Keeping customers engaged with a product or service is a difficult challenge for many businesses.
People stop using products for many reasons. They get bored, they forget about the product, they find a better alternative, and so on. Customers that leave have become a major concern for companies that offer products whose profitability depends on continuous customer engagement. Businesses are confronting the difficult challenge of retaining customers as recurring-use business models become more popular. At the same time, the availability of customer data has been expanding in recent years, and businesses are starting to see the benefits of learning from this data.
Data science techniques can be used to design data driven strategies that are effective at retaining customers, allowing business to strengthen their business models through targeted approaches. Leveraging data in the fight against churn can lead to successful strategies by understanding how to define churn in a business context, how to predict future churn events, and how to extract value from a churn data analysis.
- What is churn in a general business context.
- What are some some barriers commonly found in churn-reducing efforts and how to overcome them.
- How a financial institution decreased customer churn and credit card inactivity.
- Examples on evaluate the business benefits of a data solution to churn.