Summary
Marketers want to spend their budgets wisely by targeting people who are most likely to respond and influence others in social networks. This paper studies how to split marketing budgets across users with varying responsiveness and costs, using a new method that allows flexible, continuous spending levels rather than just all-or-nothing choices. The authors develop a scalable algorithm that can find good budget allocations even on large networks, and show through experiments that this method can do better than common simple strategies. Their work also helps marketers decide how much budget to spend by balancing the benefits of influence with its costs.
What this means in practice
- •For digital marketing teams: Optimize advertising budgets by allocating spending variably across users to maximize influence and marketing effectiveness on social platforms.
- •For social media platform operators: Design promotion tools that help clients select budget levels and allocate incentives for network-based campaign optimization.$Commercial implications: Enables sales of smarter marketing tools that improve client ROI through fine-grained budget and incentive allocation.
Authors
Abhishek K. Umrawal, Akhil Bhimaraju, Eliot W. Robson, Lav R. Varshney
Abstract
Marketing managers routinely allocate limited budgets across individuals in social networks whose responsiveness and influence vary substantially. We study the problem of Budgeted Generalized Fractional Influence Maximization (BGFIM), which captures the allocation of continuous levels of marketing spend across users in the presence of heterogeneous response and network spillovers. In this framework, adoption probabilities are governed by user-specific response functions that capture both baseline propensity and marginal responsiveness, while incentive costs vary across users. We propose a scalable solution approach based on a discrete greedy procedure with provable approximation guarantees, enabling tractable optimization under realistic budget constraints. Our formulation generalizes prior influence maximization models by allowing heterogeneous response and cost structures, providing a more flexible representation of targeted marketing interventions. Using experiments on multiple large-scale networks, we show that continuous allocation strategies can outperform standard heuristics and improve influence outcomes under heterogeneous conditions, and we illustrate how the framework can support profit-oriented decision making by identifying budget levels that balance marginal returns and costs. Overall, our results highlight the value of fine-grained budget allocation in networked marketing environments and offer a scalable approach for data-driven marketing resource allocation.