Prediction of Consumer Behaviour Regarding the Key Elements of the Packaging Design of an Educational Toy

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Article An Adaptive Machine Learning Methodology Applied to Neuromarketing Analysis: Prediction of Consumer Behaviour Regarding the Key Elements of the Packaging Design of an Educational Toy

David Juárez-Varón 1,* , Victoria Tur-Viñes 2 , Alejandro Rabasa-Dolado 3 and Kristina Polotskaya 3

1 Department of Mechanical and Materials Engineering, Universitat Politècnica de València, Camino de Vera, s/n, 46022 Valencia, Spain 2 Department of Communication and Social Psychology, Universidad de Alicante, Carretera de San Vicente del Raspeig s/n, 03690 San Vicente del Raspeig, Spain; [email protected] 3 Department of Statistics, Mathematics and Informatics, Miguel Hernández University, Avenida de la Universidad, s/n, 03202 Elche, Spain; [email protected] (A.R.-D.); [email protected] (K.P.) * Correspondence: [email protected]

Received: 13 August 2020; Accepted: 9 September 2020; Published: 19 September 2020

Abstract: This research is in response to the question of which aspects of package design are more relevant to consumers, when purchasing educational toys. Neuromarketing techniques are used, and we propose a methodology for predicting which areas attract the attention of potential customers. The aim of the present study was to propose a model that optimizes the communication design of educational toys’ packaging. The data extracted from the experiments was studied using new analytical models, based on machine learning techniques, to predict which area of packaging is observed in the ﬁrst instance and which areas are never the focus of attention of potential customers. The results suggest that the most important elements are the graphic details of the packaging and the methodology fully analyzes and segments these areas, according to social circumstance and which consumer type is observing the packaging.

Keywords: packaging; design; toy; neuromarketing; eye tracking; machine learning; predictive models; consumers; methodology; communication

1. Introduction A toy is a creation designed to stimulate and accompany a game (Espinosa 2018). It can be artisanal or industrial and it stimulates childrens’ imagination, language, memory, creativity, movement, etc., according to their age and needs. Consequently, a toy turns children into protagonists (AIJU 2019), improving the expression of their feelings, promoting positive aspects of their personality, providing learning, and helping them to grow (Peris-Ortiz et al. 2018). A game offers children the possibility of representing the world around them and their social values, by imitating or copying what they see and live in their daily lives. The child interrelates a game with their environment and previous experiences (Martínez-Sanz 2012), which allows the child to reinforce their self-image, express feelings, fears, and concerns, as well as use the game as a way to resolve conflicts (Espinosa 2018). Design is a creative act (T. B. Lawrence and Phillips 2002) that defines objects (Bloch 1995), by working with the intangible to create meaning at different cultural levels (AEFJ 2019; Starks 2014). There is a risk of standardization (Luˇcićet al. 2019), an aspect that allows the designer to operate without compromising sensitivity and creativity.

Soc. Sci. 2020, 9, 162; doi:10.3390/socsci9090162 www.mdpi.com/journal/socsci Soc. Sci. 2020, 9, 162 2 of 23

A toy is considered to be a cultural product (Jones et al. 2015) which reinforces real-life concepts. (AIJU 2019). Signs used in the design of a toy have significant meaning, which promote the interpretation of a toy and, in this way, children represent images, characters, and scenes from the real world, as well as interact with their fantasies or other children. In turn, a toy promotes physical and social competence; additionally, the child better understands their world by exploring the properties of the toy. A toy also promotes physical and mental exercise, since it stimulates the imagination. The selection of toys by adults sets the trend in consumption. Packaging is a fundamental part of a product that produces communication and sales, in addition to ensuring a product arrives in optimal condition. (American Marketing Asociation 2013). Packaging, as an element, is designed and has become a useful means to protect the product. In addition, it has a visual function (Nancarrow et al. 1998), it communicates silently (Tur-Viñes et al. 2014), and mainly fulfils the following three functions (Lamb 2008): product protection (breakage, light, temperature, etc.); product promotion (differentiation from competitors and association with the brand by design, colors, shapes, and materials); and ease of product management (storage, use, and disposal). Packaging uses visual grammar as a way of organizing signifiers and meanings, and form as a basic visual element, which become important when used together (Vilchis 2008). The toy sector has become increasingly concerned about the packaging that surrounds toys (Soluciones-Packaging 2016), since it plays a fundamental role in the toys. In addition to protecting products (many of them delicate and with small parts), packaging plays a fundamental role in communicating its value and philosophy to parents and children (K. Lawrence et al. 2015). Toy packaging must be efficient, cost-effective, and should facilitate, as much as possible, both protection of a product and attraction to a product on shelves. The choice of the game or toy must consider the transmission of social values, to promote social relationships (Espinosa 2018). An educational toy stimulates intellectual development through reasoning, attention, imagination and creativity, and mastery of language. It is an attractive resource to reinforce children’s formal content learning, such as numbers and letters, and it helps children acquire values such as respect and tolerance towards people, norms and rules, and the promotion of socialization (Luévano Torres 2013). A game is a tool through which many questions can be explained and answered (AIJU 2019). Examples of educational games include simple scientific experiments; puzzles; creative games; games that help you learn geography, history, science, etc.; as well as interactive games on electronic devices (book games, math and reading games, music games, memory and logic games, pattern recognition, games that work on social and emotional skills, applications that help you learn geography, history, science, etc., and the electronic version of creative puzzles or games). An educational toy has a different utility approach than other toys, where the different variables are work, in addition to leisure, and therefore it is interesting to know the information processing in making purchasing decisions. The analysis of consumer behavior in relation to products whose purchase decisions involve emotional aspects, requires using neuromarketing techniques and generating a large amount of data for analysis that, until now, is processed using software for each technology and basic statistics (Juarez et al. 2020; Mañas-Viniegra et al. 2020). However, when it comes to tackling complex analytical problems, the scientific community is openly committed to the data-driven approach, which generates predictive models, solely from the data itself, especially suitable to be integrated into decision support systems (DSS) (Provost and Fawcett 2013). The application of machine learning techniques in the field of eye tracking studies where there is a very high dispersion of the data, produced by the heterogeneity of the users under study, with apparently arbitrary behaviours, and therefore more difficult to predict, leads to a specific preprocessing of experimental data, in order to subsequently be able to apply discrete predictive techniques with greater tolerance to prediction error. Soc. Sci. 2020, 9, 162 3 of 23 Soc.Soc. Sci.Sci. 20202020,, 99,, xx FORFOR PEERPEER REVIEWREVIEW 33 ofof 2323

