Binge watching and advertising

David A Schweidel & Wendy W Moe Binge Watching and Advertising How users consume media has shifted dramatically as viewers migrate from traditional broadcast channels toward online channels Rather than following the schedule dictated by television networks and consuming one episode of a series each week, many viewers now engage in binge watching, which involves consuming several episodes of the same series in a condensed period of time In this research, the authors decompose users’ viewing behavior into (1) whether the user continues the viewing session after each episode viewed, (2) whether the next episode viewed is from the same or a different series, and (3) the time elapsed between sessions Applying this modeling framework to data provided by Hulu.com, a popular online provider of broadcast and cable television shows, the authors examine the drivers of binge watching behavior, distinguishing between user-level traits and states determined by previously viewed content The authors simultaneously investigate users’ response to advertisements Many online video providers support their services with advertising revenue; thus, understanding how users respond to advertisements and how advertising affects subsequent viewing is of paramount importance to both advertisers and online video providers The results of the study reveal that advertising responsiveness differs between bingers and nonbingers and that it changes over the course of online viewing sessions The authors discuss the implications of their results for advertisers and online video platforms Keywords: binge watching, online streaming video, digital advertising, digital media consumption edia consumption has changed dramatically in recent years Viewers have been moving away from watching traditional broadcast channels and toward online video consumption to gain more control over their media consumption Traditional media consumption, whereby viewers watch shows according to the schedule and sequence in which the networks broadcast them, has gradually given way to viewers determining their own viewing schedule through digital video recorders or on-demand programming (Littleton 2014) As a result of these trends, new patterns of media consumption have emerged Rather than consuming one episode of a series each week in accordance with a typical television schedule, viewers may opt to view several episodes of a single series in immediate succession Surveys have revealed that a majority of consumers prefer to watch multiple episodes of their favorite programs in a single sitting (Pomerantz 2013) A Nielsen (2013) study finds that 88% of Netflix users and 70% of Hulu Plus users reported watching at least three episodes of the same program in one day In addition to individuals consuming more content, they report doing so in a condensed period of time According to a survey conducted by Netflix and Harris Interactive in 2013, 61% of adults who stream television shows at least once a week reported that they regularly engage in “binge watching” sessions that consist of two to three episodes of a single television series in one sitting, with nearly three-quarters of respondents having positive feelings about binge watching (Netflix 2013) In its 2014 Digital Democracy Survey, Deloitte reports that 31% of respondents engaged in binge watching at least once a week, with more than 40% of respondents age 14–25 engaging in the behavior weekly (Deloitte 2015) What is binge watching?1 The Digital Democracy Survey defined the activity as “watching three or more episodes of a TV series in one sitting” (Deloitte 2015) Meanwhile, in the survey conducted by Netflix and Harris Interactive, nearly three-quarters of respondents defined binge watching as “watching between 2–6 episodes of the same TV show in one sitting” (Netflix 2013) These studies focus on viewing within a single viewing session, but Netflix further reports that for a particular serialized drama, 25% of viewers finished the 13-episode season within two days, and almost 50% did so within one week (Jurgensen 2013) A similar pattern is also reported for a sitcom Across these reports, binge watching is characterized by two common elements First, there is a heavy rate of consumption, which may occur within a single session or across multiple sessions that occur within a short period of time Second, a key feature that distinguishes binge watching from heavy usage is that binge watching is characterized by consuming multiple episodes of the same series These two characteristics are consistent with the definition for “binge watching” that Oxford Dictionaries added to its online version in 2014 (Oxford Dictionaries 2014, 2016): “watching multiple episodes of (a television program) in rapid succession, typically by means of DVDs or digital streaming.” In this research, we define “binge watching” as the consumption of multiple episodes of a television series in a short period of time M David A Schweidel is Associate Professor of Marketing, Goizueta Business School, Emory University (e-mail: dschweidel@emory.edu) Wendy W Moe is Professor of Marketing, Robert H Smith School of Business, University of Maryland (e-mail: wmoe@rhsmith.umd.edu) Rajkumar Venkatesan served as area editor for this article © 2016, American Marketing Association ISSN: 0022-2429 (print) 1547-7185 (electronic) 1We use the term “binge watching” to refer to the activity and “bingers” to refer to those individuals who engage