OTT Movie Launching Strategy

with Movie-Country Relatedness

---

2023 한국언론학회 봄철 정기학술대회
미디어 경제·경영 연구회

2023-05-19

Changjun Lee

Hanyang University

Dep. Media & Social Informatics

 changjunlee.com

Research Background

---

Global OTT Platforms and Cultural Barriers

• Emergence of global OTTs: Netflix, Disney+, etc.

• Lowering cultural barriers in media consumption

• Examples:

  • Korean TV shows: Squid Game, Extraordinary Attorney Woo (CNN 2022)

  • US-based show: Strange Things (Reporter 2022)

• However, Global mega-hits still rare, often a matter of serendipity

Research Background

---

Local Media Channels and OTTs - Challenges

• Global OTTs threaten local media channels

• Local OTTs emerge to counter competition

• Main challenges for local OTTs:

  1. Producing culturally appropriate content with global mega-hit potential

  2. Renting overseas content that appeals to local users (Wang and Jung 2023)
     

Research Background

---

Strategies for Local OTT Success

• Addressing purchasing priority and cost-effectiveness

• Creating high-quality content with worldwide resonance (Kim 2022)

• Catering to local user preferences

• Latecomer local OTT operators need additional strategies

Problem Description

---

• Previous research on OTT strategies (Park and Kwon 2019)

  • Localization, Partnership, Content differentiation, Revenue enhancement, Service optimization

• Local OTT providers adopt a multi-faceted approach

  • Assessing demand and establishing partnerships (Wang and Jung 2023)

    • Acquiring content similar in genre and quality to Netflix and Disney+

  • Developing local content repositories using local/regional languages (Sharma and Lulandala 2023)

    • Forming strategic collaborations with: Mobile network companies, Financial institutions, Technology companies

Research Gap

• Limited understanding of OTT launching strategies considering:

  • Compatibility between movies and countries

• Need for more research on country-specific content and strategies

Research Questions & Goal

---

Research Questions

• How do content and country relationships impact popularity?

• How can accurate measurements of relatedness improve content consumption patterns?

Research Goal

• Propose a method to measure movie-country relatedness density

• Test the importance of the movie-country relatedness in predicting movie popularity in specific countries

• Develop a local OTT strategy based on movie-country relatedness

Theoretical Foundations

---

The Complexity of the Cultural Industry

• Cultural goods is difficult to express as utility function (Throsby 2001)

• Cultural industry sectors: Media, Fashionable consumer goods & Services, Creative professions, Collective cultural consumption institutions (Scott 2004)

Cultural Sensitivity in International Trade

• Content carries cultural messages (Fejes 1981)

• Protection of local content from other markets

• Cultural goods deeply rooted in their place of origin (Demont‐Heinrich 2011)

• Globalization and multicultural companies impact the cultural economy

Theoretical Foundations

---

Cultural Similarity and Content-Country Compatibility

• Importance of cultural similarity in adopting foreign content (Dwyer et al. 2018; Jang, Kim, and Baek 2023; Kim 2022; and Sharma and Lulandala 2023)

• Shared cultural attributes due to content globalization

Need for a special content-country compatibility beyond cultural differences

• Borrowing the concept of product-country compatibility from economic geography (Hausmann and Hidalgo 2010)

Theoretical Foundations

---

Product-Country Compatibility

• Considers cultural, economic, and regulatory conditions impacts a product’s success in a target market (Asheim and Isaksen 2002)

• Compatibility evaluated through relatedness (Jun et al. 2020)

  • Product, Importer, and Exporter relatedness

Theoretical Foundations

---

The Importance of Product-Country Compatibility

• Widely recognized in international business and economic geography (Cavusgil et al. 2014)

• Culturally aligned products more likely to succeed

• Compatibility with economic and regulatory environment leads to profitability

Applying Product-Country Compatibility to Content

• Adapting the concept for content-country compatibility

• Assessing cultural, economic, and regulatory fit for content in target countries

• Potential to improve the success of content in foreign markets

• Content-Country Compatibility

  • Adaptation of product-country compatibility for movies and TV shows

  • Considers cultural, economic, and regulatory conditions

  • Impacts success and profitability of content in target markets

Theoretical Foundations

---

Factors Influencing Content-Country Compatibility

• Cultural factors: language, humor, storytelling style

• Economic factors: disposable income, streaming service accessibility

• Regulatory factors: censorship laws, import/export regulations

• Complex interplay of factors in evaluating potential success

In sum

• Content-country compatibility crucial for international expansion

• Align content with target country’s cultural, economic, and regulatory conditions

