Describe the purpose of this project
The renewal of a TV series for a second season is a multifaceted decision shaped by several factors. Traditionally, networks and streaming platforms have relied on audience viewership, critical perception, and financial considerations to determine whether a show continues. However, with the expansion of streaming services and globalization of content, additional factors may now influence these decisions. One possible factor is the popularity of certain genres. Genres like crime dramas or reality TV have historically shown strong audience retention, making them more likely to be renewed (What’s Behind a Show Renewal, n.d.-b). Conversely, niche or experimental genres may encounter more difficulties in securing continued production. Recognizing which genres have a higher likelihood of renewal can assist producers and platforms in optimizing their content strategies. Another factor to consider is the language of the series. In the increasingly global entertainment industry, non-English series have become increasingly popular, with platforms like Netflix and Amazon Prime investing significantly in foreign-language content (What’s Behind a Show Renewal, n.d.-b). The question remains whether language influences renewal decisions, potentially due to factors such as regional demand, dubbing and subtitling costs, and international appearance (Analyzing TV Ratings Systems - Examining the Influence of Viewership Data on Show Renewals | Common Good Ventures, 2001). Finally, average ratings are frequently considered a key factor in renewal decisions. High ratings generally indicate strong audience engagement, suggesting that a show is well-received and likely to perform well in future seasons (Analyzing TV Ratings Systems - Examining the Influence of Viewership Data on Show Renewals | Common Good Ventures, 2001). However, the correlation between ratings and renewal is complex, as other factors – such as production costs, competition, and strategic objectives of the platform – can also influence the final decision. Based on these factors, the question arises to what extent genre popularity, the language of the title, and average ratings influence the likelihood of series renewal.
The findings of this study can contribute to both academic and industry discussions by shedding light on the key factors that influence TV series renewal decisions. By identifying patterns in these decisions, this research can assist content creators, streaming platforms, and production companies in making more strategic and data-driven choices regarding future productions. Understanding these trends can also help media executives allocate resources more effectively and develop content that aligns with audience preferences. Furthermore, the automated and reproducible workflow used in this study ensures that the research process remains transparent and accessible. This not only enhances the reliability of the findings but also makes the study a valuable resource for other students, researchers, and the broader scientific community. Future studies can build upon this framework to explore additional factors influencing the renewal of TV series, contributing to a deeper understanding of decision-making in the entertainment industry.
The data for this study was obtained from IMDb via https://developer.imdb.com/non-commercial-datasets/. This website contains seven different datasets with information from IMDb. For this study, the following four datasets were used: 'title.akas', 'title.basics', 'title.episode' and 'title.ratings'. The following table gives an overview of all variables in the final dataset used for this study.
| Variable name | Variable description | Scale |
|---|---|---|
| tconst | Alphanumeric identifier of an episode | Nominal |
| parentTconst | Alphanumeric identifier of a TV series | Nominal |
| primaryTitle | The title used by the makers on promotional materials | Nominal |
| originalTitle | The original title in the original language | Nominal |
| isAdult | Wheter or not the serie is an adult show (0: non-adult, 1: adult) | Boolean |
| startYear | The release year of the title | Ratio |
| endYear | The final year a serie was produced | Ratio |
| Genre1 | The first genre of the series (some series have multiple genres) | Nominal |
| Genre2 | If available, the second genre of the series | Nominal |
| Genre3 | If applicable, the third genre of the series | Nominal |
| title | ||
| averageRating | The weigthed average of all individual user ratings | Interval |
| numVotes | The number of votes a series has received | Ratio |
| Renewed | Whether the series has at least a second season (0: not renewed, 1: renewed) | Boolean |
| Genre1_encoded | ||
| Genre2_encoded | ||
| Genre3_encoded |
- What dataset(s) did you use? How was it obtained?
- How many observations are there in the final dataset?
- Include a table of variable description/operstionalisation.
To explore influence of genre, language of the title, and average ratings on the likelihood of series renewal, we will conduct a logistic regression analysis. The renewal status will be the dependent variable, while genre, language of the title, and average rating will serve as the independent variables. According to Lee and Wang (2003), logistic regression is a useful method for analyzing binary variables because it models and predicts the probability of a specific outcome. This method is useful as it can handle both continuous and categorical predictors, making it versatile for various types of data.
For deployment, the results will be communicated through a PDF report, ensuring accessibility and clarity for potential users. The structured format will effectively present conclusions, making it easy to interpret key findings.
- Describe the gist of your findings (save the details for the final paper!)
- How are the findings/end product of the project deployed?
- Explain the relevance of these findings/product.
*Include a tree diagram that illustrates the repository structure
Explain any tools or packages that need to be installed to run this workflow.
Provide step-by-step instructions that have to be followed to run this workflow.
This project is set up as part of the Master's course Data Preparation & Workflow Management at the Department of Marketing, Tilburg University, the Netherlands.
The project is implemented by team < x > members: < insert member details>