Churn prediction is one of the best metrics to measure customer satisfaction. If there is less churn, it means the customers are very happy. But if there is a high churn rate, the number of customers leaves you. A 1% churn rate every month can translate to around 12% annually.
As per the study, it requires a lot of money to focus on getting new customers compared to keeping the number of customers you already have. Customer churn prediction is a definitive guide to make you understand how many customers are leaving the business, so if you have less churn, it can greatly impact the business revenue.
The churn is an excellent indicator of real growth. No one wants to resist change, but churn lets you make the required changes to get lost customers and focus on balancing the new customers. The business is flourishing if you see that the growth is better than the churn. The business will become small if the churn is higher than the growth.
What do you mean by churn rate?
Churn rate is a term used in customer relationship management and business to determine the percentage of customers who have stopped buying any product or service from a particular group. It can also get applicable in many ways. But the main understanding of churn rate can get related to the business of customers that have stopped completely buying anything from you.
The churn rate is the number of people who stop buying from a company over a specific period. The more people leave your company, the higher your churn rate.
Churn rates are important for companies because they allow them to see how many people are leaving their business and how much revenue they lose. The most common reason customers leave a company is because they feel that there are better alternatives for them.
Importance of customer churn prediction
There is a need to develop the right strategies for reducing the impact of the amount of impact churn can create. Data analysis and machine learning are excellent ways for Customer churn prediction. Churn prediction is an excellent way to work on devising marketing campaigns focused on the customers and have a possibility to churn. It is all grateful to the data; planning to forecast customer churn by using machine learning makes it possible.
The key to increasing customer retention rates is to know your customers better and understand their needs. Data analysis helps understand what happened, how it happened and why. As a result, you can predict future actions and outcomes based on current conditions. You can collect all relevant data from different sources like CRM systems, social media channels, e-commerce platforms and more.
Now that you have all this information, you can analyze it with software tools such as Hadoop. Spark or R programming language. These tools are used for analyzing vast amounts of data in a very short period. So you don’t have to wait long before getting results back from them.
Once you’ve analyzed all of your customer data using one or more tools mentioned above, you’ll be able to see patterns in their behaviour.
Creating the workflow for churn prediction
The process of building an ML-based application for predicting. Customer churn is a very standardized ML project-based structure that has steps, and they are as follows:
Having the goal:
It is crucial to help you understand what type of insights you require with a prediction and the analysis. It is very crucial to know the problem and collect all the requirements.
The next step is to ensure that you have enough data for your analysis. If not, it is important to find out why not and how you can get it?
Lastly, it is important to ensure that you understand what type of analysis will be required to use your predictive analytics.
Building the data source:
The next step is to specify the data source that will be important for the modelling level. There are several sources of churn data: CRM systems, customer feedback and analytics services.
CRM systems are the most common and have a lot of advantages. They provide detailed information about your customer’s behaviour and each step of his journey with your company. The disadvantage is that only a few companies have this kind of system. While the others have to use external services or analytics tools.
Analytics tools can be used when CRM data is unavailable or too broad. Analytics tools offer a great amount of information about customer behaviour. Still, it’s not as detailed as CRM data, so one needs additional manual work to get specific details about customer activity on a website or mobile apps etc.
Preparing Data: Data preparation transforms raw data into a known format for learning machine algorithms. That includes cleaning, transforming, and augmenting your data to make it more useful for machine learning tasks.
Raw data can be used for solving the problem and building the right models that need to be transformed into a known format for learning machine algorithms.
Data preparation is a critical step in building any ML model. Because this process will allow you to get the most out of your data and improve. Its accuracy by removing noise or irrelevant items.
Testing and modelling: Churn prediction is complex, expensive, and time-consuming. That is because it requires extensive data for training and testing purposes.
It is also necessary to test the accuracy of customer churn prediction models and ensure they can predict future churn events correctly.
The most common way of doing this is to perform cross-validation using different machine learning algorithms. The idea behind this approach is simple: we split our dataset into training and testing sets. Train our model using the training set and then use the testing set to test its performance.
This article covers how you can use cross-validation for performance validation in different scenarios (e.g., when you have a single or multiple models).
Regarding achieving a successful churn prediction business, hiring an experienced firm to handle the project is always advisable. It will not only drive down the costs and time of your business but also ensures that you don’t make any mistakes in handling the project.