of the show. Show A and show B are youth-oriented, magazinetype television shows. Show A, called Mo Show, represented the travels of two New Zealand presenters as they went around the Unites States visiting places and people of interest to the youth market in New Zealand. Show B, of a similar format, was more locally focused in its content and editorial. For show A, at the end of every episode, the presenters asked a question that had a multichoice answer (A, B, or C). The data were collected weekly (10 episodes) from March 16, 2004 to May 11, 2004. For show B, the same data were collected weekly (13 episodes) from November 18, 2003 to March 12, 2004. For show A, a Sony PlayStation, for show B, an AIWA home entertainment system was offered as the prize to a random caller, and this was drawn randomly at the end of the show. For show C, the set of data comes from the United States. Thirteen Seattle Mariners baseball games were televised by FSN Northwest. Like shows A and B, a "TXT and Win" promotion was implemented during each game. For the baseball game, viewers voted on the MVP from a choice of three players chosen by the commenter team (show A, B, or C), The games were played in 2004 irom September 9 through September 29. In all three cases (shows A, B, and C), the

customer sent the answer from their mobile handset to a four-digit short code in the form of an SMS message. The data that were used in the model development process included the following variables: (1) the date and time the message was sent, (2) the mobile number, and (3) the message sent by the consumer that represented the answer to the quesfion. The message as a core of the promotion represents a flexible, yet powerful component of the interacfive communication. For shows A and B, each text message sent cost U.S $0.31. For show C, the cost of each text was U.S. $0.99. The type of data obtained for this study across two countries is easily obtained for other campaigns in different geographic regions. This is because the data variables used in this study are automatically generated by the network operator when any consumer, in any region sends an SMS message. The researchers had also obtained similar data from Thailand with the same variables, but it was not used in this study because it did not represent the coupling of the mobile with the television media. Anecdotally, we also note that these types of campaigns are pervasive throughout most regions in the world. RESULTS

In the results we present the findings on the individual measures, as well as their interrelations for which we applied regression analysis and curve fitting (Sawyer and Peter, 1983). Potential Audience Diaiogue

Potenfial Audience Dialogue (PAD) measures the total potential estimated audience able to be involved in the LOOP process of dialogue (Figure 2). It is noted that this is a good measure of audience potential as the penetration of mobile phones in New Zealand and the United States is nearly 100 percent of the total

population. PAD is based on the television rating that enables us to estimate how many people, in total, are viewing each of the shows each time it is displayed. Figure 2 displays the estimated viewership (in thousands) numbers (from ACNielsen's Peoplemeter) for each show. It can be seen that show A has had an initially reasonably high viewership that has decreased with time from over 250,000 to less than 150,000 viewers. Show B started off reasonably low (approximately 130,000 viewers) then peaked at the sixth episode of this series and ended with a viewership of 70,000 viewers. The decrease after week seven reflects the fact that this show's season finished before the Christmas holiday season in New Zealand, and these shows were then repeated one month later. Note that after this time (week 7) it slowly starts increasing after it comes back on air. Show C displays an increased PAD to over 140,000 viewers after experiencing an initial trough, reflecting an increase in viewers as the baseball season develops and, in part, their fortunes as they play respective teams in the play-offs. All in all, the three shows difíer significantly in terms of viewership patterns, which might be the result of the nature/content of the program/competition. As a consequence, people get more or less involved at various stages of the programs. Active Audience Diaiogue

Active Audience Dialogue (AAD) measures the total number of viewers who have become actively involved in the LOOP dialogue (Figure 3). It measures the number of people of the potential audience (as measured by PAD) who have decided to respond at least once to the text message (stimulus). Figure 3 displays the number of active viewers for shows A, B, and C. With show A we see that the AAD fluctuates dramatically from episode to

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episode. This reflects the fact that for each show, a different question was asked. This seems to indicate that the nature of the question, along with the interests of the target audience (18-35 year olds), has had a huge impact on the number of viewers. To illustrate, the relatively low number of messages recorded for week 4 coincided with the question, "What energy source did the NZ Herald describe as 'Unsightly'?" whereas the high number associated with week 8 coincided with the question, "Should marijuana be decriminalized?" Note also that the audience was not given a standardized way of responding to each question, which could have further increased the intensity of the in-

teraction. In show C we see that the AAD seems to show a high-low pattern similar to show A as opposed to show B, which has a sharp downturn after the early peak. In the case of show C, this may be due to the fact that the baseball team played over four games and so may reflect the fortunes of this team during these play-offs. Interactive Audience Dialogue

Interactive Audience Dialogue (IAD) is a measure that uses PAD and AAD to measure the strength of the relationship between them (Figure 4). Naturally, one would like to see what relationship (if any) there is between the number of people watching the show and the number of

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viewers who texted in and become interactive. We believe that the IAD measure is a step forward in the understanding the relationship between viewers and interactive viewers. Figure 4 displays the relationship, with a straight line fit, between PAD and AAD by using linear regression method, for shows A, B, and C. Expected AAD = aßPAD.

