Patterns of Substitution between Internet and Television in the Era of Media Streaming - Evidence from a Randomized Experiment

Transcription

1 Patterns of Substitution between Internet and Television in the Era of Media Streaming - Evidence from a Randomized Experiment Filipa Reis Heinz College, Carnegie Mellon University Advisory Committee: Prof. Pedro Ferreira Prof. Miguel Godinho de Matos Prof. Michael Smith April 1, 2015 Abstract This paper investigates the relationship between TV and Internet use and how it is affected by an increase in the number of quality channels available on TV. Our work is divided in two parts. First, using data from a major European telecommunications provider we conduct an observational study to assess whether TV and Internet are complements or substitutes. We use a sample of nearly 4,000 triple-play subscribers who upgraded their service bundle during the period of analysis. Identification is achieved using a function of the time to contract expiry as an instrumental variable for TV time. Our results suggest that an increase of 1 percent in average daily TV time is associated to a decrease of 1.55 percent in the average daily download traffic. Second, we use data from a randomized experiment, conducted in collaboration with the same firm, to obtain additional evidence on the relationship between TV and Internet and the magnitude of this effect. A sample of over 5,000 heavy Internet users, also subscribers to the tripleplay service, was randomly selected to receive free access to a set of premium channels during the 2014 s Christmas holiday season. A similar sample of users was held out as a control group. Our estimate for the elasticity of substitution between TV and Internet indicates that a 1 percent increase in TV time is associated to a 0.25 percent decrease in download traffic. The two studies provide consistent evidence of substitution between TV and Internet. In practical terms, taking an average user from each sample and converting download traffic to online streaming time, we find that the decrease in online streaming time corresponds to one half and one third of the increase in TV time according to the observational and experimental analysis, respectively. To our knowledge this is one of the few works using observational data to study the relationship between TV and Internet use and the first using data from a randomized experiment for this purpose. 1

2 1 Introduction Television is by far the most popular and widely used mass media channel. Everyday, the average American citizen spends over five hours watching TV (Nielsen, 2014), significantly more time than that spent in any other leisure activity (BLS, 2014). In the past two decades, we witnessed a surge of new media and communication technologies such as the personal computer, the Internet, and mobile telephony. The impact these new technologies and, in particular of broadband Internet, had on the media industry are far from trivial e.g. (Zentner, 2008; Liebowitz, 2006; Waldfogel, 2010). Even though TV is still the leading entertainment and news source for the average person, it is starting to lose its prominence among the younger layers of the population - teenagers and young adults watch less television and spend more time online than any other demographic group (Nielsen, 2014). In fact, research has shown that while broadband penetration had no significant impact on the time people older than 35 years spend watching television, it did cause a significant reduction in the television viewing time of younger generations (Liebowitz and Zentner, 2012). If we assume the time people allot to media consumption is fixed, the arrival of new media that are functionally equivalent or superior to existing ones should lead them to partially or completely substitute one for the other (Cha and Chan- Olmsted, 2012). As broadband penetration grows, the services offered online develop accordingly; from file sharing to music and video streaming and gaming, the Internet has become a serious competitor for the attention of TV audiences. Current television business models still rely heavily on advertising revenues, which have been progressively moving online (Berman et al., 2006), posing a significant threat to their sustainability. Although in the long-term new business models will likely develop, in the short-term the industry is interested in knowing whether and how it can compete with the online threat (Berman et al., 2006). This brings us to our research questions. First, we ask whether TV and Internet 2

3 are complements or substitutes. Second, we ask whether an increase in the number of quality channels available on TV impacts TV viewing time and Internet use. We work with a major European telecommunications provider to address these questions. We start by conducting an observational study using a sample of 3967 triple-play subscribers who upgraded their service bundle (getting both a faster Internet connection and more TV channels) between September 2013 and April Data was collected on the subscribers TV and Internet use (viewing minutes and download traffic in MB, respectively) and an instrumental variable approach was used to analyze the relationship between the two channels. This approach builds on three key assumptions: first, that the timing of service upgrades is a function of the proximity to the end date of the initial lock-in period; second, that upgrades are primarily motivated by better TV; third, that the better TV service resulting from the bundle upgrade will cause an increase in TV viewing time and thus TV time will be related to the end-date of the initial lock-in. Based on these assumptions, we construct an instrumental variable for TV time that is a function of the time left to the end of the initial contract. Our results suggest that there is a negative and statistically significant relationship between the consumption of the two media channels - an increase of 1 percent in TV time is associated to a decrease of 1.55 percent in download traffic (p < 0.01). Next, and given that the results from our observational study are contingent on the quality of the instrumental variable, we seek validation for our findings using experimental data. We use data from a randomized experiment conducted with a sample of triple-play subscribers who are heavy Internet users. We chose to focus on heavy Internet users because this is the subset of the user population that has a stronger Internet preference and thus, we believe, will be less likely to trade-off Internet for TV, giving us a lower bound for the elasticity of substitution between the two channels. Half of the subscribers in this sample were granted free access to a set of premium movie and series television channels during 2014 s Christmas holidays season. Subscribers were notified about this 3

