Application areas of predictive analytics

1. Customer relationship management

 Predictive models can support the sales objectives of CRM and campaign management areas in many different ways. In the case of campaigns, for example, they can be a good basis for screening different selected target groups. By analysing past purchase and usage patterns, the affinity of a customer to buy a product or use a service can be identified. We can also predict if a customer is likely to cancel or reduce the use of a service. This allows us to propose targeted offers and individual discounts to our customers to improve cross-selling or customer retention.

2. Healthcare

Predictive analytics can also bring breakthroughs in healthcare. The various predictive models have the potential to predict the likeliness of occurrence of certain diseases, such as asthma or diabetes. In hospital care, the structured arrangement and analysis of the data collected can speed up decision-making by providing doctors with information that allows them to make more accurate diagnoses.

3. Production

 Manufacturing machines are exposed to many extreme conditions every day. Take just high temperatures, pressure changes, and sudden mechanical impacts. These can cause unexpected disruptions, breakdowns of machine operation or even bring them to a complete standstill, which can have heavy cost implications for the manufacturing company.

Data collected from machines and special sensors allow the real-time monitoring of a machine as a whole, its components, and a number of individually defined parameters. By monitoring the combination of these parameters simultaneously, predictive analytics tools can identify anomalies in machine operation and predict imminent breakdowns or downtimes. They can even help to expose the possible causes behind failures to minimise downtime and the associated costs.

4. Quality control

Predictive analytics can be used to eliminate not only machine downtime, but also errors that would result in a defective product leaving the production line. Companies can react to occurring problems in a timely manner, allowing the necessary machinery adjustments and tool settings, avoiding costly and time-consuming product recalls. Consistently high product quality guarantees the reduction of warranty claims and inspection work in the long run.

5. Retail demand forecast

Demand forecasting is one of the most common application areas of predictive analytics, helping retail companies to best meet customer needs. Forecasting with sufficient accuracy and period of time improves companies' positioning in purchasing, helps them to increase the speed of product rotation, and reduce inventory and warehouse space. Machine learning-based forecasting systems help to do this in a robust way with the efficient use of data.

Of course, predictive analytics can also be introduced effectively in many different areas. If you are interested in how predictive models can optimise your company's operations, feel free to contact us! Our experts have extensive development experience, so our cooperation is not limited to consulting and design, but also implementation and technical support.