(1)

For show A there seems to be little, if any, relationship between the viewership ñgures and the number of people watching the show. The correlation (r) between the two measures (r = 0.21) and the explained variance {R^ — 0.05) is very low

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Figure 3 Active Audience Dialogue (AAD) for Shoves A, B, and C

and not significant, which means there is no significant relationship between the number of potential and active viewers. As discussed above, this may be due to the nature of the questions and the nonstandard nature of response that is affecting audience participation. Clearly, with show B, there seems to be a reasonably strong and significant relationship between PAD and AAD (r - 0.74; R^ = 55 percent, p = 0.004). For show C, we have found no significant relationship between AAD and PAD (r - 0.46; R^ - 22 percent), which again can probably be attributed to the variation of participation within each different play-off.

Contingent Audience Loyalty

Contingent Audience Loyalty (CAL) is the proportion of the audience that has interacted in the past with this show ("repeaters") relative to the number of newcomers to the show (i.e., both first time participants and repeaters) in the LOOP's interactive dialogue for each episode. Figure 5^ allows us to understand what proportion of the audience became interactive for the

first time and repeaters in each episode, for which we have used an exponential function. In Figure 5 the data points represent the proportion of the repeaters in percentages relative to the new audience. The exponential curve was estimated based on the data points (see Equation (2)), which represents a "diminishing returns" relationship between time and the proportion of repeaters: Expected CAL ^ p(l - exp(-ß * time)).

^Please note that in the CAL charts axis X starts witb 0 and finishes with 9, 12, and 12, respectively, for shows A, B. and C. So the same number of periods is presented in Figure 5; however, it is denoted slightly differently, comph/ing with tiie rules of times series anali/sis.

(2) This diminishing returns relationship assumes that there is an exponential

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Figure 4 Interactive Audience Dialogue (IAD) for Shows A, B, and C

decrease, measured by the parameter ß, in the rate at which a show can convert people into repeaters to a constant proportion called p, of the AAD. There is a significant difference between the three shows in terms of patterns; however, the data for each of these shows represent a good fit with the proposed exponential function. The proportion of repeaters over new viewers increases throughout the episodes of show A, where the exponential curve fits some of the data points very well and the relationship seems to be significantly strong {?? = 66 percent; p = 0.004). Shows B and C represent similar patterns; however, the proportion of repeaters over new viewers increases for show C throughout the first seven weeks, whereas for show B it stops after the third

week. It can be seen that for show B approximately 25 percent and for show C over 30 percent of their viewers are coming from repeaters (i.e., have texted at least once in previous shows). However, show A is a little bit more difficult to interpret because it does not seem to be saturated, but the proportion of the repeaters is constantly increasing throughout the period of the show. Show B shows a similarly strong relationship and explained variance as show A {R^ — 62 percent; p = 0.001); however, for show C the relationship is even stronger (R^ = 84 percent, p < 0.001). Contingent Audience Wearout

The repeaters discussed above can be further investigated to see how their be-

3 8 4 JOURfiflL orflOUERTISIDGRESEHflCH September 2 0 0 8

havior changes over time. Contingent Audience Wearout (CAW) measures the proportion of repeaters who have continuously become interactive in subsequent episodes from the first time they became involved with the show (Figure 6). {Note that we are assuming that the number of texts each person sends remains relatively constant over time—this has been observed from the data.) Figure 6 displays the CAW figures for shows A, B, and C. The data points in the chart display the proportion of repeaters over the first viewers of every week, where the latter represents the base. So one (1) on X axis represents the proportion of the actual base and the repeaters in one week's time, and two (2) the proportion of the base and the number of repeaters in two weeks'

CONSUMER INTERACTIVITY WITH MOBILE AND TV MEDIA

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time, etc. It can be seen that, as time progresses, there is a geometric (or exponential) decrease in the proportion of repeaters. We may attempt to model this exponential relationship between the response rate and time as follows: Expected CAW = A exp{ß * time). (3) Here the parameter ß measures the rate at which the proportion of repeaters decreases from episode to episode. The parameter A gives us an idea of what proportion of participants actually go on to be repeaters. The following results show

the strength of the relationship, in terms of the percentage of variation explained (R^), along with the P-vafue associated (in brackets) of only the significant statistical relationship; show A, R^ - 87 percent (p < 0.001); show B, R^ - 65 percent (p < 0.001); show C, R^ - 11 percent [p 0.020). We can see that the proportion of voters who respond decreases rapidly (by approximately 80 percent and 60 percent, respectively) for each subsequent episode. For shows A and B there seems to a very rapid drop off in subsequent repeaters, decreasing to zero, whereas with show C there is the slightest decrease, from an

average of 7 percent to 3.5 percent. This may be due to the fact that people may be watching the same teams play each other (in a series of four games), but do not necessarily become involved in watching other teams. SUMMARY

Show A had an average of over 200,000 viewers (PAD) on a weekly basis; however, on average slightly less than 3,000 people interacted with the program by sending some 4,000 messages on average every week during the nine weeks. An average of 1.5 percent interacted with the

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CONSUMER INTERACTIVITY WITH MOBILE AND TV MEDIA

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