4 offer by SMS and . Data was collected on their TV and Internet activity during the treatment period and also for a short pre-treatment period (10-14 December 2014). We measure the elasticity between TV and Internet using an instrumental variable approach in which the treatment indicator serves as an instrument for TV minutes. Our results show a negative and statistically significant relationship - a 1 percent increase in TV time causes a 0.25 percent decrease in download traffic (p < 0.01). Additionally, we measure the relationship between Internet use and an alternate measure of TV use, the number of timeshift events. We find that an increase of 1 percent in the number of time-shift events is associated to a decrease of 1.19 percent in download traffic. To our knowledge, this is the first study using data from a randomized experiment to study the relationship between TV and Internet consumption. In both the observational work and randomized experiment we find evidence of substitution between TV and Internet use. Our results also show that an increase in the number of available quality TV channels leads to an increase of television viewing time and the use of the time-shift feature. This paper is structured as follows. In section two we review the literature on the impact of the Internet on other media. In section three we describe our observational study, starting by a description of the data, which is followed by our analysis and discussion of the results. Section four presents our analysis of experimental data following the same structure as chapter three. Finally, in section five we discuss the main findings and limitations of this work and conclude. 2 Literature Review Most literature studying the relationship between the Internet and other media industries has focused on the music industry e.g. (Zentner, 2006; Oberholzer-Gee and Strumpf, 2007; Liebowitz, 2008), largely motivated by the major revenue losses suffered in the early 2000 s and their connection to digital file-sharing. 4

5 Only more recently, as Internet s viability as a video distribution platform begun to develop, did researchers start to investigate its impact on movie sales (Rob and Waldfogel, 2007; Smith and Telang, 2009, 2010) and box-office revenues (Danaher and Waldfogel, 2012). Regarding the music industry, most studies have focused on the impact of file sharing on record sales, generally finding large and significant negative effects (Hui and Png, 2003; Zentner, 2006; Liebowitz, 2008), with the exception of Oberholzer-Gee and Strumpf (2007) who found no statistically significant effect. The decline in the number of physical music stores and the acceleration of their death rate has also been linked to broadband connectedness (Zentner, 2008). Concerning the movie industry, Peitz and Waelbroeck (2006) have argued that while record sales may benefit from a positive promotional effect resulting from free online sampling, movies, which are usually only consumed once, will be less likely to enjoy this benefit and, consequently, firms may suffer significant harm from movie piracy. In support of this argument, Rob and Waldfogel (2007), using survey results from a sample of university students, found that unpaid online consumption of movies did displace paid consumption. Other work has also shown that pre-release movie piracy has a negative and significant effect on international box-office revenues (Danaher and Waldfogel, 2012). However, Smith and Telang (2009) found no evidence of cannibalization between online pirated content and movie sales, leading the authors to suggest that the two products likely appeal to distinct customer segments. In a later study, the same authors found a positive and significant relationship between broadband penetration and DVD sales, suggestive that the Internet s promotional role outweighed the effect of piracy during the considered time period ( ) (Smith and Telang, 2010). Research studying the relationship between Internet and TV is still very limited. To our knowledge, most research on this topic is based on survey methodologies with the exception of Liebowitz and Zentner (2012) who use observational data on a panel of American cities to study the impact of broadband penetration on TV viewing time. The authors found that broadband penetration has a neg- 5

6 ative impact on the TV viewing time of younger viewers (those younger than 34 years old) but no significant effect on the time older individuals spend watching TV (Liebowitz and Zentner, 2012). Currently, the number of high quality options for both legal and illegal consumption of video offered by the Internet allows an increasing number of users to circumvent the traditional TV content delivery format. Moreover, the Internet offers content producers and right holders a channel for reaching audiences directly which may ultimately result in reduced access to quality content for broadcasters (D Arma, 2011), while also creating additional competition from online content aggregators. As in the current business model TV networks fight for audiences time, which is then sold to advertisers (Wilbur, 2008), the loss of audience s attention to the Internet is becoming a growing concern for TV networks (Berman et al., 2006). Also, as audiences become increasingly more fragmented, the accurate prediction and measurement of audiences grows in importance (D Arma, 2011). For these reasons, assessing the degree of substitution between TV and Internet and identifying the mechanisms behind this relationship has become a key issue for the industry stakeholders as well as media ownership policy makers (Waldfogel, 2002). 3 Observational Study This work was developed in collaboration with a large European telecommunications provider, market leader in the pay-tv segment with over one million subscriber households in the considered region. Its services include cable television and video-on-demand, broadband and mobile Internet, and fixed and mobile telephony. We were granted access to the full anonymized customer history and TV and Internet activity records for a subset of the company s customers - tripleplay (TV, Internet, and fixed-line phone) subscribers. We should note that our unit of analysis - the subscriber - corresponds to a household and that all the observed TV and Internet activity is at the household 6

