The most realistic part of doing business is that customers no longer prefer you for any reason. It is always usual to experience a loss of customers, no matter how relevant your company is or how relevant your products or services are. For this reason, companies have customer loyalty and loyalty programs in place to actively deal with the risk of losing customers. Loss management has a direct impact on profitability in any industry or product segment, from telecoms to SaaS companies and consumer products.

Depending on the resources you are looking for and what industry you operate in, it costs less to retain existing customers, as you need to advertise or campaign to attract a new customer. But understanding why customers no longer prefer your company, building a sustainable business prevents you from experiencing losses and can also make up for what happened. For this reason, customer churn analysis (customer loss analysis) is very important. It helps you understand what is right and what you are doing wrong in terms of customer satisfaction in your company. Let's take a look at what customer churn analysis is, how it is done, what are the benefits.

What is Customer Churn Rate and Analysis?

Customer churn rate is a business measurement that reflects the percentage of customers who end their relationship with a company within a certain period of time. This time period can be measured monthly, quarterly or annually, depending on the industry and product. Subscription-based companies (such as mobile service providers, SaaS, and content platforms) typically measure loss over shorter periods of time.

Customer churn is also an indicator of the company's health. While there are several reasons why customers leave, some of the most common reasons are poor service or other macroeconomic factors, such as product quality, price and recession.

The ability to predict loss is the key to preventing it. This is where machine learning comes in. Because organizations that rely solely on customer feedback to estimate customer churn analysis or loss percentages often overlook other variables that affect losses.

Today, it is much easier to develop machine learning (ML) models to predict potential losses with the amount of data companies have. Artificial Intelligence (AI) or machine learning-driven loss prediction produces more accurate results than any other prediction model available today.

Customer churn analysis is a set of methods that allows you to avoid customer loss by analyzing the frequency of purchases, visit processes and returns of customers. With this analysis, you can identify your customers who are no longer likely to prefer you and make them more loyal customers by offering attractive opportunities.

Why is Customer Churn Analysis Important?

Customer churn can be a direct reflection of the value your company provides to its customers, since this loss is not only related to customer satisfaction but also to your company's turnover, leaving customers naturally take their money with them.

However, customer churn is an indicator of the long-term health of your business, if you lose more customers than you gain or if your new customers leave without you covering any marketing and registration costs, you will lose money, which is certainly not sustainable development. Therefore, doing a customer churn analysis without losing yet means investing in the future. You can continue to manage your company without any loss of customers or with very few numbers.

How is customer churn analysis done?

To perform customer churn analysis, it is important to be able to compare data from multiple periods in order to gain insight into specific developments. Therefore, you can analyze with the following data, you can reveal the loss rate:

• The time frame in which you want to calculate customer loss,
• The number of customers your company had at the beginning of the period
• The number of customers your company has at the end of the period

So this formula expresses the number of customers who leave your company in a given period of time as a percentage of the total number of customers.

The analysis is more complicated. Because it can inform why customers stop shopping or receiving services, what kind of changes you need to make to your company. Obviously, historical data is a prerequisite for establishing a loss prediction model. But, in addition to the data, there are a few other factors that will determine how you build your loss prediction model. Here's what you need to do to perform churn analysis:

Determine the business rationale: This step is simply to understand the result you want from the machine learning algorithm. The ultimate goal in this case is:

• Prevent customer loss by identifying high-risk customers in advance
• Designing appropriate interventions to increase persistence

Collect and clean up data: The next step is data collection — figuring out which data sources will feed your loss prediction model. Companies collect customer data throughout the lifecycle through software such as CRM, web analytics, sentiment analysis tools, social listening tools, customer service software, and more.

Creating data capture services is one of the easiest and most effective ways to start collecting data for your loss prediction model. A big step in data preparation is to convert all this raw information into structured data.

Develop, extract, and select attributes: Identifying attributes is a critical part of data analysis preparation. Helps identify attributes that represent patterns of behavior related to customer interaction with a product or service. Data scientists assign measurable properties to data points that a machine learning model will handle to estimate the probability of loss.

These attributes may include contextual attributes that describe customer demographics, behavior (in the case of a mobile phone, data usage, customer service calls, international roaming usage, etc.), and other information about a customer, such as communication preferences, past purchases. Thanks to this, you can have a detailed analysis about customers, increase loyalty with promotions, discount checks.

Build predictive model: Data analysts typically approach loss estimation using multiple methods such as binary classification, logistic regression, decision trees, random forest, and others.

It is extremely difficult to accurately predict the loss of customers. However, signs can sometimes be seen by closely interested companies. Here are some strong indicators of upcoming customer churn:

• Fewer responses to newsletters
• Negative reactions on social media
• Repeated customer service complaints
• Spend less time on your website

What Are the Benefits of Customer Churn Analysis?

The most important factor in combating customer churn is to conduct analysis and develop a loss estimation model. The model not only tells you who your high-risk customers are, but also gives you insight into why. For marketers, companies and service providers, the customer churn analysis and model is important to explore the underlying causes of customer loss.

Customer retention depends on a company's ability to analyze and predict the motivations behind the cancellation and, more importantly, act accordingly. The larger your customer base, the greater the impact of customer loss. Therefore, the most important payoff of customer churn analysis is the minimization of customer loss and the prevention of reputational and financial losses. One of the important things about customer churn analysis is the storage and accurate recording of customer data. Because the importance of data in digitalization processes may be greater than you think. If you can create a profile about your customers, you can plan your future business models and provide content/services that suit your potential buyers.

In the digitized world, the importance of data is increasing day by day for every sector. Therefore, you can take advantage of the data services offered by Komtaş to protect, store it safely and improve its quality and benefit from the data you have and are constantly updated.