in the activity Journal of Marketing Vol 80 (September 2016), 1–19 DOI: 10.1509/jm.15.0258 Consistent with the reports noted previously, Google Trends reveals a sharp increase in searches for “binge watching” beginning in 2013, as illustrated in Figure Although reports have acknowledged the trend toward binge watching, we have little understanding of its implications for online video platforms Many online video services are supported by advertising revenues, as is the case in our empirical context of Hulu.com But if viewers are immersed in binge watching, are advertisements still effective? Moreover, the advertisements affect users’ subsequent viewing behaviors? Elberse and Gupta (2009) report that advertising on Hulu.com was more effective than advertising on broadcast or cable television Yet the analysis does not distinguish between users’ responsiveness to advertising when users are engaged in binge watching and when they are not If users who are binge watching are less responsive to advertising, this may give advertising-supported online video platforms pause in terms of encouraging such behavior In this research, we propose a model of viewing behavior and advertising response and apply it to data from Hulu.com, a popular online video platform Our modeling framework decomposes users’ viewing behavior into (1) the decision to continue the viewing session after each episode, (2) whether the next episode viewed is from the same or a different series, and (3) the time elapsed between sessions, in an effort to identify binge watching behavior and factors that affect this behavior, including advertisements In addition to these three components of viewing behavior, we simultaneously model users’ responsiveness to advertisements shown during episodes This modeling approach allows us to examine how advertising is related to viewing behavior, in terms of the effect that binge watching has on advertising response, as well as how advertisements affect viewing behavior Our analysis provides empirical evidence in the context of online video consumption that viewing begets more viewing (Kubey and Csikszentmihalyi 2002), suggesting that binge watching is at least in part a malleable behavior (in addition to being a user-specific tendency or trait) We also find that advertisements shown during a viewing session can deter binge watching behavior and in fact shorten the length of the viewing session Finally, we find that when viewers engage in binge watching, they are less responsive to advertising In particular, we show how advertising responsiveness differs between users who have a propensity to engage in binge watching and those users who shift in and out of binge watching states These findings have significant implications for advertisers and online video platforms supported by advertising revenues The remainder of this manuscript proceeds as follows We next provide a review of related research We then describe our data before presenting our modeling framework and model specification Finally, we present our results and conclude with a discussion of implications and directions for future work FIGURE Google Trend’s Index for “Binge Watching” 100 Indexed Volume of Search Activity 90 80 70 60 50 40 30 20 10 Ja n Ma 2, 01 r Ma 2, y 011 Ju , 20 11 l Se 2, 01 p No 2, v 011 Ja , 20 n Ma 2, r 012 Ma , y 012 Ju , 20 12 l Se 2, p 012 No , 20 v 12 Ja , 20 n Ma 2, 2 r Ma 2, y 013 Ju , 20 l 13 Se , 20 p 13 No 2, v 13 Ja , 20 n 13 Ma , 01 r Ma 2, y 014 Ju , 20 l 14 Se , 20 p 14 No , v 014 Ja , 20 n Ma 2, r Ma 2, y 015 Ju , 20 15 l Se 2, p 15 No 2, v 015 Ja , 20 n 15 Ma 2, r 16 ,2 01 Week / Journal of Marketing, September 2016 Related Research In this section, we provide a brief review of the literature related to binge watching We draw on multiple streams of literature from behavioral, economic, and medical research Our intention is not to engage in testing particular theories using the observational data available to us Rather, we provide this discussion to motivate our analysis of binge watching behavior and our expectations for how the behavior relates to advertising What Is Binge Behavior? The psychological and medical literature considers binge behavior an addiction (e.g., Gold, Frost-Pineda, and Jacobs 2003), research into which has shown that individuals often engage in such behaviors to escape reality Binge behavior, in general, has been defined by psychological researchers as an “excessive amount in a short time,” such as binge eating or binge drinking (e.g., Heatherton and Baumeister 1991; Leon et al 2007) This raises the question: Do such addictive behaviors extend to television consumption? Kubey and Csikszentmihalyi (2002) delve into this question by examining the addictive nature of television and comparing it to substance dependence The authors note that electroencephalogram (EEG) studies of individuals watching television have found that people “reported feeling relaxed and passive” and reveal that they exhibited “less mental stimulation” (p 76) In addition to proposing the association of a relaxed feeling with viewing that continues throughout a viewing session, the authors also contend that this association is negatively reinforced by the stress that viewers experience when the viewing session ends As Kubey and Csikszentmihalyi (2002, p 77) note, “Viewing begets more viewing,” suggesting that viewers exhibit a tendency to continue the viewing session to maintain their current state of mind In the online