7 level. We have no information regarding household composition, such as number of people, gender, age, or occupation, or the demographic characteristics of the decision maker regarding the triple-play service. Therefore, we are unable to control for these factors in our analysis or characterize our sample in terms of the average or more frequent household characteristics. However, Prince (2012) has found that triple-play subscriptions were positively correlated with income, education, and household size, but generally uncorrelated with age (Prince, 2012, p. 26). We start by addressing our first research question on whether TV and Internet are complements or substitutes. The main difficulty in studying this relationship lies in the fact that each household choses to use TV and Internet at its own discretion. Household choices are reflected at two main levels. First, each household purchases the service that better fits its needs and preferences. Thus, we expect subscribers with stronger media preferences to subscribe to higher quality bundles. In the case of the triple-play subscribers considered, the choice of service bundles is quite limited: there is a higher quality service bundle combining a fast Internet connection and a large number of TV channels and a lower quality service bundle combining a slower Internet connection and a lower number of TV channels. The difference in Internet connection speed between the lower and the higher quality bundle is 90 Mbps and the difference in the number of TV channels offered is greater than 30. This service offering prevents us from observing subscribers who have the same Internet connection speed but a different number of TV channels, or vice-versa, as the quality of the two items is always coupled together. Second, each household allocates time to each of these channels at its own discretion. In this work, we attempt to circumvent these empirical limitations by looking only at subscribers who upgraded their service bundle during our period of analysis. This means that all the households in our sample are originally subscribers of the lower quality bundle who, during the time period analyzed, upgraded their 7

8 service to the higher quality bundle 1. Although the actual motivations behind bundle upgrades are unobserved, two main possibilities can be listed. One is a change in the subscriber s available budget: a subscriber may have a strong media preference (prefer the high quality bundle) but, due to financial constraints, subscribe to the lower quality bundle and then upgrade to the higher quality bundle once its available budget increases. Alternatively, a change in the company s service offering, a promotion, or competitive threat, may result in a lower service price. A second possibility is a change in the subscriber s preferences: a subscriber s media preference may increase causing the initial subscription of the lower quality bundle to fail to satisfy the new customer needs. This change in preferences may concern any of the services offered in the triple-play bundle or any combination of them. Alternatively, the company may change the composition of the offered bundles (e.g. change the number of TV channels offered in each bundle) thus inducing the preference change in the consumer. The motivations listed above are unobservable to us and, consequently, cannot be accounted for. However, there is one service characteristic that influences not the decision to upgrade but the timing of the upgrade and that is the service s lock-in period: new subscribers of the company s triple-play service sign a contract with a lock-in period of 24 months; current subscribers of the triple-play service who wish to change their service bundle sign a contract with lock-in period of 12 months. Usually, when a subscriber is approaching the end of a lock-in period (typically when the subscriber is 3 or less months away from the end of the contract), the company will proactively contact the customer to advertise its services and try to re-sign the subscriber into a new one-year contract. The time at which the subscriber is presented with this decision is a direct result of the 1 In our sample there is an exception to this rule: there are 165 subscribers (corresponding to 4.2 percent of the sample) who have an initial service bundle with an intermediate Internet connection speed. This bundle had been discontinued from the company s service offering during our period of analysis. Nonetheless, these observations are included in our analysis as all these users upgrade to the high quality bundle. 8

