A gradual approach for maximising user conversion without compromising experience with high visual intensity website elements

The visual intensity of website elements is commonly used in online marketing to steer user behaviour. The overuse of techniques to attract user attention can ultimately lead to an inferior user experience and even user churn [11]; [13]. However, marketing elements that are insufficiently visible will fail to generate beneficial interactions for the website operator due to the phenomenon of banner blindness, which was first identified by Benway and Lane [1] and later confirmed by other researchers [2]; [16]. With many dependencies affecting user experience, website developers and marketers have looked for ways to analyse the effects of interactivity on web user attitude [15]. Research suggests that more control to web users within interactive content to minimise negative impact should be provided [5]. For effective marketing, it is important to determine the point at which conversion represented by the number of acquired interactions relative to the number of visitors is maximised while influence is minimised to reduce adverse impact [17]. Dedicated elements of interface affecting user behaviour are designed to generate interactions [6], while other strategies assess the interaction of users and product [7]. Such is typically achieved by using interactive objects such as banner ads, pop-ups, landing pages, recommending interfaces or other active elements within websites that use call-to-action messages. Currently, efforts to find a sweet spot between the optimal levels of visual intensity have fallen short, with most studies based on breaking cognitive processes [17] and static approaches with repeated exposures of the same object [11], aggregated measures for positive and negative results with attempts to use fuzzy modelling of the levels of influence and sustainable marketing strategies [9].

Research questions arise regarding how to find the optimal level of visual intensity of web elements to attract user attention without negatively affecting user experience. What is the best way to keep visual intensity at the lowest possible level while acquiring acceptable results?

The motivation of the present study is to propose an approach for detecting the optimal level of visual intensity where interactions can be elicited without unnecessarily invading users with content based on higher intensity. Therefore, a gradual approach for adjusting the visual intensity of web interfaces towards maximised conversions and lower negative influence on user experience was developed. The main goal is searching dynamically for a balance between conversion within an interface and negative effects represented by the decline in user experience. Visual intensity and effects of compelling stimuli are explored to influence target users’ arousal state. Elements with high intensity can negatively affect web users’ experience, and low-intensity elements do not affect arousal state, which leads to lower influence on user behaviour. While it is difficult to adjust the intensity to optimal level a priori, the goal of the proposed gradual adjustment is searching for appropriate levels for each user or audience. The problem was investigated from two perspectives: first, by increasing the level of visual intensity of interactive content and second, by decreasing it. This gradual approach makes it possible to search for the saturation point beyond which increased intensity fails to increase the number of interactions.

This approach extends earlier studies by assigning visual intensity levels to elements to scale influence and monitor positive and negative feedback. Previous studies explored the problem of overcoming low click-throughs by using constant and varying banners featuring call-to-action messages with different texts and flashing elements [4]. Differences were not scaled between exposures, and only clicks were monitored, without taking into account negative effects. Other research based on sequential exposition of animations [3] reported a wear-out effect for repeated static and animated content without content changes between repetitions [10]. Previous studies have also revealed that during repeated exposures, users may ignore advertising content due to a learned enduring disposition [14] or repetition blindness [12].

In contrast to previous studies that have altered the type of stimulus to overcome the habituation effect [8], the present study explores how changes in visual intensity affect the habituation effect and identifies the saturation point. The advantage of the present approach it the possibility to observe the growth of response and find the best-converting version to impact business goals and user experience positively. Experiments were conducted on real audiences, not in laboratory setting like in most of earlier studies.

An experiment within a real-online environment where user behaviour was measured to investigate optimal levels of visual intensity of used elements was conducted. The approach was verified by using an experimental object with different levels of visual intensity attracting user attention and motivating users to perform interactions in the form of clicks. Elements used were different colours with low- and high-contrast background, low- and high-intensity flashing effect, and verbal call-to-action texts with scaled influence. The intensity of visual elements was scaled from low levels with neutral colours, through medium levels using more contrasting colours, towards highest intensity with flashing effects.

While experimental verification was based on selected characteristics of visual elements, the proposed approach could be used with scaled sizes, sound effects with varying intensities and other techniques to attract user attention with the use of visual elements.

The paper is structured as follows. Section 2 provides a literature review in the area of influence on web user behaviour. Section 3 presents the approach for incrementally adjusting the level of visual influence with the empirical study in Section 4. Results of experiments are presented in Section 5 and they are discussed in Section 6. Section 7 outlines conclusions and provides a summary of the presented research.

A gradual approach for maximising user conversion without compromising experience with high visual intensity website elements

Jarosław Jankowski
Juho Hamari
Jarosław Wątróbski

Citation: Jankowski, J., Hamari, J., & Wątróbski, J. (2019). A gradual approach for maximising user conversion without compromising experience with high visual intensity website elements. Internet Research, 29(1), 194-217. DOI: https://doi.org/10.1108/IntR-09-2016-0271

Please see the paper for full details: 