environment, this relates to the concept of “flow” (e.g., Ghani and Deshpand´e 1994; Hoffman and Novak 1996), which characterizes immersive experiences in which the user is in a state of focused concentration, intrinsic enjoyment, and time distortion Researchers have also linked experiencing flow to addictive behaviors In the context of video games, Chou and Ting (2003) find that individuals who experience flow are more likely to become addicted They also find evidence to suggest that experiencing flow is an intermediary step through which repetitive behaviors contribute to addictive behaviors As Chou and Ting (2003) note, addictive behaviors have been viewed in various ways depending on the field of study Economists have proposed the theory of rational addiction, which posits that individuals who exhibit addictive behaviors may be maximizing their utility and that past consumption can have a substantial impact on the utility derived from future consumption (e.g., Becker and Murphy 1988) In the marketing literature, Gordon and Sun (2015) develop a dynamic model of rational addiction to examine the impact cigarette taxes on consumption behavior Under rational addiction theory, binges can arise from cyclical behavior (e.g., Becker and Murphy 1988; Dockner and Feichtinger 1993) Using eating as an example, Becker and Murphy (1988) describe individuals alternating between periods of overeating and dieting in order to enjoy consuming food while also maintaining their weight In the context of binge watching, we may find that users take longer to initiate a new viewing session after a binge experience because they may derive more utility from other activities Advertising and Binge Watching Advertisements shown during a viewing session can be seen as an interruption to the experience We can liken the effect to that of advertising interruptions during an online browsing session Previous studies have shown that online browsers frequently enter a state of flow (Hoffman and Novak 1996) Advertisements shown during these sessions interrupt the flow state and can adversely affect the browsing experience Along these lines, Moe (2006) finds that pop-up promotions that interrupt an online shopping session shorten the duration of the session and encourage users to exit the site By the same token, we would expect that advertisements shown during a viewing session might interrupt the viewing experience and consequently contribute to an increase in viewers’ tendencies to end the session Binge watching behavior can also have an impact on advertising responsiveness As noted previously, research on binge and addiction behavior outside the context of binge watching has shown that users engage in addiction behaviors as an escape from reality (e.g., Gold, Frost-Pineda, and Jacobs 2003) In other words, individuals engaged in a binge state are immersed in an alternate reality In the context of binge watching, this alternate reality is created by the video content, and advertisements shown during these sessions can be seen as unwelcome reminders of the viewer’s true reality Thus, our expectation is that viewers engaged in binge watching will be less responsive to advertisements than viewers not engaged in binge watching because they prefer to remain immersed in the context of the series they are viewing Modeling Framework In this section, we conceptually describe our modeling framework before presenting the data and the methodological details of the model Advertising-supported platforms that provide streaming video content have an interest in two types of behaviors of their users: viewing behavior and advertising responsiveness In this article, we simultaneously model viewing behavior and advertising responsiveness at the level of the individual user while allowing the two to be related Our goal is to capture characteristics of viewing behavior that may indicate binge watching behaviors and relate those characteristics to how the user responds to advertising To model viewing behavior, we consider users’ viewing decisions at the end of each episode they have viewed Consistent with prior research on live television viewing (e.g., Rust and Alpert 1984; Rust, Kamakura and Alpert 1992; Shachar and Emerson 2000), we decompose a viewing session into a series of choices made by users First, after each episode, we model users’ decisions to continue their viewing session by watching another episode (of any program) Second, we model users’ decisions to watch another episode of the same Binge Watching and Advertising / program, an option not considered in studies of live television viewing because this decision is facilitated by today’s streaming video services Third, if a user decides to conclude the current viewing session, we consider the time until the user returns to the platform to begin a new viewing session Finally, we simultaneously model the user’s response to any advertising to which he or she is exposed Specifically, we examine the number of advertisements on which a user clicks, out of the total number of advertisements to which the user was exposed during an episode, as a binomial process Overall, our modeling framework allows us to examine both how advertisements affect viewing and how viewing behaviors affect the user’s response to advertisements In modeling both advertising and viewing decisions, we allow for heterogeneity across users and recognize that users’ tendencies for each behavior may be correlated In addition to variation in