Customer Profiling in the Ambit of Gaming: portraying lifestyles
Perfiles de clientes en el ámbito del juego: Retratando estilos de vida
Matheus José Machado Dutra
Escola Superior de Propaganda e Marketing de Porto Alegre. Brasil.
matheusjmdutra@gmail.com
Abstract
Customer profiling is a tool for lifestyle visualization that combines creative imagination techniques with storytelling and statistical methods to fabricate easily interpretable representations of the consumer’s everyday life. This paper suggests employing the data-driven method allied with the storytelling approach for modeling engaging gamer profiles. Developing a profile is a sensible process that utilizes data and statistics to produce credible results. Therefore, the methodology principally involved the exploratory factor analysis for uncovering latent variables that influence the gaming lifestyle and the non-hierarchical cluster analysis to separate data cases with different characteristics and group members with similar attributes. The analyzed dataset gathered 1.588 participants from 80 countries. The results support the customer profiling methodology as a valid instrument for consumer behavior analysis and lifestyle visualization.
Keywords
Marketing; lifestyle; consumer behavior; segmentation; gamer.
Resumen
La elaboración de perfiles de cliente es una herramienta de visualización de estilos de vida que combina técnicas de imaginación creativa con métodos narrativos y estadísticos para fabricar representaciones fácilmente interpretables de la vida cotidiana del consumidor. Este artículo propone emplear el método basado en datos aliado con el enfoque de la narración para modelar perfiles de jugadores atractivos. El desarrollo de un perfil es un proceso sensato que utiliza datos y estadísticas para producir resultados creíbles. Por lo tanto, la metodología implicó principalmente el análisis factorial exploratorio para descubrir las variables latentes que influyen en el estilo de vida de los jugadores y el análisis de conglomerados no jerárquico para separar los casos de datos con características diferentes y los miembros del grupo con atributos similares. El conjunto de datos analizados reunió a 1.588 participantes de 80 países. Los resultados respaldan la metodología de elaboración de perfiles de clientes como instrumento válido para el análisis del comportamiento del consumidor y la visualización del estilo de vida.
Palabras clave
Marketing; estilo de vida; comportamiento del consumidor; segmentación; gamer.
1. Introduction
Customer profiling is a lifestyle visualization tool that combines creative imagination techniques with statistical methods to fabricate easily interpretable representations of the consumer’s everyday life (Wansink, 1994). It is a hybrid approach focused on ascribing meaning to data-driven prototypes by placing the consumer model in ordinary life situations. Moreover, it employs fictional devices to recreate the personal routine and describe the needs, motivations, and goals of a representative of a market segment while portraying which social, cultural, and individual aspects influence each prototype.
For this article, the objective is to use the customer profiling technique as an avenue to humanize data and offer the decision-makers an alternative tool for customer behavior analysis. By applying the quantitative methods allied with narrative elements, the proposition is to build a story reproducing the daily journey of a particular group - the gamers (Wansink, 1994). On this account, this research centers around the gamer category, an innate subculture of the digital environment recognized for having a set of behavioral, cognitive, habitual, and attitudinal characteristics that define their identity and way of living – the so-called gaming lifestyle (Machado Dutra, 2021). Thus, this paper answers the following research question:
- Is the customer profiling technique efficient when describing lifestyles in the ambit of gaming?
To properly address the looming question, a survey was conducted to facilitate the creation of detailed customer profiles. Gamers’ responses were collected from three gaming systems: mobile (n= 700) console (n= 408) and portable computer (n= 480), totalizing 1588 valid responses. The goal of the questionnaire was to assess the participants’ opinions on six lifestyle topics associated with gaming: technology, microtransactions, consumption, streaming and media, eSports, and gaming performance. Concerning the statistical analysis, to reduce the number of variables and identify latent constructs, the first procedure was the exploratory factor analysis, followed by the cluster analysis, whose objective was to group data instances based on a series of specified variables (Field, 2013).
2. Literature Review
2.1. The Art of Creating Customer Profiles
In times of information overload, there is a concern regarding the value of customer profiling when there are multiple alternatives that one can employ to study consumers. New forms of individual-level data that enable the creation of sophisticated segments have arisen, and part of the profiling magic has faded away. Automated marketing systems and interactional design facilitated personalization, microtargeting, and one-to-one operations (Nielsen, 2012; Salminen et al., 2021).
When the topics revolve around marketing and interactional design, customer profiling is a technique conceived to develop hypothetical prototypes of real consumers based on composite data (Wansink, 1994). The purpose of the customer profiling method is to avoid the creation of opaque or blurred portrayals as it requires creative force, rigor, and sophistication to give soul, flesh, and blood to characters.
Theoretically, customer profiles offer engaging narratives more relatable to decision-makers than mere numbers and sheets as it provides an immersive opportunity to comprehend complex target groups (Wasinsk, 1994; Salminen et al., 2018; Salminen et al., 2021). The psychological benefits of the customer profiling tool derive from one’s ability to feel empathy and identify with the prototypes created (Madsen and Nielsen, 2009; Nielsen, 2012; Salminen et al., 2018).
Complementary to traditional marketing segmentation techniques, the customer profiling process differentiates from other methods by including literary and creative elements in its procedure (Wansink, 1994). It considers the past, present, and future in order to portray the desired picture. Like any other craft, customer profiling is subject to its creator’s empathy and mastership. If a developer lacks contextual information and qualitative or quantitative data, it may result in the image of a faceless ghost.
Despite the benefits of implementing a customer prototype to assist the segmentation process, Wansink (1994) warns that the generalization of qualities and traits of several unique individuals in the shape of a single user representation masks diversity and peculiarities within that group. To address this matter, the author reiterates the necessity to incorporate the statistical method and accessory procedures such as laddering and creative inference.
2.2. The Essence of a Customer Profile
Wansink’s (1994) goal is to facilitate empathy by transforming data into fictitious characters and setting them in daily activities representing their lifestyle. The success of a customer profile is the result of the creative capacity, inferential reasoning, and analytical skills of its developer. The design of believable models requires a collection of characteristics and details of different classes to confer a degree of realism and authenticity to each fictional entity. These attributes are of many sorts, and each case dictates what to include or suppress.
