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Machine learning as an effective paradigm for persuasive message design

Abstract

The impact of the development of the internet and new communications channels on the marketing industry pushed practitioners to devise new tools and approaches for influencing consumer attitudes and behaviors towards products and services. This has led to new insights into persuasive message design. In general, persuasive advertisement messaging can be viewed as a combination of context, i.e., a message, and additional affiliated components such as images, video, and special graphics. It is composed out of two main attribute categorizations: (1) textual content such as a product description or affiliated message and (2) sensible content such as product image, color, or even scent. In a competitive market in which consumers are constantly exposed to a hyper-abundance of products that also contain sensible attributes, it is crucial to design persuasive messages that will maximally appeal to desired consumers and evoke their positive response. Yet only a few studies have focused on the effective design of persuasive advertisement messages characterized by two integrated elements. As such, this research focuses on effective persuasive message design with integrated product scent and color attributes. We demonstrate how a machine learning process can be utilized to generate optimal persuasive messages by estimating the contribution of each message attribute to the final class attribute: the purchase intention response. Our results show that several prediction algorithms can enhance consumer response value. In addition, correlations between several attributes affiliated with the message can be derived by graph theory-based estimation. This research thus provides insight into attribute values important for management decisions, with implications for effective persuasive message design. Ultimately, this may lead to higher response rates for marketing practitioners in an increasingly competitive market.

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References

  • Alriksson, S., Öberg, T.: Conjoint analysis for environmental evaluation. Environ. Sci. Pollut. Res. 15(3), 244–257 (2008)

    Article  Google Scholar 

  • Bakshy, E., Hofman, J.M., Mason, W.A., Watts, D.J.: Everyone’s an influencer: quantifying influence on Twitter. In: Proceedings of the 4th ACM International Conference on Web Search and Data Mining, pp. 65–74 (2011)

  • Biehal, G., Stephens, D., Curio, E.: Attitude toward the ad and brand choice. J. Advert. 21(3), 19–36 (1992)

    Article  Google Scholar 

  • Belch, G.E., Belch, M.A.: Advertising and Promotion: An Integrated Marketing Communications Perspective, 11th edn. McGraw-Hill, New York (2016)

    Google Scholar 

  • Beltagui, A., Candi, M., Riedel, J.C.: Setting the stage for service experience: design strategies for functional services. Journal of Service Management 27(5), 751–772 (2016)

    Article  Google Scholar 

  • Biswas, D., Labrecque, L.I., Lehmann, D.R., Markos, E.: Making choices while smelling, tasting, and listening: the role of sensory (dis) similarity when sequentially sampling products. J. Mark. 78(1), 112–126 (2014)

    Article  Google Scholar 

  • Bloch, P.H.: Product design and marketing: reflections after fifteen years. Prod. Innov. Manag. 28(3), 378–380 (2011)

    Article  Google Scholar 

  • Bond, R.M., Fariss, C.J., Jones, J.J., Kramer, A.D., Marlow, C., Settle, J.E., Fowler, J.H.: A 61-million-person experiment in social influence and political mobilization. Nature 489(7415), 295–298 (2012)

    Article  Google Scholar 

  • Bradford, K.D., Desrochers, D.M.: The use of scents to influence consumers: the sense of using scents to make cents. J. Bus. Ethics 90(2), 141–153 (2009)

    Article  Google Scholar 

  • Brown, S.L., Eisenhardt, K.M.L.: Product development: past research, present findings, and future directions. Acad. Manag. Rev. 20(2), 343–378 (1995)

    Article  Google Scholar 

  • Chang, C., Yen, C.: Missing ingredients in metaphor advertising: the right formula of metaphor type, product type, and need for cognition. J. Advert. 42(1), 80–94 (2013)

    Article  Google Scholar 

  • Cherven, K.: Mastering Gephi Network Visualization. Packt Publishing Ltd., Birmingham (2015)

