Skip to main content
SpringerLink
Log in

Dynamic segmentation of loyalty program behavior

  • Original Article
  • Published:
Journal of Marketing Analytics Aims and scope Submit manuscript

Abstract

Loyalty programs have evolved in recent years to become a key component of customer relationship management. The creation of huge databases from these loyalty programs has created a need for methodologies capable of generating meaningful insights from analysis of the large quantities of longitudinal behavioral data flowing from them. Our research utilizes a group trajectory modeling approach to generate managerially important segments among members of a retail loyalty program based on the dynamics of their behaviors following the launch of the program. We profile the segments and discuss the managerial implications of such findings as segment sizes, spending trajectories, and possible drivers of differences among the groups, evaluating these issues relative to the effectiveness of the loyalty program design.

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

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Figure 1
Figure 2
Figure 3

Similar content being viewed by others

References

  • Allaway, A.W., Gooner, R.M., Berkowitz, D. and Davis, L. (2006) Deriving and exploring behavior segments within a retail loyalty card program. European Journal of Marketing 40 (11/12): 1317–1339.

    Article  Google Scholar 

  • Blattberg, R.C., Kim, B.-D. and Neslin, S.A. (2008) Database Marketing: Analyzing and Managing Customers. New York, NY: Springer.

    Book  Google Scholar 

  • Bryk, A.S. and Raudenbush, S.W. (1992) Hierarchical Linear Models in Social and Behavioral Research: Applications and Data Analysis Methods. Newbury Park, CA: Sage Publications.

    Google Scholar 

  • Collins, L.M. and Flaherty, B.P. (2002) Latent class models for longitudinal data. In: A.L. McCutcheon and J.A. Hagenaars (eds.) Applied Latent Class Analysis. Cambridge, UK: Cambridge University Press, pp. 287–303.

    Chapter  Google Scholar 

  • Creamer, M. (2007) P&G primes its pinpoint marketing. http://adage.com/article/news/p-g-primes-pinpoint-marketing/116545/, accessed 7 May 2007.

  • Davenport, T.H. and Harris, J.G. (2007) Competing on Analytics: The New Science of Winning. Boston, MA: Harvard Business School Publishing Corporation.

    Google Scholar 

  • Dorotic, M., Bijmolt, T. and Verhoef, P.C. (2012) Loyalty programmes: Current knowledge and research directions. International Journal of Management Reviews 14 (3): 217–237.

    Article  Google Scholar 

  • Dowling, G.R. and Midgley, D.F. (1988) Identifying the coarse and fine structures of market segments. Decision Sciences 19 (4): 830–847.

    Article  Google Scholar 

  • Everitt, B.S. and Hand, D.J. (1981) Finite Mixture Distributions. London, UK: Chapman and Hall.

    Book  Google Scholar 

  • Ferguson, R. and Hlavinka, K. (2007) The COLLOQUY loyalty marketing census: Sizing up the US loyalty marketing industry. Journal of Consumer Marketing 24 (5): 313–321.

    Article  Google Scholar 

  • Fox, E.J., Metters, R. and Semple, J. (2003) Every House a Warehouse: An Inventory-Theoretic Model of Retail Shopping Behavior. working paper, Cox School of Business, Southern Methodist University, Dallas, TX, 20 August.

  • Fox, E.J., Montgomery, A.L. and Lodish, L.M. (2004) Consumer shopping and spending across retail formats. The Journal of Business 77 (2): 525–560.

    Google Scholar 

  • Frank, R.E., Massy, W.F. and Wind, Y. (1972) Marketing Segmentation. Englewood Cliffs, NJ: Prentice-Hall.

    Google Scholar 

  • Goldstein, H. (1995) Multilevel Statistical Models. London, UK: Edward Arnold.

    Google Scholar 

  • Haviland, A. and Nagin, D.S. (2005) Causal inference with group-based trajectory models. Psychometrika 70 (3): 557–578.

    Article  Google Scholar 

  • Heckman, J.J. and Singer, B. (1984) Econometric duration analysis. Journal of Econometrics 24 (1/2): 63–132.

    Article  Google Scholar 

  • Henderson, C.M., Beck, J.T. and Palmatier, R.W. (2011) Review of the theoretical underpinnings of loyalty programs. Journal of Consumer Psychology 21 (3/4): 256–276.

    Article  Google Scholar 

  • Jeffreys, H. (1961) Theory of Probability. New York, NY: Oxford University Press.

    Google Scholar 

  • Jones, B.L., Nagin, D.S. and Roeder, K. (2001) A SAS procedure based on mixture models for estimating developmental trajectories. Sociological Methods & Research 29 (3): 374–393.

    Article  Google Scholar 

  • Jones, B.L. and Nagin, D.S. (2007) Advances in group-based trajectory modeling and a SAS procedure for estimating them. Sociological Methods & Research 35 (4): 542–571.

    Article  Google Scholar 

  • Kim, B.-D., Shi, M. and Srinivasan, K. (2001) Reward programs and tacit collusion. Marketing Science 20 (2): 99–120.

    Article  Google Scholar 

  • Kivetz, R. and Simonson, I. (2003) The idiosyncratic fit heuristic: Effort advantage as a determinant of consumer response to loyalty programs. Journal of Marketing Research 40 (4): 454–467.

    Article  Google Scholar 

  • Kopalle, P.K. and Neslin, S.A. (2003) The economic viability of frequency reward programs in a strategic competitive environment. Review of Marketing Science 1 (1): 1–39.