users’ tendencies, we account for shifts in behavior that reflect prior viewing and advertising responsiveness Data Data Description The data for our empirical analysis consist of the video viewing behavior of 9,873 registered users of Hulu.com from February 28, 2009, to June 29, 2009.2 While maintaining a library including both movies and television programs, Hulu com “had a brand promise that was clear and distinctive: Hulu is where you go for network TV” (Hansell 2009) This served as a point of differentiation compared with other popular online video portals, such as YouTube, that were populated primarily with user-generated content (e.g., Elberse and Gupta 2009) Due to this positioning, discussions have occurred in the popular press about Hulu com’s impact on broadcast and cable television’s business models (e.g., Learmonth 2009; Rose 2008, 2009) Following its 2009 Super Bowl advertisement, online conversation about Hulu increased more than 250% (Eshman 2009) In April 2009, Hulu announced a deal to make content from Disney available to Hulu users, including episodes of primetime hits such as Lost, Grey’s Anatomy, Desperate Housewives, Ugly Betty, Samantha Who?, Scrubs, and Private Practice, as well as content from ABC Family and Disney Channel (Kilar 2009a) The five most popular shows on Hulu in 2009 were Saturday Night Live, Family Guy, The Office, The Simpsons, and Naruto Shippuden (a Japanese anime series), and an episode of Family Guy was the most played full episode (Kilar 2009b) The data for each individual user consist of an event log that indicates the videos viewed.3 Each episode of a program 2The data set employed in this study was previously employed by Schwartz et al (2011) and Zhang, Bradlow, and Small (2013) We refer interested readers to these studies for additional details of the data We excluded data from 164 users who had sessions that exceeded 24 hours in length, due to concerns about the veracity of the data 3We use the terms “viewers” and “users” interchangeably Typical of many panel data sets, our data set does not allow us to distinguish among multiple individuals in the same household / Journal of Marketing, September 2016 is divided into multiple segments The event log records the time at which each video segment began, as well as information about the video segment, including the title of the television series and episode, the season of the series to which the episode belongs, and the episode number within the season In addition to the series and episodes that users viewed, the event log also contains information on the advertising to which users were exposed The advertising data include a time-stamp at which the advertisement was served to the user, as well as the program and episode in which the advertisement aired Our advertising data also indicate whether an individual took action and clicked on the advertisement Over 1.1 million advertisement impressions were recorded in our data, with users clicking on 9,317 advertisements (.84% of the advertisements).4 In Figures 2–4, we provide histograms that show the distribution of the number of episodes viewed by users, the number of unique programs viewed by users, and the number of viewing sessions conducted by users, respectively Although most users viewed several episodes, we find variation in the number of viewing sessions that users conducted While 25% of users conducted just one viewing session, approximately an equal proportion conducted ten or more viewing sessions We also see that the number of series that users viewed follows a bimodal distribution While 35% of users viewed only one or two series, more than 25% of users viewed ten or more different series We provide descriptive statistics based on users’ behaviors across all viewing sessions in our data in the upper portion of Table We define a viewing session as a period of video viewing separated by one hour or more of inactivity In the data, 9,873 users were responsible for 104,414 viewing sessions, an average of 10.58 sessions per user during the four-month data period We provide descriptive statistics about the composition of these sessions in the lower portion of Table Although Table provides a summary of viewing behavior at the level of the session, such statistics not shed light on the heterogeneity that exists across users They also not provide insight into the relationship between the volume of online video consumption and the content consumed To investigate these factors at the session level, we present the number of programs viewed conditional on the length of the session in Table According to the session-level data presented in Table 1, in at least 50% of sessions, viewers watched two or more episodes, and in at least 50% of the sessions, viewers constrained their viewed episodes to a single series Additionally, the lower bound of the interquartile ranges presented in Table is equal to one series, irrespective of the number of episodes viewed In other words, for each session length considered, at least 25% of sessions involved viewers watching episodes from a single series These statistics suggest the prevalence of binge watching behavior in the data 4During the data period, all videos on the site could be viewed free of charge, and the service was strictly ad-supported In June 2010, after the data period concluded, Hulu introduced the Hulu Plus subscription service 50% 50% 45% 45% 40% 40% 35% 30% 25% 20% 15% 35% 30% 25% 20% 15% 10% 10% 5% 5% 0% 0% 10+ To further illustrate the prevalence of binge watching behavior in our data, we show the joint distribution of the number of episodes and unique series viewed in a session in Table We see that 63.3% of viewing sessions consisted of a single series, and 18.5% consisted of multiple episodes of a single series We next consider the fraction of users who engaged in different types of viewing sessions We consider three types of viewing sessions: (1) single-episode sessions, (2) multiepisode sessions that consist of episodes from a single series, and (3) multiseries sessions that consist of episodes from multiple series We find that 81.1% of users conducted at least one single-episode session, 49.0% of users conducted at least one multiepisode viewing session that consists of episodes from a single series, and 69.0% users conducted at least one multiseries session FIGURE Distribution of the Number of Series Viewed 50% 45% 40% 35% 30% 25% 20% 15% 10% 5% 0% Series Viewed 10+ Viewing Sessions Episodes Viewed Fraction of Users FIGURE Distribution of the Number of Viewing Sessions Fraction of Users Fraction of Users FIGURE Distribution of the Number of Episodes Viewed 10+ Taken together with Table 3, with nearly half of users viewing multiple episodes of a single program in a session, these statistics provide model-free evidence for the presence of binge watching in our data While our exploratory analysis suggests that binge watching does occur, it does not enable us to discern whether such behavior is driven by user-specific traits or by recent viewing behavior To disentangle these competing explanations, as well as to understand how this behavior is related to users’ advertising responsiveness, we develop a joint model of viewing behavior and advertising response We next describe the key variables in our empirical analysis before presenting our modeling framework Variable Specification For our model, we first create a set of dependent variables that characterize the various components of users’ viewing and advertising response decisions These variables are user-level and time-varying from episode to episode The viewing decisions we are interested in reflect the length, variety of programming, and frequency of viewing sessions Because a viewing session is only observed when at least one episode is viewed, we characterize the length of a session according to a user’s decision of whether or not to continue the session after viewing an episode In other words, we construct a binary variable equal to if the user views another episode and equal to if the user chooses to end the viewing session (CONTINUE) To represent the variety of the viewing session, we again consider the user’s decision after viewing an episode Conditional on the user viewing another episode, we construct a binary variable equal to if the next episode is from the same series as the episode just completed and equal to if it is from a different series (SAME) Finally, if the user chooses to end the viewing session, we then consider the frequency of viewing sessions by computing the time (in days) until the next viewing session (FREQUENCY) To model advertising click-through behavior, for each episode, we count the number of advertisements on which a Binge Watching and Advertising / TABLE Descriptive Statistics By User Viewing sessions Episodes viewed Programs viewed Ads shown Ads clicked Season finales views Series finale views By Session Episodes viewed Programs viewed Ads shown Ads clicked Intersession time (days) M SD 10.58 34.01 8.60 111.70 94 2.16 98 19.31 114.57 12.62 368.39 4.14 8.23 4.36 3.21 1.66 10.56 09 3.73 10.67 1.18 39.68 95 9.84 Mdn 95% Range 22 0 [1, [1, [1, [0, [0, [0, [0, 86 90% Range 68] 229.68] 43] 797.03] 8] 17] 8] [1, [1, [1, [1, [0, [0, [0, [1, 12] [1, 5] [0, 40] [0, 1] [.05, 32.63] IQR 27] 79] 21] 255.20] 2] 2] 2] [2, [3, [2, [7, [0, [0, [0, [1, 6] [1, 3] [0, 19] [0, 0] [.07, 7.93] 10] 27] 10] 79] 1] 1] 1] [1, 3] [1, 2] [3, 11] [0, 0] [.33, 2.25] Notes: IQR = interquartile range, representing the range that defines the middle 50% of observations user clicks (CLICKTHRU) out of the total number of advertisements to which the user is exposed Taken together, this yields the click-through rate for each episode In addition, we construct a number of covariates that are expected to influence the viewing and advertising clickthrough decisions These covariates are designed to capture the effects of temporal factors (time of day, day of week, etc.), program variation (e.g., genre), and content to which a user was previously exposed, including program content and advertising We provide a list of the covariates employed in our analysis in Table First, we consider the effects of viewing variables that capture previous viewing behavior Specifically, we consider a breadth variable, which is measured as the number of different series viewed in a session up until the point of the behavioral decision being considered, and a depth variable, which is measured as the number of episodes from the current series viewed thus far in the viewing session We expect these covariates to affect both the viewing decisions and advertising responsiveness We construct episode-by-episode breadth and depth measures (BREADTH_EPISODE and DEPTH_ EPISODE) that are used to model within-session behaviors (CONTINUE and SAME), as well as measures that summarize breadth and depth within the previous session (BREADTH_ SESSION and DEPTH_SESSION) and that are used to model intersession