mohitgoel92 / minimising-churn-rate-of-subscription-products

We will be using Logistic Regression to predict which users will churn.

Minimising Churn Rate Of Subscription Products

Subscription products often are the main source of revenue for companies across all industries, such as Netflix. These products may come in the form of a 'one size fits all' overcompassing subscription, or in multi-level memberships. Regardless of how they structure their memberships, or what industry they are in, companies usually try to minimise customer churn (a.k.a. subscription cancellations). To retain their customers, these companies are required to firstly identify behavioural patterns that act as a catalyst in disengagement with the product.

Market: The target audience is the entire company's subscription base. They are the ones the company wants to keep.

Product: The subscription products that customers are already enrolled in can provide value that users may not have imagined, or they may have forgotten.

Goal: The objective of this model is to predict which users are likely to churn, so that the company can focus on re-engaging these users with the product. These efforts can be notifications about the benefits of the product, especially focusing on features that are new or that the user has shown to value.

In this case study we will be working for a fintech that provides a subscription product to its users, which allows them to manage their bank accounts (saving accounts, credit cards ... etc), provides them with personalised coupons, informs them of the latest low-APR loans available in the market, and educates them on the best available methods to save money (e.g. videos or saving money on taxes, free courses on financial health ... etc).

We are in charge of identifying users who are likely to cancel their subscription so that we can start building new features that they may be interested in. These features can increase the engagement and interest of our users towards the product.

By subscribing to the membership, our customers have provided us with the data on their finances, as well as how they handle those finances through the product. We also have some demographic information we acquired from them during the sign-up process.

Financial data can often be unreliable and delayed. As a result, companies can build their marketing models using demographic data, and data related to finances handled through the product itself. Therefore, we will be restricting ourselves to only using that type of data. Furthermore, product-related data is more indicative of what new features we should be creating as a company.

In this project, we will be taking a different approach for data pre-processing which should result in a deeper understanding of data engineering.