9 time she initially subscribed the service. For these reasons, we believe that the timing of the end of the subscribers initial lock-in period is fairly exogenous in our sample. In order to achieve identification two additional assumptions are needed. These concern which media channel is the main driver for the choice to upgrade and the way TV consumption is affected by the upgrade. Our second assumption is that upgrades are primarily motivated by the better TV service. The following arguments in favor of this assumption are worth noting. First, according to an independent survey conducted by a market research firm to a sample of 1025 households, representative of the region s household population, 48 percent of respondents stated that the most relevant service for their home is TV whereas only 13 percent stated that it is the Internet. Considering only the respondents who had both TV and Internet in their homes, 70 percent stated that TV is the most important service compared to 23 percent who chose the Internet. Second, the provider we work with was originally a pay-tv provider and it is still primarily known by the market as such. The Internet service is considered by the company and presented to the market as an add-on to pay- TV. The TV service, namely, the number and content of base and premium channels and the additional service features such as video on demand and time-shift TV are the primary focus of the company s marketing campaigns. Third, we expect that for the average user the difference of over 30 channels between the two availble bundles to be more significant than the difference of 90 Mbps in Internet connection. In fact, for most households, even including those with multiple Internet users, the Internet connection speed offered in the lower quality bundle should be sufficient to satisfy their Internet usage needs. Finally, our third assumption is that TV time is positively related to the enddate of the initial contract. This assumption results from the combination of our two previous assumptions and evidence showing that after the upgrade, users significantly increase their TV viewing time. 9

10 Summing up, our identification strategy relies on three main assumptions: i) that the timing of upgrade is a function of how close the subscriber is to the end of the lock-in period of the original service bundle, ii) that the upgrade is primarily motivated by a better TV service, iii) that TV time is related to the proximity to the end of the initial lock-in period. Based on these assumptions construct an instrument for TV time which gives us the probability of upgrade at each time point, modeled as a function of the number of days left to the end of the initial lock-in period. We then use two-stage least squares to model Internet use as a function of TV time and obtain an estimate for the elasticity between these two channels. The following sections will detail the data used in this study as well as our empirical strategy and results. 3.1 Data and Descriptive Statistics The data set used in this study is a 10-month panel from mid-2013 to mid-2014 for a sample of triple-play subscribers who upgraded their service bundle between September 2013 and April For each subscriber, we extracted its complete profile information and TV and Internet activity logs for the period between mid- June 2013 (the earliest date for which data is available) and the end of May We eliminated from our sample all subscribers who were not active in the 30 days before and the 30 days after the service upgrade as well as those who churned during the period considered. Our final sample includes 3967 subscribers. Figure 1 shows the number of subscribers in our data set at each point in time relative to their service upgrade date: setting the date of bundle upgrade to 0, we plot the number of subscribers in the sample in the days preceding and following the change. We can see that as we get further away from the date of upgrade there are increasingly fewer subscribers. These are households who upgraded their service close to the start or end date of our period of analysis. In order to avoid biasing results by using data from fewer users, we only consider the period of 100 days preceding and following the date of service upgrade of each subscriber (depicted in the plot by the two red lines). The change in the service 10

11 bundle triggers a set of technical procedures some of which take place remotely in the company s databases and some of which take place locally such as any necessary changes in the customers equipment (e.g. set-top boxes). While these procedures take place there are often failures in data collection. For this reason, the 25 days preceding the activation of the new service will be excluded from the analysis as the activity logs from this period are unreliable Number of Subscribers Number of Days from Bundle Upgrade Figure 1: Number of subscribers per date relative to day of service upgrade Figure 2 shows the number of service upgrades per calendar day. On average, there are 12.4 upgrades day (standard deviation of 13.3, minimum of 0, and maximum of 60). The number of upgrades is higher during the second half of our period of analysis, which corresponds to the first semester of Table 1 presents the summary statistics for our sample of users before the upgrade in service. The table includes the main variables of interest as well as some additional subscriber characteristics. On average, before upgrading their service, subscribers in our sample used about 440 MB of downloads per day and watched around 180 minutes of television. The average Internet connection was 31 Mbps, the average tenure for the TV and Internet service was 5.6 years and 3.3 years, respectively. Finally, the average monthly expense was EUR 45 for the base services and EUR 0.8 for premium services (e.g. video-on-demand). Table 2 presents the summary statistics for our sample of users after the upgrade in service. On average, after upgrading their service, the subscribers 11

12 60 Number of Bundle Upgrades Day ID Figure 2: Number of service bundle upgrades per date Table 1: Sample summary statistics - before upgrade Statistic N Mean St. Dev. Min Max Download traffic (MB) 290, , , TV minutes 290, , Internet connection speed 290, TV tenure (in months) 290, Internet tenure (in months) 290, Base expense (month) 290, Premium expense (month) 290, in our sample used about 470 MB of downloads per day and watched around 220 minutes of TV. The average Internet connection speed was 120 Mbps, the average tenure for the TV service was 5.8 years and 3.6 years for the Internet service. Finally, the average monthly expense was EUR 58 for the base services and EUR 0.3 for premium services. Figure 3 shows the percentage change in the mean number of TV minutes viewed by our subscriber sample relative to the first day observed. The x-axis shows time relative to the date of service upgrade. Before the upgrade, the mean 12