    Google Scholar 

  • Chowdhury, R.M., Olsen, G.D., Pracejus, J.W.: Affective responses to images in print advertising: affect integration in a simultaneous presentation context. J. Advert. 37(3), 7–18 (2008)

    Article  Google Scholar 

  • Delbaere, M., McQuarrie, E.F., Phillips, B.J.: Personification in advertising. J. Advert. 40(1), 121–130 (2011)

    Article  Google Scholar 

  • Destino, G., de Abreu, G.T.F.: Network boundary recognition via graph-theory. In: 2008 5th Workshop on Positioning, Navigation and Communication, ‏pp. 271–275. IEEE (2008)

  • Dostál, P., Lin, C.Y.: Business applications of fuzzy logic. In: The Oxford Handbook of Computational Economics and Finance,1st edn, pp. 360–396. Oxford University Press (2018)

  • Elbert, S.P., Dijkstra, A., Rozema, A.D.: Effects of tailoring ingredients in auditory persuasive health messages on fruit and vegetable intake. Psychol. Health 32(7), 781–797 (2017)

    Article  Google Scholar 

  • Ellen, P.S., Bone, P.F.: Does it matter if it smells? Olfactory stimuli as advertising executional cues. J. Advert. 27(4), 29–39 (1998)

    Article  Google Scholar 

  • Font, X., Elgammal, I., Lamond, I.: Greenhushing: the deliberate under communicating of sustainability practices by tourism businesses. J. Sustain. Tour. 25(7), 1007–1023 (2017)

    Article  Google Scholar 

  • Fornito, A., Zalesky, A., Bullmore, E.: Fundamentals of Brain Network Analysis. Academic Press, San Diego (2016)

    Google Scholar 

  • Funke, S.: Topological hole detection in wireless sensor networks and its applications. In: Proceedings of the 2005 Joint Workshop on Foundations of Mobile Computing, pp. 44–53. ACM (2005)

  • Grömping, U.: Variable importance assessment in regression: linear regression versus random forest. Am. Stat. 63(4), 308–319 (2009)

    Article  Google Scholar 

  • Gvili, Y., Levy, S., Zwilling, M.: The sweet smell of advertising: the essence of matching scents with other ad cues. Int. J. Advert. 37(4), 568–590 (2018)

    Article  Google Scholar 

  • Hall, E.: What's that smell in the movie theater? it's an ad. Advert. AdAge Publishing. https://adage.com/article/news/smell-movie-theater-ad/129864 (2008). Accessed 24 July 2008

  • Hing, N., Vitartas, P., Lamont, M.: Understanding persuasive attributes of sports betting advertisements: a conjoint analysis of selected elements. J. Behav. Addict. 6(4), 658–668 (2017)

    Article  Google Scholar 

  • Hitt, R., Perrault, E., Smith, S., Keating, D.M., Nazione, S., Silk, K., Russell, J.: Scientific message translation and the heuristic systematic model: insights for designing educational messages about progesterone and breast cancer risks. J. Cancer Educ. 31(2), 389–396 (2016)

    Article  Google Scholar 

  • Holbrook, M.B., Batra, R.: Assessing the role of emotions as mediators of consumer responses to advertising. J. Consum. Res. 14(3), 404–420 (1987)

    Article  Google Scholar 

  • Hosmer, D.W., Lemeshow, S.: Applied Logistic Regression, 2nd edn. Wiley, Hoboken (2000)

    Book  Google Scholar 

  • Ji, M., Zhang, D., Xie, F., Zhang, Y., Zhang, Y., Yang, J.: Semisupervised community detection by voltage drops. Math. Probl. Eng. 2016, 1–10 (2016)

    Google Scholar 

  • Kohonen, T.: Self-organized formation of topologically correct feature maps. Biol. Cybern. 43(1), 59–69 (1982)

    Article  Google Scholar 

  • Kohonen, T., Honkela, T.: Kohonen network. Scholarpedia 2(1), 1568 (2007)

