A customer who signs up once is not guaranteed to be a lifelong customer and companies offering subscription services expect a certain degree of churn and account for this in their planning. Estimates put an average acceptable annual churn rate for a subscription business such as a SaaS business at between five and seven percent.
Any increase in the average churn rate is a trigger for root cause analysis at the very least and more aggressive measures to predict and stem this attrition, at the most.
Predicting churn, with the help of statistical techniques as well as technologies like AI and ML, is a proactive approach that many companies use. Knowing which customers are likely to churn and possibly why, enables companies to offer tailored offers and discounts to entice users to stay, and to improve any aspects of the customer experience that are causing the customer to leave.
However, churn prediction is a complex problem to solve and it’s one that companies continuously grapple with. The goal is to bring it down to the most optimum level and this is an ongoing process as the factors that influence churn continually change and evolve. Churn numbers are never static and even technologically sophisticated streaming giants like Netflix and Hulu have felt the pinch at different times.
Customer behavior is often complex and influenced by a multitude of factors. Subscribers may churn for various reasons, such as dissatisfaction with the service, changes in personal circumstances, financial constraints, competition from other providers, or simply loss of interest. Predicting the exact combination of factors that will lead to churn for each customer is difficult.
Accurate prediction of churn requires comprehensive and high-quality data. However, obtaining relevant and reliable data can be challenging. Businesses may not have access to complete customer data, including historical usage patterns, feedback, or external data sources that may impact churn. Limited data can hinder the accuracy of churn prediction models.
Churn prediction becomes more challenging when considering time-dependent factors. Customer behavior and preferences can change over time, and the factors influencing churn may evolve as well. Predicting churn accurately requires capturing and accounting for these dynamic aspects, which can be complex.
Training churn prediction models typically requires historical data from customers who have churned and those who have not. However, the availability of data can create sample bias. For instance, if historical data primarily consists of customers who churned, the model may not adequately capture the behavior of retained customers, leading to inaccurate predictions.
Predicting churn solely based on individual customer data may overlook important interactions and external factors. Customer interactions with the service, such as engagement levels, content consumption, or support interactions, can provide valuable insights into their likelihood of churn. Additionally, external factors like market trends, competitor actions, or economic conditions can influence churn rates, making it challenging to predict accurately.
Before we can answer that question, let’s look at what AutoAI is. AutoAI offers the capability to automate the entire lifecycle of AI development. This automation includes all the tasks beginning with data preparation, feature engineering, model selection, hyperparameter tuning, and model deployment, to data app or dashboard creation to showcase the results. AutoAI does what otherwise would need a team of specialized data scientists and other professional resources, and does it faster and more efficiently.
With this in mind, AutoAI offers a great many benefits when dealing with a churn prediction problem.
AutoAI leverages advanced machine learning techniques and algorithms to analyze complex customer behavior. It can automatically explore and identify patterns, correlations, and dependencies in customer data that may influence churn. By experimenting with various modeling approaches, AutoAI can uncover intricate relationships and capture the nuances of customer behavior, allowing for more accurate churn prediction.
When faced with limited data, AutoAI can still deliver meaningful results. It employs techniques such as data imputation, feature engineering, and feature selection to overcome data limitations. AutoAI can intelligently handle missing or incomplete data, identify relevant features, and transform the data to enhance its predictive power. This enables organizations to generate reliable churn predictions even when data availability is constrained.
AutoAI recognizes the significance of time-dependent factors in churn prediction. It incorporates time series analysis and forecasting methods to account for temporal patterns and dynamics. By considering factors such as seasonality, trends, and historical behaviors, AutoAI can capture the time-dependent nature of churn and generate predictions that reflect the evolving customer churn probabilities over time.
AutoAI can mitigate sample bias, which refers to imbalances or biases in the training data that may affect the churn prediction model's performance. It incorporates techniques such as resampling, stratification, or weighting to address sample bias issues. By ensuring representative and unbiased training data, AutoAI helps create robust churn prediction models that generalize well to diverse customer populations.
AutoAI can effectively capture interactions and account for external factors that impact churn prediction. It employs feature engineering techniques that enable the model to learn and capture complex interactions between variables. Additionally, AutoAI can integrate external data sources, such as demographic data, market trends, or social media data, to enhance the predictive accuracy by incorporating relevant external factors into the churn prediction model.
In this way, AutoAI empowers businesses to accurately predict the churn of their customers, and develop strategies to manage and reduce the rate at which customers leave a platform or service. Along with this increased visibility, AutoAI can be used to see rapid results which can be to be continually iterated upon as the cycles of churn and retention continue. With its simple processes and automation, it is a tool that can be transformative for marketing, revenue operations, customer success, and any other teams whose performance is tied closely to churn and allied metrics.
Talk to RapidCanvas today to learn more about our AutoAI solution to predict and manage churn.