13 Table 2: Sample summary statistics - after upgrade Statistic N Mean St. Dev. Min Max Download traffic (MB) 339, , , TV minutes 339, , Internet connection speed 339, TV tenure (in months) 339, Internet tenure (in months) 339, Base expense (month) 339, Premium expense (month) 339, TV activity remains stable at the initial level but at the time of upgrade there is significant increase in the average television activity that then persists over time. Figure 4 shows an equivalent depiction for the average daily download activity in MB. Unlike television activity, Internet usage seems to follow an increasing trend from the start. Although there seems to be a slight increase in the average daily Internet usage level following the service upgrade, this is a much smaller change relative to that observed in television activity. This lends some support to our assumption regarding television being the main driver behind triple-play bundle upgrades. Figure 5 presents an histogram of the number of days to (or from) the end date of the initial lock-in period at the date of service upgrade. We should note that 26 percent of the subscribers in our sample were initially signed into a 24- month contract while the remaining subscribers had 12-month contracts. The figure shows that a small number of upgrades took place during the first year of contract of those with a two-year contract, while the majority of upgrades took place towards the end of the lock-in period. The first and smaller wave of upgrades includes subscribers who chose to upgrade their service during the first year of contract despite being locked-in a 2-year subscription. The motivations for 13

14 Percentage change in average daily TV time Time from upgrade Figure 3: Mean television time (in minutes) per day (relative to date of service change) Percentage change in average daily download traffic Time from upgrade Figure 4: Mean downloads traffic (in MB) per day (relative to date of service change) 14

15 these early upgrades are unknown to us but they may relate to any of the following reasons: changes in the customer s preferences or available budget, changes in the service offering or price, or responses from the provider to offers presented to the customer by competitors. The second and larger wave of upgrades includes those subscribers who, as expected, decide to upgrade their service as their initial contract comes to an end. This plot lends strong support to our assumption that service upgrade is a function of the end date of the initial lock-in period. The combined evidence of figures 5 and 3 also lends support to our assumption regarding the positive relationship between TV time and the end-date of the initial contract. 400 Count of service upgrades Number of days to/from end date of initial contract Figure 5: Number of days to/from end of contract at date of bundle upgrade 3.2 Analysis The goal of this analysis is to estimate the elasticity between TV and Internet use. As there are instances where the download traffic and/or TV time of a subscriber are zero, we add one unit to the daily amount of downloads (in MB) and to the daily number of TV minutes and then take the logarithm of these variables as indicated in the model below (i is a household, t is day). log(downloadsmb + 1) it = β 1 log(t V minutes + 1) it + δ t + ɛ it (1) 15

16 The coefficient of interest is β which gives us the percent change in the amount of downloads associated to a one percent change in average daily TV minutes. We include a control for each calendar day in order to account for any possible season effects in TV and Internet use. When estimating this model by ordinary least squares we are unable to overcome the problem of endogeneity resulting from the fact that the usage levels of both media channels result from each subscriber s choices. We address this limitation using the instrumental variable approach described in the previous section. We do the following to construct our instrumental variable: for each consumer in each day, we identify the end date of her current contract (or last contract if it already expired). This date was determined long time ago when the household signed the contract and thus assumed exogenous. An instrumental variable could be a dummy variable indicating whether this date has passed. However, we know that the likelihood of upgrading the service does not increase from one day to the next as soon as the original contract expires. Instead, it increases smoothly over time, peaking towards the end date of the contract because the firm typically calls subscribers close to contract expiry to try to re-sign them into a new contract. Therefore, we use the distribution of upgrade dates to smooth out the former instrument. This transformation is monotonic and similar for all households. The equation for the first stage regression is presented below. Again, we include a control for each calendar day in order to account for any possible season effects in TV and Internet use or the timing of the upgrades. log(t V minutes + 1) it = α time2endlockin it + δ t + ɛ it (2) Based on our assumptions, we expect that as subscribers get closer to the enddate of their initial contract, their probability to upgrade will increase, peaking at that date. Also, based on our assumptions and descriptive statistics, we expect TV time to increase following the upgrade. Thus, we anticipate that the coefficient of time2endlockin will be positive, indicating that after the end-date of 16

