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2022 | OriginalPaper | Chapter

5. Empathetic Connection

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Abstract

This chapter explores the increased interest in consumers’ emotional private life. Algorithmic systems allow marketing organisations to create new in-depth profiles of consumers’ psychology, measure sentiment in the digital marketplace, and decipher emotional cues during the offline-online customer journey. At the same time, the expansion of language-based interfaces enables marketers to engage in new human-like interactions with consumers, generating a sense of social attachment in consumers while auctioning-o intimate spaces of their lives. This development drives consumers’ surveillance and predictive personalisation into the depths of human emotions and affective lives, which trigger new dynamics of hidden persuasion and social affection in algorithmic marketing.
Footnotes
1
Ventura (2019), p. 3.
 
2
Parmar (2015).
 
3
Venture Beat (2018).
 
4
Neil (2020).
 
5
Goleman (2006).
 
6
McArthur (2014).
 
7
White and Bloomberg (2020).
 
8
Burns (2020).
 
9
TechNative (2020).
 
10
Kunze (2016).
 
11
The increased uptake of empathetic strategy in marketing is reflected in a growing interest in marketing literature. See, e.g., C. Brooks (2016); Barile (2020).
 
12
Kotler and Keller (2016, p. 110) explain that new marketing “requires emphatic listening and immersive research into what is known as digital anthropology. Once the human side of the customers has been uncovered, it is time for brands to uncover their empathetic side. Brands need to demonstrate empathetic attributes that can attract customers and build human-to-human connections.”
 
13
In his seminal work on the subject, Wells (1975) defined psychographic research in marketing as “a form of quantitative research, delimiting of demographics, that is intended to assign psychological dimensions to consumers.” For an introduction to current psychographic techniques and methods, see (Gunter and Furnham 2014).
 
14
Matz and Netzer (2017); Alexander et al. (2020).
 
15
Lazer et al. (2009).
 
16
Matz et al. (2020).
 
18
For example, Graves and Matz (2018) claims that “no marketer wants to present a message that is off-key or even irrelevant; personality science offers the chance to empathize with individuals, and engage them with the message, advertisement, or content in a way that is more likely to resonate with them.”
 
19
For example, the Open-Source Psychometrics Project lists forty-eight available databases distinguished by different variables of the interviewee (such as age, gender, and country) and different psychometric measurements they use. Available at https://​openpsychometric​s.​org/​_​rawdata/​.
 
20
Azucar et al. (2018).
 
21
Visual DNA, “Know Your Customer: Smart Online Marketing through Unique Personality Profiling,” 2014, https://​www.​visualdna.​com/​wp-content/​uploads/​2014/​09/​VisualDNA_​WHY_​Know_​Your_​Customer.​pdf.
 
23
Writing in the Netflix Technology Blog, Chandrashekar et al. (2017) comment that “if the artwork representing a title captures something compelling to you, then it acts as a gateway into that title and gives you some visual ‘evidence’ for why the title might be good for you.”
 
24
Schneider et al. (2017).
 
25
Hu and Pu (2010).
 
26
Chen et al. (2013).
 
27
Oxford English Dictionary (2021).
 
28
Vences et al. (2020).
 
29
Vignal Lambret and Barki (2018).
 
30
Rambocas and Gama (2013).
 
31
Brandwatch (2019).
 
32
In a discussion that covers many such applications, Arbel and Shapira (2020) suggest that the ability to pinpoint unsatisfied consumers who are likely to publicize or pursue a complaint against the company hinders collective consumer activism, as complaining consumers can be selected for targeted promotions and special care.
 
33
For example, Gómez-Adorno et al. (2016) presented a lexical resource to preprocess social-network data based on neural networks and includes systems of nonverbal mechanisms: emoticons.
 
