Pricing service maintenance contracts using predictive analytics

TL;DR

This paper proposes a data-driven approach using predictive analytics to set differentiated maintenance service fees based on machine profiles, aiming to improve profitability and reduce adverse selection in service contracts.

Contribution

It introduces a novel calibration of predictive models for pricing maintenance contracts tailored to machine characteristics, enhancing profitability and customer segmentation.

Findings

Differentiated tariffs outperform uniform pricing in profitability.

Predictive models effectively identify high-cost machine profiles.

The approach reduces adverse selection risks.

Abstract

As more manufacturers shift their focus from selling products to end solutions, full-service maintenance contracts gain traction in the business world. These contracts cover all maintenance related costs during a predetermined horizon in exchange for a fixed service fee and relieve customers from uncertain maintenance costs. To guarantee profitability, the service fees should at least cover the expected costs during the contract horizon. As these expected costs may depend on several machine-dependent characteristics, e.g. operational environment, the service fees should also be differentiated based on these characteristics. If not, customers that are less prone to high maintenance costs will not buy into or renege on the contract. The latter can lead to adverse selection and leave the service provider with a maintenance-heavy portfolio, which may be detrimental to the profitability of the service contracts. We contribute to the literature with a data-driven tariff plan based on the calibration of predictive models that take into account the different machine profiles. This conveys to the service provider which machine profiles should be attracted at which price. We demonstrate the advantage of a differentiated tariff plan and show how it better protects against adverse selection.