17 the initial lock-in period the subscribers in our sample increased TV time. The second stage equation is: log(downloadsmb + 1) it = β 2 log(t V minutes + 1) it + δ t + ɛ it (3) The coefficient of interest is β 2 which gives us the percent change in the number of downloads associated to a one percent change in average daily television minutes. A positive sign of β 2 would indicate complementarity between the two media channels while a negative sign would be indicative of substitution. As before, we include a dummy variable for each calendar day. 3.3 Results Tables 3 and 4 present our regressions results using pooled least squares. All standard errors are clustered at the household (subscriber) level. Table 3 presents the first stage results, corresponding to model (2) in the above section. As expected, our instrument is positively associated to TV time and this relationship is statistically significant (p < 0.1). This is in line with our expectations and confirms the appropriateness of our instrument. Table 4, presents the regression results from both ordinary least squares (column 1) and the second stage regression (column 2) corresponding to models (1) and (3) in the previous section, respectively. As expected, the OLS results produce a positive coefficient for TV time (p < 0.01). As previously discussed, we believe this to be caused by the fact that TV time is endogenous and that users with stronger media preferences will consume more of both channels. When TV time is instrumented by time2endlockin we observe a sign flip in its relationship to Internet use: according to our IV results, a one percent increase in television time is associated to a 1.55 percent decrease in downloads (p < 0.01). As a robustness check, we run the same regressions with data aggregated at the week level. These results are presented in Appendix 2 and are consistent with the estimates presented in the present section. 17

18 Table 3: Regression results - first stage Dependent variable: log(tvminutes+1) time2endlockin (0.102) Constant (0.118) day Yes Observations 598,686 R Adjusted R Note: p<0.1; p<0.05; p<0.01 Cluster robust standard errors in parentheses 18

19 Table 4: Regression results - second stage Dependent variable: log(downloadsmb+1) (1) (2) log(tvminutes+1) (0.007) (0.030) Constant (0.142) (0.240) day Yes Yes Observations 598, ,686 R Adjusted R Note: p<0.1; p<0.05; p<0.01 Cluster robust standard errors in parentheses 19

20 This analysis provides us with some preliminary evidence of substitution between TV and Internet at the individual level. There is one main limitation in this approach that concerns the representativeness of our sample and hence our ability to extend our findings to the population of triple-play subscribers. On this issue, we do not claim that the subset of users who initially subscribe to the lower quality triple play bundle and later decide to upgrade to the higher quality bundle is representative of triple-play subscribers in general. Instead, we take our results as an indication of the likely presence of substitutability between these two channels in this population but not as definite proof of its existence. On average, the subscribers in our sample increase their TV time by approximately 40 minutes following the service upgrade, which represents an increase of 22 percent. Our results suggest that, all else constant, we should observe a reduction of download traffic of 34 percent or approximately 150 MB. Considering that 5 minutes of YouTube streaming at a speed of 720p, which is a popular resolution, will use 37.5 MB of data (the vast majority of which are downloads), a reduction of 150 MB of download traffic may be translated into a reduction of 20 minutes in YouTube streaming time. Our results thus suggest that, if we convert download traffic to streaming minutes, the reduction in online video streaming time should correspond to about half of the increase in TV time. 4 Experiment We now use data from a randomized experiment to validate our findings from the observational study. The randomized experiment was conducted in collaboration with the same telecommunications provider and consisted in the following: a sample of 5,643 triple-play subscribers who were heavy users of the Internet service were randomly selected and assigned to a treatment group while another set of 5,764 randomly selected subscribers of the same service, also heavy Internet users, was held as control group. This process ensured that the two groups would only differ on whether or not they received treatment (Gupta, 2011) and is the 20

21 gold standard for inferring causal relationships in social sciences (Bapna and Umyarov, 2012). Subscribers in the treatment condition received free access to a set of premium movie and series channels during 2014 s Christmas holiday season. Subscribers were notified about this offer by both SMS and . No setup action was needed by the subscribers in order to be able to access these channels as the service provider performed the channel activation remotely. Due to the large scale of this operation, the activation of the channels was done in four waves starting on December 15th and ending on December 18th. The assignment of subscribers to each activation wave was random. Our analysis will focus on the period between December 19th and December 31st when all users in our treatment group had access to the premium channels. By using a sample of heavy Internet users we expect our analysis to provide us conservative estimates of the effect of treatment on TV and Internet use, as we believe that users who prefer the Internet channel will be less likely to substitute Internet time for television time. Additionally, we expect this sample to over represent younger demographic groups who have been shown to be the heavier Internet users and also those less interested in the television service (Nielsen, 2014). Thus, we believe our results will be not only conservatively estimating the effect that our treatment would have in the general population but also informative of the expected impact of such treatment on younger generations and consequently future consumers. In this study we also look into an alternate measure of TV use, time-shfit events. Time-shift events are a feature of the television service of this provider that automatically records the contents streamed on television and keeps them available to the viewer for a period of one week. This means viewers are able to rewind the contents that are streaming as well as watch any of the automatically recorded contents at their own leisure. The time-shift feature significantly increases the menu of contents available to viewers who are no longer bound by the contents that are being broadcasted at any given point in time. We believe this 21