Despite the customary usage of demographic and psychographic information for segmentation and prototyping, Salminen et al. (2021) and Nielsen (2019), who extensively studied the development of data-driven models, contributed to broadening the knowledge about the subject and improving Wansink’s (1994) work. For instance, Jung, Salminen, and Jansen (2021) highlight the importance of demographically appropriate names for data-focused prototypes. Madsen and Nielsen (2009) underline the inclusion of scenarios and storytelling ingredients when introducing fictional models. Salminen et al. (2021) examine the impacts of picturesque styles when creating empathic prototypes.
However, Wansink’s (1994) method diverges from others essentially in four points: defending the benefits of less tied-to-data profile development, which creates space for an imaginary laddering process to address unanswered questions and exercise creative inference. By focalizing on unobservable or latent traits attempting to externalize internal dispositions. Creating each narrative in an informal writing style facilitates comprehension of a broader audience and potentially increases emotional empathy. Lastly, directing attention to which activities consumers engage in and fit lifestyle and consumption choices in the narrative.
In Wansink’s (1994) vision, the outcome of the customer profiling technique produces similar generalization levels as the most prevalent methods. It is also considerably more accurate when predicting membership status and reaches a superior degree of empathy due to the accessible language, presentation, and personal narrative style. As a lifestyle visualization tool, the customer profile methodology requires knowledge about the way of living and the daily life of its target market – gamers. Therefore, the coming section discusses gaming and engagement.
2.3. Engagement as a Profiling Element
This section treats how engagement is a determinant parameter for customer profiling and the formulation of a gaming lifestyle concept (Machado Dutra, 2021). The idea is to contemplate online games as another type of social media, in line with Jiménez-Marín, González and Galiano-Coronil (2021) or Kaplan and Haenlein’s (2010) proposals. This way, it becomes clearer to understand how gaming and other social media practices blend and affect the daily life of the gamer segment.
Across fields, there is a lack of consensual definition for engagement since it can be considered either a state or a process. Or whether it manifests on the behavioral, emotional, physical, or cognitive level Kahn (1990). Consequently, in this study, it is prudent to evaluate engagement within the scope of marketing and media, given that there is no conclusive explanation about this matter in the context of gaming.
Engagement in marketing, media, and behavioral literature is a multidimensional affective component that describes the amount or intensity of an individual’s cognitive, emotional, and behavioral investment towards a cause, a brand, or an organization (Shao, 2009; Kaplan & Haenlein, 2010). It gauges the level of commitment people manifest concerning a particular activity.
Anchored to the marketing and media theory and following the gaming lifestyle logic, engagement manifests itself in online and offline arenas in the forms of commitment, immersion, and participation in digital worlds and virtual communities (Drury, 2008; Machado Dutra, 2021). These variables can manifest in four online habits: social media activity, streaming consumption, eSports support, and virtual gatherings.
These four social activities are prevalent practices in the routine of many gamers. They are responsible for the progress of widespread gaming culture and local gaming communities (Başar, Erciş & Ünal, 2018). By encouraging participation, interaction, and sharing among users, gamers can disseminate their views, opinions, beliefs, and content of interest within their orbit (Kaplan & Haenlein, 2010).
If playing games, watching live streams, spectating eSports matches, and consuming user-generated content are considered all forms of leisure, according to Roberts (2006), they would be formative components of an individual’s lifestyle. In this regard, it would not be far out to believe that leisure styles and consumption choices function as criteria to differentiate gamers within-groups and between-groups. Therefore, it is plausible to conceive a link between engagement, lifestyle, and the customer profiling technique.
3. Methodology
Detailed visualization of the customer and an appropriate profiling process require relevant information about the targeted audience. For the first part, to avoid disadvantageous generalizations, the total sample is separated into three gaming systems: mobile, console, and portable computer (PC). This approach intends to highlight the differences and similarities in each gaming system. This way, it is possible to observe any discrepancies in the answers of each group of respondents.
3.1. Sample and Data Collection
The gathering of the sample dataset was through the public post system on the Reddit platform. Reddit is a network mesh structured around self-made communities created by its users. These self-made communities are called subreddits – virtually organized spaces with distinct rules, slang, humor, and goals (Medvedev, Lambiotte, Delvenne, 2019). Mostly, the objective of these communities is to share user-made content, cover pertinent news, discuss specific topics, ask for advice, and answer questions.
The main page of each subreddit discloses information about the community, number of members, online users, updates, official links, and rules. Following the regulation, the forum moderators were alerted through private messages of the conduction of this survey. Granted permission, each publication presented a special introductory letter with specific jargon directed to each gaming community. The letters utilized accessible vocabulary and invited, in an informal tone, all the members to partake in the survey. Table 1 lists the 24 chosen subforums to share the questionnaire.
Table 1. Reddit Subforums
|
Mobile |
r/grandorder |
r/DokkanBattleCommunity |
r/Genshin_Impact |
r/PUBGMobile |
|
r/DragonballLegends |
r/SaintSeiyaAwakening |
r/FFBraveExvius |
r/pokemongo |
|
|
Console |
r/FIFA; |
r/HadesTheGame |
r/AnimalCrossing |
r/Halo |
|
r/CallOfDuty |
r/bloodborne |
r/NoMansSkyTheGame |
r/MonsterHunterWorld |
|
|
PC |
r/FortNiteBR |
r/EscapefromTarkov |
r/ffxiv |
r/wow |
|
r/GTAV |
r/leagueoflegends |
r/csgo |
r/VALORANT |
Source: own elaboration
The dataset covered gamers with many different profiles and backgrounds. This research counted with 1.588 valid responses divided into three subsets: mobile (n= 700), console (n= 408) and portable computer (n= 480). The participants were volunteers from 80 countries randomly selected who fit the criteria. The requirements for validation included playing frequency (at least once a week) and playing periodicity (having played online games for no less than 12 months). Participants who did not meet the criteria were excluded. Table 2 provides a summary of participants’ demographic data.