    Article  Google Scholar 

  • Lal, R. and Bell, D.E. (2003) The impact of frequent shopper programs in grocery retailing. Quantitative Marketing and Economics 1 (2): 179–202.

    Article  Google Scholar 

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

    Article  Google Scholar 

  • Mauri, C. (2003) Card loyalty - a new emerging issue in grocery retailing. Journal of Retailing and Consumer Services’ 10 (1): 13–25.

    Article  Google Scholar 

  • Meyer-Waarden, L. (2007) The effects of loyalty programs on customer lifetime duration and share of wallet. Journal of Retailing 83 (2): 223–236.

    Article  Google Scholar 

  • Muthén, B.O. (1989) Latent variable modeling in heterogeneous populations. Psychometrika 54 (4): 557–585.

    Article  Google Scholar 

  • Muthén, B.O. (1997) Latent variable modeling with longitudinal and multilevel data. In: A.E. Raftery (ed.) Sociological Methodology. Boston, MA: Blackwell Publishers, pp. 453–480.

    Google Scholar 

  • Muthén, L.K. and Muthén, B.O. (2004) Mplus User’s Guide. Los Angeles, CA: Muthén & Muthén.

    Google Scholar 

  • Nagin, D.S., Lynam, D., Raudenbush, S.W. and Roeder, K. (1999) Analyzing developmental trajectories: A semi-parametric, group-based approach. Psychological Methods 4 (2): 139–157.

    Article  Google Scholar 

  • Nagin, D.S. and Tremblay, R.E. (1999) Trajectories of boys’ physical aggression, opposition, and hyperactivity on the path to physically violent and non violent juvenile delinquency. Child Development 70 (5): 1181–1196.

    Article  Google Scholar 

  • Nagin, D.S. and Tremblay, R.E. (2001) Analyzing developmental trajectories of distinct but related behaviors: A group-based method. Psychological Methods 6 (1): 18–34.

    Article  Google Scholar 

  • Nagin, D.S. (2005) Group-Based Modeling of Development Over the Life Course. Cambridge, MA: Harvard University Press.

    Book  Google Scholar 

  • Nagin, D.S. and Tremblay, R.E. (2005) Developmental trajectory groups: Fact or a useful statistical fiction? Criminology 43 (4): 873–904.

    Article  Google Scholar 

  • Roehm, M.L., Pullins, E.B. and Roehm, Jr. H.A. (2002) Designing loyalty-building programs for packaged goods brands. Journal of Marketing Research 39 (2): 202–213.

    Article  Google Scholar 

  • Rogers, E.M. (2003) Diffusion of Innovations. New York, NY: Simon and Schuster.

    Google Scholar 

  • Schwarz, G. (1978) Estimating the dimension of a model. The Annals of Statistics 6 (2): 461–464.

    Article  Google Scholar 

  • Sharp, B. and Sharp, A. (1997) Loyalty programs and their impact on repeat-purchase loyalty patterns. International Journal of Research in Marketing 14 (5): 473–486.

    Article  Google Scholar 

  • Steinhoff, L. and Palmatier, R.W. (2013) Understanding the Effectiveness of Loyalty Programs. Working paper, Marketing Science Institute [13-105].

  • Taylor, G.A. and Neslin, S.A. (2005) The current and future sales impact of a retail frequency reward program. Journal of Retailing 81 (4): 293–305.

    Article  Google Scholar 

  • Titterington, D.M., Smith, A.F.M. and Markov, U.E. (1985) Statistical Analysis of Finite Mixture Distributions. New York, NY: John Wiley & Sons.

    Google Scholar 

  • Verhoef, P.C., van Doorn, J. and Dorotic, M. (2007) Customer value management: An overview and research agenda. Marketing: Journal of Research and Management 3 (2): 105–120.

    Google Scholar 

  • Wedel, M. and DeSarbo, W.S. (2002) Market segment derivation and profiling via a finite mixture model framework. Marketing Letters 13 (1): 17–25.

    Article  Google Scholar 

  • Wedel, M. and Kamakura, W.A. (2000) Market Segmentation: Conceptual and Methodological Foundations. Boston, MA: Kluwer Academic Publishers.

    Book  Google Scholar 

  • Willett, J.B. and Sayer, A.G. (1994) Using covariance structure analysis to detect correlates and predictors of individual change over time. Psychological Bulletin 116 (2): 363–381.

    Article  Google Scholar 

  • Yi, Y. and Jeon, H. (2003) Effects of loyalty programs on value perception, program loyalty, and brand loyalty. Journal of the Academy of Marketing Science 31 (3): 229–240.

    Article  Google Scholar 

  • Zeithaml, V., Rust, R.T. and Lemon, K.N. (2001) The customer pyramid: Creating and serving profitable customers. California Management Review 43 (4): 118–142.

    Article  Google Scholar 

  • Ziliani, C. and Bellini, S. (2004) From loyalty cards to micro-marketing strategies: Where is Europe’s retail industry heading? Journal of Targeting, Measurement and Analysis for Marketing 12 (3): 281–289.

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. The University of Alabama, 169 Alston Hall, Box 870225, Tuscaloosa, 35487, Alabama, USA

    Arthur W Allaway

Authors
  1. Arthur W Allaway
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Giles D'Souza
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. David Berkowitz
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Kyoungmi (Kate) Kim
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Arthur W Allaway.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Allaway, A., D'Souza, G., Berkowitz, D. et al. Dynamic segmentation of loyalty program behavior. J Market Anal 2, 18–32 (2014). https://doi.org/10.1057/jma.2014.2

Download citation

  • Received:

  • Revised:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1057/jma.2014.2

Keywords

Search

Navigation