22 feature should prove particularly valuable for the consumption of entertainment contents such as movies and series as it essentially creates a content library of a significant size. The set of premium movie and series channels that were given as treatment included this time-shift feature. 4.1 Data and Descriptive Statistics For the duration of the experiment, data was collected on each household daily Internet use and TV viewing activity. We were also able to collect data on an additional measure of TV use - the number of time-shift events. We should note, however, that we are unable to distinguish how much time is spent on streaming events and how much is spent on time-shift consumption, we are only able to know the number of times subscribers used the time-shift feature in each day. Additionally, to ensure that control and treatment groups were equivalent on all observed metrics prior to treatment, data was collected for a short pre-treatment period - from the 10th to the 14th of December Table 5 presents summary statistics of the main variables of interest and some additional controls for the treatment and control groups in the pre-treatment period. Additionally, the table includes the differences in means and their significance level and well as the KS (Kolmogorov-Smirnov) statistic and p-value. A small KS p-value is an indicator that the treatment and control groups were sampled from populations following different distributions. The KS p-values presented in table 5 ensure us that this was not the case as the random selection of the two groups did result in two equivalent groups. Also, in this table, we can see that the average subscriber in our sample viewed an average of 210 minutes of television per day during this period, did about 0.85 time-shift events, and used around 2 GB of downloads. The average Internet connection was above 100 Mbps, the average tenure for the television service was 8 years, and 6 years for the Internet service. Finally the average base revenue was EUR 55 and the average premium revenue was EUR As a further check, we conduct a linear hypothesis test to confirm whether 22

23 Table 5: Balance table for treatment and control groups tx.mn tx.sd ct.mn ct.sd std.eff.sz stat p ks ks.pval Downloads 2, , , , TV minutes N timeshift Internet connection TV tenure Internet tenure Base expense Premium expense treatment and control groups followed the same trends prior to treatment. Table 6 presents the regression results of television time, number of time-shift events, and Internet activity as a function of the treatment indicator, time dummies, and the interactions between treatment and time. None of the interaction coefficients are statistically significant. Appendix 1, contains the results of the three corresponding linear hypothesis tests. All tests confirm the quality of the randomization procedure indicating no significant differences between the activity of the control and treatment groups. Table 7 presents the summary statistics for the subscribers in the control group during the treatment period. On average, during this period, these subscribers used about 2GB of downloads per day and watched around 215 minutes of television. Their average Internet connection was over 100 Mbps, their average tenure for the TV service was 8 years and 6 years for the Internet service. Finally, their average monthly expense was EUR 55 for the base services and 60 cents for premium services. Table 8 presents the same information for the subscribers in the treatment group. On average, during this period, these subscribers used about 2 GB of downloads per day and watched around 230 minutes of television. 23

24 Table 6: Regression - time trends for treatment and control groups Dependent variable: log(tvminutes+1) N.Time-shift log(downloadsmb+1) (1) (2) (3) treated (0.046) (0.029) (0.040) day (0.046) (0.029) (0.040) day (0.046) (0.029) (0.042) day (0.046) (0.031) (0.043) day (0.046) (0.030) (0.040) treated*day (0.065) (0.041) (0.056) treated*day (0.066) (0.041) (0.059) treated*day (0.066) (0.045) (0.061) treated*day (0.065) (0.043) (0.057) Constant (0.032) (0.020) (0.028) Observations 56,680 56,680 56,680 R Adjusted R Note: p<0.1; p<0.05; p<

25 The remaining variables take the same values as in the control group. Table 7: Summary statistics - Control group Statistic N Mean St. Dev. Min Max Downloads traffic (MB) 71,607 1, , , TV minutes 74, , Number of time-shift events 74, Internet connection speed 74, TV tenure (in months) 74, Internet tenure (in months) 74, Base expense (month) 73, Premium expense (month) 73, Table 8: Summary statistics - treatment group Statistic N Mean St. Dev. Min Max Download traffic (MB) 70,154 1, , , TV minutes 72, , Number of time-shift events 72, Internet connection speed 72, TV tenure (in months) 72, Internet tenure (in months) 72, Base expense (month) 72, Premium expense (month) 72, Figure 6 shows the average time spent by users in the control and treatment group watching the set of premium channels in each day of our period of analysis. In the graph, we can clearly see that both groups had similar viewing times prior 25

