Obviously, everyone in life has to leave at some point.
Sorry to start out with a bummer sentence, but what we mean is customers churning is just a part of business. Like an episode of Modern Love, churning teaches us about trust, loyalty, breaking points, and out-of-our control circumstances that lead to someone leaving.
But what if you can predict a customer leaving and figure out what you can do to stop them from leaving?
Why Predicting Churn is So Important for Subscription-Based Businesses
Most SaaS and telecom products are subscription based, meaning the company's revenue depends on how many users are paying monthly, weekly, or yearly.
Using your data, you can detect relationships between a user churning and your product attributes. This is an amazing ability for product managers, customer success managers, sales teams, and designers.
Once you detect relationships between attributes, you can begin to optimize to decrease churn and increase retention to increase revenue.
The amazing thing is you can do this without knowing code or having technical knowledge of machine learning. All you need is a tabular dataset with quantitative information (CSV file, or data source such as Salesforce, Hubspot, etc.)
Customer Churn Definition
Real quick, let’s define what customer churn is:
Customer Churn: When a customer ends their relationship with a company, product or service. This could be in many different capacities, such as website churn, subscription churn, newsletter churn, content churn, etc.
Churn Rate: The percentage or ratio of users who churn within a time period. This is important to measure because you can use this data to identify trends and relationships between variables. Learning about and dissecting your churn rate is the key to developing customer retention and which customers to put your time and resources into. The churn rate impacts the bottom line and can make it or break it for businesses.
What Kind of Data Do You Need to Predict Customer Churn?
As mentioned, you want to measure churn rate, but also variables you can easily track that might attribute to churn.
In one of our first posts on an introduction to creative data predictions, we outline the data prediction process. The first step is defining a business problem.
In this case, the business problem can be:
How do we identify which high-value customers will churn and what attributes affect them churning?
Beneficial Data Attributes to Collect for Churn
Let’s list out valuable data points.
We can largely classify a dataset into five different sections:
- Identifying Data: This can be a unique customer ID, name, or code.
- Demographical/Basic Customer Data: Basic info about the customer such as age, income, education, location, etc.
- Product Data: The customer's usage of the service such as type of plan, tenure, number of products used, etc.
- Support Data: Info on how the customer interacts with customer support, number of interaction, topics asked, and satisfaction ratings.
- Payment Data: Quantitive data on how much the user is spending on their plans, their monthly spend, weekly spend, micro-transaction data, etc.
Here is a spreadsheet explaining that in relation to a Churn Dataset from AT&T.
Other contextual attributes that would be interesting to discover relationships between are:
- Overdue balances
- Credit score
- User income
- Company title
- Device type
- Time they use the product
Exploring Why Customers Churn With Sample Data
Sometimes the conversations you have about your data is hard and can uncover some ugly truths.
In the technical machine learning realm, the process would be to prepare and clean data, build a Logistic Regression model, train it and test it, and then start making predictions. However, we’re going to take a simpler approach where you can click a few buttons.
Going back to the problem, Input this question: "Which customers will churn and why?" let's explore the results of a codeless data prediction as it relates to churn.
When you login to Obviously AI, you can do some amazing things in under a minute.
- You can use sample data inside our data store to model your dataset after.
- You can predict churn with sample AT&T telecom data to get an idea of the type of attributes related to churn.
- You can use your own data to make predictions related to churn.
We'll use sample data to use as an example.
Simply choose the column "churn" and hit Go.
You'll instantly be taken to the data dialog where you can customize your prediction and filter out columns you don't want to use. For this prediction, the identifier column is the unique user ID because you want to identify which users are most and least likely to churn. The prediction column is obviously Churn.
If you wish, read more on how our no-code algorithms work after you start predicting. Each algorithm is trained, tested, and customized in under a minute.
Let's start predicting.
Instantly you can see the top drivers related to churn such as monthly charges, total charges, type of internet service and more.
Using the interactive graph you can see the predicted churn related to the attribute. For the image above, customers with a monthly spend of $86.23 have a 34.3% chance of churning.
You can start to explore the top drivers to improve the customer experience. Here you can see customers are more likely to churn if they have paperless billing.
Diving into the deeper details, you can see how payment methods compare to each other when it comes to churn. You can quickly see those who are paying by electronic check are more likely to churn. This can mean the payment process for electronic check is difficult and your product can be geared towards the user using automatic payments instead to increase retention.
Customer Retention Remains One of the Biggest Problems in Business
While churn is a hard egg to crack when improving a product, you can use historical data to predict the best ways to improve retention and allocate resources.
Predicting with data gives you answers instead of moving blindly into product optimization. The best part about codeless predictions is non-technical teams don't have to wait on their data science teams to get answers. They can use no-code machine learning to quickly get insight and take actions from their predictions.