Strategies to mitigate the cannibalization effect between subscription video-on-demand and transactional video-on-demandopen access
- Authors
- Baek, Hyunmi; Jang, Moonkyoung; Kim, Seongcheol
- Issue Date
- Jun-2024
- Publisher
- Elsevier B.V.
- Keywords
- Cannibalization effect; OTT; SVOD; TVOD; VOD pricing plan; VOD service provider
- Citation
- Digital Business, v.4, no.1
- Journal Title
- Digital Business
- Volume
- 4
- Number
- 1
- URI
- https://scholarworks.bwise.kr/gachon/handle/2020.sw.gachon/90271
- DOI
- 10.1016/j.digbus.2024.100073
- ISSN
- 2666-9544
- Abstract
- Video-on-demand (VOD) service providers face the challenge of making strategic decisions regarding the allocation of movies to specific pricing plan libraries. This study delves into the cannibalization effect that occurs when a movie transitions from the transactional video-on-demand (TVOD) library to the subscription video-on-demand (SVOD) library. We conducted a comprehensive analysis using user purchasing and viewing data for 1541 movies offered by SK Broadband, South Korea's leading VOD service provider. Our analysis employed propensity score matching and difference-in-difference methodologies. The results of our initial analysis reveal that allocating movies to SVOD leads to an 192% increase in the number of views while simultaneously 52% decreasing TVOD sales. Additionally, we investigate the characteristics of movies that are particularly effective for SVOD servicing. To accomplish this, we scrutinized 236 movies available in SK Broadband's SVOD service using a cost-effectiveness analysis. Our second analysis demonstrates that certain categories of movies, including foreign films, non-G-rated movies, animations, and films not allowed as Download-To-Own (DTO), are more suitable for allocation to SVOD libraries. Furthermore, movies with lower box office viewership numbers exhibit greater success when allocated to SVOD. This study offers insights into the strategic considerations that VOD service providers should embrace, emphasizing a content feature-driven approach to managing pricing plan combinations. © 2024 The Authors
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