Bundling, Cord-Cutting and the Death of TV as We Know It
Bibliographic record
Abstract
CASE BODY The pay-TV industry faces a revolution both in US and world-wide. On one hand, popularity of pay-TV programming is at an all-time high as pay-TV networks such as HBO and Showtime invest in lavishly produced programming that is well-received by both viewers and critics. Traditional pay-TV providers, which include cable and satellite TV companies, reach over 130 million households in US and an average household spends seven hours a day watching TV (FCC, 2013). On other hand, consumers who have long been at mercy of cable and satellite TV providers are now finding a variety of new ways to get their favorite TV content. New entrants in pay-TV market including Netflix and Amazon provide alternatives to traditional cable TV at a significantly lower cost. More consumers choose to cord and receive their video programming on demand from a free or a paid subscription service. Some industry observers have even proclaimed the death of TV as we know (Yarow, 2015). The rising price of traditional pay-TV offerings is noticed by consumers and regulators alike. According to Federal Communications Commission (2014), average cost of a monthly expanded basic cable subscription rose from $27.88 in 1998 to $64.41 in 2013. A separate research report from NPD Group states that average cable TV monthly bill in U.S. rose from $40 in 2001 to $86 in 2011, and is projected to rise to $123 per month in 2015 (Kritsonis, 2013). Figure 1 compares growth in price of cable TV and inflation rate measured by Consumer Price Index. Over this period, cable TV prices have been rising at four times rate of general inflation. As prices of traditional pay-TV access continue to increase, there is an emerging narrative suggesting that best way to cut those bills down and encourage more competition from providers is to offer consumers opportunity to select individual channels they purchase. This is known as pricing. Recently, Republican Senator John McCain and Democratic Senator Richard Blumenthal sponsored a bill that would require pay-TV operators to offer a-la-carte pricing. McCain asserts that special interest groups have stacked regulatory deck in favor of preserving an outdated business model and advocates benefits of a-la-carte selection of channels (Kritsonis, 2013). In Canada, broadcasters are now required to offer a low cost base package of local and educational channels to consumers. Beyond base service, Canadians are now able to subscribe to individual channels or small bundles of channels that, by law, must be reasonably priced. The Canadian system came to be known as pick and pay. (Lazarus, 2015) This push to a-la-carte pricing represents a major departure from traditional pricing in pay-TV. For many years, pay-TV market in US has been dominated by providers bundling individual channels they offer into packages. Despite increases in number of channels offered to consumers in these bundles, a typical consumer only chooses to watch a few channels on a regular basis. Figure 2 shows number of channels received and watched in an average TV household. In 2013, a typical consumer watched only 17 channels out of 189 available in programming bundle. While bundling is common in many product markets--with McDonald's Happy Meals and Microsoft Office software suite providing two of better-known examples--it has become pervasive in pay-TV industry. Most consumers see no other option but to purchase their TV channels in a bundle. Bundling may help firm realize economies of scale and economies of scope in product delivery. Proponents of bundling suggest that it is essential for survival of niche channels that cater to specific interests or minorities and will not have sufficient support to be offered in a-la-carte environment. Critics of bundling claim that it forces consumers to buy channels that they never watch. …
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.006 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.014 | 0.015 |
| Scholarly communication | 0.010 | 0.013 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.006 | 0.012 |
| Insufficient payload (model declined to judge) | 0.013 | 0.002 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".