Purpose – The purpose of this paper is to develop and test a method that can gradually find a sweet spot between user experience and visual intensity of website elements to maximise user conversion with minimal adverse effect.
Design/methodology/approach – In the first phase of the study, the authors develop the method. In the second stage, the authors test and evaluate the method via an empirical study; also, an experiment was conducted within web interface with the gradual intensity of visual elements.
Findings – The findings reveal that the negative response grows faster than conversion when the visual intensity of the web interface is increased. However, a saturation point, where there is coexistence between maximum conversion and minimum negative response, can be found.
Practical implications – The findings imply that efforts to attract user attention should be pursued with increased caution and that a gradual approach presented in this study helps in finding a site-specific sweet spot for a level of visual intensity by incrementally adjusting the elements of the interface and tracking the changes in user behaviour.
Originality/value – Web marketing and advertising professionals often face the dilemma of determining the optimal level of visual intensity of interface element. Excessive use of marketing component and attention-grabbing visual elements can lead to an inferior user experience and consequent user churn due to growing intrusiveness. At the same time, too little visual intensity can fail to steer users. The present study provides a gradual approach which aids in finding a balance between user experience and visual intensity, maximising user conversion and, thus, providing a practical solution for the problem.


[1] Benway, J.P. and Lane, D.M. (1998), “Banner blindness: web searchers often miss obvious links”, ITG Newsletter, Vol. 1 No. 3, pp. 1-22, available at: www.internettg.org/newsletter/dec98/banner_ blindness.html (accessed 20 May 2017).
[2] Burke, M., Hornof, A., Nilsen, E. and Gorman, N. (2005), “High-cost banner blindness: ads increase perceived workload, hinder visual search, and are forgotten”, ACM Transactions on Computer- Human Interaction, Vol. 12 No. 4, pp. 423-445.
[3] Baccot, D., Choudary, O., Grigoras, R. and Charvillat, V. (2009), “On the impact of sequence and time in rich media advertising”, Proceedings of the 17th ACM International Conference on Multimedia in Beijing, China, October, New York, NY, pp. 849-852.
[4] Chatterjee, P. (2005), “Changing Banner ad executions on the web: impact on clickthroughs and communication outcomes”, in Menon, G. and Rao, A. (Eds), Proceedings of Advances in Consumer Research, ACR 2004, Association for Consumer Research, Duluth, MN, pp. 51-57.
[5] Chatterjee, P. (2008), “Are unclicked ads wasted? Enduring effects of banner and pop-up ad exposures on brand memory and attitudes”, Journal of Electronic Commerce Research, Vol. 9 No. 1, pp. 51-61.
[6] Haubl, G. and Murray, K.B. (2001), “Recommending or persuading? The impact of a shopping agent’s algorithm on user behaviour”, Proceedings of the 3rd ACM Conference on Electronic Commerce in Tampa, New York, NY, pp. 163-170.
[7] Harbich, S. and Hassenzahl, M. (2011), “Using behavioral patterns to assess the interaction of users and product”, International Journal of Human-Computer Studies, Vol. 69 Nos 7-8, pp. 496-508.
[8] Hsieh, Y.C., Chen, K.H. and Ma, M.Y. (2012), “Retain viewer’s attention on banner ad by manipulating information type of the content”, Computers in Human Behavior, Vol. 28 No. 5, pp. 1692-1699.
[9] Jankowski, J., Kazienko, P., Wątróbski, J., Lewandowska, A., Ziemba, P. and Zioło, M. (2016), “Fuzzy multi-objective modeling of effectiveness and user experience in online advertising”, Expert Systems with Applications, Vol. 65, pp. 315-331, available at: www.sciencedirect.com/science/ article/pii/S095741741630447X
[10] Lee, J., Ahn, J.H. and Park, B. (2015), “The effect of repetition in internet banner ads and the moderating role of animation”, Computers in Human Behavior, Vol. 46, pp. 202-209, available at: www. sciencedirect.com/science/article/pii/S0747563215000205
[11] Moe, W.W. (2006), “A field experiment to assess the interruption effect of pop-up promotions”, Journal of Interactive Marketing, Vol. 20 No. 1, pp. 34-44.
[12] Mancero, G., Wong, W. and Amaldi-Trillo, P. (2007), Looking But Not Seeing: Does Perceptual Depth Reduce Change Blindness? (No. IDC-TR-2007-3-001), Middlesex University, London.
[13] Nielsen, J.H. and Huber, J. (2009), “The effect of brand awareness on intrusive advertising”, iSociety for Consumer Psychology Conference, February, San Diego, CA, available at: www3.nd.edu/~markdept/020812/sss/EffectofBrandAwarenessonIntrusiveAdvertising091009.pdf (accessed 20 May 2017).
[14] Sun, Y., Lim, K.H., Peng, J.Z., Jiang, C. and Chen, X. (2008), “Why and when will banner blindness occur? An analysis based on the dual processing theory”, Proceedings of Americas Conference on Information Systems, AIS Electronic Library, p. 259.
[15] Teo, H.H., Oh, L.B., Liu, C. and Wei, K.K. (2003), “An empirical study of the effects of interactivity on web user attitude”, International Journal of Human-Computer Studies, Vol. 58 No. 3, pp. 281-305.
[16] Wong, C.Y. (2001), “Is banner ads totally blind for us?”, Extended Abstracts on Human Factors in Computing Systems, in Seattle, New York, NY, pp. 389-390.
[17] Zha, W. and Wu, H.D. (2014), “The impact of online disruptive ads on users’ comprehension, evaluation of site credibility, and sentiment of intrusiveness”, American Communication Journal, Vol. 16 No. 2, pp. 15-28.

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