• Increases likelihood of success in international business endeavors

A New Way of Measuring Content-Movie Compatibility

---

Related Density

eq(1)

\[ RD_{r,i} = \frac{∑_{j∈r, j≠i}∅_{i,j} }{∑_{j≠i}∅_{i,j}}×100 \]

• Use relatedness density index to measure content-movie compatibility

• \(RD_{i,j}\) denotes the related density of region \(r\) to content \(i\)

• \(∅_{i,j}\) denotes the relatedness between content \(i\) and \(j\) measured by considering the co-occurrence of top-tiered lists

Hypothesis

---

H1

Movies and TV shows exhibit varying degrees of compatibility with different countries, and this compatibility predicts a given country’s likelihood of including that content in its top 20 list

H2

Despite the importance of content–country compatibility, global mega-hit content that achieves widespread popularity regardless of its compatibility with a particular country may occasionally emerge. This ubiquity of popular content predicts a given country’s likelihood of including that content in its top 20 list.

Methods

---

Overview

• Data source: Flixpatrol’s monthly top 20 movies in 80 countries from January to December 2021

• Final sample: 1,939 movies (1,861,440 observations)

• Focusing on Netflix as a global OTT service provider

• Dependent variable: \(ENT-TOP20_{c,m,(t+1)}\) - new entry of a movie \(m\) in a country \(c\)’s top 20 list in the period \(t+1\)

• Independent variables:

  • Country–movie relatedness density at period t: \(∅_{c,m,t}\)

  • Movie ubiquity: \(U_{m,t}\)

• Covariates:

  • Movie-specific fixed effects \(Z_{m,t}\): Netflix Original status, production country, genre, Rotten Tomatoes score, and IMDB score

• Model: Account for factors influencing a movie’s inclusion in a country’s top 20 list