    Article  Google Scholar 

  • Lai, H.H., Lin, Y.C., Yeh, C.H.: Form design of product image using grey relational analysis and neural network models. Comput. Oper. Res. 32(10), 2689–2711 (2005)

    Article  Google Scholar 

  • Leder, H., Belke, B., Oeberst, A., Augustin, D.: A model of aesthetic appreciation and aesthetic judgments. Br. J. Psychol. 95, 489–508 (2004)

    Article  Google Scholar 

  • Lee, Y.H., Mason, C.: Responses to information incongruency in advertising: the role of expectancy, relevancy, and humor. J. Consum. Res. 26(9), 156–169 (1999)

    Article  Google Scholar 

  • Lobo, J.M., Jiménez-Valverde, A., Real, R.: AUC: a misleading measure of the performance of predictive distribution models. Glob. Ecol. Biogeogr. 17(2), 145–151 (2008)

    Article  Google Scholar 

  • Lin, Y.C., Lai, H.H., Yeh, C.H.: Consumer-oriented product form design based on fuzzy logic: a case study of mobile phones. Int. J. Ind. Ergon. 37(6), 531–543 (2007)

    Article  Google Scholar 

  • Luchs, M.G., Naylor, R.W., Irwin, J.R., Raghunathan, R.: The sustainability liability: potential negative effects of ethicality on product preference. J. Mark. 74(5), 18–31 (2010)

    Article  Google Scholar 

  • Lwin, M.O., Morrin, M., Chong, C.S.T., Goh, S.X.: Odor semantics and visual cues: what we smell impacts where we look, what we remember, and what we want to buy. J. Behav. Decis. Mak. 29(2–3), 336–350 (2016)

    Article  Google Scholar 

  • Masoud, M.Z., Jaradat, Y., Jannoud, I., Al Sibahee, M.A.: A hybrid clustering routing protocol based on machine learning and graph theory for energy conservation and hole detection in wireless sensor network. Int. J. Distrib. Sens. Netw. 15(6), 1550147719858231 (2019)

    Article  Google Scholar 

  • McAlister, L.: A dynamic attribute satiation model of variety-seeking behavior. J. Consum. Res. 9, 141–149 (1982)

    Article  Google Scholar 

  • MacKenzie, S.B., Lutz, R.J., Belch, G.E.: The role of attitude toward the ad as a mediator of advertising effectiveness: a test of competing explanations. J. Mark. Res. 23(2), 130–143 (1986)

    Article  Google Scholar 

  • Mehrabian, A., Russell, J.A.: An Approach to Environmental Psychology. MIT Press, Cambridge (1974)

    Google Scholar 

  • Mick, D.G.: Meaning and mattering through transformative consumer research. Adv. Consum. Res. 33(1), 1–4 (2006)

    Article  Google Scholar 

  • Mitchell, A.A., Olson, J.C.: Are product attribute beliefs the only mediator of advertising effects on brand attitude? J. Mark. Res. 18(3), 318–332 (1981)

    Article  Google Scholar 

  • Molina, M.D., Sundar, S.S., Le, T., Lee, D.: “Fake news” is not simply false information: a concept explication and taxonomy of online content. Am. Behav. Sci. (2019) https://doi.org/10.1177/0002764219878224

    Article  Google Scholar 

  • Nicks, D.: These scientists just sent smells via smartphone. Time 6(17). http://time.com/2891850/scents-smartphone (2014) Accessed 17 June  2014.

