Skip to main content
SpringerLink
Log in

Making Digital Freemium Business Models a Success: Predicting Customers’ Lifetime Value via Initial Purchase Information

  • Research Paper
  • Published:
Business & Information Systems Engineering Aims and scope Submit manuscript

Abstract

In digital freemium business models such as those of online games or social apps, a large share of overall revenue derives from a small portion of the user base. Companies operating in these and similar businesses are increasingly constructing forecasting models with which to identify potential heavy users as early as possible and create special retention measures to suit those users’ needs. In our study, we observe three digital freemium companies that sell virtual credits and investigate to what extent initial purchase information can be used to determine a given customer’s lifetime value. We find that customers represent higher future lifetime values if they (a) make a purchase early after registration, (b) spend a significant amount on their initial purchase, and (c) use credit cards to purchase credits. In addition, we see that users tend to spend increasing amounts on subsequent purchases.

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

Fig. 1
Fig. 2

Similar content being viewed by others

References

  • Aarts H, Verplanken B, Knippenberg A (1998) Predicting behavior from actions in the past: repeated decision making or a matter of habit? J Appl Soc Psychol 28(15):1355–1374

    Article  Google Scholar 

  • Alves J, Lange S, Riedmiller M (2014) Case study: behavioral prediction of future revenues in freemium games. In: NC2 Workshop, 36th Ger Conf Pattern Recognit (GCPR)

  • Amit R, Zott C (2001) Value creation in e-business. Strat Manag J 22(6–7):493–520

    Article  Google Scholar 

  • AppAnnie (2015) iOS top app charts, 4 January 2015. http://www.appannie.com/apps/ios/top/?device=iphone. Accessed 05 Jan 2015

  • Bapna R, Umyarov A (2012) Do your online friends make you pay? A randomized field experiment in an online music social network. http://www.heinz.cmu.edu/download.aspx?id=3660. Accessed 25 Mar 2015

  • Berger PD, Nasr NI (1998) Customer lifetime value: marketing models and applications. J Interact Mark 12(1):17–30

    Article  Google Scholar 

  • Blattberg RC, Malthouse EC, Neslin SA (2009) Customer lifetime value: empirical generalizations and some conceptual questions. J Interact Mark 23(2):157–168

    Article  Google Scholar 

  • Borle S, Singh SS, Jain DC (2008) Customer lifetime value measurement. Manag Sci 54(1):100–112

    Article  Google Scholar 

  • Bostan B (2009) Player motivations: a psychological perspective. CIE 7(2):1–26

    Article  Google Scholar 

  • Chan SL, Ip WH (2011) A dynamic decision support system to predict the value of customer for new product development. Decis Support Syst 52(1):178–188

    Article  Google Scholar 

  • Choi D, Kim J (2004) Why people continue to play online games: in search of critical design factors to increase customer loyalty to online contents. CyberPsychol Behav 7(1):11–24

    Article  Google Scholar 

  • Cleophas C, Ehmke JF (2014) When are deliveries profitable? Bus Inf Syst Eng 6(3):153–163

    Article  Google Scholar 

  • Dwyer RF (1997) Customer lifetime valuation to support marketing decision making. J Interact Mark 11(4):6–13

    Google Scholar 

  • Fader PS, Hardie BG (2007) How to project customer retention. J Interact Mark 21(1):76–90

    Article  Google Scholar 

  • Fader PS, Hardie BG, Lee KL (2005) RFM and CLV: using iso-value curves for customer base analysis. J Market Res 42(4):415–430

    Article  Google Scholar 

  • Farrell J, Saloner G (1985) Standardization, compatibility, and innovation. RAND J Econ 16(1):70–83

    Article  Google Scholar 

  • Feinberg RA (1986) Credit cards as spending facilitating stimuli: a conditioning interpretation. J Consum Res 13(3):348–356

    Article  Google Scholar 

  • Fickel N (2001) Sequenzialregression: Eine neodeskriptive Lösung des Multikollinearitäts-problems mittels stufenweise bereinigter und synchronisierter Variablen. VWF, Berlin

    Google Scholar 

  • Freedman DA (2009) Statistical models: theory and practice. Cambridge University Press, New York

    Book  Google Scholar 

  • Gerlach M, Cleophas C, Kliewer N (2013) Airline codeshare alliances. Bus Inf Syst Eng 5(3):153–163

