Referral Priority Program: Leveraging Social Ties via Operational Incentives

Yang, Luyi; Debo, Laurens G.

doi:10.1287/mnsc.2018.3034

Cited by 14 publications

(12 citation statements)

References 37 publications

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“…By contrast, the full-priority scheme cannot stimulate referrals and results in the same equilibrium outcomes as FIFO. Note that the full-priority referral scheme's failure to generate referrals when the base market size is too small has also been established in Yang and Debo (2019). However, instead of concluding referrals are out of the question, we show that under those circumstances, a carefully-designed referral program that involves strategic delay can still "save the day.…”

Section: Numerical Illustrationsmentioning

confidence: 88%

“…While we build on their model, we also take one step further by endogenizing both the scheduling policies (in the main model) and the capacity (in an extension). In particular, we complement Yang and Debo (2019) by showing that even when the referral program based on full priority backfires or fails to work, the firm may still generate referrals and acquire more customers by launching a carefully designed referral program based on partial priority or strategic delay.…”

Section: Related Literaturementioning

confidence: 99%

“…Finally, to the best of our knowledge, the only other paper that studies referral priority programs is Yang and Debo (2019), who restrict attention to the full-priority referral scheme under fixed capacity. While we build on their model, we also take one step further by endogenizing both the scheduling policies (in the main model) and the capacity (in an extension).…”

Section: Related Literaturementioning

confidence: 99%

“…It has been previously identified in Yang and Debo (2019) that a referral program which grants full priority to customers with a successful referral can backfire in the sense that launching such a program may harm the system throughput. Since Yang and Debo (2019) only compare the full-priority scheme with the non-referral FIFO case, their recommendation in this situation is simply to forsake referrals altogether. By contrast, our paper takes one step further by identifying a carefully calibrated partial-priority referral scheme-as a middle ground between FIFO and full priority-that can improve the FIFO system throughput and achieve the first best, despite the undesirable performance of the full-priority referral scheme.…”

Section: Numerical Illustrationsmentioning

confidence: 99%

“…Our conversations with Waitlisted.co suggest that many firms are eager to launch a referral priority program, but often lack guidance on whether they should do it and if so, how. Nevertheless, the academic literature on this topic is tenuous, and to the best of our knowledge, the only existing paper is Yang and Debo (2019), who consider two specific mechanisms, FIFO and full priority, but are silent on the broader mechanism design question.…”

Section: Introductionmentioning

confidence: 99%

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Invite Your Friend and You’ll Move Up in Line: Optimal Design of Referral Priority Programs

Yang

2021

M&SOM

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Problem definition: This paper studies the optimal design of referral priority programs, in which customers on a waiting list can jump the line by inviting their friends to also join the waiting list. Academic/practical relevance: Recent years have witnessed a growing presence of referral priority programs as a novel customer-acquisition strategy for firms that maintain a waiting list. Different variations of this scheme are seen in practice, raising the question of what should be the optimal referral priority mechanism. Methodology: I build an analytical model that integrates queuing theory into a mechanism design framework in which the objective of the firm is to maximize the system throughput, that is, accelerate customer acquisition as much as possible. Results: My analysis shows that the optimal mechanism has one of the following structures: full priority; partial priority; first in, first out (FIFO); and strategic delay. A full-priority (partial-priority) scheme enables referring customers to get ahead of all (only some) nonreferring ones. A FIFO scheme does not provide any priority-based referral incentive. A strategic-delay scheme grants full priority to referring customers but artificially inflates the delay of nonreferring ones. I show that FIFO is optimal if either the base-market size or the referral cost is large. Otherwise, partial priority is optimal if the base-market size is above a certain threshold; full priority is optimal at the threshold base-market size; strategic delay is optimal if the base-market size is below the threshold. I also find that referrals motivate the firm to maintain a larger capacity and therefore can surprisingly shorten the average delay, even though more customers sign up and strategic delay is sometimes inserted. Managerial implications: My paper provides prescriptive guidance for launching an optimal referral priority program and rationalizes common referral schemes seen in practice.

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Section: Numerical Illustrationsmentioning

confidence: 88%

Section: Related Literaturementioning

confidence: 99%

Section: Related Literaturementioning

confidence: 99%

Section: Numerical Illustrationsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Invite Your Friend and You’ll Move Up in Line: Optimal Design of Referral Priority Programs

Yang

2021

M&SOM

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Impact of altruism preference difference on the optimal reward structure in online referral marketing

Jiang,

Zhu,

Zhang

2023

Manage Decis Econ

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Social media have significantly boosted the prevalence of online referral marketing, through which firms use referral reward programs (RRPs) to promote their existing customers to disseminate or refer product information within social connections. We start with the idea that customer referral motives include firm‐offered rewards and altruistic motives to examine the optimal RRPs by developing nested Stackelberg game models among a firm, a rational (absolute) altruistic sender, and a recipient. The findings show that both product factors and customer factors influence the effectiveness of RRPs, and taking customers altruistic motives into account helps avoid reward waste and makes the optimal reward structure shift away from rewarding the sender. Furthermore, the optimal reward structure is closely related to the tie strength between the sender and the recipient and varies with altruism preference differences.

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The effect of customer awareness on priority queues

Wang

Fang

2022

Naval Research Logistics

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Pay‐for‐priority system is believed to be an efficient service mechanism in congested systems since it introduces service differentiation that prioritizes those who are more delay‐sensitive. However, in practice, not all customers are aware of the provision of such auxiliary service (i.e., priority access). Does the lack of awareness or ignorance of priority service make the social welfare or customer surplus worsen off? To answer this question, we establish a queueing‐game‐theoretic model by capturing the strategic interactions between service provider and customers to examine the effect of customer awareness on the priority queues. Our main results are as follows. First, we confirm that increasing the level of customer awareness indeed improves the revenue of service provider, and it triggers a higher priority premium price. Second, perhaps surprisingly, we find that under the profit‐maximizing priority price, the social welfare as well as the customer surplus are both nonmonotone in the level of customer awareness, that is, full or no customer awareness can be suboptimal for the social welfare and customer surplus. Third, despite the common belief that priority is socially efficient in congested systems, we demonstrate that the optimal information levels for social welfare and customer surplus are decreasing in the congestion level, and the full customer awareness is optimal only when the system load is relatively low. Finally, to reach the maximal social welfare or customer surplus, some regulation policies are proposed, whereby the social planner can provide advertising subsidy to (levy tax on) the service provider (advertising agency) when the system load is low (high).

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Referral Priority Program: Leveraging Social Ties via Operational Incentives

Cited by 14 publications

References 37 publications

Invite Your Friend and You’ll Move Up in Line: Optimal Design of Referral Priority Programs

Invite Your Friend and You’ll Move Up in Line: Optimal Design of Referral Priority Programs

Impact of altruism preference difference on the optimal reward structure in online referral marketing

The effect of customer awareness on priority queues

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