Problem definition: Firms providing products and services to low-income base of the pyramid (BOP) customers are increasingly utilizing independent contractor agents rather than employees in their distribution models. We empirically investigate the best way to help agents perform better. Academic/practical relevance: BOP customers represent one-third of the world’s economy but make five United States dollars or less daily. Providing goods and services to these customers is difficult for traditional firms because most retail activity occurs at small-scale independent outlets. Improving agent performance can help firms reach customers in this environment. We enhance the literature on agent-based models in BOP settings, decision making, technology in developing economies, and field experiments. Methodology: In partnership with a Tanzanian mobile money operator, we perform a randomized, controlled trial with 4,771 agents to examine how differing types of guidance, and whether in-person training is offered, impact agents’ inventory management. Mobile money is a platform whereby firms in developing economies provide financial services to customers via cell phones. Mobile money agents service customer withdrawals and deposits as branchless banking outlets. Every day, they decide how much money to stock to service customers’ transactions, from which they earn commissions. Results: We find that those agents given only explicit recommendations (as opposed to summary statistics or both) who were invited to in-person training (as opposed to simply received an automated notification) improve their performance. Agents in other treatments showed no statistically significant change. The effect is concentrated in agents who never replenished their money at a bank and whose money inventory levels were low in the pretreatment period. Managerial implications: We show empirically how firms can better manage agents, thereby improving the value proposition of serving BOP customers. We show the utility of segmentation based on agent heterogeneity. This can improve firm performance, agent profits, and customer service.

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Mobile money systems, platforms built and managed by mobile network operators to allow money to be stored as digital currency, have burgeoned in the developing world as a mechanism to transfer money electronically.Mobile money agents exchange cash for electronic value and vice versa, forming the backbone of an emerging electronic currency ecosystem that has potential to connect millions of poor and "unbanked" people to the formal financial system. Unfortunately, low service levels due to agent inventory management are a major impediment to the further development of these ecosystems. This paper describes models for the agent's inventory problem, unique in that sales of electronic value (cash) correspond to an equivalent increase in inventory of cash (electronic value). This paper presents a base inventory model and an analytical heuristic that are used to determine optimal stocking levels for cash and electronic value given an agent's historical demand. When tested with a large sample of transaction-level data provided by an East African mobile operator, both the base model and the heuristic improved agent profitability by reducing inventory costs (defined here as the sum of stockout losses and cost of capital associated with holding inventory). The heuristic increased estimated agent profits by 15% relative to profits realized through agents actual decisions, while also offering substantial computational advantages relative to the base model.

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Problem definition: Mobile money systems—platforms built and managed by mobile network platform operators (MMPOs) to allow money to be stored as digital currency—connect millions of poor and “unbanked” people to the formal financial system. Unfortunately, low service levels because of the suboptimal management of cash and digital currency (e-float) inventory impede the development of these ecosystems. Accordingly, we seek to answer the question of how agents should manage inventories of cash and e-float. Academic/practical relevance: This paper extends inventory theory to the mobile money context, unique in that sales of cash generate inventory of e-float and vice versa. In doing so, we address a key pain point for an emerging sector that improves lives at the base of the pyramid. Methodology: We develop an analytical heuristic to determine initial stocking levels for cash and e-float and analyze its performance on simulated and actual data. Results: By partnering with an MMPO, we tested the performance of the heuristic inventory policy with data from more than 35 million transactions. The heuristic captured 99.9998% of the optimal profit on simulated data and, on actual data, we found that following the recommendations could increase agents’ profits by an average of 15.4%. Managerial implications: We develop a pragmatic inventory policy that performs nearly optimally. We also analyze under which conditions the performance deteriorates and examine heterogeneity among agents with respect to the heuristic’s impact on their performance. Thus, we equip MMPOs with guidance as to whom to target and how. By contributing to service level and profit improvements, this work can make mobile money a more effective financial inclusion tool in the developing world as well as improve the livelihoods of agents. Supplemental Material: The online appendix is available at https://doi.org/10.1287/msom.2022.1175 .