Daily, everybody is confronted with 10.000 – 13.000 advertisement messages, which deludes the effectiveness of classical marketing. The measures lose in attracting the consumers attention (Weimann/ Schneider/ Brocke 2016; Meske/ Kroll 2017; Jung et al. 2018). Therefore, we have investigated the phenomenon of digital nudging as a new approach for marketers to encourage online customers to buy company preferred decisions without being noticed (Thaler/ Sunstein 2010; Mandel/ Johnson 2015; Meske/ Potthoff 2017; Jung et al. 2018; Tandler 2019). To do that, we instrumented an online experiment with 160 participants to gain more context information about digital nudging and its effects on the customers decision-making behavior and their experience along the customer journey.
Digital Nudging is highly varied
Digital Nudging describes the influence of the decision environment on digital user interfaces through the targeted use of design elements (Mirsch/ Lehrer/ Jung 2017; Weinmann/ Schneider/ vom Brocke 2016). Digital nudging can be realized in form of various methods. The following graph presents definitions and the psychological effects of the three most popular methods.
Graph 1: Digital nudging methods
Even if digital nudging is a quit famous concept in online marketing, researches about the approach are limited. For this reason, it was the aim to gain more context information about digital nudging and its effects on the customers decision-making behavior and their experience along the customer journey by instrumenting an online experiment.
How we obtained the data
The experiment was realized on the website of a German logistics group with 160 participants, which were subdivided into three nudged treatment groups and one control group which hasn’t been nudged. To test all the three digital nudging methods in the pre-purchase, purchase and post-purchase phase, 16 web pages were created in total. Every phase had a different focus and decision risk. In the pre-purchase phase, the participants had to choose a business division of the company, in the purchase phase they had to choose a service offer and in the post-purchase phase they had to choose a long-term service package. At the end, the participants rated their choice intention for every purchase alternative and the overall customer experience on a seven-point scale from “not at all likely” to “very likely”.
Our results are ground-breaking for digital nudging research
Result 1: The effects of digital nudging rely on the purchase-phases
The first research was about the effect differences of digital nudging in the purchase phases.
The graph shows that with digital nudging there was an increased choice intention for the company’s favored option in every purchase phase. The biggest increase was in the pre-purchase phase with 24% from 3.67 to 4.55, followed by the purchase phase with 16% and the post-purchase phase with 7%. A reason for the result could be that the involvement of the human being is comparably the lowest in the pre-purchase phase and that digital nudging is more effective with a low involvement, because of the inattention of the observer.
Graph 2: Effect differences of digital nudging in the purchase phases
Graph 3: Digital nudging’s effectiveness along the customer journey
Result 2: The digital nudging methods affect the purchase behavior in different ways
In the second research question, the focus was on the effect differences of the digital nudging methods. The results show that there is a difference of the methods effectiveness between the purchase phases. For example, in the pre-purchase phase the default effect has the lowest effectiveness with 18%, compared to 26% for the social norms and 28% for the decoy effect. But in the purchase and the post-purchase phase, the default effect is the most effective method with an 21% and 13% increase. A reason for the result could be that all methods with their different design elements apply to different psychological principles which leads to this high effect diversity.
Graph 4: Effect differences of the digital nudging methods in the purchase phases
Result 3: The Customer Experience is also influenced positively by digital nudging
The third research focuses on the influence of digital nudging on the customer experience. The customer experience was measured by the positivity of impressions of the website visitors, which was increased by 9% from 5.13 to 5.61. The result is so interesting because it shows for the first time, that digital nudging can also influence the participants attitude while using a digital touchpoint. A possible reason is that digital nudging leads to various positive effects that influence the customer experience, like enhancing the price and quality level of a company.
Graph 5: Effect of digital nudging on the customer experience
Result 4: The observer’s age decides how effective digital nudging is
A final analysis pointed out that the participants age influences the effectiveness of digital nudging. Participants within the age interval 30-39 were influenced the most with an increased choice intention about 41%. Participants in the age interval 20-29 were influenced moderately with 8%, while the age interval 40-79 shows nearly no influence. An explanatory approach is that older people consume content more attentively which would reduce the effectiveness of digital nudging.