Table 2. Demographic Characteristics of the Sample
|
Mobile (n= 700) |
Console (n= 408) |
PC (n= 480) |
|||||
|
Variable |
% |
n |
% |
n |
% |
n |
|
|
Gender |
Male |
85.29 |
597 |
74,82 |
306 |
83.78 |
403 |
|
Female |
12 |
84 |
20.78 |
85 |
13.51 |
65 |
|
|
Other |
2.71 |
19 |
4.40 |
18 |
2.70 |
13 |
|
|
Age Group |
Less than 17 |
8.57 |
60 |
19.34 |
57 |
7.48 |
36 |
|
18-24 |
78.43 |
549 |
69.93 |
286 |
81.08 |
390 |
|
|
More than 35 |
13 |
91 |
16.14 |
66 |
11.43 |
55 |
|
|
Education Level |
None/primary |
4.43 |
31 |
6.11 |
25 |
6.03 |
29 |
|
Secondary |
29.86 |
209 |
38.88 |
159 |
34.30 |
165 |
|
|
Tertiary |
65.71 |
460 |
55.01 |
225 |
59.67 |
287 |
|
|
Employment Status |
Other |
50.86 |
356 |
55.15 |
225 |
57.71 |
277 |
|
Working |
49.14 |
344 |
44.85 |
183 |
42.29 |
203 |
|
|
Number in Household |
Live alone |
14.57 |
102 |
12.75 |
52 |
21.67 |
104 |
|
More than one person |
85.43 |
598 |
87.25 |
356 |
78.33 |
376 |
|
Source: own elaboration
Although relatively minor differences exist, the respondents in each subset displayed similar demographic characteristics. It is perceptible a majority of young educated males across the three groups. While the mobile players show a split result concerning employment status, console and PC boast a significantly wider margin of employed gamers. As for the household, a superior share of participants lives with one or more individuals within the same space.
So, to complement this data, Table 3 presents a set of the player’s habits, including their favorite gaming platform, preferred days of the week to play, gaming session duration, if they ever made microtransactions, and their monthly spending. This way, it’s possible to portray a more matching characterization of the gamers.
The players’ habits are one of the parameters when analyzing how gamers live and comport. It presents some descriptive characteristics of the sample and highlights primary behavioral differences suchlike the gaming platform used. The data indicates that mobile players are engaged principally in mobile (41.98%) and computer systems (31.87%).
Console players tend more to console (43.36%) and PC (30.34%) gaming. The idea that contemporary gamers share interests across multiple platforms, not limited exclusively to one, is getting widely popular. Part of this is due to the maturity of a gaming way of living. Regarding the PC players, they represent the only group of the sample that the majority (58.03%) of participants choose its number-one gaming system.
When examining the gaming routine, a fair share of mobile players (50.91%) dedicates part of their time to everyday gaming. Due to the accessibility to mobile devices and the variety of options and genres, it is not difficult for a regular phone user to start gaming. Following the same logic, it is easily manageable for an individual to incorporate such practices into the daily routine.
Table 3. Player Habits
|
Mobile (n= 700) |
Console (n= 408) |
PC (n= 480) |
|||||
|
Item |
% |
n |
% |
n |
% |
n |
|
|
Gaming platform regularly used (multiple selection choice) |
PC |
31.87 |
501 |
30.34 |
240 |
58.03 |
448 |
|
Console |
19.4 |
301 |
43.36 |
343 |
21.76 |
168 |
|
|
Mobile |
41.98 |
660 |
19.85 |
157 |
13.73 |
106 |
|
|
Virtual reality |
0.83 |
13 |
2.78 |
22 |
2.72 |
21 |
|
|
Web browser |
5.41 |
85 |
3.16 |
25 |
3.50 |
27 |
|
|
Other |
0.51 |
8 |
0.51 |
4 |
0.26 |
2 |
|
|
Preferred days of the week to play (multiple selection choice) |
Everyday |
50.91 |
532 |
39.62 |
273 |
36.44 |
309 |
|
Monday |
2.78 |
29 |
2.47 |
17 |
4.01 |
34 |
|
|
Tuesday |
2.49 |
26 |
2.76 |
19 |
4.36 |
37 |
|
|
Wednesday |
3.25 |
34 |
2.90 |
20 |
4.48 |
38 |
|
|
Thursday |
2.39 |
25 |
3.19 |
22 |
4.95 |
42 |
|
|
Friday |
8.71 |
91 |
11.90 |
82 |
12.5 |
106 |
|
|
Saturday |
16.08 |
168 |
20.32 |
140 |
17.57 |
149 |
|
|
Sunday |
13.40 |
140 |
16.84 |
116 |
15.68 |
133 |
|
|
Duration of a gaming session |
Less than one hour |
7.43 |
52 |
1.47 |
6 |
1.04 |
5 |
|
1-2 hours |
34.43 |
241 |
29.1 |
119 |
23.08 |
111 |
|
|
3-4 hours |
39 |
273 |
50.12 |
205 |
46.36 |
223 |
|
|
5-6 hours |
11.43 |
80 |
13.2 |
54 |
19.54 |
94 |
|
|
More than seven hours |
7.71 |
54 |
6.11 |
25 |
9.98 |
48 |
|
|
Ever spent on microtransactions |
Yes |
82.43 |
577 |
75.55 |
309 |
79.42 |
382 |
|
No |
17.57 |
123 |
24.45 |
100 |
20.58 |
99 |
|
|
Monthly spending in microtransactions (USD) |
Less than five |
52.29 |
366 |
69.19 |
283 |
47.19 |
227 |
|
6-10 |
15.43 |
108 |
7.82 |
32 |
12.68 |
61 |
|
|
11-20 |
8.71 |
61 |
9.78 |
40 |
19.96 |
96 |
|
|
21-30 |
6.86 |
48 |
4.16 |
17 |
7.69 |
37 |
|
|
More than 31 |
16.71 |
117 |
9.05 |
37 |
12.47 |
60 |
|
Source: own elaboration
Despite the considerable share of console (39.62%) and computer (36.44%) players choosing to play every day, the percentages on weekdays marginally rose in contrast to mobile gaming, and both console computer gamers displayed superior distributions on Friday, Saturday and Sunday. Concerning the duration of a gaming session, all three groups (39% for mobile, 50.12% for console, and 46.36% for PC) regularly play from three to four hours. Coming in second, mobile (34.43%), console (29.1%), and PC (23.08%) gamers enjoy shorter sessions, from one to two hours.
All the groups showed percentages above 75% in microtransactions activity with a higher concentration of monthly spending of less than five USD. In truth, each group presented meaningful differences in their in-game monthly expenditure. For instance, console players (69.19%) exhibited the highest value for the less than five USD intervals. The most inclined to spend from six to 10 USD were the mobile gamers (15.43%). While the PC players (19.96% and 7.69%) were more prone to spend ranging from 11 to 30 USD compared to the other groups. Mobile gamers (16.71%) were the most interested in spending more than 31 USD. As for the geographic distribution of the sample, Table 4 presents the top five countries per subset.