Methods

---

Data at a glance

cid	mid	Month	entry	rel_density	Titles	IMDB	Rotten	mov_ubq	top20	genre_n	prod_ctry_n	netf_org
c1	m15	3	0	0.1270	1939	5.9	78	0.025	0	Etc	Etc	0
c2	m15	3	0	0.1263	1939	5.9	78	0.025	0	Etc	Etc	0
c3	m15	3	0	0.1543	1939	5.9	78	0.025	0	Etc	Etc	0
c4	m15	3	0	0.1153	1939	5.9	78	0.025	0	Etc	Etc	0
c5	m15	3	0	0.3119	1939	5.9	78	0.025	0	Etc	Etc	0
c6	m15	3	0	0.1118	1939	5.9	78	0.025	0	Etc	Etc	0
c7	m15	3	0	0.1306	1939	5.9	78	0.025	0	Etc	Etc	0
c8	m15	3	0	0.1796	1939	5.9	78	0.025	0	Etc	Etc	0
c9	m15	3	0	0.1463	1939	5.9	78	0.025	0	Etc	Etc	0
c10	m15	3	0	0.1230	1939	5.9	78	0.025	0	Etc	Etc	0
c11	m15	3	0	0.1209	1939	5.9	78	0.025	0	Etc	Etc	0
c12	m15	3	0	0.1214	1939	5.9	78	0.025	0	Etc	Etc	0
c13	m15	3	0	0.1694	1939	5.9	78	0.025	0	Etc	Etc	0
c14	m15	3	0	0.2612	1939	5.9	78	0.025	0	Etc	Etc	0
c15	m15	3	0	0.1982	1939	5.9	78	0.025	0	Etc	Etc	0
c16	m15	3	0	0.1171	1939	5.9	78	0.025	0	Etc	Etc	0
c17	m15	3	0	0.1246	1939	5.9	78	0.025	0	Etc	Etc	0
c18	m15	3	0	0.1692	1939	5.9	78	0.025	0	Etc	Etc	0
c19	m15	3	0	0.2191	1939	5.9	78	0.025	0	Etc	Etc	0
c20	m15	3	0	0.1540	1939	5.9	78	0.025	0	Etc	Etc	0
c21	m15	3	0	0.1464	1939	5.9	78	0.025	0	Etc	Etc	0
c22	m15	3	1	0.4379	1939	5.9	78	0.025	1	Etc	Etc	0
c23	m15	3	0	0.1185	1939	5.9	78	0.025	0	Etc	Etc	0
c24	m15	3	0	0.1156	1939	5.9	78	0.025	0	Etc	Etc	0
c25	m15	3	0	0.1156	1939	5.9	78	0.025	0	Etc	Etc	0
c26	m15	3	0	0.1687	1939	5.9	78	0.025	0	Etc	Etc	0
c27	m15	3	0	0.1540	1939	5.9	78	0.025	0	Etc	Etc	0
c28	m15	3	0	0.1000	1939	5.9	78	0.025	0	Etc	Etc	0
c29	m15	3	0	0.1917	1939	5.9	78	0.025	0	Etc	Etc	0
c30	m15	3	0	0.1835	1939	5.9	78	0.025	0	Etc	Etc	0
c31	m15	3	0	0.1799	1939	5.9	78	0.025	0	Etc	Etc	0
c32	m15	3	0	0.0789	1939	5.9	78	0.025	0	Etc	Etc	0
c33	m15	3	0	0.1296	1939	5.9	78	0.025	0	Etc	Etc	0
c34	m15	3	0	0.1398	1939	5.9	78	0.025	0	Etc	Etc	0
c35	m15	3	0	0.1630	1939	5.9	78	0.025	0	Etc	Etc	0
c36	m15	3	0	0.1261	1939	5.9	78	0.025	0	Etc	Etc	0
c37	m15	3	0	0.1688	1939	5.9	78	0.025	0	Etc	Etc	0
c38	m15	3	0	0.2059	1939	5.9	78	0.025	0	Etc	Etc	0
c39	m15	3	0	0.1233	1939	5.9	78	0.025	0	Etc	Etc	0
c40	m15	3	0	0.1111	1939	5.9	78	0.025	0	Etc	Etc	0
c41	m15	3	0	0.1672	1939	5.9	78	0.025	0	Etc	Etc	0
c42	m15	3	0	0.1404	1939	5.9	78	0.025	0	Etc	Etc	0
c43	m15	3	0	0.1136	1939	5.9	78	0.025	0	Etc	Etc	0
c44	m15	3	0	0.1241	1939	5.9	78	0.025	0	Etc	Etc	0
c45	m15	3	0	0.1950	1939	5.9	78	0.025	0	Etc	Etc	0
c46	m15	3	0	0.1222	1939	5.9	78	0.025	0	Etc	Etc	0
c47	m15	3	0	0.1140	1939	5.9	78	0.025	0	Etc	Etc	0
c48	m15	3	0	0.1258	1939	5.9	78	0.025	0	Etc	Etc	0
c49	m15	3	0	0.1204	1939	5.9	78	0.025	0	Etc	Etc	0
c50	m15	3	0	0.1151	1939	5.9	78	0.025	0	Etc	Etc	0
c51	m15	3	0	0.1882	1939	5.9	78	0.025	0	Etc	Etc	0
c52	m15	3	0	0.1754	1939	5.9	78	0.025	0	Etc	Etc	0
