In the modern economy, an organization’s clientele constitute its single most important and valuable resource. In order for a firm to successfully keep customers, it is necessary to comprehend their requirements as well as the patterns of their behavior. What factors contribute to a high churn rate from the customer, and why should companies be concerned about it?
The capacity of a company to keep its customers or users for an extended length of time is referred to as customer retention. Client retention helps a company improve its revenue and fosters customer loyalty. A company’s brand value suffers, which in turn leads to a drop in revenue when the rate at which it loses customers is greater than the rate at which it wins new customers.
A churn rate is considered to be high for a company when,
- The features and functionalities that are offered by the product are not meeting the expectations of the consumers.
- A growing number of errors and problems with the product.
- The product offered by the competitor is more cutting-edge.
- Distinction between the subscription and pricing plans.
Customers Are Being Lost to Competitors
The needs of the clients could be satisfied by a great number of comparable items or platforms. If a user wants to buy shoes, for instance, they can do so at a number of different e-commerce websites, all of which sell shoes with comparable characteristics. In this particular scenario, one e-commerce website can be regarded as an alternative to, or a replacement for, another. In a similar vein, one product might be able to replace another with comparable functionality, which would result in churn of the customer.
The following are some of the preventative actions that can be taken to reduce client turnover:
- Identifying the customers most likely to defect and targeting them with enticing promotions, product enhancements, and other forms of marketing.
- Conducting an analysis of customer turnover as it occurs and determining the causes that are playing a key influence.
- Keeping up with the competition, continually improving products in order to fulfill the needs of customers and incorporate any new features that may become available.
When a new feature is added to a product, it will either assist keep the customers or cause them to leave the product if the new feature is not well welcomed.
How to Forecast the Loss of Customers
With the assistance of a churn prediction model, we have devised a strategy for determining whether a new feature added to a product helps increase or decrease the retention of users, i.e., whether they move to a competitor’s product or not after using the new feature; in other words, if they move to a competitor’s product or not after using the new feature.
It is necessary to collect historical data on the customer’s use of both the company’s product and the competitor’s product in order to investigate the amount of time spent on the products before and after the implementation of the new feature.
Information on each customer’s activity before and after they started using the new feature, broken down by period.
Due to the fact that different customers may have engaged with the newly added functionality for the first time on various days, the pre- and post-engagement periods will be distinct for each individual customer based on the date of their initial interaction with the newly added functionality.
Before feeding the data into the model, performing feature engineering on the variables in order to achieve better performance from the model.
Input variables for the model are now being prepared, including Customer Type, Group, Product Usage Time, and Feature Usage Time.
Identifying Customers Who Are Likely to Leave
Evaluation of the churn criterion is required in order to establish whether or not a user has churned.
Determining whether customers have churned as a result of the new feature engagement is essential in this scenario. Churn refers to users who were customers of the company’s product prior to the new feature engagement but have since switched to the product of a competitor as a result of the engagement.
Variables such as the amount of time spent using the product and the amount of time engaged with the product can be used to define the “primary product.”
Model for the Prediction of Customer Churn
- The formulation of the issue:
The primary purpose is to find out if there is a loss of clients once they have engaged with features. As a result, it is a problem of binary classification, and we need to construct the model in accordance with that.
- Analysis of the Data Based on Exploration (EDA):
To begin, we need to conduct an exploratory data analysis (EDA) on the data that we have so that we may gain a better understanding of the variables and discover the links between those variables and the target variable, which is whether or not the customers would churn. In Feature Engineering, we will be making use of the information gathered from this location in order to achieve higher performance.
- Engineering of Functional Features:
If you want the model to have a high degree of accuracy in its predictions, you should make sure that the dataset is well-balanced (that is, it should have an equal split of churn and not churn labels).
In the event that there are any category variables (for example, Customer Type, Group), decode them to integers.
By generating buckets that are based on the currently available numbers, some of the numerical variables, such as the amount of time spent using the Product and the Features, can be transformed into categories.
It is possible that some variables do not have a direct relationship with the target variable; as a result, it may be necessary to create a new variable by combining existing variables that do not have a direct relationship with each other or with the target variable itself in order to achieve a higher level of accuracy.
When all of these procedures have been accomplished, several classification models can be used to establish whether or not the new feature boosted the amount of customers who retained their subscriptions. After that, the model that delivers the best results can be chosen to use for deriving the final result.
- Contributions to the Model
The amount of time a user spends using a product can be compared to both the user’s history Pre and Post data to determine whether or not the user has churned.
It is necessary to translate the data into one record for each user, complete with information on the amount of time spent on our product, the amount of time spent on the product of a rival, the amount of time spent on the new feature, customer type, group, and so on. The model will take this into consideration as an input.
- Logistic Regression
Construct a model using logistic regression, then adjust the hyperparameters until you have the model that best fits the data. We are able to assess the probability of customer churn or retention based on the output of the model, which takes into account a new set of significant predictor variables.
- The Tree of Decisions
A decision tree model generates a yes or no branch constantly until all of the data points are used or based on the limit that we specify. This model will assist us in determining which group the consumers belong to and will do it in a timely manner (either churn or not churn). We are able to calculate the conditions necessary to identify churn based on the branches.
- Metrics Regarding Performance
We are able to choose the model that has higher performance for the end result based on the precision, recall, and accuracy.
We are able to determine the Churn / Retention probability once the customer has engaged with the new feature in our product by using the output of the model. On the basis of this information, we are able to draw the conclusion of whether or not the new feature has assisted in the retention of clients.
The churn model is able to assist us in determining whether or not the users have churned as a result of the newly added functionality. In addition, we can utilize the model to regularly watch and assess the customers’ needs and preferences. We are able to use a methodology that is comparable across a variety of industries in order to ascertain the rate of customer turnover and the factors that played a vital part in assisting businesses in reducing the rate of customer churn.