Sometimes your customers simply leave the English way: without even saying “Good bye”. This is not just one lost customer, this is actually losing additional income and losing investment. Customer churn is more dangerous thing than it might seem. This is why churn prediction is of high importance for your business.
What is customer churn?
Customer churn is what happens when a relationship of a customer with a company comes to the end. Customer churn rate is a rate at which a business is losing its clients. And while for subscription business a high customer churn can be equal to death, for e-commerce business model it is more typical to think about relationship with a client in terms of retention, rather than churn. It is easy to see how customers leave in subscription business (they simply press “Unsubscribe”), but how to know that customer is churned in e-commerce?
Why to predict customer churn?
Customer churn prediction is highly important in e-commerce. The information about potential churners is valuable, because it allows you to take action when you still have time, and stop your customers from leaving. It is an essential action not only because you can lose additional revenue with every churn customer, but also because the buyer’s spending before the churn date might not cover the initial spending on acquiring this buyer. It’s a matter of a fact, that retaining an existing buyer is cheaper than getting a new one. It costs five time as much to attract a new customer than to keep an existing one.
SaaS companies spend a lot of time and money to acquire new customers, this is why making customers stay as long as possible is crucial.
Churned customers: why do buyers leave?
Churn score is one of the decisive retention metrics. In subscription based business, it is easy to calculate the churn rate. But how do you know that customer is churned in e-commerce?
There might be a lot of reasons why your clients leave you and switch to your competitors, so it is not that easy to predict a probability of churn. For example, Harvard Business Review says that a high churn rate is a result of poor acquisition efforts, when many firms are attracting wrong customers.
Even though there is no single answer on how to predict churn, still there are many similarities in behavior of customers who are about to leave: they might stop replying to your email newsletters, not entering their website profiles, not searching for new products or start complaining about your brand.
Machine learning to predict customer churn
One of the key methods to predict customer churn is machine learning. Specialized algorithms, used by companies, are adopted to specific problems and can perform such tasks as identifying obvious or latent features of customer’s behavior. It helps to understand better what are the reasons that people keep buying and what makes them leave. These algorithms can identify which buyers can become VIP and bring you most of the profit.
With enough of business specific information, machine learning is a powerful tool to find out the potential churners. After the potential churners are discovered, the company can use different marketing tools to stop buyers from leaving or to win them back. Different companies use a variety of methods to persuade customers to stay with them, e.g. special offers, discounts, personal messages reminding about the importance of this exact customer to the company etc. It might be useful to read about these 9 case studies that will help you reduce customer churn rate.
Data Science is a powerful tool in predicting churn for a company. The program is being trained how to foresee the future behavior of customers, based on real cases and a wide range of available data, such as customer order history, provided customer info and website activity. The customer, with every new activity on the website, constantly feeds the machine learning with new amount of valuable info. Every new interaction with the website, with its social media platforms, with the email newsletters becomes a dataset, that can be used by the system later to identify the churned customers in e-commerce. The more diverse is the behavior of customers, the more scenarios the program can adjust to. It only improves the probability of the accurate forecast.
Predicting customer churn with segmentation and customer lifetime value approach
One of the accurate approaches to predict customer churn is calculating customer lifetime value, that is used in Amazon Prime Subscription Model. This method of predicting customer churn approved itself as an effective technique for different e-commerce industries (e.g. Amazon Prime Subscribers Hit 80 million).
Secondly, the customer micro segmentation is in the core of customer churn prediction. The segmentation is constantly updated basing on the customers data changes. The right customer segmentation is a defining element in creation of so-called segment route history of each client. With its help it is much easier to understand and predict where, when and why the customer might churn.
StackTome helps your business benefit from social marketing segmentation and other customer segmentation models.
Customer churn prediction: step by step
In customer churn prediction, your main goal is to detect which of your buyers are about to leave. This process includes three main steps:
Data gathering starts with so-called feature engineering. Comparing your particular buyer to a similar group of customers helps you make a better prediction. There are specific features of every customers group, it includes some particular information about them. You can do the comparison based on these specific pieces of data. It can be customer’s demographic information, age, educational background, the history of interaction with your website (like last log in or last search on the website, type of device used for interaction with your brand), purchase periods, amounts of sales, and a lot of other valuable information.
