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Predictive analytics can help your organization forecast future outcomes based on historical data and analytics techniques such as machine learning.
It is a category of data analytics aimed at making predictions about future outcomes based on historical data and analytics techniques such as statistical modeling and machine learning. The science of predictive analytics can generate future insights with a significant degree of precision.

Customer churn prediction is one of the most popular Big Data use cases in business. In simple terms customer churn describes when an existing customer, subscriber or user stops using your service, subscription or making business with you.

The use of fraud detection analytics using predictive data science methodologies enables companies to discover potential fraudulent activity before it occurs. Fraudulent activity is a high-cost threat that can compromise the integrity of your company as well as cripple your bottom line.

Analyzing customer behavior and identifying segments of customers that demonstrate similar behavior patterns will help businesses to understand customer preferences and create a unique customer experience.

For those companies who have been collecting machine data for years, an incredible opportunity exists in making this data actionable. Utilizing actionable data can offer an invaluable competitive advantage by enabling companies to streamline operational processes, optimize demand forecasting and better understand their customers’ propensity to buy. In particular, predictive maintenance (PdM) is a core benefit of making machine data actionable, as it can decrease downtime and waste, leading to greater organizational efficiency.

Churn is part and parcel of running a SaaS company. Some customers won’t get enough value out of your product. Some will switch to a competitor. Some will go out of business. It’s unfortunate, but it’s also inevitable. However, you should do your best to keep churn as low as possible, because otherwise, it can do serious damage to your business. So, how can you prevent churn from cutting the customer lifecycle short?