c53	m15	3	0	0.1378	1939	5.9	78	0.025	0	Etc	Etc	0
c54	m15	3	0	0.2130	1939	5.9	78	0.025	0	Etc	Etc	0
c55	m15	3	0	0.1080	1939	5.9	78	0.025	0	Etc	Etc	0
c56	m15	3	0	0.1263	1939	5.9	78	0.025	0	Etc	Etc	0
c57	m15	3	0	0.1897	1939	5.9	78	0.025	0	Etc	Etc	0
c58	m15	3	0	0.1332	1939	5.9	78	0.025	0	Etc	Etc	0
c59	m15	3	0	0.1585	1939	5.9	78	0.025	0	Etc	Etc	0
c60	m15	3	0	0.1833	1939	5.9	78	0.025	0	Etc	Etc	0
c61	m15	3	0	0.1689	1939	5.9	78	0.025	0	Etc	Etc	0
c62	m15	3	0	0.1324	1939	5.9	78	0.025	0	Etc	Etc	0
c63	m15	3	0	0.1908	1939	5.9	78	0.025	0	Etc	Etc	0
c64	m15	3	0	0.1266	1939	5.9	78	0.025	0	Etc	Etc	0
c65	m15	3	0	0.2060	1939	5.9	78	0.025	0	Etc	Etc	0
c66	m15	3	0	0.1905	1939	5.9	78	0.025	0	Etc	Etc	0
c67	m15	3	0	0.1013	1939	5.9	78	0.025	0	Etc	Etc	0
c68	m15	3	0	0.1776	1939	5.9	78	0.025	0	Etc	Etc	0
c69	m15	3	0	0.1318	1939	5.9	78	0.025	0	Etc	Etc	0
c70	m15	3	1	0.7204	1939	5.9	78	0.025	1	Etc	Etc	0
c71	m15	3	0	0.1292	1939	5.9	78	0.025	0	Etc	Etc	0
c72	m15	3	0	0.1203	1939	5.9	78	0.025	0	Etc	Etc	0
c73	m15	3	0	0.1377	1939	5.9	78	0.025	0	Etc	Etc	0
c74	m15	3	0	0.1966	1939	5.9	78	0.025	0	Etc	Etc	0
c75	m15	3	0	0.1256	1939	5.9	78	0.025	0	Etc	Etc	0
c76	m15	3	0	0.1461	1939	5.9	78	0.025	0	Etc	Etc	0
c77	m15	3	0	0.1598	1939	5.9	78	0.025	0	Etc	Etc	0
c78	m15	3	0	0.1376	1939	5.9	78	0.025	0	Etc	Etc	0
c79	m15	3	0	0.1283	1939	5.9	78	0.025	0	Etc	Etc	0
c80	m15	3	0	0.1151	1939	5.9	78	0.025	0	Etc	Etc	0
c1	m15	4	0	0.1270	1939	5.9	78	0.000	0	Etc	Etc	0
c2	m15	4	0	0.1263	1939	5.9	78	0.000	0	Etc	Etc	0
c3	m15	4	0	0.1543	1939	5.9	78	0.000	0	Etc	Etc	0
c4	m15	4	0	0.1153	1939	5.9	78	0.000	0	Etc	Etc	0
c5	m15	4	0	0.3119	1939	5.9	78	0.000	0	Etc	Etc	0
c6	m15	4	0	0.1118	1939	5.9	78	0.000	0	Etc	Etc	0
c7	m15	4	0	0.1306	1939	5.9	78	0.000	0	Etc	Etc	0
c8	m15	4	0	0.1796	1939	5.9	78	0.000	0	Etc	Etc	0
c9	m15	4	0	0.1463	1939	5.9	78	0.000	0	Etc	Etc	0
c10	m15	4	0	0.1230	1939	5.9	78	0.000	0	Etc	Etc	0
c11	m15	4	0	0.1209	1939	5.9	78	0.000	0	Etc	Etc	0
c12	m15	4	0	0.1214	1939	5.9	78	0.000	0	Etc	Etc	0
c13	m15	4	0	0.1694	1939	5.9	78	0.000	0	Etc	Etc	0
c14	m15	4	0	0.2612	1939	5.9	78	0.000	0	Etc	Etc	0
c15	m15	4	0	0.1982	1939	5.9	78	0.000	0	Etc	Etc	0
c16	m15	4	0	0.1171	1939	5.9	78	0.000	0	Etc	Etc	0
c17	m15	4	0	0.1246	1939	5.9	78	0.000	0	Etc	Etc	0
c18	m15	4	0	0.1692	1939	5.9	78	0.000	0	Etc	Etc	0
c19	m15	4	0	0.2191	1939	5.9	78	0.000	0	Etc	Etc	0
c20	m15	4	0	0.1540	1939	5.9	78	0.000	0	Etc	Etc	0
c21	m15	4	0	0.1464	1939	5.9	78	0.000	0	Etc	Etc	0
c22	m15	4	0	0.4379	1939	5.9	78	0.000	0	Etc	Etc	0
c23	m15	4	0	0.1185	1939	5.9	78	0.000	0	Etc	Etc	0
c24	m15	4	0	0.1156	1939	5.9	78	0.000	0	Etc	Etc	0
c25	m15	4	0	0.1156	1939	5.9	78	0.000	0	Etc	Etc	0
c26	m15	4	0	0.1687	1939	5.9	78	0.000	0	Etc	Etc	0