35
Jin and Wang (2018).
 
36
Johnston et al. (2014).
 
37
TechCrunch (2017).
 
38
Klaib et al. (2021).
 
39
Threatpost (2017).
 
40
Noldus, 2020, last accessed 20 December 2021, https://​www.​noldus.​com/​facereader.
 
41
Affectiva (2020).
 
42
Schmidt (2018).
 
43
Keats (2011). More extensively on the subject, see Grant and Waite (2013).
 
44
CMO (2019).
 
45
VMO, 2020, last accessed 20 December 2021, http://​valmorganoutdoor​.​com/​dart/​.
 
46
Gatti et al. (2018; Charara 2020; iClarified 2013).
 
47
Farr (2020).
 
48
McStay (2018).
 
49
McStay (2018), p. 2.
 
50
Iacoboni (2013).
 
51
Renvoisé and Morin (2007).
 
52
Packard (1957).
 
53
Packard (1957), p. 37.
 
54
Gountas et al. (2019).
 
55
See, e.g., (Pradeep 2010).
 
56
For an introduction to the field, with a short history of it, see Morin (2011). The authors recognise that “neuromarketing is here to stay. And it will evolve, like humans—and even brands—do. Consumers like you may never see the difference in the messages that are refined or produced because of gaining a better understanding of our buying decision process. Ethical issues will continue to surface but standards have already been adopted to make sure that neuromarketing research is conducted with respect and transparency” (p. 135).
 
57
As early as 1997, Geral Zaltman at Harvard, one of the strongest proponents of neuromarketing, suggested that virtually all consumer purchases are influenced by subconscious stimuli or reactions, even when the individual claims to have made a rational choice, and neuromarketing is a vital development in the effort to probe their desires.
 
58
Morin (2011), p. 131. For example, in one of the most popular studies in this area conducted in 2003 by Pepsi and Coca-Cola, a group of consumers were asked to drink either Pepsi or Coca-Cola while their brains were being scanned by an fMRI (functional magnetic resonance imaging) machine (McClure 2004). The study showed that different areas of the brain lit up depending on whether or not subjects were aware of the brand they were consuming, and that a strong brand such as Coca-Cola had the power to “own” a piece of our frontal cortex and stimulate pleasant feelings.
 
59
Blakeslee (2004).
 
60
Olteanu (2015).
 
61
Wolpe (2019).
 
62
Bhandari (2020).
 
63
Marketing professor Giuliano Noci (2018) describes this transition as “bio-marketing”: a new human-centred approach to marketing is envisaged in the world of digital disintermediation where “bio-data is the new raw material for business and marketing research is based on biometric signals and brain activity to provide authentic interpretations of why an individual displays inevitable reaction when exposed to a marketing stimulus” (p. 2).
 
64
Bartholomew (2017), p. 118.
 
65
Poldrack and Yarkoni (2016).
 
66
Ariely and Berns (2010).
 
67
Venkatesan and Thangadurai (2017).
 
68
Stark (2018).
 
69
Nemorin (2017).
 
70
Ekman and Friesen (1969).
 
71
Ekman (1993).
 
72
Aaker et al. (1988).
 
73
Beer (2016): “The difficulty is how we might go about understanding metrics as affective. The problem is where to start if we want to think about how measurement is felt, how it is embodied, and how it can be seen to be experienced emotionally. Clearly, this produces for us a set of questions and possibilities that stretch far beyond the capacity of this chapter alone. What I would like to do here then is to open these questions by drawing upon work on affect theory and the sociology of emotions. This conceptual framework can provide us with the beginnings of a toolkit for analysing what I call here affective measures and for understanding how metric power works through the production of uncertainty.”
 
74
Barrett (2017).
 
75
Barrett (2017), p. 36.
 
76
Purdy et al. (2019).
 
77
Rose (2001).
 
78
McStay (2018), pp. 20–21.
 
79
Ellul et al. (1964).
 
80
Nemorin and Gandy (2017).
 
81
Nemorin and Gandy (2017), p. 23.
 
82
Kramer et al. (2014).
 
83
Kramer et al. (2014), p. 8788.
 
84
Matz et al. (2017).
 
85
A reconstruction of the Cambridge Analytica case can be found in Harris (2018; Cadwalladr and Graham-Harrison 2018; Adams 2018).
 