durations (FREQUENCY) In addition to the breadth and depth measures, we include an indicator variable to account for whether a user has viewed the current series in an earlier viewing session (PRIORVIEW) Second, throughout these viewing sessions, users are exposed to advertisements Users’ exposures to advertisements vary with the amount of content they consume; that is, longer programs include more advertising than shorter programs We construct a time-varying covariate, EXPOSURES, that represents the number of advertisements to which a user has been exposed in each episode Because viewers’ interactions with advertisements may affect their subsequent viewing decisions, we also consider the impact of the number advertisements on which the user has clicked in each episode, captured by the variable CLICKTHRU Finally, we specify a number of control variables to capture the effects of content and temporal differences (time of day, day of week, etc.) on both viewing and advertising click-through decisions Drawing on the data provided by Hulu.com, we identify 18 genres in our data and construct a series of 17 indicator variables (one for each genre, with action/ TABLE Unique Series Viewed, by Number of Episodes in the Session Number of Episodes 10+ Percentage of Sessions M SD Mdn 44.79 21.71 11.96 6.90 4.24 2.68 1.73 1.25 89 3.89 1.6 2.04 2.39 2.66 2.92 3.06 3.29 3.35 3.51 49 84 1.14 1.42 1.65 1.87 2.04 2.25 2.76 2 2 3 3 95% Range [1, [1, [1, [1, [1, [1, [1, [1, [1, [1, Notes: IQR = interquartile range, representing the range that defines the middle 50% of observations / Journal of Marketing, September 2016 1] 2] 3] 4] 5] 6] 7] 8] 9] 11] 90% Range [1, [1, [1, [1, [1, [1, [1, [1, [1, [1, 1] 2] 3] 4] 5] 6] 6] 6] 7] 7] IQR [1, [1, [1, [1, [1, [1, [1, [1, [1, [1, 1] 2] 3] 3] 4] 4] 4] 5] 5] 5] TABLE Numbers of Episodes and Series Viewed, Across Sessions Number of Episodes Number of Series 5+ 51 44.76% 8.58% 13.13% 4.01% 3.43% 4.52% 2.06% 1.70% 1.53% 1.60% 3.88% 3.14% 2.53% 1.92% 3.21% adventure being our baseline genre) We also construct a covariate to represent whether the episode just viewed was a season finale (SEASON_FINALE) or a series finale (SERIES_FINALE) Finally, we define a set of variables to capture month, day-of-week, and time-of-day effects For day of week, we differentiate between weekdays (Monday–Thursday), Friday, Saturday, and Sunday, again constructing indicator variables whereby weekdays are considered the baseline For time of day, we divide the day into parts according to Headen, Klompmaker, and Rust (1979) Specifically, we construct indicator variables for early morning (7 A.M.-10 A.M.), daytime (10 A.M.-5 P.M.), early fringe (5 P.M.-8 P.M.), and prime time (8 P.M.-11 P.M.) The late fringe period of 11 P.M.-7 A.M is set as our baseline Model Model Specification Figure provides a depiction of our modeling framework, including the user decisions we consider and the variables that influence them Our model is made up of four decisions: (1) whether to continue the viewing session by viewing another episode (CONTINUE); (2) if the session is continued, whether to watch a video from the same or a different series (SAME); (3) how much time elapses until the next viewing session begins (FREQUENCY); and (4) whether to click on an advertisement (CLICKTHRU) We develop our model by first considering the intrasession viewing behaviors of whether to continue a viewing session and whether to watch the same series We then present TABLE Variable Descriptions Variable Name Description BREADTH_EPISODEits Number of series previously viewed in session s by user i prior to video t; operationalized as log(1 + number of series previously viewed) and mean-centered DEPTH_EPISODEits Number of episodes of the current series previously viewed in session s by user i prior to video t; operationalized as log(1 + number of episodes previously viewed) and mean-centered BREADTH_SESSIONis Number of series viewed in session s by user i; operationalized as log(1 + number of series viewed) and mean-centered DEPTH_SESSIONis The maximum number of episodes of a single series viewed by user i in session s; operationalized as log(1 + number of episodes viewed) and mean-centered PRIORVIEWits Indicator variable for whether or not user i has previously viewed the same series as video t of session s in an earlier viewing session EXPOSURESits Number of advertisements shown to user i in video t of session s CLICKTHRUits Number of advertisements clicked by user i in video t of session s SEASON_FINALEits Indicator variable for whether video t in session s viewed by user i is a season finale SERIES_FINALEits Indicator variable for whether video t in session s viewed by user i is a series finale MONTHits Month in which video t viewed by user i in session s began (March, April, May, June) DAYits Day of the week on which video t viewed by user i in session s began (Monday–Thursday, Friday, Saturday, Sunday) DAYPARTits Day part in which video t viewed by user i in session s began (early morning, daytime, early fringe, prime time, late fringe) GENREits Program genre of video t viewed by user i in session s (action/adventure, animation and cartoons, comedy, drama, family, food and leisure, home and garden, horror and suspense, music, news and information, other, reality and game shows, science fiction, sports, talk and interviews, unknown, video games, web) Binge Watching and Advertising / FIGURE Depiction of Modeling Framework Viewing Variables Viewing