c27	m15	4	0	0.1540	1939	5.9	78	0.000	0	Etc	Etc	0
c28	m15	4	0	0.1000	1939	5.9	78	0.000	0	Etc	Etc	0
c29	m15	4	0	0.1917	1939	5.9	78	0.000	0	Etc	Etc	0
c30	m15	4	0	0.1835	1939	5.9	78	0.000	0	Etc	Etc	0
c31	m15	4	0	0.1799	1939	5.9	78	0.000	0	Etc	Etc	0
c32	m15	4	0	0.0789	1939	5.9	78	0.000	0	Etc	Etc	0
c33	m15	4	0	0.1296	1939	5.9	78	0.000	0	Etc	Etc	0
c34	m15	4	0	0.1398	1939	5.9	78	0.000	0	Etc	Etc	0
c35	m15	4	0	0.1630	1939	5.9	78	0.000	0	Etc	Etc	0
c36	m15	4	0	0.1261	1939	5.9	78	0.000	0	Etc	Etc	0
c37	m15	4	0	0.1688	1939	5.9	78	0.000	0	Etc	Etc	0
c38	m15	4	0	0.2059	1939	5.9	78	0.000	0	Etc	Etc	0
c39	m15	4	0	0.1233	1939	5.9	78	0.000	0	Etc	Etc	0
c40	m15	4	0	0.1111	1939	5.9	78	0.000	0	Etc	Etc	0
c41	m15	4	0	0.1672	1939	5.9	78	0.000	0	Etc	Etc	0
c42	m15	4	0	0.1404	1939	5.9	78	0.000	0	Etc	Etc	0
c43	m15	4	0	0.1136	1939	5.9	78	0.000	0	Etc	Etc	0
c44	m15	4	0	0.1241	1939	5.9	78	0.000	0	Etc	Etc	0
c45	m15	4	0	0.1950	1939	5.9	78	0.000	0	Etc	Etc	0
c46	m15	4	0	0.1222	1939	5.9	78	0.000	0	Etc	Etc	0
c47	m15	4	0	0.1140	1939	5.9	78	0.000	0	Etc	Etc	0
c48	m15	4	0	0.1258	1939	5.9	78	0.000	0	Etc	Etc	0
c49	m15	4	0	0.1204	1939	5.9	78	0.000	0	Etc	Etc	0
c50	m15	4	0	0.1151	1939	5.9	78	0.000	0	Etc	Etc	0
c51	m15	4	0	0.1882	1939	5.9	78	0.000	0	Etc	Etc	0
c52	m15	4	0	0.1754	1939	5.9	78	0.000	0	Etc	Etc	0
c53	m15	4	0	0.1378	1939	5.9	78	0.000	0	Etc	Etc	0
c54	m15	4	0	0.2130	1939	5.9	78	0.000	0	Etc	Etc	0
c55	m15	4	0	0.1080	1939	5.9	78	0.000	0	Etc	Etc	0
c56	m15	4	0	0.1263	1939	5.9	78	0.000	0	Etc	Etc	0
c57	m15	4	0	0.1897	1939	5.9	78	0.000	0	Etc	Etc	0
c58	m15	4	0	0.1332	1939	5.9	78	0.000	0	Etc	Etc	0
c59	m15	4	0	0.1585	1939	5.9	78	0.000	0	Etc	Etc	0
c60	m15	4	0	0.1833	1939	5.9	78	0.000	0	Etc	Etc	0
c61	m15	4	0	0.1689	1939	5.9	78	0.000	0	Etc	Etc	0
c62	m15	4	0	0.1324	1939	5.9	78	0.000	0	Etc	Etc	0
c63	m15	4	0	0.1908	1939	5.9	78	0.000	0	Etc	Etc	0
c64	m15	4	0	0.1266	1939	5.9	78	0.000	0	Etc	Etc	0
c65	m15	4	0	0.2060	1939	5.9	78	0.000	0	Etc	Etc	0
c66	m15	4	0	0.1905	1939	5.9	78	0.000	0	Etc	Etc	0
c67	m15	4	0	0.1013	1939	5.9	78	0.000	0	Etc	Etc	0
c68	m15	4	0	0.1776	1939	5.9	78	0.000	0	Etc	Etc	0
c69	m15	4	0	0.1318	1939	5.9	78	0.000	0	Etc	Etc	0
c70	m15	4	0	0.7204	1939	5.9	78	0.000	0	Etc	Etc	0
c71	m15	4	0	0.1292	1939	5.9	78	0.000	0	Etc	Etc	0
c72	m15	4	0	0.1203	1939	5.9	78	0.000	0	Etc	Etc	0
c73	m15	4	0	0.1377	1939	5.9	78	0.000	0	Etc	Etc	0
c74	m15	4	0	0.1966	1939	5.9	78	0.000	0	Etc	Etc	0
c75	m15	4	0	0.1256	1939	5.9	78	0.000	0	Etc	Etc	0
c76	m15	4	0	0.1461	1939	5.9	78	0.000	0	Etc	Etc	0
c77	m15	4	0	0.1598	1939	5.9	78	0.000	0	Etc	Etc	0
c78	m15	4	0	0.1376	1939	5.9	78	0.000	0	Etc	Etc	0
c79	m15	4	0	0.1283	1939	5.9	78	0.000	0	Etc	Etc	0
c80	m15	4	0	0.1151	1939	5.9	78	0.000	0	Etc	Etc	0