In order to build the right church predictive model, you have to know your business model as well. Your predictive model also depends a lot on how you define churn for exactly your company. You should know what churn means to your ecommerce business and how it is represented, in order to transform churn into an element of machine learning. You can build an accurate customer churn model using statistics and data mining. With the use of complex data processing algorithms, you can discover a lot of latent deviations in customer behavior, and predict the outcomes of it.
Testing the model on existing clients.
Once the customer churn predictive model is built, it is time to test it to see if all the factors were considered, and if it works for retaining your customers. You can make some specific adjustments to your model, based on the results you get, in order to achieve the desired performance. Once you customize your customer churn model, you will see its benefits over automatically generated one. It allows you to be flexible and adjust your model to the growing needs of your business.
Churn prediction challenges
While churn prediction modeling seem to be significantly improved by nowadays technologies, it still faces a lot of challenges and risks.
Building an accurate predictive churn model is quite a challenge, even for professional data scientists. None of the solutions you find will always totally solve the problem. Even churn modeling is defined differently. Also there is no single churn method that will work out for all the situations. The key to getting more accurate results for customer churn prediction is to narrow down the methods that will work exactly for your business. On the other hand, it is still recommended to compare different methods of customer churn prediction, to find out the most effective ones. The most common challenges business face while making customer churn prediction, are the following ones:
Low quality data.
The information used for customer churn prediction is usually quite messy and far from the format appropriate for churn modeling. Bringing these loads of data into a suitable form includes feature engineering and extract-transformation-load process. This process is quite boring and thus challenging, as you should thoroughly identify the helpful features, extract SQL scripts from databases, remove outliers info and all the possible data transformation. It is a labour-intensive part of customer churn modeling, that can cause many challenges, if not accurately done.
Low customer churn rate.
You might be surprised how a low churn rate can be a problem, instead of a desired result. If there is a severe imbalance, where the non-churners is the majority of customers, and its amount is much bigger than churned customers, then the overall accuracy of customer churn model goes down. In this case the machine learning pays less attention to minority (churned customers) and, as a result, it gets more difficult to predict the behavior of customers. There are a couple of methods to solve imbalance problem, such as down/up sampling approach.
How to reduce customer churn in e-commerce
Solving one problem does not solve the whole situation. Many e-commerce companies thing that once they fix one problem in their business, all the other issues will be fixed too. The customer churn can be caused by many factors, and in order to see the customer dissatisfaction roots, you have to make a thorough research of all the possible problems. It can include pricing, customer service problems, product installation difficulties, deficient payment methods, delivery delays and many other.
There are some simple ways to reduce customer churn in e-commerce business:
Improve your customer support.
Investing in customer support is always a win-win. When your customers can get timely support and guidance, there is a doubt they will want to leave. The innovations nowadays are turning customer support into effective tool to reduce customer churn. If the buyers can get help not only by calls, but via text messages, in-app customer support service or helpful blogs or video tutorials, they feel more protected. The fastest your customer support is, the less time your customer has to decide to quit and switch to your competitors.
Provide additional service.
One of the reasons your customers leave is not getting enough from your business and finding this opportunity at your competitors business. Analyze your rivals, look at what they are offering to their clients and think on improvement of your service. Go extra mile and offer some additional service. For example, HubSpot offers software tools for their clients and, additionally, provides them with free tutorials on how to use it. When your clients can get everything they need at your company, then the risk of customer churn goes down.
Pay more attention to the feedback.
Do not take customers reviews for granted, pay attention to the good ones, and, especially to negative feedback. The last one usually demonstrates why your customer become churned. Give your buyers an opportunity to speak to your business, and react to what they say. Try to reach your clients before they need you, it will show them that they are important to you and you are ready to invest your time to learn their needs better. Customer frustration can lead to customer churn, but it clearly explains you where your business needs improvements.
Do not underestimate the power of emails.
Not only emails help you to stay in touch with your customers, but they are a perfect platform for storytelling, and are pushing your users back to your website or application. E-mails move your clients toward their goal (even though they might not guess it). The great examples of using email newsletters to reduce customer churn can be DropBox, Grammarly and Autopilot. These companies mastered the art of emails, and effectively use it to retain customers.
Do you need more guarantees to reduce customer churn rate and retain more clients? StackTome can help you do that – it could never be easier to make your customers stay.