    Article  Google Scholar 

  • Glady N, Baesens B, Croux C (2009) A modified Pareto/NBD approach for predicting customer lifetime value. Expert Syst Appl 36(2):2062–2071

    Article  Google Scholar 

  • Gneiser MS (2010) Value-based CRM – the interaction of the triad of marketing, financial management, and IT. Bus Inf Syst Eng 2(2):95–103

    Article  Google Scholar 

  • Goldsmith RE, Newell SJ (1997) Innovativeness and price sensitivity: managerial, theoretical and methodological issues. J Prod Brand Manag 6(3):163–174

    Article  Google Scholar 

  • Goldstein DG, Gigerenzer G (2009) Fast and frugal forecasting. Int J Forecast 25(4):760–772

    Article  Google Scholar 

  • Guo Y, Barnes S (2009) Virtual item purchase behavior in virtual worlds: an exploratory investigation. Electron Commer Res 9(1–2):77–96

    Article  Google Scholar 

  • Gupta S, Hanssens D, Hardie B, Kahn W, Kumar V, Lin N, Ravishanker N, Sriram S (2006) Modeling customer lifetime value. J Serv Res 9(2):139–155

    Article  Google Scholar 

  • Hinz O, Skiera B, Barrot C, Becker JU (2011) Seeding strategies for viral marketing: an empirical comparison. J Market 75(6):55–71

    Article  Google Scholar 

  • Hochman J (2015) The cost of pay-per-click (PPC) advertising – trends and analysis. https://www.hochmanconsultants.com/articles/je-hochman-benchmark.shtml. Accessed 25 Mar 2015

  • Huber PJ (1967) The behavior of maximum likelihood estimates under nonstandard conditions. In: 5th Berkeley Symposium Mathematical Statistics Probability, Berkeley, pp 221–233

  • Jain D, Singh SS (2002) Customer lifetime value research in marketing: a review and future directions. J Interact Mark 16(2):34–45

    Article  Google Scholar 

  • Kim N, Mahajan V, Srivastava RK (1995) Determining the going market value of a business in an emerging information technology industry: the case of the cellular communications industry. Technol Forecast Soc 49(3):257–279

    Article  Google Scholar 

  • Kim SY, Jung TS, Suh EH, Hwang HS (2006) Customer segmentation and strategy development based on customer lifetime value: a case study. Expert Syst Appl 31(1):101–107

    Article  Google Scholar 

  • Latham GP, Locke EA (1991) Self-regulation through goal setting. Organ Behav Hum 50(2):212–247

    Article  Google Scholar 

  • Lehdonvirta V (2009) Virtual item sales as a revenue model: identifying attributes that drive purchase decisions. Electron Commer Res 9(1–2):97–113

    Article  Google Scholar 

  • Lehmann S, Buxmann P (2009) Pricing strategies of software vendors. Bus Inf Syst Eng 1(6):452–462

    Article  Google Scholar 

  • Lessmann S, Voß S (2010) Customer-centric decision support. Bus Inf Syst Eng 2(2):79–93

    Article  Google Scholar 

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

    Article  Google Scholar 

  • Malthouse EC, Blattberg RC (2005) Can we predict customer lifetime value? J Interact Mark 19(1):2–16

    Article  Google Scholar 

  • Najar V, Rajan MV (2005) Measuring customer relationships: the case of the retail banking industry. Manag Sci 51(6):904–919

    Article  Google Scholar 

  • Ngai EWT, Xiu L, Chau DCK (2009) Application of data mining techniques in customer relationship management: a literature review and classification. Expert Syst Appl 36(2):2592–2602

    Article  Google Scholar 

  • Prelec D, Simester D (2001) Always leave home without it: a further investigation of the credit-card effect on willingness to pay. Mark Lett 12(1):5–12

    Article  Google Scholar 

  • Pretorius K (2013) Google adwords CPC inflation. http://www.mediavisioninteracti-ve.com/blog/paid-search/google-adwords-cpc-inflation/. Accessed 25 Mar 2015

  • Reichheld FF (2001) The loyalty effect: the hidden force behind growth, profits, and lasting value. Harvard Business Press, Boston

    Google Scholar 

  • Reinartz WJ, Kumar V (2000) On the profitability of long-life customers in a noncontractual setting: an empirical investigation and implications for marketing. J Mark 64(4):17–35