Table 4. Geographic Distribution of the Sample
|
Item |
Mobile (n= 700) |
Console (n= 408) |
PC (n= 80) |
||||||
|
% |
n |
% |
n |
% |
n |
||||
|
Countries |
USA |
37.49 |
361 |
USA |
46.27 |
273 |
USA |
42.66 |
317 |
|
Canada |
6.44 |
62 |
UK |
10.34 |
61 |
Canada |
7.94 |
59 |
|
|
Philippines |
5.50 |
53 |
Canada |
7.46 |
44 |
UK |
7.40 |
55 |
|
|
Brazil |
3.95 |
38 |
Germany |
3.90 |
23 |
Germany |
5.79 |
43 |
|
|
Germany |
3.53 |
34 |
Australia |
3.73 |
22 |
Australia |
3.63 |
27 |
|
Source: own elaboration
Considering the three subsets, participants from 80 countries responded to the survey. It’s perceptible that gamers from the United States of America form the great bulk of respondents. In fact, except for the Philippines, the majority of the players reside in western countries.
3.2. Materials
The survey was designed to collect descriptive and analytical information about the gaming community, considering the focus of this study on the narrative development of a gamer customer profile. For this reason, the structure of the questionnaire comprehended three types of questions: (1) questions developed using a five-point Likert Scale to measure the level of agreement or disagreement regarding the statements, (2) single-selection questions, and (3) multiple-selection questions. There were 58 questions altogether - five about demography and five about habits. The rest were composed by the assertions divided into six topics – technology, microtransactions, consumption, streaming and media, eSports, and performance.
In an attempt to create a rounded profile of a gamer consumer, it was essential to develop a scale that included both in-game and out-game aspects of a player’s life. Thus, the questionnaire adapted questions from multiple sources: about technology - Tobbin and Adjei (2012) investigated acceptance decisions of early and late adopters of mobile technology. Regarding consumption, Evers and Weeda (2015) present a comprehensive study elucidating the relationship between microtransactions, social perception, and in-game competence. Komutanont, Nuangjamnong, and Dowpiset (2020) provide information about the reason to spectate Multiplayers Online Battle Arenas (MOBAs) live gameplays. Lastly, as for the eSports topic, Qian et al. (2019) introduce an esports online spectatorship scale designed to identify what motivates people to watch eSports in an online environment.
The survey lasted 21 days, one week to collect responses from each group. The questions were developed in American English and Brazilian Portuguese, and the response rate was superior to 64%, ensuring this research was meaningful to its target audience.
3.3. Design and Procedures
Apart from using demographic and psychographic data, the customer profiling technique encourages multivariate analysis to comprehend the various objective and subjective relationships among variables. Consequently, the software suite IBM SPSS 26 was utilized for data processing and statistical solutions. About the methodology, this paper concentrates on two principal procedures: the exploratory factor analysis (EFA) and the cluster analysis.
3.3.1. Exploratory Factor Analysis
The theory behind the exploratory factor analysis discusses that its purpose is to identify order and structure to the dataset by conveying meaningful and reasonable explanations of unobservable characteristics of individuals. The intention is to uncover attributes that cannot be directly measured (Watkins, 2018).
Thereby, the resulting factors are a selection of latent variables that affect the in-game and out-game behavior of the gamer community and characterize their lifestyle. Those factor models contribute by assisting in the clustering phase and describing customer profiles as leading attributes.
The EFA is a statistical technique to discover unobserved theoretical variables that influence empirical phenomena. It assembles a collection of intercorrelated variables to form a factor (Field, 2013). In social sciences, these factors represent unobserved qualities of people. Therefore, the sample must be capable of reflecting the characteristics of a population.
To reproduce population parameters, the number of participants is of complex decision. Costello and Osborne (2005) mention that it is imperative to understand the nature of data to achieve solid indicators. Strict rules to estimate sample size are slowly vanishing. But, as a rule of thumb, analyses should follow 20:1 subject to item ratios for more stable factorial models.
Another point for consideration is the appropriateness of the data. It is necessary to verify the intercorrelation among variables to justify the employment of the EFA. The Keiser-Meyer-Olkin (KMO) tests the general suitability of the data and identifies if the EFA is an adequate statistical treatment for this type of data (Field, 2013).
Likewise, Bartlett’s test of sphericity is an auxiliary statistical tool to ensure that each variable correlates only with itself and assess the overall correlation between factors (Field, 2013). Variables should be perfectly correlated with themselves and significantly correlated with other items to ratify that they belong to the same factor.
Another estimation is the total variance which is a standard index that demonstrates through percentages of variance how influential a factor model appears to be (Field, 2013). In the final results were included only factors with higher rates.
Lastly, the Cronbach’s Alpha test is a measure of internal consistency that indicates the reliability of each factor (Bonnet & Wright, 2015). Since a measuring instrument is reliable when the test scores do not present measurement errors, Cronbach’s alpha tests if the correlation between all variables assembled under a particular factor measure the same conception.
3.3.2. Cluster Analysis
To develop statistically supported customer profiles and fabricate hypothetical narratives, the leading method of this project is the cluster analysis. It is a tool for data classification and group aggregation or disaggregation. Its purpose is to organize and identify interpretable data structures. Also, to distinguish one from the others, considering their differences (external heterogeneity) and similarities (internal homogeneity) (Zhou, Shu & Tang, 2010).
For this research, the choice was a non-hierarchical process using K-means as the clustering heuristic algorithm. K-means is an algorithm that tries to minimize the distance of the elements to a set in an iterative way, which depends on a particular parameter (k = the number of clusters) defined ad hoc by the researcher (Shanti & Sivabalakrishnan, 2014).
The criterion to decide the number of clusters was the level of interaction between each factor in relation to the number of predetermined groups. The index that reveals this relationship is the F-score - the product of the F-test called factorial analysis of variance, a type of ANOVA test (Field, 2013). Its purpose is to analyze discrepancies among means and understand how two or more factors interact with the response variable.
The cluster analysis uses Euclidean distance to approximate data cases with similar characteristics around a cluster center and separate cluster centers with unrelated attributes. In this respect, each cluster center is a point in a dimensional space resulting from its relationship with each factor model (Zhou, Shu & Tang, 2010). So, its location is dependent on the nature of the input variables. The final center is the measure that informs the position of each cluster center.
