Customer Churn Prediction
Keeping existing customers satisfied is the key to having a steady revenue stream. We can help you predict customer churn and prevent it.
What is customer churn?
Customer churn refers to the business scenario in which your customers stop using your company’s product or service. This is also known as customer attrition, turnover or defection. Predicting customer churn refers to the method of calculating this customer loss. For online businesses, this can mean a customer who has not returned to the website for a particular amount of time or a customer who has not renewed his subscription. For retailers running brick-and-mortar stores, it can mean a loss of regular shoppers.
The importance of reducing customer churn
Customer churn is something that every company should try to reduce, whether it is in the B2C or the B2B segment. This is because 80% of sales come from 20% of the customers. While this figure is not set in stone for all businesses, the fact remains that customer loyalty is the key to generating a steady revenue stream, no matter which industry you are in.
If you are running a mom-and-pop store in a small community and know all your customers by their first name, this is easy enough to do. By interacting with them on a regular basis, you know when they are dissatisfied with your store and even other factors such as customer lifetime value. You can also carry out customer segmentation, create cross-selling and upselling opportunities, carry out market basket analysis and even perform sentiment analysis.
But what do you do when you have customers across hundreds of locations or thousands of products on your website which are bought by millions of customers? How do you identify at-risk customers?
This is where data science comes in. To get actionable insights from multiple datasets you need to carry out data analysis and implement data visualization tools to see the results in real-time and in easy-to-understand business terms.
Data Science Proof of Value
Looking for an effective way to improve your churn rate and increase revenue? We can help you build predictive model on your datasets to determine what actions customers take before you lose them.
Calculating customer churn is not always easy
Calculating customer churn involves a lot of variables. If you are growing your subscriber base by 10% every month and have a customer churn rate of 10% for the same period, you are still losing revenue since acquiring new customers involves expenditure in sales and marketing activities.
If you want to calculate your churn rate accurately you need to ask yourself these questions –
A. What is the customer lifetime value (CLV) of your customers?
This metric is closely interlinked with customer churn. It is important to measure which customers are more profitable to your organization and take steps to retain them instead of having a generalized approach towards customer retention.
B. What is your conversion rate?
If you are offering free trial subscriptions or big discounts to lure in customers, how many of them become paid subscribers or repeat customers later on? This also ties into revenue churn and measuring the customer churn per month and churn per year.
C. What is your activity churn?
This is one of the most important factors in calculating customer churn. For example, if a paid subscriber to a newspaper site has decreased his visits to the website, the probability of him not renewing his subscription has increased. For an eCommerce store, this involves tracking the behavior of regular users to know the likelihood of them switching over to another site for their shopping needs.
The usual churn measurement methods only look at the cancellation rates by which time it is too late to bring back the customer. Even if the customer can be convinced to come back, it usually involves offering him discounts which might not be the best move in terms of revenue generation.
The right time to address customer churn is when you notice a drop in usage instead of waiting for the customer to cancel his subscription. In order to estimate future churn rates, businesses need to adopt predictive analysis and carry out predictive churn modeling.
Make the most of your data with SoftwebIoT
Contact us today to know how our data science and visualization services can add value to your organization.
How Softweb Solutions can help you reduce customer churn
We will use predictive models on your datasets to determine what actions customers take before you lose them. This is done in three steps.
Step 1: Integration of your existing customer data sources so that the datasets don’t just sit in silos but can be analyzed to get a 360-degree view of your customers.
Step 2: The data visualization tools that we provide will help you understand the customer’s journey so that you know which actions are commonly associated with customer churn.
Step 3: Data science will be used to perform customer segmentation and clustering. This involves using predictive modeling to spot risks and unlock hidden insights.
Stop waiting and start retaining your customers with our customer churn prediction services
By working with our data scientists, you can improve your customer retention by understanding which actions are ideal for strengthening customer relationships. Contact us today to know how our data science and visualization services can transform your company.