    Article  Google Scholar 

  • Reinartz WJ, Kumar V (2003) The impact of customer relationship characteristics on profitable lifetime duration. J Mark 67(1):77–99

    Article  Google Scholar 

  • Rosset S, Neumann E, Eick U, Vatnik N (2003) Customer lifetime models for decision support. Data Min Knowl Disc 7(3):321–339

    Article  Google Scholar 

  • Rust RT, Verhoef PC (2005) Optimizing the marketing interventions mix in intermediate-term CRM. Mark Sci 24(3):477–489

    Article  Google Scholar 

  • Rust RT, Moorman C, Bhalla G (2010) Rethinking marketing. Harv Bus Rev 88(1/2):94–101

    Google Scholar 

  • Schmittlein DC, Peterson RA (1994) Customer base analysis: an industrial purchase process application. Mark Sci 13(1):41–67

    Article  Google Scholar 

  • Schmittlein DC, Morrison DG, Colombo R (1987) Counting your customers: who are they and what will they do next? Manag Sci 33(1):1–24

    Article  Google Scholar 

  • Shaw MJ, Subramaniam C, Tan GW, Welge ME (2001) Knowledge management and data mining for marketing. Decis Support Syst 31(1):131–141

    Article  Google Scholar 

  • Soman D (2001) Effects of payment mechanism on spending behavior: the role of rehearsal and immediacy of payments. J Consum Res 27(4):460–474

    Article  Google Scholar 

  • STATA (2015) Nbreg – negative binomial regression. www.stata.com/manuals13/rnb-reg.pdf. Accessed 25 Mar 2015

  • Takac C, Hinz O, Spann M (2011) The social embeddedness of decision making: opportunities and challenges. Elec Mark 21(3):185–195

    Article  Google Scholar 

  • Taylor JW (1977) A striking characteristic of innovators. J Mark Res 14(1):104–107

    Article  Google Scholar 

  • Teece DJ (2010) Business models, business strategy and innovation. Long Range Plan 43(2):172–194

    Article  Google Scholar 

  • UCLA (2015) Stata data analysis examples: negative binomial regression. http://www.ats.ucla.edu/stat/stata/dae/nbreg.htm. Accessed 25 Mar 2015

  • Veit DE, Clemons E, Benlian A, Buxmann P, Hess T, Spann M, Kundisch D, Leimeister JM, Loos P (2014) Business models – an information systems research agenda. Bus Inf Syst Eng 6(1):45–53

    Article  Google Scholar 

  • Venkatesan R, Kumar V (2004) A customer lifetime value framework for customer selection and resource allocation strategy. J Mark 68(4):106–125

    Article  Google Scholar 

  • Wagner TM, Benlian A, Hess T (2014) Converting freemium customers from free to premium – the role of the perceived premium fit in the case of music as a service. Elec Mark 24(4):259–268

    Article  Google Scholar 

  • White H (1980) A heteroskedasticity-consistent covariance matrix estimator and a direct test for heteroskedasticity. ECTA 48(4):817–838

    Google Scholar 

  • Wübben M, von Wangenheim F (2008) Instant customer base analysis: managerial heuristics often “get it right”. J Mark 72(3):82–93

    Article  Google Scholar 

  • Zhang JQ, Dixit A, Friedmann R (2010) Customer loyalty and lifetime value: an empirical investigation of consumer packaged goods. J Mark Theory Pract 18(2):127–140

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Chair of Information Systems, Electronic Markets, Technische Universität Darmstadt, Hochschulstraße 1, 64289, Darmstadt, Germany

    Sebastian Voigt &  Oliver Hinz

Authors
  1. Sebastian Voigt
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Oliver Hinz
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Sebastian Voigt.

Additional information

Accepted after two revisions by Prof. Dr. Bichler.

Electronic supplementary material

Below is the link to the electronic supplementary material.

Supplementary material 1 (PDF 187 kb)

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Voigt, S., Hinz, O. Making Digital Freemium Business Models a Success: Predicting Customers’ Lifetime Value via Initial Purchase Information. Bus Inf Syst Eng 58, 107–118 (2016). https://doi.org/10.1007/s12599-015-0395-z

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s12599-015-0395-z

Keywords

Search

Navigation