Advertisement

Springer Nature is making Coronavirus research free. View research | View latest news | Sign up for updates

Planning the supply of rewards with cooperative promotion considerations in coalition loyalty programmes management

Abstract

Coalition loyalty programmes (CLPs) are owned and operated as for-profit enterprises. We consider the ordering decisions of rewards that arise in this context, under a general setting in which not only is the demand for rewards uncertain, but also the CLP firm offers bonus points, a very common cooperative promotion mechanism used in loyalty programmes. The rewards are acquired either at a wholesale ‘discounted’ cost or at a wholesale ‘non-discounted’ cost by the CLP firm from its multiple commercial partners and supplied to customers seeking to redeem their accumulated ‘reward points’, subject to commercial partners’ capacities for offering rewards, the firm’s overall budget for purchasing rewards, and its control policy on points liability. We formulate the problem as a stochastic linear programme with recourse and solve it using a sampling-based heuristic solution procedure previously discussed in the literature. We report on the managerial applicability of our model in dealing with the redemption budget spending resulting from changes in demand variability, changes in the redemption budget, and the control of liability levels within a reasonable range.

This is a preview of subscription content, log in to check access.

Figure 1

References

  1. Aeroplan (2011a). Group Aeroplan Inc. Annual information form for the financial year ended December 31, 2010, http://aimia.com/content/dam/aimiawebsite/financial_reports/2010/Annual%20Report/Annual-Information-Form-2010.pdf, accessed 25 March 2013.

  2. Aeroplan (2011b). Q4 2010 Financial Highlights. http://aimia.com/content/dam/aimiawebsite/financial_reports/2010/Q4/2010-Q4-Financial%20highlights%20presentation.pdf, accessed 25 March 2013.

  3. Birge JR and Louveaux FV (1997). Introduction to Stochastic Programming. Springer: New York.

  4. Cachon GP (2003). Supply chain coordination with contracts. In: Graves S and de Lok T (eds). Handbooks in Operations Research and Management Science: Supply Chain Management. Elsevier Science B.V.: North Holland, pp 229–331.

  5. Cao YH, Nsakanda AL and Diaby M (2012). A stochastic linear programming modelling and solution approach for planning the supply of rewards in loyalty reward programs. International Journal of Mathematics in Operational Research 4 (4): 400–421.

  6. Diaby M and Nsakanda AL (2008). Coping with revenue recognition in the loyalty reward programs industry: A stochastic modeling approach. In: Proceedings of the 2008 American Conference on Applied Mathematics, WSEAS Press: Cambridge, MA, pp 79–86.

  7. Gandomi A and Zolfaghari S (2011). A stochastic model on the profitability of loyalty programs. Computers & Industrial Engineering 61 (3): 482–488.

  8. Hofer D (2008). Reinforcing the value of frequent flyer miles. White paper, Loylogic Inc., http://www.loylogic.com/index.php/component/docman/doc_download/3-reinforcing-the-value-of-frequent-flyer-miles-.pdf, accessed 25 February 2013.

  9. Kadar M and Kotanko B (2001). Designing loyalty programs to enhance value growth. Mercer on Transport and Travel 8 (2): 28–33.

  10. Karabuk S (2008). Production planning under uncertainty in textile manufacturing. Journal of the Operational Research Society 59 (4): 510–520.

  11. Kim BD, Shi M and Srinivasan K (2001). Reward programs and tacit collusion. Marketing Science 20 (2): 99–120.

  12. Kim BD, Shi M and Srinivasan K (2004). Managing capacity through reward programs. Management Science 50 (4): 503–520.

  13. Labbi A and Berrospi C (2007). Optimizing marketing planning and budgeting using Markov decision processes: An airline case study. IBM Journal of Research and Development 51 (3/4): 421–431.

  14. Lee RC and Wright WE (1994). Development of human exposure factor distributions using maximum-entropy inference. Journal of Exposure Analysis and Environmental Epidemiology 4 (3): 329–341.

  15. Lewis M (2005). Incorporating strategic consumer behaviour into customer valuation. Journal of Marketing 69 (4): 230–238.

  16. Liu Y (2007). The long-term impact of loyalty programs on consumer purchase behavior and loyalty. Journal of Marketing 71 (4): 19–35.

  17. LoyaltyOne (2013). Air Miles reward program, http://www.loyalty.com/coalition-loyalty/air-miles/, accessed 23 April 2014.

  18. Mak WK, Morton DP and Wood RK (1999). Monte Carlo bounding techniques for determining solution quality in stochastic programs. Operations Research Letters 24 (1): 47–56.

  19. Meyer-Waarden L (2008). The influence of loyalty program membership on customer purchase behavior. European Journal of Marketing 42 (1/2): 87–114.

  20. Mimouni-Chaabane A and Volle P (2010). Perceived benefits of loyalty programs: Scale development and implications for relational strategies. Journal of Business Research 63 (1): 32–37.

  21. Nsakanda AL, Diaby M and Cao Y (2011). An aggregate inventory-based model for predicting redemption and liability in loyalty reward programs industry. Information System Frontiers 13 (5): 707–719.

  22. Oracle (2005). Ensuring customer loyalty: Designing next-generation loyalty programs. Oracle White Paper series, p 41, Oracle Corporation: Redwood Shores, CA, http://www.oracle.com/us/products/applications/siebel/047108.pdf, accessed 15 December 2012.

  23. Santoso T, Ahmed S, Goetschalckx M and Shapiro A (2005). A stochastic programming approach for supply chain network design under uncertainty. European Journal of Operational Research 167 (1): 96–115.

  24. Shapiro A (2003). Monte Carlo sampling methods. In: Ruszczyński A and Shapiro A (eds). Handbooks in Operations Research and Management Science: Stochastic Progarmming. Elsevier Science B.V.: North Holland, pp 353–425.

  25. Shapiro A and Homem-de-Mello T (1998). A simulation based approach to two-stage stochastic programming with recourse. Mathematical Programming 81 (3): 301–325.

  26. Shapiro A and Homem-de-Mello T (2000). On the rate of convergence of optimal solutions of Monte Carlo approximations of stochastic programs. SIAM Journal of Optimization 11 (1): 70–86.

  27. Sodhi MS and Tang CS (2011). Determining supply requirement in the sales-and-operations-planning (S&OP) process under demand uncertainty: A stochastic programming formulation and a spreadsheet implementation. Journal of the Operational Research Society 62 (3): 526–536.

  28. Tsao HY, Pitt L and Campbell C (2010). Analysing consumer segments to budget for loyalty and promotion programmes and maximize market share. Journal of the Operational Research Society 61 (10): 1523–1529.

  29. Zhang ZJ, Krishna A and Dhar SK (2000). The optimal choice of promotional vehicles: Front-loaded or rear-loaded incentives? Management Science 46 (3): 348–362.

  30. Zhu X and Sherali HD (2009). Two-stage workforce planning under demand fluctuations and uncertainty. Journal of the Operational Research Society 60 (1): 94–103.

Download references

Acknowledgements

We are grateful to the Editor and the anonymous referees for their helpful comments, which contributed to the improvements of the earlier versions of this paper.

Author information

Correspondence to Aaron L Nsakanda.

Rights and permissions

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

Cao, Y., Nsakanda, A. & Diaby, M. Planning the supply of rewards with cooperative promotion considerations in coalition loyalty programmes management. J Oper Res Soc 66, 1140–1154 (2015). https://doi.org/10.1057/jors.2014.81

Download citation

Keywords

  • loyalty reward programmes
  • aggregate planning
  • stochastic linear programming
  • optimization
  • supply chain management