Data Science

Predict Churn Rate and Customer Retention with Data Science

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By Pere Munar, on 6 July 2023

Predicting churn rate is one of the essential applications of Data Science for startups. By identifying customers who are likely to stop purchasing or unsubscribing from a service, businesses can take proactive measures to retain them.

In this article, we delve into predicting churn rates using data analysis and effectively enhancing your customer retention rate.

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Predict Churn Rate and Customer Retention with Data Science


What Is Churn Rate?

The churn or customer cancellation rate refers to the percentage of customers who stop purchasing products or services or unsubscribing within a specific timeframe. Estimating when people are likely to stop using a service is essential to proactively implementing strategies to retain customers and prevent them from switching to competitors. And data science is essential to accomplishing this objective.

While churn measurement is commonly associated with email marketing, it is also carried out in other areas to analyze the loss of customers.


Why Is It So Important?

Retaining customers is more profitable than acquiring new ones. It can cost up to five times more to gain a new customer than to maintain an existing one. When you convince a customer to buy from you again, you benefit from their prior decision to choose your business over the competition. Assuming their experience has been positive, they are more inclined to continue their loyalty without going through the decision-making process again. This means a significant portion of your marketing efforts is already accomplished.

Customer retention should therefore be a key component of your marketing strategy, and monitoring your churn rate becomes crucial in assessing your success in retaining customers and evaluating the effectiveness of your efforts.


Difference Between Churn Rate and Revenue Churn Rate

Churn rate is the rate or percentage of customers who have discontinued purchasing your products or services or unsubscribed in a certain period. Revenue churn rate provides insight into these customer abandonments' revenue.

If your company only offers one product or service, revenue churn may be less relevant, since understanding the churn rate alone can give you an idea of the losses incurred. However, the revenue churn rate becomes valuable in identifying which specific offerings are experiencing lower sales if you have a range of products or services.


Factors Leading to an Increase in Churn Rate


Price of Products or Services

Price is a primary factor leading customers to switch to competitors or discontinue their association with a brand. For instance, when a customer discovers a comparable solution at a more competitive price, it significantly increases the likelihood of their defection. To prevent this, you should consistently reinforce the value of your brand, highlight what sets you apart, and assist customers in maximizing the benefits of your solution.


Not Delivering on Promises

Promising outstanding results to convert a potential customer without the ability to deliver is an ineffective strategy. When customers feel deceived, it can lead to distrust and diminish the chances of building long-term loyalty. While it may yield short-term gains, it is damaging in the medium to long term.


Unsatisfactory User Experience

Not all customer issues stem from pricing or products and services. The root cause lies in how customers interact with your brand online. Is your website functioning correctly? Is it user-friendly and intuitive? Does the payment process inspire confidence?


Poor Customer Service

Exceptional customer service is fundamental. When implemented effectively, it can turn customer doubts or issues into an opportunity to foster loyalty. However, poor customer services can be a significant factor that drives customers away. The speed of your response, the quality of treatment, personalized attention, and the availability of diverse communication channels can make or break customer relationships.


How to Measure Churn Rate

Measuring churn rate is a straightforward process. You can use the following formula:

Churn rate = (number of customers who canceled the service in a given period/number of existing customers at the beginning of the period) x 100

To better illustrate this, let's consider an example. Suppose you start the month with 300 customers, and at the end of the month, you have 150 customers:

Churn rate = (150 customers who canceled the service) / (300 customers at the beginning of the month) x 100 = 50%

In this case, your churn rate would be 50%.

It's important to note that the ideal churn rate varies depending on your industry, product or service, and business style. For instance, in SaaS (software as a service) businesses, a 5% or 7% churn rate per year is feasible and common. Understanding your specific industry benchmarks will help you assess the health of your customer retention efforts.


How to Predict Churn Rate Step by Step

In order to make accurate predictions, it is crucial to have a comprehensive database that captures customer interactions with your brand over time. This data should include indicators such as subscription cancellations or periods of inactivity.

With this valuable information and the use of supervised learning algorithms, you can identify which users are likely to stay and which ones may not, and understand the underlying reasons. When a new user enters the system, the algorithm can analyze their behaviors and assess the probability of them abandoning the brand.

If you’re interested in leveraging the power of data analysis and customer clustering, our team of skilled data scientists at Cyberlink can evaluate your database and provide you with insights into its potential. Feel free to contact us for an assessment and make your company data-driven!

Data science consulting with Cyberclick

Pere Munar

Data Scientist en Cyberclick. PhD en Astrofísica por la Universitat de Barcelona con más de diez años de experiencia en investigación mediante el análisis e interpretación de datos. En 2019 redirige su carrera profesional hacia el mundo del Data Science cursando el Postgrado en Data Science y Big Data de la UB, así como participando en el programa Science To Data Science (S2DS) en Londres. Actualmente forma parte del equipo de Data Science y SEM de Cyberclick.

Data Scientist at Cyberclick. PhD in Astrophysics from the University of Barcelona with more than ten years of research experience through data analysis and interpretation. In 2019 he redirected his professional career to the world of Data Science by graduating in Data Science and Big Data from the UB, as well as participating in the Science To Data Science (S2DS) program in London. He is currently part of Cyberclick's Data Science and SEM team.