Is the service-based revenue model working in Digitalization?
One of the most used and successful business revenue models used in the market is the service-based revenue model. The models work in the favor of customers where revenues are generated based on services offers on time or on a project or as a retained service provider for the customers.
Service revenue models by the companies can generate revenue through streams such as:-
- Implementation fees
- Consulting fees
- Customer support fees
- Customization fees
The revenue model got an increasing demand in the manufacturing industries for digitalization. It complements the product-based revenue model. Once the product has been sold to the customer, a huge number of data is generated by the sensors collected via IoT devices. The generated data can be used for actionable insights. Hence the company gets to keep its customer tightened up with valuable after-sale services and also let them know what’s new in their company.
The manufacturing companies from different industries using the service-based-revenue model are:-
- Rolls Royce: The company is using the predictive maintenance service-based revenue model on big data-powered AI. The technique is used to ensure the corrective measures are being taken to their highest possible limit to achieve the least number of possible breakdowns of its machines. The method is implemented in jet engines where the customer pays money for the service being offered to let not it break down rather than going for the repairment of the originality and losing its touch. A well-designed preventive method. Rolls-Royce also offers to sell the power on an hourly basis in its engines rather than selling the entire engine. The added advantage of Rolls Royce assuming the total responsibility for maintenance and support of engines.
- Caterpillar: The company is a heavy equipment manufacturer. In comparison to Rolls Royce’s service-based revenue model, the company uses sensors on rented machines for data gathering from generators, engines, GPS, air conditioning systems, and fuel meters. The data is used by the company’s asset intelligence platform for predictive maintenance services. Examples like marine division. It uses to identify a direct correlation between the fuel amount and power used by refrigerated containers. The data was useful to analyze the optimum operating conditions for the container and hence only the modification of the power output from the generator was needed. The operation resulted in an hourly saving of US$30.
Even though, both the companies are different in their work field from one another. But found the resolution with a common predictive analysis method. It shows how the IoT data is not meant for one industry but industry to the industry even in a smaller way can help to save the cost. Or it can help you to earn money even with preventive measures.