Viacom18: Creating a Sustainable Video Streaming (OTT) Business in India
Bibliographic record
Abstract
Viacom18, a Reliance Industries Ltd. company, acquired the streaming rights for the popular Indian Premier League (IPL) cricket tournament for 2023-2027 for a whopping $2.89 billion. Unlike the previous rights holder, Disney+Hotstar, which put the streaming behind a paywall, Viacom18 decided to offer free streaming, betting on increased advertisement revenue from a massive increase in viewership. However, the free streaming for the 2023 IPL season only brought in advertisement revenues of $276 million against a projection of $450 million. The Viacom18 team needed to rethink its mix of advertising and subscription revenue. It had three options: a pure advertising-supported model (AVoD); a subscription model with packages for the IPL, possibly including a cheaper package with ad-supported content; and a blended business model that would combine the AVoD model with a small subscription fee (sachet pricing) specifically for the IPL. If the advertising-based model had created a large enough user base, it would have attracted advertisers and generated a profit. In the 2023 season, however, it produced only 60% of projected revenues, which was insufficient to pay the IPL licensing fee. On the other hand, a subscription-based model, although likely to create more revenue per user, would dramatically reduce the customer base because of Indian consumers' price sensitivity. Such a model also might not meet revenue expectations.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.030 | 0.010 |
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".