LONDON--(BUSINESS WIRE)--Quantzig, a leading analytics advisory firm that delivers customized analytics solutions, has announced the completion of their new article on the challenges enterprises face in building an effective churn model. Building and deploying an effective customer churn analysis model is crucial for businesses to succeed in today’s competitive business environment. Customer acquisition always costs heavily on the financial pockets of businesses. Therefore, predictive churn model has become one of the most appealing solutions for businesses to retain customers and maximize profits.
“Understanding the factors behind customer churn and estimating the risks associated with individual customers are crucial to the design and development of a data-driven retention strategy,” says an advanced analytics expert from Quantzig.
In order to succeed in retaining customers who would otherwise abandon the business, companies must be able to predict which customers are going to churn through predictive churn model. Furthermore, this can tell about which marketing actions will have the greatest retention impact on each customer.
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Challenges in Building a Predictive Churn Model
Lack of “silver bullet” methodology
Building a predictive churn model is a daunting task for businesses. One of the major roadblocks that enterprises face is the selection of a suitable churn modeling approach. But there is no single approach to build a predictive churn model that can work in most situations. Therefore, the best solution is to compare the performance of several models and find out the most suitable method for your business.
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Features and exploratory analysis
While building predictive churn models businesses face several challenges and churn risks such as unavailability of information, target leakage, and the need for optimal feature transformations. Businesses need to have the required skills and domain expertise to build effective predictive churn models. With the help of exploratory analysis businesses can decipher the irregularities, outliers, and, relationships between different functions, which wouldn’t be possible with domain knowledge alone.
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Quantzig is a global analytics and advisory firm with offices in the US, UK, Canada, China, and India. For more than 15 years, we have assisted our clients across the globe with end-to-end data modeling capabilities to leverage analytics for prudent decision making. Today, our firm consists of 120+ clients, including 45 Fortune 500 companies. For more information on our engagement policies and pricing plans, visit: https://www.quantzig.com/request-for-proposal.