Nowadays, many online movie theaters face a number of challenges, such as intense competition, lack of quality content, high advertising costs, and difficulty retaining customers. Big data analysis and machine learning can help solve these problems.
Through big data analytics, online movie theaters can gain valuable insights into their customers’ preferences, behaviors, and genres of movies and TV shows. This will allow Internet resource owners to create and offer more personalized content, improve recommender systems and predict demand for their content. Machine learning can also help optimize ad campaigns, predict LTV (customer lifetime value), and predict traffic and revenue.
Many people know that the success of Netflix, Kinopoisk and the like is largely due to the cool recommendation algorithms that are hidden inside their sites and applications. Thousands of man-hours, hundreds of thousands and even millions of dollars have been spent on the development of these algorithms! Does this mean that a small online cinema has almost no chance to stay afloat in this market? Not at all. If you use the opportunities that data science gives us, you can create recommender systems of quite a decent level even with a small budget.
Let’s take as an example that we have an online cinema and we need to analyze all the available data and build a good recommender system. This process will be built in several stages.
1. Analytical base
To conduct a qualitative study of an online cinema business, you need to have the following business reports:
- user behavior report (views, ratings, comments),
- revenue report (payments, subscriptions),
- report on advertising campaigns (contextual advertising, targeted, etc.),
- and content report (movies, series, genres).
Ideally, the data will include information about different segments of users, movies, series, views, ratings, payments, and advertising campaigns. Data sources can range from internal storage systems to external APIs, from market research to scraping competitor sites.
2. Data Processing
It is understood that the online cinema is operational and already has some history. The main entities in the online movie theater data warehouse may include tables with information about users, movies, series, views, ratings, payments, and advertising campaigns. This is typically a SQL database that typically uses a star schema where the data is split into multiple tables where the facts are views, ratings and payments and the dimensions are users, movies and ad campaigns.
Not always all this data is structured and stored in one database. Difficulties in data processing may arise due to different schemes and structures of databases, the use of different DBMS, and also due to the presence of a lot of unstructured data, such as text comments on the site, or images.
The key task of analysts and data scientists when working with such data is their cleaning, filtering and competent aggregation. Of course, much also depends on database administrators, in particular, the quality of uploading this data to the server. Basic data quality checks in online cinemas can include checking for required fields, checking data format, checking for duplicates, checking for invalid values, checking for compliance with business rules, etc.
The data pouring process involves collecting data from various sources, transforming it, and loading it into a data warehouse. Data retrieval involves running queries to retrieve the required information from the data store. Data pipelines can be built using ETL (Extract, Transform, Load) tools to automate data loading and transformation processes.
3. Options for improving business efficiency
Let’s assume that we are faced with the task of creating a Data project that can improve the performance of an online cinema business. Let’s say it’s the development and implementation of a more accurate and personalized recommender system.
To solve this problem, we first need to analyze the main metrics of this business, to understand where there are strengths and weaknesses. We can carry out the following studies:
- Analysis of the customer base. It is necessary to understand who our main target audience is: men or women, young or old, what are their interests, profession, hobbies? An example of such a report in Google Docs: https://docs.google.com/spreadsheets/.
- Content Analysis: We need to understand what films and series we offer for viewing, do we have many new films or mostly old films? What genres do we offer and how much are they in demand among the modern audience? An example of such a report in Google Docs: https: //docs.google.com/spreadsheets/. Analysis example in PySpark: https://colab. research.google.com/.
- Analysis of user interaction with our platform: We need to understand how much our users like to leave reviews, which films, which genres? Look for insights in this data, find the most loyal and disloyal users. Sample report: https://docs. google.com/spreadsheets/.
Next, we can start creating a recommender system. To do this, we can use the universal programming language Python and machine learning methods such as logregression, collaborative filtering or content-based approaches, as well as neural networks (example of this code: https://colab.research.google.com/).
In the future, this code can be embedded on a website or application in order to improve the quality of recommendations. Of course, after lengthy A / B tests, as well as an unbiased analysis of indicators using statistical methods.
Required roles in a data team may include Data Quality Analyst (responsible for data quality checks), Data Engineer (responsible for cleaning and preprocessing data), Data Scientist (builds models and performs data analysis), and ML Engineer (runs models in production).
Conclusions
Big data analytics and machine learning have great potential to improve the business of online movie theaters. Using data to make informed decisions, optimizing advertising campaigns, creating personalized content and improving recommender systems will help attract and retain customers, increase revenue and competitiveness of online cinema.
However, the successful implementation of a data strategy requires certain resources and expertise. It is important to have a well-organized analytical base that provides access to the necessary data, as well as data processing processes that provide high-quality information. The data team, which includes data quality specialists, data engineers, data scientists, and machine learning engineers, also plays an important role in the successful implementation of the strategy.