86
Following Wiener, Floridi (2019) argues that marketing not only controls the digital interface but also treats us humans as an interface to get our attention. Similarly, thanks to the digital evolution, politics has moved towards marketing and adopted the same techniques to treat us as interfaces and win our votes.
 
87
A review of existing studies is provided by Teeny et al. (2020).
 
88
Odekerken-Schröder et al. (2003).
 
89
Wheeler et al. (2005).
 
90
Li (2016).
 
91
Fleming and Petty (2000).
 
92
Cacioppo and Petty (1982).
 
93
Levin (2017).
 
95
Bhatia and Bhatia (2011). From the description: “Emotional targeting, by contrast, remains a vast, potent, and largely untapped resource on the Web. In dramatically increasing fashion, users can express themselves emotionally on the Web. Social networking and real-time interaction provide huge levels of emotionally rich communication. Rich media, including audio and video, provide unprecedented opportunities for users to share their emotions and emotionally charged experiences. Furthermore, if facilitated, tapped, and recognized, direct and indirect online behavioral clues abound regarding users’ emotions and emotional patterns.”
 
96
Conversioner, 2020, accessed 12 December 2021, https://​www.​conversioner.​com/​glossary/​emotional-targeting.
 
97
Damasio (1994). Damasio’s “somatic marker hypothesis” puts forward the idea of a mechanism by which emotions guide (or bias) behaviour and decision-making, such that rationality requires emotional input. This hypothesis he sets in contrast to what he identifies as René Descartes’s error, consisting in a dualist separation of mind and body, rationality and emotion.
 
98
See, e.g., Agrawal et al. (2013). The research brings together the literature on discrete emotions and biased processing of information. Studies provide evidence that incidental emotions differ in their response to preference-inconsistent (vs. preference consistent) information due to their differences in agency appraisals.
 
99
Achar et al. (2016).
 
100
Yan et al. (2016).
 
101
Di Muro and Murray (2012).
 
102
Wegener et al. (1994).
 
103
Heo and Lee (2018); Jones (2018).
 
104
MarTech Series (2018).
 
105
Dominik Felix, How to Create a Chatbot Without Coding a Single Line, 2016 available at https://​chatbotsmagazine​.​com/​how-to-create-a-chatbot-without-coding-a-single-line-e716840c7245.
 
106
SocialBee, How to Integrate Chatbots in Your Social Media Marketing Strategy, 2020, available at https://​socialbee.​io/​chatbots-social-media/​. Customers are reluctant to install new apps, and they often use services within most common messaging applications to connect with the brand directly.
 
107
For example, through the AI-powered computational platform that Facebook offers for developers, marketers can build a chatbot that is then hosted on the Facebook platform itself, on the company’s profile page, and available for conversation with customers and anyone who visits the webpage. When customers converse with the bot, the Facebook server sends webhooks to the URL of the business server, which automatically responds directly to the Facebook platform. See Facebook for Developers, 2020, https://​developers.​facebook.​com/​docs/​messenger-platform/​.
 
109
The taxonomy is provided by Adamopoulou and Moussiades (2020).
 
110
Van den Broeck et al. (2019).
 
111
Tuzovic and Paluch (2018).
 
112
Ultimate.ai (2020).
 
113
Other virtual-assistant applications are Microsoft’s Cortana and Huawei’s Celia.
 
114
K. Cronin, “Alexa: How Will Voice Impact My Mobile Marketing,” 2017, https://​martechtoday.​com/​alexa-will-voice-impact-mobile-marketing-208766.
 
115
Dawar and Bendle (2018).
 
116
Lee and Cho (2020).
 
118
The different typologies of marketing interactions are modelled after Lee and Cho (2020).
 
119
Cognizant, “The Coming Intelligent Digital Assistants Era and Its Impact on Online Platforms,” 2017, available at https://​www.​forrester.​com/​report/​By+2024+575+Mill​ion+EU5+Househol​ds+Will+Have+Sma​rt+Speakers/​-/​E-RES158859.
 