26 to treatment but, as treatment starts to be implemented, a significant gap appears between the vieing times of the two groups. This provides us with some visual evidence that the set of channels offered to subscribers did succeed in capturing viewers attention 2. Figure 7 shows a similar graph for the number of time-shift events in the set of premium channels given as treatment. In this graph we can observe a very similar pattern to that of figure 6 whereby treatment and control groups have a very similar activity level prior to treatment but a large gap grows between the two groups once treatment is in place. As before, this provides graphical evidence that viewers used the time-shift feature to consume the contents of this set of channels. 40 Average daily minutes watched in premium channels Day Figure 6: Average daily minutes viewed in premium channels 2 We should note that even when channels are not activated, channel locations are available to the subscribers but their contents are locked. Some of these channels will regularly stream trailers promoting their series and movies. As a result, subscribers who do not have access to these channels may still have a positive view time due to a combination of zapping events and time spent watching the channels promotional contents. 26

27 0.20 Average daily number of time shift events in premium channels Day Figure 7: Average daily number of time-shift events in premium channels 4.2 Analysis The purpose of this analysis is to estimate the elasticity between TV and Internet use. We start by looking at the relationship between the time spent watching TV and the amount of downloads. Given that there are instances in which TV time or the amount of downloads is equal to zero, we will add one unit to each of these variables in order to be able to apply logarithmic transformations. Our empirical strategy is similar to that used in our observational study, namely, we use a two-stage least squares approach in which TV time is instrumented by the treatment assignment indicator. The model below is the first-stage regression. As before, we include a control for each calendar day. log(t V minutes + 1) it = α 1 treated i + δ t + ɛ it (4) The coefficient of treated indicates whether treatment assignment is a good intrument for television time. We expect that receiving the set of premium channels led subscribers to increase their TV use, thus we expect α 1 to be positive. 27

28 The model below is the second-stage regression. The coefficient of interest is β 3, which is the estimate for the elasticity between TV and Internet. Based on our previous results, we expect to find a negative coefficient, providing additional evidence of substitution between the two channels. log(downloadsmb + 1) it = β 3 log(t V minutes + 1) it + δ t + ɛ it (5) Next, we investigate this relationship further by looking at an alternate measure of television use, time-shift events. Our analytical approach is the same as before - TV use, now measured as the number of time-shift events, is instrumented by treatment assignment and then the relationship between the number of time-shift events and the amount of downloads in estimated through two-stage least squares. As before, we add one unit to the number of time-shift events and estimate a constant elasticity model. The model below presents the first-stage regression. log(nt imeshiftevents + 1) it = α 2 treated i + δ t + ɛ it (6) The coefficient of treated indicates whether treatment assignment is a relevant intrument for the number of time-shift events. We expect that receiving the set of premium channels led subscribers to increase the use of the time-shift feature, thus we expect α 2 to be positive. Finally, the model below gives us the second-stage regression. The coefficient β 4 will give us the percentage change in the amount of downloads resulting from a 1 percent increase in the number of time-shift events. Given that this is an alternate measure of TV use we expect β 4 to be negative, providing additional evidence of substitution between the two channels. log(downloadsmb + 1) it = β 4 log(nt imeshiftevents + 1) it + δ t + ɛ it (7) 28

29 4.3 Results Table 9 presents our estimates for the elasticity of substitution between TV time and Internet use. The first column gives us the relationship between TV time and download traffic: a 1 percent increase in TV time is associated to a 0.25 percent reduction in download traffic (p < 0.01). These result lend support to our findings from the observational study regarding the existence of substitution between TV and Internet. As expected, the estimated magnitude for the elasticity between the two channels is smaller than that obtained in the observational study, given that our experiment targetted heavy Internet users who should be less likely to give up Internet use for new TV channels. The second column gives us the relationship between the number of time-shift events and download traffic: a 1 percent increase in the number of time-shift events is associated to a decrease of percent in download traffic (p < 0.01). This result, and the prevalence of time-shift to watch the new premium channels, provides preliminary evidence that the ability to time-shift may be an important driver for the users to substitute Internet use for TV consumption. Looking at our results in terms of minutes of TV watched and streamed, if the average user in our sample were to increase her TV time by 10 percent or approximately 20 minutes, this would cause a decrease of 2.5 percent in download traffic, corresponding to around 50 MB. A similar calculation to the one presented in section 3.3 shows that this would roughly correspond to 7 minutes of YouTube streaming time. This means that although the rates of substitution found in our observational and experimental work differ, in practical terms (and due to the differences in the average TV and Internet use between the samples used in the two studies) they translate to similar substitution rates between TV time and online streaming time. Table 10 presents the corresponding first-stage results. Column 1 shows the impact of treatment on TV time: on average, treatment assignment caused an increase of 9.7 percent in daily TV time (p < 0.01). Column 2 shows the impact of 29