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Behavioral and Attitudinal Customer Loyalty in the Power Sector

An Explorative Investigation of the Effectiveness of Reward Programs

Verhaltensbezogene und einstellungsbezogene Kundenloyalität im Energiemarkt

Eine explorative Untersuchung der Wirkung von Bonusprogrammen

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Abstract

The emergence of smart grids changes the customer-utility relationship. To facilitate the transition towards a sustainable, reliable and economically viable energy system, utilities need to develop smart grid products and services that have strong customer acceptance and enable different customer segments to engage in energy efficiency. Thus, integrating customer feedback on innovative smart grid services early in the innovation process is of crucial importance. Further, energy providers need to increase customer loyalty and invest in relationship marketing in order to survive and be successful in a competitive market environment.

This article presents the findings of a five-month field experiment that investigated the effectiveness of different reward programs in increasing customer loyalty and customer feedback provision in the energy sector. The results demonstrate that reward programs have a positive effect on behavioral (customer feedback provision) and attitudinal (e. g. satisfaction with the energy provider) aspects of customer loyalty. The reward type matters, however. While monetary reward programs are effective in increasing customer feedback provision, only social reward programs can improve attitudinal aspects of customer loyalty. Energy providers should therefore consider tailoring the reward type to meet their program objectives when employing reward programs.

Zusammenfassung

Das Aufkommen von Smart Grids verändert die Beziehung zwischen Endkunden und Energieversorgern. Um den Übergang in ein nachhaltiges, sicheres und wirtschaftlich tragbares Energiesystem zu erleichtern, müssen Energieversorger Smart Grid Produkte und Dienstleistungen entwickeln, welche eine hohe Kundenakzeptanz aufweisen und verschiedene Kundensegmente dazu bringen, sich an Energieeffizienz-Massnahmen zu beteiligen. Kundenfeedback über innovative Smart Grid Dienstleistungen frühzeitig in den Innovationsprozess zu integrieren ist daher von entscheidender Bedeutung. Darüber hinaus müssen Energieversorger die Kundenloyalität erhöhen und in Beziehungsmarketing investieren, um im kompetitiven Marktumfeld zu überleben und erfolgreich zu sein.

Dieser Beitrag präsentiert die Forschungsergebnisse eines fünfmonatigen Feldexperimentes, welches die Wirkung von verschiedenen Bonusprogrammen auf die Steigerung von Kundenloyalität und Erhöhung von Kunden Feedback im Energiemarkt untersuchte. Die Ergebnisse zeigen, dass Bonusprogramme sowohl verhaltensbezogene (Kunden Feedback) als auch einstellungsbezogene Aspekte (z. B. Zufriedenheit mit dem Energieversorger) von Kundenloyalität verbessern. Dabei kommt es jedoch auf die Art der Belohnung an. Während monetäre Bonusprogramme vor allem das Feedback-Verhalten der Kunden positiv beeinflussen, wirken Bonusprogramme mit sozialen Belohnungen besser auf einstellungsbezogene Aspekte von Kundenloyalität. Bei der Einführung von Bonusprogrammen sollten Energieversorger daher die Wirkung unterschiedlicher Belohnungsarten beachten und deren Einsatz mit den unternehmerischen Zielen abstimmen.

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Notes

  1. For example, in Germany the switching rates of household increased from 11 % in 2007 to 39.5 % in 2015 (Statista 2016).

  2. Some consumers may have several providers, e. g. one for each house/apartment they own or rent. However, they do not buy services from different providers for a single house/apartment.

  3. The remaining customers were assigned to 4 other reward programs of interest to the Swiss energy provider. Those reward programs were not of interest for the present study. Information on the 4 other reward programs can be obtained from the author.

  4. Tasks 3 and 8 were online surveys administered with Unipark. Customers needed to provide a unique code (sent in the announcement email of the task) to start the surveys. This code was used to find out which customer had completed the task.

  5. Table 8 (appendix) provides a detailed description of the content of each task and indicates how customers liked the tasks.

  6. The materials, stimuli and procedures for the field study were developed in cooperation with the energy provider to ensure fit with the communication strategy and the company brand. All materials were in German and were translated for the purpose of this paper.

  7. The current task (task 4) was signaled in light red. Tasks which were not completed and closed were signaled in grey (e. g. task 3).

  8. Customers, who wished to participate in the program but had no online access, could contact the university (contact provided on the letter) and request a paper version of the tasks. No customer used this option.

  9. Customers could only know about the different reward programs by reading the general terms and conditions which they had to accept (by setting a check in a box) when signing up for the customer program. To read the general terms and conditions, the customer had to click on a button, which opened another tab in the browser containing them.

  10. A one-way ANOVA with all customers (including those who completed no task) yielded no significant results. However, as this study is not only interested in customer adoption of a program but also in customer loyalty behavior of participants of the customer program, selecting only participants for the analyses can be justified. The scale for average number of completed tasks thus ranged from 1 to 8.

  11. The assumption of homogeneity of variances was violated, Levene (2, 71) = 12.32, p < 0.001. Thus, the Welch F-ratio is reported.

  12. The correlation matrix of all items is provided in the appendix. Items which were assumed to belong to a common factor had a stronger correlation with each other than with items assumed to belong to the other factor.

  13. According to Rucker et al. (2011), an indirect effect may also occur in the absence of a total effect. Thus, the existence of a total effect on the dependent variable is not necessary for investigating indirect effects in a mediation analysis.

  14. The author assumes that the effect will become significant with a larger sample size (see limitations and further research).

  15. Information obtained orally from the partner utility.

  16. Image of the energy provider (3 items) and satisfaction with the energy provider (2 items) provided acceptable reliability with Cronbach alphas between 0.72 and 0.86. Further, all 7 items for measuring attitudinal company loyalty provided acceptable test-retest reliability with significant (p < 0.01) correlation coefficient ranging between 0.38 and 0.61.

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Acknowledgements

I wish to thank Repower Ltd. for enabling and financially supporting this research project. In particular, I thank F. Joos, M. Gerber and L. Mautone for their help and the very good cooperation in designing the customer program. Additionally, I would like to thank G. Collenberg and M. Utiger for their support in setting up the customer program on the customer online portal. Further, I acknowledge funding from the Swiss National Science Foundation for a Doc.Mobility grant, project no. P1SGP1_158812, of which this contribution is a part.

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  1. Institute for Economy and the Environment, University of St. Gallen, Tigerbergstrasse 2, 9000, St. Gallen, Switzerland

    Karoline Gamma

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Correspondence to Karoline Gamma.

Appendix

Appendix

1.1 Tasks of the Customer Program

The author and a master student designed all tasks in close cooperation with the marketing department of Repower. All tasks were administered online on the customer portal.

Table 8 Tasks descriptions and evaluations
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1.2 Factorability of the Items used for Factor Analysis

Table 9 Correlation Matrix of items used for factor analysis
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Gamma, K. Behavioral and Attitudinal Customer Loyalty in the Power Sector. Z Energiewirtsch 40, 211–232 (2016). https://doi.org/10.1007/s12398-016-0186-3

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