Predictive Analytics: A ‘Crystal Ball’ for Business

Mar 30, 2017

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Production faults, misinvestments, frauds or machine failures cause significant costs for enterprises. Predictive Analytics helps to avoid it.

The technology is a subfield of Business Intelligence. It is focused on forecasting possible events in the future. Historical and recent data from company and business environment are used as a basis for the analysis. This form of AI is becoming an important tool for reliable forecasts in companies.

In fact, the application scenarios of Predictive Analytics are immense. Here are just a few examples.

Fraud detection

Rules and algorithms automatically detect irregularities: from incorrect invoices to balance sheet manipulation. Suspicious transactions are automatically stopped and manually checked.

Keeping skilled employees

Sometimes there is nothing to indicate that an employee considers finding a new job: an excellently educated person has worked in the same department for over 5 years. However, it is striking that he or she recently attended numerous education courses. A forecast model can carry out an analysis of the likelihood of a job change of employees, so the HR team can timely react.

Predictive maintenance

Algorithms constantly analyze the behavior of machines using historical data. The system calculates the right time for the next inspection and holds the required spare parts in stock.

Payment practice improvement

Open claims on suppliers and business partners are always a dilemma for a company. The liquidity suffers if there are many bills that have to be paid. However, every company has an experience under what conditions the customers punctually and reliably pay, e.g. due to discounts ensuring clients to pay invoices quickly. The cash forecasting process takes this data into account.

Predictive quality

By parameterization of production stages, it is possible to timely identify defective products and remove them from the manufacturing process. However, it requires the appropriate criteria based on patterns from sensor data indicating quality defects.  

Churn management

Predictive Analytics can predict whether a customer is liable to churn in order to convince the person to stay. Especially it is relevant for the telecom industry. Besides lower prices and better conditions of competitors, a customer can churn for the following reasons: long processing and waiting time, constantly changing contact persons, impolite behavior of service staff, etc.

Up-selling potential identification

In up-selling, a vendor offers customers an added value in form of better quality or service. For example, a mobile phone contract upgrade: for just 5 dollars more a customer gets a tariff including more free minutes and internet flat rate.

Algorithms estimate the individual potential based on the previous behavior of a customer and inform whether it would be worth to contact the person.