Will a customer be a reliable payer? Will a student stay enrolled until the end of the course? Does someone have the right profile to purchase a specific product? What’s the sales projection for the next six months? You don’t need a crystal ball to answer these questions. Data science can help.

Predicting answers is possible when you have historical data, statistical knowledge, experience, and a good software solution capable of handling the heavy lifting of calculations and data correlation. Known as predictive analytics, this field allows companies to improve planning and make more accurate decisions, even in uncertain environments and a constantly evolving world. Predictive analytics doesn’t just focus on past data, as business intelligence (BI) often does; it also uses statistical models to project the future.

What Can You Do With Data Science?

At Stefanini Scala, our specialists create statistical models with the support of IBM SPSS, which includes SVM – Support Vector Machine, a modeler capable of mapping and categorizing data through mathematical functions. It may sound complicated, but what matters is that we can help you:

• Forecast financial performance for the coming months
• Predict demand for better planning
• Develop budget forecasts to ensure the financial health of your company
• Forecast inventory levels, because accurate inventory management is crucial
• Predict the performance of marketing campaigns, whose results may seem impossible to anticipate
• Detect the risk of fraud, whether in insurance, online retail, etc.
• Enable predictive maintenance of equipment, anticipating which equipment will fail
• Make various statistical predictions, such as the length of time someone will stay in the ICU or the best product offer for a specific group of consumers.
• Analyze customer lifecycle.

EXAMPLES

All of Stefanini Scala’s work starts with understanding your business. Then, we study what data is needed and what is available in databases before arriving at the statistical models to be implemented.

To define the customer lifecycle, for example, we study customer profiles and the marketing activities that can keep them engaged for as long as possible, preventing them from switching to competitors. If we know the customer, we can offer a specific product, credit, investment, or create a personalized offer. We can make them buy more.

The telecommunications industry, for example, suffers from customer churn. The work of predictive analytics in this case includes scoring customers so that the higher the score, the greater the chance of losing that customer within a certain period of time (churn rate). Monitoring the churn rate is crucial because it means having the opportunity to create special offers to reverse the churn trend.

Predictive analytics can also determine which customers are gold, silver, or bronze, based on their future purchase potential. And it can define which products each group is likely to buy. Can you imagine the possibilities?

All areas—Industry, Retail, Healthcare, Education—can benefit. And at Stefanini Scala, we have a specialized team ready to help you.

Learn more here.