120
Marketing Charts (2017).
 
121
Gal and Elkin-Koren (2017).
 
122
Gal and Elkin-Koren (2017), p. 323.
 
123
Smith (2020).
 
124
Boldly (2014); O’Brien (2020).
 
125
As Breazeal and Forest have articulated, “for our purposes as robot designers, it seems reasonable [to] construct a robot with an infant-like appearance, which could encourage people [to] switch on their baby-scheme and treat it as a cute creature in need of protection and care.” Caudwell and Lacey (2019).
 
126
Review (2015). The description of Jibo on the Jibo.inc company website is available at at https://​jibo.​com/​2015/​07/​15/​jibo-the-worlds-first-living-character-property/​.
 
127
Elly Strang, “Google Empathy Lab founder Danielle Krettek on Why It’s Time for Businesses to Reflect Humanity and Match Their EQ to Their IQ,” 2018, available at https://​www.​linkedin.​com/​pulse/​google-empathy-lab-founder-danielle-krettek-why-its-time-elly-strang/​.
 
128
TechCrunch (2016).
 
129
Peon, L. (2017). Voice Technology Demands to Be Heard Campaign. Retrieved January 14, 2018, from https://​www.​campaignlive.​co.​uk/​article/​voice-technology-demands-heard/​1438482.
 
130
Feine et al. (2019).
 
131
Gartenberg (2019). See also Amazon’s Alexa website at https://​www.​amazon.​com/​b?​ie=​UTF8&​node=​20591210011.
 
132
On Amazon’s announcement that Shaquille O’Neal and Melissa McCarthy will lend their voices to Alexa, see Porter (2021).
 
133
Smith (2020), p. 13.
 
134
Luo et al. (2019).
 
135
Lanier et al. (2013).
 
136
Aggarwal and McGill (2007).
 
137
Touré-Tillery and McGill (2015).
 
138
Aggarwal and McGill (2007).
 
139
Ngai (2015).
 
140
Araujo (2018).
 
141
Ischen et al. (2019).
 
142
Ischen et al. (2020).
 
143
Wagner and Schramm-Klein (2019).
 
144
Cambre and Kulkarni (2019).
 
145
Heyman and Gelman (1999).
 
146
Voorveld and Araujo (2020).
 
147
Woods (2018).
 
148
Dellaert et al. (2020).
 
149
Jones (2018).
 
150
One purpose for the viewing data was to analyse advertising effectiveness. With Vizio TV data, third parties could analyse a household’s behaviour across devices, for example, “(a) whether a consumer has visited a particular website following a television advertisement related to that website, or (b) whether a consumer has viewed a particular television program following exposure to an online advertisement for that program.” Another purpose for the viewing data was to better target household members on their other digital devices.
 
151
See Luckerson (2014), noting Google’s expectation “that users will be using [Google] services and viewing [Google] ads on an increasingly wide diversity of devices in the future, and thus [Google] advertising systems are becoming increasingly device-agnostic).” See also Determann and Perens (2017) describing “behavioral data” that smart-car developers “can monetize for advertising and other purposes.”
 
152
Aksu et al. (2018).
 
153
Maschable (2013).
 
154
Russey (2019).
 
155
Ziegeldorf et al. (2014).
 
156
Hoy (2018).
 
157
Zheng et al. (2018); Leenes and De Conca (2018).
 
158
Sandel (2012).
 
159
Marquand (2004).
 
160
Buttimer (1980).
 
161
See Allen (1999, p. 725), observing that physical privacy is violated “when a person’s efforts to seclude or conceal himself or herself are frustrated.”
 
162
Zuboff (2019), who, at the end of her book, calls upon a “right to a sanctuary”: “the human need for a space of inviolable refuge has persisted in civilized societies from ancient times but is now under attack as surveillance capital creates a world of “no exit” with profound implications for the human future at this new frontier of power”.
 
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Metadata
Title
Empathetic Connection
Author
Federico Galli
Copyright Year
2022
DOI
https://doi.org/10.1007/978-3-031-13603-0_5