4. Results
The first part of the Results section is dedicated to the exploratory factor analysis and all its concerning procedures. The second part introduces the cluster analysis and the customer profiles.
4.1. Appropriateness of the Dataset
Table 5 reports the adequacy of the sample. When applying the factor analysis to a dataset, the correlation matrix should not match its identity matrix. Variables should be perfectly correlated with themselves and significantly correlated with other items to ratify that they belong to the same factor.
Table 5. Adequacy test for the EFA
|
Kaiser-Meyer-Olkin Measure |
.871 |
|
|
Bartlett’s sphericity Test |
Approx. Chi-square. |
27248.736715 |
|
DF |
1081 |
|
|
Sig. |
,000 |
Source: own elaboration
The KMO index scored 0.871 on a scale of 0 to 1, which is an optimal score according to Field (2013). Bartlett’s sphericity reached a significance of (p <0.05), confirming that the matrix can be factored.
4.2. Reliability and Consistency
The Cronbach’s reliability alpha is one of the most commonly utilized indexes of reliability in social sciences, and under specific circumstances, its measurements may not be so effective (Bonnet & Wright, 2015; Taherdoost, 2016). In this regard, Bonnet and Wright (2015) argue that, although customary, it is inappropriate to inform only the sample value for Cronbach’s alpha since the reliability may vary across different testing environments or subpopulations with peculiar characteristics. Table 6 displays the label of each factor, the value of alpha for each gaming system, and the integrated value for the dataset.
Table 6. Cronbach’s Alpha for each Factor
|
Factors |
Mobile |
Console |
PC |
Global Value |
|
eSports Factor |
.852 |
.845 |
.847 |
.852 |
|
Performance Factor |
.737 |
.684 |
.737 |
.722 |
|
Privilege Factor |
.708 |
.728 |
.700 |
.711 |
|
Spectating Factor |
.903 |
.917 |
.887 |
.902 |
Source: own elaboration
Bonnet and Wright (2013) suggest that values between 0.7 and 0.8 are reliable. Nonetheless, on a similar note, Field (2013) understands that depending on what is being measured, acceptable scores can reach lower values from 0.5 to.6.
4.3. Total Variance Explained
Table 7 shows the variance percentages of the top four factors with eigenvalues greater than 1.8, which is an acceptable standard for a factor model to be effective. Lower scores suggest that the factors explain less information than a single item would have.
Table 7. Total variance explained
|
Component |
Total |
Initial Eigenvalues |
|
|
% variance |
% cumulative |
||
|
Spectating Factor |
8.368 |
17.804 |
17.804 |
|
eSports Factor |
3.428 |
7.295 |
25.099 |
|
Performance Factor |
2.999 |
6.380 |
31.479 |
|
Privilege Factor |
1.932 |
4.110 |
35.589 |
Source: own elaboration
Results in Table 7 indicate that four factors can cumulatively explain 35.589% of the total factorial solution – a decent level of variance for only four factors (Field, 2013). Note that the Spectating Factor is responsible for 17.804% of the entire model and presents an eigenvalue of 8.368.
4.4. The Four Factors
The Four Factors represent the leading attributes associated with engagement and the gaming lifestyle. More than that, they function as narrative ingredients figuring each story - part of the customer profile. Emphasize that the proposal of this paper is not to develop a measuring instrument, notwithstanding the factors were crucial elements to elaborate each customer prototype.
4.4.1. Spectating Factor
The Spectating Factor characterizes those players who believe that the habit of watching live streams is a significant part of their lives. As a modality of participatory media, live streams encourage the production of user-generated video content and promote interaction between the audience and the streamer. Moreover, the streaming phenomenon is associated with the surge of new stereotypes, social labeling, herd mentality, intergroup bias, and parasocial relationships.
In a media-oriented society, parasocial contact is the concept that explains the seemingly intimate and genuine connection between live stream spectators and the performers. It is a dialogical networked interaction through which the streamer communicates with plenty of recipients and creates the impression of closeness and direct contact - it is a one-to-many interaction (Thompson, 2018).
Parasocial relationships involve the simultaneous online presence of the performer and the audience despite their geographical position (Thompson, 2018). It also requires both the viewers and the streamer to speak under the same code to avoid misunderstandings and guarantee the desired meaning of the dialog. Otherwise, symbolic cues and slangs are ineffective for communication.
Curiously enough, throughout the internet and digital worlds, it is not uncommon to witness congregations of people sprout around influential online figures they never actually met. These internet celebrities give people enough reason to gather, share similar behaviors and beliefs, organize, and differentiate themselves from other groups (Komutanont, Nuangjamnong, and Dowpiset, 2020). Schiappa, Gregg, and Hewes (2005) argue that inter and intragroup relationships within cyberspace mold a person’s worldview, induce new habits, and impact the form one perceives different social categories. In short, the streaming environment functions as an interactive realm in which players live virtual experiences that have real-life implications.
The social facet is one of the many reasons to spectate and commit to this habit. Sjöblom et al. (2017) identified five types of constructs that justify stream viewing. The first one is on the cognitive level. It expresses the desire to learn from streamers and improve as a player. The second one is in the affective domain. It is associated with the ability to identify or idolize the streamer and empathize with the community of viewers. The third and fourth ones are on the personal and social ambit. They are related to the competence for association and membership, developing an identity, and recognition among peers. Lastly, the fifth corresponds to tension release and escapism as means to deviate from the lack of satisfaction in private life.
It is noticeable that the relationship between the audience and the streamer is vital to the spectating experience. The mental sensation of engagement drives the viewers to participate and interact with others (Komutanont, Nuangjamnong and Dowpiset, 2020). In turn, real-time dialogs produce the feeling of emotional connectedness that is also a theoretical item in the next factor model. The following section treats the spectacle of eSports and how it is a supplement to the spectating topic and, by itself, a unique factor.
4.4.2. eSports Factor
The eSports Factor represents another spectating opportunity with its own set of characteristics. Intimately connected to the Spectating Factor, the eSports Factor addresses how the competitive scenery is structured from a managerial standpoint. As an external part of the contemporary gaming experience, eSports evolved from amateur underground competitions to dynamic international leagues, now associated with big brands, sponsors, and the traditional media (Macey et al., 2020).
