Use of Pricing and Revenue Analytics to Support Menu Strategy in Global Fast Food Organizations

• Usman Javed

  Mehran University of Engineering and Technology, Department of Telecommunication Engineering, Jamshoro–Hyderabad Road, Jamshoro, Sindh, Pakistan

  Author

• Noman Siddiqi

  Balochistan University of Information Technology, Engineering and Management Sciences, Faculty of Telecommunication and Networking, Airport Road, Quetta, Balochistan, Pakistan

  Author

Global fast food organizations operate in markets characterized by intense price competition, high operating leverage, and rapidly evolving customer expectations. Menu strategy, encompassing price levels, price structures, and product configurations, has become a central managerial lever for balancing traffic growth and unit economics across heterogeneous markets. At the same time, transactional data from digital channels and in-store systems have expanded the opportunities to embed pricing and revenue analytics into day-to-day decision making. This paper examines how pricing and revenue analytics can support menu strategy in global fast food chains, with particular attention to the integration of demand modeling, menu architecture, and operational constraints. The discussion covers the construction of data assets, the selection of econometric and machine learning methods for estimating price response, and the formulation of optimization problems that align menu design with revenue and margin objectives. The paper also considers the role of experimentation and simulation in evaluating menu changes before large scale deployment, and outlines organizational and governance factors that influence the adoption of analytics driven menu processes. While the focus is on fast food organizations with international footprints, the conceptual framework is relevant for multi unit food service networks that face similar constraints in capacity, service time, and brand positioning. The aim is to provide a technically oriented treatment that connects modeling choices to practical menu decisions without assuming a single universally optimal approach.

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