Case Study
Behavioural economics for infrastructure growth
Prioritising behavioural economics use cases to drive revenue and customer value
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Prioritising behavioural economics use cases to drive revenue and customer value
CamIn works with early adopters to identify new opportunities enabled by emerging technology.
of CamIn’s project team comprised of leading industry and technology experts
Our infrastructure client sought to identify high-impact behavioural economics use cases to improve customer experience and increase revenue. CamIn identified 10 high-value use cases, prioritising 4 opportunities capable of delivering measurable revenue uplift within three years.
Opportunity Compass
The client was performing strongly but identified untapped revenue potential across its mobility and infrastructure assets.
They aimed to explore how behavioural economics, combined with AI, could unlock new commercial opportunities and improve customer experience.
Given limited in-house expertise, they required a structured approach to identify viable use cases. The objective was to prioritise high-impact opportunities capable of increasing revenue per customer by an estimated 5-10% within three years.
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12 | Behavioural economics models were systematically assessed to determine relevance across business units and identify potential intervention areas linked to commercial and operational performance. |
30+ | Potential interventions were identified and evaluated against technical feasibility, implementation requirements, and regulatory considerations across infrastructure and mobility environments. |
10 | High-value use cases were prioritised based on commercial viability, scalability, and expected impact on customer experience and revenue generation. |
4 | Priority use cases were selected for execution, with defined pathways to proof-of-concept development and near-term deployment across core business units. |
Identified 10 validated use cases, with 4 prioritised for immediate development across key business units.
Client is developing proof-of-concept solutions and allocating budgets for implementation within three months.
Estimated 5-10% increase in revenue per customer and multi-million-dollar revenue uplift over three years.
Download our detailed case study to learn more about how CamIn and our hand-selected expert project team delivered these results for our client.
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Behavioural economics in infrastructure and mobility refers to the application of human decision-making insights to influence how people use physical assets such as airports, highways, and transport systems. It combines data, psychology, and technology to shape behaviours that improve commercial outcomes, operational efficiency, and safety.
In practice, this means designing interventions such as pricing models, nudges, and digital interfaces that guide user behaviour in predictable ways. When combined with AI and real-time data, these approaches can be scaled across complex infrastructure ecosystems, enabling operators to move from passive asset management to active demand shaping and revenue optimisation.
Infrastructure operators face structural pressure on margins due to high capital intensity, regulatory constraints, and limited pricing flexibility. Traditional optimisation approaches focus on cost reduction, but behavioural economics opens a complementary pathway by increasing revenue per user and improving asset utilisation without major capital expenditure.
It also enables more precise management of demand peaks, congestion, and safety risks. For example, influencing traveller behaviour can reduce bottlenecks, improve throughput, and enhance customer satisfaction. This has direct implications for commercial revenue, particularly in retail-heavy environments such as airports.
Importantly, behavioural interventions are often low-cost and fast to implement compared to physical infrastructure upgrades. For Heads of Strategy and Innovation, this creates a portfolio of quick-win opportunities that can be tested and scaled with relatively low risk, while building capabilities in data-driven decision making.
Behavioural economics is moving from isolated pilots to systematic deployment across infrastructure portfolios. The most valuable opportunities sit at the intersection of pricing, customer experience, and operational efficiency.
Airports are increasingly dependent on non-aeronautical revenue, yet many still rely on static pricing and generic retail strategies. Behavioural economics enables more granular segmentation of passenger behaviour, allowing operators to influence dwell time, spending patterns, and movement through terminals.
Quick wins include dynamic pricing for parking and premium services, nudging passengers towards underutilised retail zones, and personalised offers through mobile platforms. These can deliver immediate uplift in revenue per passenger with minimal infrastructure changes.
Mid-term opportunities involve integrating behavioural models into airport digital ecosystems, linking flight data, passenger profiles, and real-time congestion metrics. This allows for adaptive interventions such as targeted promotions or queue management strategies.
Long-term, airports can evolve into fully orchestrated commercial environments where behavioural data continuously informs layout design, tenant mix, and service offerings. This shifts the role of the airport from landlord to active revenue optimiser.
Toll road operators traditionally rely on fixed pricing models, which limit their ability to manage demand and maximise revenue. Behavioural economics introduces the ability to influence route choice, travel time, and payment behaviour.