Over the past decade, the gaming paradigm has changed. What once was a leisure activity is now one of the fastest-growing forms of new media and a competitive sport with multiple disciplines. Due to a mass of committed supporters and increasing numbers in the audience, electronic sports conquered the genuine recognition of the market as a profitable business investment (Julkunen et al., 2021).
Of collaborative nature, the eSports industry embraces several different business organizations, independent institutions, and individual actors to create a dynamic network of relations. The competitive gaming scene is of complex mold, and its origin is difficult to trace from a business management standpoint. Julkunen et al. (2021) say that because of its tight ties to technology and ever-changing structure, its organization is highly dependent on the synchronous function of each pillar: game developers, professional teams and players, leagues and tournaments organizers, sponsors, streaming platforms, fans, and larger audience, and sports governing bodies.
More and more, newcomer companies are joining the competitive gaming business. The adhesion of non-gaming enterprises raised the eSports industry to a higher level, not only in revenue but in complexity. The eSports experience is even closer to traditional sports (Qian et al., 2019). Viewership, news, consumption, and brand relationship are all affected by the arrival of new firms in the scenery (Macey et al., 2020).
When discussing business competition, it is fundamental to remember that the field of eSports marketing is still in maturation and its properties are yet to be defined. Nonetheless, it is possible to affirm that competitive gaming viewership struggles with individual streamers and traditional sports in the battle for the audience (Macey et al., 2020). Fan attention is split among many screens, streaming platforms, agents, and organizations (Komutanont, Nuangjamnong and Dowpiset, 2020).
Since there are too many game titles, it is very demanding to seize the consumers’ attention. Competitive gaming broadcasters must invest in meaningful advertising strategies and aim for the right niche to raise engagement rates in each segment. In truth, engagement and viewing are two essential ingredients that define the likelihood of eSports industry success (Sjöblom et al., 2017).
4.4.3. Performance Factor
The Performance Factor treats the sense of achievement in the ambit of gaming. This factor intends to understand how competition motivates players to polish their gaming skills and strive for even higher ranks on the ladder. More specifically, the motivational appeal and the feeling of competence derived from resolving gaming tasks, mastering challenges, and demonstrating ability.
Competition is a formative element of most electronic games. It either describes an individual or a party of players challenging computer-controlled enemies, environmental obstacles, or facing enemy players in belligerent activity (García & Murillo, 2020). Competition is a common mechanism that instigates gamers to improve and seek extrinsic rewards. More than that, it is a component of human nature.
In the light of the theory, performance and self-efficacy are all types of goal structures utilized to comprehend behavior associated with the success or failure of a particular task. The concept of goal is generally considered a future-focused hypothetical representation of a person’s end state (Hulleman et al., 2010). This notion of goal must not be mistaken for the desired outcome, albeit as a process that determines an individual’s comportment in face of achievement situations. Kaplan and Maehr (2007) emphasize that goals-as-purposes is not a universally agreed conceptualization.
To clarify how goal-structures function in the ambit of gaming, especially in competitive play, it is necessary to distinguish self-efficacy from performance-approach goals and mastery-approach goals. Self-efficacy is an ex-post self-evaluation of the amount of effort expended to achieve aimed in-game results (Trepte & Reinecke, 2011). In contemporary games, especially in competitive titles, it has become common practice the inclusion of interactive dashboards to inform players about their performance. Games offer exclusive feedback with performance indexes as parameters for assessment and comparison among players.
While performance is a multidimensional concept that defines the degree of proficiency associated with a specific endeavor and the act or process of performing a particular activity. Performance-goals refer to individuals who aim to accumulate recognition for their performance. According to Ziegler, Dresel, and Stoeger (2008), performance-goals have two psychological ramifications: a normative component that expresses the desire to perform better than others and an appearance component when one tries to look skilled or better to other people. In online games, performance manifests as ranking systems which allocate players according to their feats, victories, possessions (e.g., currency or equipment), skills, or power level.
Finally, mastery-goals represent an approach of complex composition with multifarious psychological branches. Although a common root exists grounded on the ideas of learning and skill development, literature presents different interpretations of mastery-goals: an intrinsic motivation based on curiosity, the objective to excel or develop a specific competence, the fulfillment of one’s true potential, the ability to perform better than in the past, a preference for challenging activities, and the pursuit for absolute mastery (Hulleman et al., 2010). Regardless of the diversity in interpretations, the mastery-goals theory is sufficiently consistent when explaining a player’s internal disposition to strive and exceed prior performance.
4.4.4. Privilege Factor
From the early 2010s, monetization practices in games went through a significant transformation marked by the emergence of microtransactions – small amounts of real currency exchanged for in-game products or virtual currency (Zendle, Meyer, Ballou, 2020). The Privilege Factor describes the relationship between macrotransactions and the advantages exclusive to a selected few.
In the earliest stages, microtransactions were part of a monetization system in free-to-play games that allowed players to make small purchases and improve the gaming experience (Zendle, Meyer, Ballou, 2020). At some point, the situation changed, and games started to offer more options for sale. The sales catalog went from selling cosmetic items and exclusive mounts to boosters, unbalanced power-ups, and unique equipment with better status. Initially, only those who could afford to spend a small sum of money would relish the microtransactions system (Darakjian, 2016). This paradigm shift explains the rise of the pay-to-win scheme and led to the first studies about in-game purchases and customer behavior in virtual worlds.
With the popularization of microtransactions and the implementation of new payment methods, in-game purchases became the norm. Many titles introduced purchasing alternatives according to the game genre and the organizations’ interest (Darakjian, 2016). Among the options, it is commonly seen: the season pass, players receive extra rewards by playing the game, completing missions and leveling up their accounts, tie-in sales, when players can obtain a specific virtual asset only if they agree on buying another tied good or service, and macrotransactions, players started to spend large, in some cases unreasonable, amounts of money in exclusive in-game services and products.
Initially, games recompensed players’ skill, patience, and determination. Now, more than ever, players who spend more have the capacity to disrupt the power balance in a game. This phenomenon mimics the concept of privilege, which characterizes particular groups who benefited from entitlements and advantages granted to them, not earned by merit, ability, or hard work (Black & Stone, 2005).