Graph 6: Moderating effects of the participants age on the digital nudging’s effectiveness
Our vision for digital nudging
The study presents digital Nudging as an exciting approach to guide the decision behavior of customers on a digital touchpoint and moreover to improve their customer experience. An unknown complexity of the digital nudges was shown, where the effectiveness depends on a high variety of underlying conditions like the purchase phase, the used method and the visitor’s characteristics. Hence, there is a need for further practical experiences and researches to increase the effectiveness of digital nudging by a stronger person and context orientation. Within the experiment, only the company website was analyzed as a digital touchpoint – but there are many more touchpoints along the customer journey. Therefore, the aim of the research is to get the ball rolling and to inspire practitioners to test further touchpoints to make digital-customer-journeys more attractive in the future.
References
Mirsch, T., Lehrer, C., Jung, R. (2017), “Digital Nudging: Altering User Behavior in Digital Environments”, Proceedings of the 13th business informatics conference, 634-648.
Weimann, M., Schneider, C., vom Brocke, J. (2016), “Digital Nudging”, Business & Information Systems Engineering, 58 (6), 433-436.
Meske, C., Potthoff, T. (2017), “The DINU-Model – A Process Model for the Design of Nudges”, 25. European Conference on Information Systems, 1-11.
Jung, R., Mirsch, T., Rieder, A., Lehrer, C. (2018), “Mit Digital Nudging Nutzererlebnisse verbessern und den Unternehmenserfolg steigern”, Controlling – Zeitschrift für erfolgsorientierte Unternehmenssteuerung, 30 (5), 12-18.
Thaler, R., Sunstein, C. (2010), “Nudge: Wie man kluge Entscheidungen anstößt”, Vol. 14. Berlin: Ullstein.
Mandel, N., Johnson, E. (2002), “When Web Pages Influence Choice: Effects of Visual Primes on Experts and Novices”, Journal of Consumer Research, 29 (2), 235-245.
Tandler, M. (2019), Nudging (accessed September 25, 2019), https://de.ryte.com/
wiki/Nudging.
Huber, J., Payne, J., Puto, C. (1982), Adding asymmetrically dominated alternatives: Violations of regularity and the similarity hypothesis, Journal of Consumer Research, 9 (1), 90-98.
Tversky, A., Kahneman, D. (1991), “Loss aversion in riskless choice: A Reference-Dependent Model”, Quarterly Journal of Economics, 106 (4), 1039-1061.
Slaughter, J., Sinar, E., Highhouse, S. (1999), “Decoy effects and attribute-level inferences”, Journal of Applied Psychology, 84 (5), 823-828.
Pettibone, J., Wedell, D. (2007), “Testing alternative explanations of phantom decoy effects”, Journal of Behavioral Decision Makings, 341, 323-341.
Oehlrich, M. (2019), Wissenschaftliches Arbeiten und Schreiben, 2. Vol. Wiesbaden: Springer.
Thomas, A. (1992), Grundriss der Sozialpsychologie, Vol. 2. Göttingen: Hogrefe.
Sunstein, C. (1996), “Social Norms and Social Roles”, Columbia Law Review, 96 (4), 903-968.
Cialdini, R., Trost, M. (1998), “Social influence: Social norms, conformity and compliance”, In Gilbert, D., Fiske S., Lindzey, G. (Ed.), The Handbook of Social Psychology, Vol. 4. New York City: McGraw-Hill.
Kulbe, A. (2009), Grundwissen Psychologie, Soziologie und Pädagogik, Vol. 2. Stuttgart: Kohlhammer.
Chang, E., Milkman, K., Chugh, D., Akinola, M. (2019), “Diversity Thresholds: How Social Norms, Visibility, and Scrutiny relate to Group Composition”, Academy of Management Journal, 62 (1), 144-171.
Ritov, I., Baron, J. (1992), “Status Quo and Omission Biases”, Journal of Risk and Uncertainty, 5 (1), 29-61.
McKenzie, C., Liersch, M., Finkelstein, S. (2006), “Recommendations implicit in policy defaults”, Psychological Science, 17 (5), 414-420.
Thaler, R., Sunstein, C. (2010), “Nudge: Wie man kluge Entscheidungen anstößt”, Vol. 14. Berlin: Ullstein.