To succeed in today’s complex business scenario, companies need to build and deploy an effective customer churn analysis model in order to monitor churn rate and maximize customer retention. Acquiring new customers always costs heavily and this makes the predictive churn model appealing for businesses that aim at retaining customers and maximizing profits. Although predicting customer churn seems to be easy initially it involves several challenges. We have listed some of these challenges in order to help companies adopt preventive measures before moving ahead in the process.
Our customer churn analysis helps companies to measure churn risk and enhance the effectiveness of retention campaigns. Want to know how? Get in touch with our experts right away.
Table of Contents
Challenges of Building a Predictive Churn Model
1. Lack of a ‘silver bullet’ methodology
One of the major challenges that companies face in building a predictive churn model revolves around the selection of a suitable churn modeling approach. But there is no single methodology to build a predictive churn model that can work in most situations. Machine learning techniques are mostly used by businesses due to their efficiency and ability to categorize and manipulate complex data sets. The approach of survival analysis, on the other hand, uses survival and hazard functions to predict which customer will churn during a particular period. So, the best solution to deal with this challenge is to compare the performance of several models and identify the most effective method for your business.
Quantzig helps its clients by offering churn prediction models to design a data-driven customer retention strategy. Request a FREE proposal to gain better insights.
2. Features and exploratory analysis
Businesses face several roadblocks and churn risks at this stage of building predictive churn models such as lack of information, target leakage, and the need for optimal feature transformations. Along with domain knowledge, businesses must also have the required skills and creativity to build robust predictive churn models. Therefore, it is important that companies execute careful exploratory analysis and build auxiliary models before embarking on building an overall churn prediction model. Exploratory analysis can also help in revealing reciprocity, irregularities, outliers, and, relationships between different functions, which wouldn’t be possible with domain knowledge alone.
Our customer analytics solutions help businesses precisely predict future buying behaviors of customers and deliver relevant offers. Request a FREE demo to know more.
3. Validating churn model performance
For accurate customer churn analysis, it’s essential to choose the correct metric to optimize and validate datasets. The precision of a churn model not only impacts performance but also affects decision-making. As such, businesses need to employ different strategies to validate the performance of a churn model prior to its implementation. Also, businesses need to monitor several versions of the churn model to identify problems.