4.5. The Four Clusters
This section is dedicated to the development of four customer profiles. The proposal involves introducing and narrating the life stories of four different gamers in daily situations. Each gamer profile represents a group of consumers with distinctive characteristics. However, before presenting the representatives, it is necessary to demonstrate how the grouping procedure happened. Table 8 presents the number of cases in each cluster and the names of each representative from the customer profiles.
Table 8. Number of cases in each Cluster
|
Cluster 1 – Representative: Matt Havertz |
433.000 |
|
Cluster 2 – Representative: Maicon dos Santos |
324.000 |
|
Cluster 3 – Representative: Anna Canterbury |
371.000 |
|
Cluster 4 – Representative: Emannuel Rizal |
461.000 |
|
Valid |
1588.000 |
Source: own elaboration
The number of cases demonstrates which representative share more distinguishing qualities across the broader segment of players. Emmanuel Rizal is the representative of the largest cluster, while Maicon dos Santos represents the smallest cluster. Results suggest that there are no underrepresented groups of players. Following, Table 9 indicates the positioning of each cluster center.
Table 9. Final Centers of Each Cluster
|
Cluster |
||||
|
Factor |
1 |
2 |
3 |
4 |
|
Spectating Factor |
.78879 |
-.59779 |
-.56756 |
.13602 |
|
eSports Factor |
-.68043 |
-.17729 |
-.49845 |
1.16484 |
|
Performance Factor |
.25712 |
-1.21155 |
.49665 |
.21031 |
|
Privilege Factor |
-.46800 |
-.30784 |
.86284 |
-.03846 |
Source: own elaboration
Table 10 displays the results of the F-test (factorial ANOVA). Note that the larger the F-statistic, the better the corresponding factor is in discriminating clusters.
Table 10. ANOVA test
|
Cluster Mean Square |
df |
Error Mean Square |
df |
F |
Sig. |
|
|
Spectating Factor |
171.075 |
3 |
.678 |
1585 |
252.290 |
.000 |
|
eSports Factor |
309.446 |
3 |
.416 |
1585 |
743.523 |
.000 |
|
Performance Factor |
205.370 |
3 |
.613 |
1585 |
334.927 |
.000 |
|
Privilege Factor |
134.144 |
3 |
.748 |
1585 |
179.339 |
.000 |
Source: own elaboration
According to Table 10, the most influential factor is the eSports Factor, and the least impactful is the Privilege Factor. Overall, the results confirm that the clusters are sustainable and reach a satisfactory level of significance (Sig.).
All the statistical operations served as the foundation for the customer profiling method. To achieve the desired effect and offer marketing professionals a consistent picture of the gamers’ lifestyle, the design of each profile contains fictitious literary elements combined with the collected data.
4.5.1. Matt Havertz
Matt is a 20-year-old American who lives in a suburb of Sacramento, California. He is dedicated exclusively to his undergraduate education at Sacramento State University, where he studies Production Engineering. Matt loves eSports and is a die-hard fan of Cloud 9. He follows all of his team’s matches on streaming platforms and is very dedicated when training, reviewing his matches, and analyzing pro plays. Matt watches the best highlights of the week on YouTube Saturday night.
Figure1. Matt Havertz Profile Overview
Despite being a critic of the latest performances of Cloud 9, he remains loyal to the team. As a social media enthusiast, he uses forums, such as Reddit, every day to discuss games, catch up on the news and work as a forum moderator. He takes time to develop his image on all the social networks he is involved in and is recognized for it. His biggest concern is how his fellow students see him, so he always tries to do as well as he can when he plays and spends substantial amounts on virtual purchases to give himself some advantage.
Due to the considerable amount of time spent on social media, Matt is on good terms with some League of Legends influencers and streamers. In fact, from time to time, he donates small sums of money when his favorite streamer, TF Blade, performs a flashy move or freaks out. Of course, he keeps these donations a secret so his parents will not call him out.
It is funny to say, but Matt is not that sociable out-game. Actually, from his active engagement on social media, people perceive him as a chatterbox when, in reality, he is painfully shy. He does have a bunch of friends, although all of them are gamers or anime lovers. No, no girls were found on his contacts list.
As far as in-game purchases go, Matt regularly spends USD 25.00 per month on various microtransactions. These purchases are conversation topics in both the virtual and real worlds. Whenever he gets a new skin, he often attracts much praise. However, note that, as he purchases regularly, Matt does not get any sense of emotion from purchasing since it has become routine.
As a matter of fact, spending money on skins and season passes is nothing new for Matt. He began when he was 16 years old and got hooked pretty quickly. Just because his budget has grown over the years does not mean he can spend just like that. Despite his age, he is immature. It is a secret, but he was not admitted to the internship last summer. It is all his parents’ money. Why does he do that? He sure likes being in the spotlight, but only in-game.
4.5.2. Maicon dos Santos
Figure 2. Maicon dos Santos Profile Overview
Maicon dos Santos is a young adult who began his life in a neighborhood west of Rio de Janeiro, Bangu. Working as a motorcycle courier and being the eldest son in a family of four, Maicon tries to raise money to buy his mother and other younger siblings a new home. In Bangu, opportunities are scarce. Consequently, Maicon did not conclude his secondary education since he had to start working at an early age to help raise the household income.
Maicon started gaming in his free time, in between deliveries. His colleagues introduced him to Free Fire, a survival shooter game for mobile. Initially, he only watched streams on Nimo TV. As he quickly got the gist of the game, Maicon saw a great chance. What if he became a professional streamer? In his head, he could team up with his friends and make up a live comedy show while playing his favorite game. Additionally, he could improve his financial situation by working as a streamer in his off time.
The young man is very aware that becoming a professional streamer is a tough challenge, especially in the beginning. But, as a man of faith, Maicon believes this is his way. About the subject, Maicon grew up in a very religious environment. He is a fervent evangelical Christian and truthfully believes the Lord will provide and suffice his needs. For this reason, he is always sharing religious content on his social media mixed with Nimo TV profile. Most of his followers think he is more a Christian influencer than a comedy streamer.
4.5.3. Anna Canterbury
Figure 3. Anna Canterbury Profile Overview
Anna is a very easy-going girl, and everybody loves to get together with her. At first sight, nobody could tell she was a gamer, and it is even harder to imagine that she is also an amateur streamer. Her journey as a gamer started in university, more precisely, in the undergraduate course of Journalism at Dublin City University.