Descriptive Statistics

---

vars	levels	stats
cid	80 unique values
mid	1939 unique values
top20	0	1688923 (99.0%)
	1	17397 (1.0%)
entry	0	1690759 (99.1%)
	1	15561 (0.9%)
netf_org	0	1487200 (87.2%)
	1	219120 (12.8%)
rel_density	Mean ± SD	0.2 ± 0.2
mov_ubq	Mean ± SD	0.0 ± 0.1
IMDB	Mean ± SD	6.3 ± 1.1
Rotten	Mean ± SD	53.6 ± 25.9
genre_n	Action	196240 (11.5%)
	Adventure	44880 (2.6%)
	Animated	118800 (7.0%)
	Comedy	293920 (17.2%)
	Crime	66880 (3.9%)
	Documentary	39600 (2.3%)
	Drama	190080 (11.1%)
	Etc	438240 (25.7%)
	Romance	81840 (4.8%)
	Science Fiction	86240 (5.1%)
	Thriller	149600 (8.8%)
prod_ctry_n	Etc	478720 (28.1%)
	France	51040 (3.0%)
	Germany	24640 (1.4%)
	India	45760 (2.7%)
	Japan	43120 (2.5%)
	Nigeria	29920 (1.8%)
	South Korea	41360 (2.4%)
	Spain	27280 (1.6%)
	United Kingdom	75680 (4.4%)
	United States	888800 (52.1%)
Month	2	155120 (9.1%)
	3	155120 (9.1%)
	4	155120 (9.1%)
	5	155120 (9.1%)
	6	155120 (9.1%)
	7	155120 (9.1%)
	8	155120 (9.1%)
	9	155120 (9.1%)
	10	155120 (9.1%)
	11	155120 (9.1%)
	12	155120 (9.1%)

• Variables in the study:

  • TOP20 Dummy = 1: 1.0%

  • Entry Dummy = 1: 0.9%

  • Netflix Original: 12.8%

  • Related Density: mean = 0.2, sd = 0.2

  • Movie Ubiquity: mean = 0.0, sd = 0.1

  • Month in 2021: each month has 9.1% of the total sample

Descriptive Statistics by Entry Status

---

vars	levels	0 (N=1690759)	1 (N=15561)	p
rel_density	Mean ± SD	0.2 ± 0.2	0.5 ± 0.2	<.001
mov_ubq	Mean ± SD	0.0 ± 0.0	0.4 ± 0.4	<.001
IMDB	Mean ± SD	6.4 ± 1.1	6.3 ± 1.0	<.001
Rotten	Mean ± SD	53.6 ± 25.9	53.6 ± 26.2	.856
netf_org	0	1478616 (87.5%)	8584 (55.2%)	<.001
	1	212143 (12.5%)	6977 (44.8%)
genre_n	Action	194049 (11.5%)	2191 (14.1%)	<.001
	Adventure	44209 (2.6%)	671 (4.3%)
	Animated	117181 (6.9%)	1619 (10.4%)
	Comedy	291354 (17.2%)	2566 (16.5%)
	Crime	66324 (3.9%)	556 (3.6%)
	Documentary	39272 (2.3%)	328 (2.1%)
	Drama	188705 (11.2%)	1375 (8.8%)
	Etc	435456 (25.8%)	2784 (17.9%)
	Romance	80986 (4.8%)	854 (5.5%)
	Science Fiction	85354 (5%)	886 (5.7%)
	Thriller	147869 (8.7%)	1731 (11.1%)
prod_ctry_n	Etc	476365 (28.2%)	2355 (15.1%)	<.001
	France	50398 (3%)	642 (4.1%)
	Germany	24273 (1.4%)	367 (2.4%)
	India	45511 (2.7%)	249 (1.6%)
	Japan	42856 (2.5%)	264 (1.7%)
	Nigeria	29871 (1.8%)	49 (0.3%)
	South Korea	41131 (2.4%)	229 (1.5%)
	Spain	26914 (1.6%)	366 (2.4%)
	United Kingdom	75066 (4.4%)	614 (3.9%)
	United States	878374 (52%)	10426 (67%)

• 1,706,320 observations, 15,561 (0.9%) movies newly entered top 20

• Statistically significant differences (p < .001):

  • Related Density

  • Movie Ubiquity

  • Netflix Original

  • Genre and Producing Country proportions

• No statistically significant differences:

  • IMDB Score

  • Rotten Tomatoes Score

Model

---

An econometric model we used to test our hypotheses

\[ TOP20_{c,m,(t+1)}=β_1 ∅{c,m,t}+β_2 U{m,t}+β_3 Z_{m,t}+ϵ_{c,m,t} \]

As TOP20 entry is binary, it is a Generalized Linear Model (GLM) that aims to examine the impact of country-movie relatedness density (∅), movie ubiquity (U), and other covariates (Z) on the entry of a movie into the top 20 list of a given country at period t+1.

• Here, we use Logit as a link function for GLM

\[ P(TOP20=1) = \frac{1}{1 + e^{-f(x)} } \]

Results

---

vars	levels	0 (N=1690759)	1 (N=15561)	OR (multivariable)
rel_density	Mean ± SD	0.2 ± 0.2	0.5 ± 0.2	494.51 (458.15-533.75, p<.001)
mov_ubq	Mean ± SD	0.0 ± 0.0	0.4 ± 0.4	24815.68 (22008.67-27980.69, p<.001)
IMDB	Mean ± SD	6.4 ± 1.1	6.3 ± 1.0	1.09 (1.06-1.12, p<.001)
Rotten	Mean ± SD	53.6 ± 25.9	53.6 ± 26.2	1.00 (1.00-1.00, p<.001)
netf_org	0	1478616 (87.5%)	8584 (55.2%)
	1	212143 (12.5%)	6977 (44.8%)	0.36 (0.34-0.39, p<.001)
genre_n	Action	194049 (11.5%)	2191 (14.1%)
	Adventure	44209 (2.6%)	671 (4.3%)	1.22 (1.07-1.38, p=.003)
	Animated	117181 (6.9%)	1619 (10.4%)	0.86 (0.77-0.95, p=.003)
	Comedy	291354 (17.2%)	2566 (16.5%)	0.90 (0.82-0.98, p=.012)
	Crime	66324 (3.9%)	556 (3.6%)	1.19 (1.05-1.34, p=.005)
	Documentary	39272 (2.3%)	328 (2.1%)	1.77 (1.53-2.04, p<.001)
	Drama	188705 (11.2%)	1375 (8.8%)	0.99 (0.90-1.08, p=.776)
	Etc	435456 (25.8%)	2784 (17.9%)	1.10 (1.01-1.19, p=.021)
	Romance	80986 (4.8%)	854 (5.5%)	1.04 (0.93-1.16, p=.502)
	Science Fiction	85354 (5%)	886 (5.7%)	1.22 (1.09-1.35, p<.001)
	Thriller	147869 (8.7%)	1731 (11.1%)	0.90 (0.81-0.99, p=.030)
prod_ctry_n	Etc	476365 (28.2%)	2355 (15.1%)
	France	50398 (3%)	642 (4.1%)	1.53 (1.34-1.74, p<.001)
	Germany	24273 (1.4%)	367 (2.4%)	1.30 (1.09-1.54, p=.003)
	India	45511 (2.7%)	249 (1.6%)	2.12 (1.84-2.44, p<.001)
	Japan	42856 (2.5%)	264 (1.7%)	1.78 (1.52-2.07, p<.001)
	Nigeria	29871 (1.8%)	49 (0.3%)	0.64 (0.47-0.86, p=.003)
	South Korea	41131 (2.4%)	229 (1.5%)	1.11 (0.91-1.36, p=.288)
	Spain	26914 (1.6%)	366 (2.4%)	1.86 (1.56-2.21, p<.001)
	United Kingdom	75066 (4.4%)	614 (3.9%)	1.07 (0.95-1.21, p=.281)
	United States	878374 (52%)	10426 (67%)	1.61 (1.51-1.71, p<.001)
Month	2	153762 (9.1%)	1358 (8.7%)
	3	153697 (9.1%)	1423 (9.1%)	1.28 (1.16-1.41, p<.001)
	4	153768 (9.1%)	1352 (8.7%)	1.18 (1.07-1.31, p=.001)
	5	153683 (9.1%)	1437 (9.2%)	1.09 (0.98-1.21, p=.107)
	6	153745 (9.1%)	1375 (8.8%)	1.03 (0.93-1.15, p=.553)
	7	153773 (9.1%)	1347 (8.7%)	0.92 (0.83-1.03, p=.141)
	8	153598 (9.1%)	1522 (9.8%)	0.95 (0.85-1.05, p=.330)
	9	153743 (9.1%)	1377 (8.8%)	0.79 (0.70-0.88, p<.001)
	10	153655 (9.1%)	1465 (9.4%)	1.12 (1.01-1.24, p=.029)
	11	153528 (9.1%)	1592 (10.2%)	0.99 (0.89-1.09, p=.783)
	12	153807 (9.1%)	1313 (8.4%)	1.00 (0.90-1.11, p=.964)