Behavior Continue Same Frequency Do I continue or stop watching? (pits) Do I watch the same or a different series? (qits) How long before I watch again? (h(t)) • • • • • • • • • Control Variables • • • • • • GENRE DAY MONTH DAYPART SEASON_FINALE SERIES_FINALE DEPTH_EPISODE DEPTH_EPISODE2 BREADTH_EPISODE BREADTH_EPISODE2 DEPTH_SESSION DEPTH_SESSION2 BREADTH_SESSION BREADTH_SESSION2 PRIORVIEW Ad Variables Advertising Exposures • • EXPOSURES CLICKTHRU Advertising Clickthrough Do I click on the ad? (rits) the model component for intersession timing decisions Finally, we describe our model for viewers’ advertising responsiveness For user i who has viewed t videos in the current session s, we model two binary decisions: (1) the decision to continue session s by viewing another video (CONTINUE) and (2) conditional on viewing another video, the decision to watch the same series or a switch to a different program (SAME) As shown in Figure 5, these decisions are affected by viewing variables based on previous viewing activity, ad variables, and control variables After user i views video t, we model the probability with which the user continues the current session as pits: (1) logitðpits Þ = + q1 DEPTH EPISODEits + q2 DEPTH EPISODE2its + q3 BREADTH EPISODEits + q4 BREADTH EPISODE2its + q5 PRIORVIEWits + q6 EXPOSURESits + q7 CLICKTHRUits + q8 SEASON FINALEits 26 + q9 SERIES FINALEits + å q GENRE j its j = 10 29 + å 32 qj DAYits + j = 27 å q MONTH j its j = 30 36 + å q DAYPART j its , j = 33 where is an individual-level intercept such that = a + g i1 , with g i1 a random effect with mean that captures variation across users We allow for nonlinear effects of the viewing variables DEPTH_EPISODE and / Journal of Marketing, September 2016 BREADTH_EPISODE If we consider binge watching an addictive behavior whereby “viewing begets more viewing,” as proposed by Kubey and Csikszentmihalyi (2002), we should expect DEPTH_EPISODE (captured by q1 and q2) to have a positive effect on pits Likewise, the impact of BREADTH_EPISODE (reflected by q3 and q4) is expected to be negative because continued viewing of a given program is more likely in such an addictive state in which BREADTH_EPISODE of viewing is low The coefficient q5 accounts for the effect of user i having viewed the current program prior to episode t of session s on the decision to continue the current viewing session The effects of ad variables (EXPOSURES and CLICKTHRU) to which user i is exposed during episode t of session s on the decision to continue viewing session s are captured by q6 and q7 If we assume that advertising breaks up the flow of a binge watching session and discourages further viewing, we should expect that q6 < and q7 < The remaining coefficients (q8–q36) capture variation in the decision to continue the session that is related to control variables, including whether video t is a season (q8) or series (q9) finale, the genre viewed in video t (q10–q26), and day and time (q27–q36) at which video t is viewed In the special case where q = 0, then logit(pits) = and the length of the viewing session (in episodes) follows a shifted geometric distribution at the user level Following those who employ this individual-level model for discrete-time customer base analysis (e.g., Fader and Hardie 2009), we accommodate heterogeneity across users We also allow the likelihood of continuing the viewing session to shift depending on the content that a user views We employ a similar binary logit model for a user’s decision to continue viewing the same program, as opposed to viewing a different program Conditional on user i continuing viewing session s, we specify the probability with which video t + is from the same series as video t as qits: (2) logitðqits Þ = bi + y1 DEPTH EPISODEits (4) + y4 BREADTH + y5 PRIORVIEWits + y6 EXPOSURESits + y7 CLICKTHRUits + y8 SEASON FINALEits + y9 SERIES FINALEits å y GENRE j its å y DAY + j = 27 32 + j å y MONTH j its + å y DAYPART j = 33 j its , where the user-level intercept is specified as bi = b + g i2 As in our specification of pits, our specification of qits enables us to distinguish the user’s general tendency to watch the same program (through g i2) from the impact of previously viewed content (through y1–y5) If we assume that consuming multiple episodes of the same series increases addictive behavior, then we should expect to observe a tendency to continue viewing the same series as depth increases Likewise, if limited breadth contributes to addictive viewing behavior focused on a single series, then we are more likely to observe a tendency to switch series as breadth increases The impact of ad variables on this component of viewing behavior is reflected in y6 and y7 The coefficients y8-y36 capture the effects of the control variables that account for content and temporal differences Whereas Equations and characterize the user decisions within a single viewing session, our third model component (FREQUENCY) looks across viewing sessions At the conclusion of viewing session s, we model the time until the start of the next viewing session using a proportional hazard model (e.g., Seetharaman and Chintagunta 2003) We assume a Weibull distribution for the baseline hazard (e.g., Helsen and Schmittlein 1993; Schweidel, Fader, and Bradlow 2008; Seetharaman and Chintagunta 2003), which accommodates increasing or decreasing hazards and nests the constant exponential hazard process as a special case The baseline Weibull hazard is