For the class of Journalism Portfolio, Anna had to cover ESI London. An event that brought the most influential heads of the eSports industry to debate the future of competitive gaming. She was delighted, and at the end of the convention, Anna made up her mind – she was going to follow the gaming path. Unsure about what to do or where to go, she received an unexpected invitation on her social media. Anna felt awkward at first. A young male she has never seen before has requested her presence at the Game Awards Show, one of the most celebrated gaming ceremonies.
Anna freaked out. It was an all-inclusive trip to Los Angeles not to cover the ceremony but as an honors guest. Who was this man? A few years later, at the age of 27, the not-so-young Anna calls him husband. That man was the owner of an eSports team in the Republic of Korea, and now he lives in Dublin as a strategic manager in the Activision/Blizzard studio.
So the couple could spend more time together, Anna’s husband asked her to work a part-time job. The ginger girl accepted the suggestion since her beloved would bear all their expenses. From then on, their routine involves going to pubs for a drinking night, dining in fine restaurants, and enjoying some of her favorite coffee drinks: the iced latte and the Frappuccino. During the weekends, they play together League of Legends while streaming as a couple. That is her time to release tension and set the world on fire as a main Brand player.
4.5.4. Emmanuel Rizal
Figure 4. Emmanuel Rizal Profile Overview
Emmanuel always dreamt of being a boxer like his idol, the star Manny Pacquiao. Unfortunately, the kid neither had the talent nor the physical build for the sport. He tried playing basketball, his second favorite modality, but was not tall enough. Emmanuel thought he could succeed playing sepak takraw, but he was terrible and would not show signs of improvement.
A lost cause, many would say. Indeed the boy was, at least for sports. Desolated, Emmanuel would always help his father with the cleaning chores in his hair salon near the center of Manila after school. It was a quiet life, albeit satisfying. Emmanuel lost his mother in a car accident, and his father alone would take care of him. They both trusted and supported each other.
In school, Emmanuel was not very popular. His social circle consisted of his two best friends, who spent the entire break playing mobile games. The young Filipino did not have his smartphone, so his former friends started to push him aside. However, his father was strongly connected to him and very caring. A few weeks later, when Emmanuel silently arrived home, a gift was waiting for him – a flagship smartphone.
In possession of a powerful device in his hands, Emmanuel appeared before his past friends and flaunted them: I’ll defeat you in any game we play”. Fate/Grand Order was not a game about skills. On the contrary, it was about luck, and the young Filipino was blessed. Proof of that was his loving father - a man that would not measure any sacrifices to see his son happy.
5. Discussion
A customer profile design with satisfactory prevalence requires empirical evidence to support the claims that they illustrate factual information about groups of individuals. On the one side, there is no need to create a data-focused profile for the sake of making it realistic. On the other side, if a customer profile is not tied-to-real data, it can be considered a creative sketch at best (Salminen et al., 2018).
So what’s the best way to create credible customer profiles? It is a narrative profiling process supported by empirical data. Developing realistic scenarios to situate fictional characters is more advantageous than unmixed methods (Wasinsk, 1994). It is a humanistic approach to empower the user and make representations that go beyond a user-centered scheme (Madsen & Nielsen, 2010; Nielsen, 2012). It draws the customer profile design closer to film scriptwriting.
By this approach, the decision-maker can see the life-movie of the customer profile portrayed. This format is used to express verbally the life story of an individual who represents a group of people, just like a scene from a movie. Also, it is a suited technique to externalize a character’s internal feelings and motivations, which are determinants for a well-grounded profiling process (Wasinsk, 1994; Nielsen, 2012; Salminen et al., 2021).
With this in mind, a concept of a gaming lifestyle is very fit since it frames minor and major aspects of a gamer’s behavior, routine, and habits (Machado Dutra, 2021). This process aids the creation of more engaging and relatable profiles, especially when in combination with a good narrative. Story-telling facilitates the immersion required for designing complex individuals (Madsen & Nielsen, 2010).
The downside of this approach is that it may hinder universalization. It is up to the development team to define the degree of representativeness on a scale of how fictional or realistic the profile should be. It can be challenging to grasp minor details about one’s life - to the point of creating a true-to-life customer profile - thus, creativity comes into play (Wasinsk, 1994).
Customer profiling is a tool to communicate creatively instead of reports and piles of paper (Salminen et al., 2021). It should avoid stereotypical representations focusing on the balance between commonalities and individualities. In this regard, lifestyle data provides enough information to make inferences about the individual’s life and fuel representative narratives for profiling scenarios.
5.1. Managerial Implications
The main managerial implication of this research was to suggest an alternative tool for customer lifestyle visualization in the era of privacy-protected user analytics. The relevancy of this topic is due to the advent of specific legislation concerning data privacy and the law enforcement towards governmental and private institutions to safeguard sensitive information.
It is natural to expect that, at some point in the future, enterprises and other organizations will not be able to recur to user analytics for commercial purposes by virtue of recent legislation that encourages fair information practices. With that in mind, current marketers must adapt to the new legal framework and seek other targeting and segmentation solutions. A viable option would be employing the customer profiling technique, which utilizes creative inference when there is a shortage of customer information.
6. Conclusion
The customer profiling technique does fulfill its purpose as a lifestyle visualization tool. In the case of the gamer segment, it allows decision-makers to understand what unarticulated needs this segment has and perceive that customers are not a bundle of shallow statistics. Risk being too narrow, the customer profiling method diverts from traditional approaches by resorting to creativity when the data lacks information and focusing on latent attributes instead of superficial or objective characteristics. In conclusion, customer profiling is a powerful tool for generating insights about clients and assisting in market segmentation operations.
6.1. Limitation and Recommendations for
Further Work
The principal limiting factor was the prevalence of North American respondents, which restricts the generalization capability of the research and, despite the sample size (N = 1.588), potentially increases ascertainment bias. Possibly, to avoid distortions in data collection, one should include respondents from other gaming communities instead of an array of subreddits.
For future study: (1) inclusive sample composition to augment data representativeness. (2) The employment of the customer profiling technique in other market segments. (3) The appliance of more advanced statistical methods such as the partial least squares equation modeling (PLS-SEM), which estimates the relationships between the observed data and latent variables; (4) Investigate to which extent creative inference positively contributes in lieu of data usage.
6.2. Disclosure Statement
No potential conflict of interest was reported by the author.
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