  • Olney, T.J., Holbrook, M.B., Batra, R.: Consumer responses to advertising: the effects of ad content, emotions, and attitude toward the ad on viewing time. J. Consum. Res. 17(4), 440–453 (1991)

    Article  Google Scholar 

  • Olteanu, A., Varol, O., Kiciman, E.: Distilling the outcomes of personal experiences: a propensity-scored analysis of social media. In: Proceedings of the 20th ACM Conference on Computer-Supported Cooperative Work and Social Computing (2017)

  • Opsahl, T., Hogan, B.: Modeling the evolution of continuously-observed networks: communication in a Facebook-like community (2010). arXiv preprint arXiv:1010.2141

  • Petty, E.R., Ostrom, T.M.: Historical foundations of the cognitive response approach to attitudes and persuasion. In: Petty, E.R., Ostrom, T.M., Brock, T.C. (eds.) Cognitive Responses in Persuasion, pp. 5–29. Psychology Press, New-York (2014)

    Chapter  Google Scholar 

  • Seva, R.R., Helander, M.G.: The influence of cellular phone attributes on users’ affective experiences: a clultural comparison. Int. J. Ind. Ergon. 39(2), 341–346 (2009)

    Article  Google Scholar 

  • Steinmetz, K.: Follow your nose: food company launches ‘smell-vertising’ for potato ads. Time 2(10). https://newsfeed.time.com/2012/02/10/follow-your-nose-food-company-launches-smell-vertising-for-potato-ads (2012) Accessed 10 Feb 2012.

  • Suka, M., Yamauchi, T., Yanagisawa, H.: Comparing responses to differently framed and formatted persuasive messages to encourage help-seeking for depression in Japanese adults: a cross-sectional study with 2-month follow-up. BMJ Open 8(11), e020823 (2018)

    Article  Google Scholar 

  • Verganti, R.: Design, meanings, and radical innovation: a metamodel and a research agenda. Prod. Innov. Manag. 25, 436–456 (2008)

    Article  Google Scholar 

  • Villarino, J., Font, X.: Sustainability marketing myopia: the lack of persuasiveness in sustainability communication. J. Vacat. Mark. 21(4), 326–335 (2015)

    Article  Google Scholar 

  • Wauters, B., Brengman, M., Mahama, F.: The impact of pleasure evoking colors on the effectiveness of threat (fear) appeals. Psychol. Mark. 31(12), 1051–1063 (2014)

    Article  Google Scholar 

  • Yang, S.M., Nagamachi, M., Lee, S.Y.: Rule-based inference model for the Kansei engineering system. Int. J. Ind. Ergon. 24(5), 459–471 (1999)

    Article  Google Scholar 

  • Zhu, W., Zeng, N., Wang, N.: Sensitivity, specificity, accuracy, associated confidence interval and ROC analysis with practical SAS implementations. In: NESUG Proceedings: Health Care and Life Sciences, Baltimore, Maryland, vol. 19, p. 67 (2010)

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Correspondence to Moti Zwilling.

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Appendix 1: Items by variable

Appendix 1: Items by variable

Variables and items
PAD pleasure
1. Happy/unhappy
2. Pleased/annoyed
3. Satisfied/unsatisfied
4. Contented/melancholic
5. Hopeful/despairing
6. Relaxed/bored
PAD arousal
1. Stimulated/relaxed
2. Excited/calm
3. Frenzied/sluggish
4. Jittery/dull
5. Wide awake/sleepy
PAD dominance
1. In control/cared for
2. Controlling/controlled
3. Dominant/submissive
4. Influential/influenced
5. Autonomous/guided
6. Important/awed
Attitude toward the AdAad
1. Good/bad
2. Like/dislike
3. Irritating/not irritating
4. Interesting/uninteresting
Attitude toward the brandAb
1. Bad/good
2. Dislike/like
3. Unpleasant/pleasant
4. Poor quality/good quality
Purchase intentionPI
1. Possible/impossible
2. Probable/improbable
3. Likely/unlikely
Maximum product pricePriceMax
1. What is the maximum price you would pay for liquid soap of the brand listed in the advertisement in the 750 ml packaging?

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Zwilling, M., Levy, S., Gvili, Y. et al. Machine learning as an effective paradigm for persuasive message design. Qual Quant 54, 1023–1045 (2020). https://doi.org/10.1007/s11135-020-00972-0

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Keywords

  • Advertising
  • Product design
  • Symbolic design
  • Machine learning