Quick wins include time-based pricing incentives, targeted communication to shift traffic away from peak periods, and simplified payment experiences that reduce leakage and improve compliance.
Mid-term opportunities involve integrating behavioural insights into traffic management systems, enabling dynamic tolling strategies that respond to real-time conditions. This can improve throughput while increasing yield per vehicle.
Long-term, operators can develop mobility platforms that shape end-to-end journeys, influencing not just road usage but multimodal transport decisions. This positions toll operators as active participants in broader mobility ecosystems rather than standalone asset owners.
In construction, behavioural economics is underutilised despite its potential to improve safety and productivity. Human factors are a major contributor to delays, cost overruns, and incidents.
Quick wins include redesigning workflows and interfaces to reduce cognitive load, using behavioural nudges to improve compliance with safety protocols, and incentivising productivity-enhancing behaviours on-site.
Mid-term opportunities involve embedding behavioural insights into project management systems, linking performance data with targeted interventions to improve decision making and coordination.
Long-term, behavioural models can inform the design of future infrastructure projects, ensuring that assets are built with user behaviour in mind from the outset. This can reduce lifecycle costs and improve long-term utilisation.
Mobility ecosystems are becoming more interconnected, with users expecting seamless journeys across multiple modes. Behavioural economics provides a mechanism to influence how users navigate these ecosystems.
Quick wins include incentives for off-peak travel, personalised journey recommendations, and behavioural prompts that encourage the use of higher-margin services.
Mid-term opportunities involve platform-level integration, where behavioural data informs service design, pricing, and partnerships across mobility providers.
Long-term, operators can create adaptive mobility ecosystems where user behaviour is continuously shaped to optimise system-wide efficiency and revenue. This requires advanced data infrastructure but offers significant competitive advantage.
The application of behavioural economics at scale depends on a set of enabling technologies that allow operators to collect, analyse, and act on behavioural data in real time.
AI is central to identifying behavioural patterns and predicting user responses to different interventions. It enables segmentation at a level of granularity that is not possible with traditional analytics.
Strengths include the ability to process large datasets and generate real-time insights. Weaknesses relate to data dependency and the need for high-quality inputs. Poor data governance can limit effectiveness.
Opportunities lie in combining predictive models with automated decision systems, allowing operators to deploy dynamic pricing, personalised offers, and adaptive nudges. The main threat is over-reliance on opaque models, which can create regulatory and reputational risks if outcomes are not transparent.
Mobile applications, digital signage, and integrated platforms provide the primary channel for delivering behavioural interventions. These interfaces are critical for influencing user decisions at key moments.
Strengths include direct access to users and the ability to deliver personalised, context-aware messaging. Weaknesses include user adoption challenges and fragmentation across platforms.
Opportunities include integrating behavioural insights into every touchpoint of the customer journey, from trip planning to post-journey engagement. The threat lies in poor user experience design, which can reduce trust and limit effectiveness.
Behavioural economics relies on continuous data flows from multiple sources, including sensors, transaction systems, and user interactions. Robust data infrastructure is therefore essential.
Strengths include the ability to provide a unified view of user behaviour and system performance. Weaknesses include high implementation costs and integration complexity.
Opportunities include real-time optimisation of operations and revenue streams, as well as the ability to test and refine interventions. The main threat is data silos, which prevent organisations from capturing the full value of behavioural insights.
IoT technologies enable the collection of granular behavioural data, such as movement patterns, dwell times, and environmental conditions. This provides the foundation for many behavioural interventions.
Strengths include high-resolution data and the ability to monitor physical environments in real time. Weaknesses include deployment costs and maintenance requirements.
Opportunities include using sensor data to trigger targeted interventions, such as redirecting passenger flows or adjusting service levels. The threat lies in privacy concerns and regulatory scrutiny, which can limit data usage.
Dedicated platforms for testing and deploying behavioural interventions are emerging, allowing organisations to run controlled experiments and measure impact.
Strengths include the ability to validate hypotheses and scale successful interventions. Weaknesses include the need for organisational alignment and capability building.
Opportunities include creating a continuous improvement loop where behavioural insights are systematically tested and refined. The threat is that without strong governance, experimentation can become fragmented and fail to deliver strategic value.