Results

---

Key Findings

• Related Density and Movie Ubiquity positively associated with dependent variable in all models

  • Suggests relatedness to other popular movies and worldwide popularity important for top 20 appearance

• Other significant predictors in Model 2 & Model 3:

  • Positive: Higher IMDB score

  • Negative: Higher Rotten Tomatoes score

  • Netflix Original: Positive in Model 1, Negative in Model 3

  • Genre and Producing Country: Significant variation in coefficients

Discussion

---

Highlight

• Related Density and Movie Ubiquity positively associated with top 20 list appearance

• Hypotheses supported: Content-country compatibility and global popularity impact top 20 list inclusion

• Other significant predictors: IMDB Score, Rotten Tomatoes Score, Netflix Original, Genre, Producing Country, Monthly F.E.

• Findings suggest importance of considering country–movie related density for OTT movie launching strategy

Extension - OTT launching strategy

---

OTT Content Launching Strategy Map

• X-axis: Country–movie Relatedness density

• Y-axis: Movie Ubiquity

• Quadrant 1: Low-risk, high-benefit section

  • High investment, high return potential due to global popularity
  • Suggested strategy: Local OTT providers focus on movies in Quadrant 1

• Quadrant 2: High ubiquity, low related density

  • Moderate-risk option due to limited local appeal

• Quadrant 4: Low ubiquity, high related density

  • Low-cost, low-profit, but potential for unexpected profits due to local user compatibility

Extension - OTT launching strategy

---

Country-Specific OTT Content Launching Maps: Argentina (December 2021)

• Red dots: Top 20 content

• Black dots: Not yet in top 20

• Examples of famous movies with low compatibility: A Castle for Christmas, A Boy Called Christmas, Single All the Way

• Examples of locally compatible, less known movies: I Feel Pretty, Waiting for the Hearse

Extension - OTT launching strategy

---

Future Plans

• Robustness check

• Explore more..

  • e.g.)

  • Explore

• Wrapping up the results and find a target journal

Finalize this talk

Thanks for your attention

You can find this presentation here

• https://changjunlee.com/blogs/posts/9_ott_strategy_talk.html/

Any questions & suggestions?

Changjun Lee

Hanyang University

Dep. Media & Social Informatics

 changjunlee.com

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