given by (3) bðtÞ = luðltÞ u-1 (5) Xis = g i3 + w1 DEPTH SESSIONis + w2 DEPTH SESSION2is + w3 BREADTH SESSIONis + w5 its 36 j = 30 where + w4 BREADTH SESSION2is 29 j = 10 SðtÞ = exp EPISODE2its + ! Â Ã u bðuÞexpðXis Þdu = exp -ðltÞ expðXis Þ , ðt + y2 DEPTH EPISODE2its + y3 BREADTH EPISODEits 26 and control variables to affect how quickly a user returns to the website to begin a new viewing session The resulting survival function is then given by , where l > and u > The survival function can then be written as a function of the hazard rate h(t), which is given by h(t) = b(t)exp(Xis), where b(t) is the baseline Weibull hazard rate and Xis captures the impact of covariates corresponding to session s for user i As described in Equations and and illustrated in Figure 5, we allow viewing variables, ad variables, 5We compared the proposed model that uses the Weibull distribution as the baseline hazard with the model that employs the exponential distribution as the baseline hazard Although we did not find any substantive differences between the specifications, on the basis of the logmarginal density, the model that uses the Weibull hazard better fits the data We therefore present the results corresponding to this model å EXPOSURES å CLICKTHRU i$s + w6 i$s + w7 maxðSEASON FINALEi$s Þ + w8 maxðSERIES FINALEi$s Þ 25 + + 28 å w GENRE j iNis s + å w DAY j j=9 j = 26 31 35 å w MONTH j j = 29 iNis s + iNis s å w DAYPART j iNis s , j = 32 and Xis captures the impact of observed covariates and unobserved heterogeneity across users The user-specific random effect g i3 has a mean of and captures unobserved differences across users As described in Table 4, the measures of DEPTH_SESSIONis and BREADTH_ SESSIONis provide measures of viewing depth and breadth that are calculated according to viewing behavior in session s For example, if a user has viewed three episodes of one series and one episode of another, the maximum depth during the session is If a user is addicted to a series and has watched many episodes of a given program in a session (i.e., DEPTH_SESSION is high), then we might expect the user to return to view more episodes in a relatively short amount of time, reflected by w1 and w2 Alternatively, if users exhibiting binging behavior cycle between different activities (e.g., Becker and Murphy 1988), we might expect increased depth to increase the time until the next viewing session begins To account for the potential impact of ad variables on the time until user i begins session s + 1, we aggregate the advertising exposure (w5) and advertisement clicks (w6) throughout session s by summing these variables across all videos that comprise the viewing session We also account for the content viewed during session s and the time of session s The coefficients w7 and w8 account for the presence of a season or series finale, respectively, in session s If an episode viewed in session s is a season finale, max(SEASON_FINALEits) = 1; otherwise, max(SEASON_FINALEits) = We control for the genre of the final episode viewed by user i in session s (which we denote video Nis) to account for the most recently viewed content Similarly, we control for the time (day part, day of week, and month) at which video Nis is viewed We use a binomial distribution to model the number of advertisements on which user i clicks in episode t of session s, according to the number of advertisements to which the user is exposed We specify user i’s probability of clicking on an advertisement during video t of session s, rits, using a Binge Watching and Advertising / binary logit model (e.g., Chatterjee, Hoffman, and Novak 2003; Dr`eze and Hussherr 2003; Hoban and Bucklin 2015; Urban et al 2014), where rits is affected by the viewing variables and control variables shown in Figure 5: (6) logitðrits Þ = d i + f1 DEPTH EPISODEits + f2 DEPTH EPISODE2its + f3 BREADTH EPISODEits + f4 BREADTH EPISODE2its + f5 PRIORVIEWits + f6 SEASON FINALEits + f7 SERIES FINALEits 24 27 å f GENRE + j=8 j its å f DAY + j 30 34 å f MONTH + j = 28 its j = 25 j its + å f DAYPART j = 31 its , j where d i = d + g i4 and g i4 is a user-specific intercept; f1 and f2 capture the impact of the depth of viewing of the series viewed in video t; f3 and f4 account for the breadth of viewing that has occurred in the session prior to viewing video t; and f5 controls for the impact of prior exposure to the series In addition to viewing variables, we also include control variables that account for whether or not video t is a season (f6) or series finale (f7), the genre of video t (f8–f24), and when video t is viewed (f25–f34).6 If we assume that binge watching reduces viewers’ responsiveness to advertisements, we should anticipate that the impact of depth on the click-through probability (captured by f1 and f2) is negative To complete our model specification, Equation provides the joint likelihood function Let Yits = when the next video that user i chooses to view is from the same series as video t of session s, let Yits = when user i chooses to view a different program, and let Yits = when user i decides to end session s after viewing video t Let dis denote the time between sessions s and s + for user i Combining the intrasession viewing decisions (whether to continue the session and whether to view the same series), episode-level advertising response, and proportional hazard model of intersession durations, the likelihood of user i’s behavior is given by (7) & Si ∏ Li = s=1 N is ∏ ðpits Þ1ðYits