2.2. ResultsResults

2.1.2.1. ResultsResults InterpretationInterpretation frfr fromomom NeuromarketingNeuromarketing Approach Approach AfterAfter showing showing each each product product (image) (image) separately separately on on a a screen screen for for 30 30 s s (estimated(estimated time time so so thethe detailsdetails ofof interestinterest can can be be seen, seen, as as itit isis notnot possiblepossible toto opop openenen thethe toy),toy), biometricbiometric eyeeye trackingtracking waswas used,used, withwith thethe aimaim of of simulating simulating a a consumer’s consumer’s experienceexperience whenwhen tata takingkingking productproduct 11 oror 22 from from store store shelves. shelves. FigureFigure 11 showsshows areas areas of of interest interest (AOI) (AOI) of of the the Educa Educa packaging packaging design. design.

Figure 1. Product 01. Image of the Educa packaging with its areas of interest (AOI). Source: Prepared FigureFigure 1.1. ProductProduct 01.01. ImageImage ofof thethe EducaEduca papackagingckaging withwith itsits areasareas ofof interestinterest (AOI).(AOI). Source:Source: PreparedPrepared by the authors. byby thethe authors.authors. Figure2 shows a heat map of the distribution of the di ﬀerent ﬁxings on the packaging, for all Figure 2 shows a heat map of the distribution of the different fixings on the packaging, for all usersFigure with the 2 shows following a heat parameters: map of the distribution of the different fixings on the packaging, for all usersusers withwith thethe followingfollowing parameters:parameters: Gaze (accumulated time displayed), 30 s; • •• GazeGaze (accumulated(accumulated timetime displayed),displayed), 3030 s;s; Size (focus representation size), 35%; • •• SizeSize (focus(focus representationrepresentation size),size), 35%;35%; •Transparency (level of transparency of the representation), 40%. • • TransparencyTransparency (level(level ofof transparentransparencycy ofof thethe representation),representation), 40%.40%.

Figure 2. Heat map of Educa packaging for all consumers. Source: Prepared by the authors. FigureFigure 2.2. HeatHeat mapmap ofof EducaEduca packagingpackaging forfor allall consconsumers.umers. Source:Source: PreparPrepareded byby thethe authors.authors.

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Once the areas of interest (AOI) were identiﬁed, which in this case corresponded to the Educa Soc. Sci. 2020, 9, x FOR PEER REVIEW 4 of 23 brand packaging (a total of seven areas of interest), the software allowed us to draw conclusions drawn regardingOnce the attentionthe areas of interest users (Table (AOI)1 were). identified, which in this case corresponded to the Educa brand packaging (a total of seven areas of interest), the software allowed us to draw conclusions Tabledrawn 1. regardingInformation the fromattention the set of ofusers users, (Table about 1). areas of interest of the Educa toy, using eye tracking. Source: Prepared by the authors. Table 1. Information from the set of users, about areas of interest of the Educa toy, using eye tracking. AOI Ave Source: Prepared by the authors. Ave Ave Duration Total Time to Ave AOI AOI AOI Viewers Ave Time Time Revisitors (sec—U = Viewers 1st Ave Fixations Name StartAOI (s) Duration (#) Time to Viewed Viewed (#) AOI User Viewers Total(#) ViewAve Time Time Ave (#) Revisitors Start (sec—U = 1st (s) (%) Name Controlled) (#) Viewers (#) (s)Viewed (s) Viewed Fixations (#) (#) (s) User View (%) AOI 0 0Controlled) 30 22 25(s) 7.63 1.79 5.97 6.09 20 AOI 1AOI 0 0 0 30 30 22 24 25 25 7.63 5.67 1.79 5.04 5.97 16.80 6.09 15.17 20 24 AOI 2AOI 1 0 0 30 30 24 22 25 25 5.67 12.82 5.04 0.44 16.80 1.47 15.17 2.18 24 9 AOI 3AOI 2 0 0 30 30 22 14 25 25 12.82 9.30 0.44 0.68 1.47 2.26 2.18 2.78 9 9 AOI 4AOI 3 0 0 30 30 14 23 25 25 9.30 5.49 0.68 0.73 2.26 2.45 2.78 4.39 9 18 AOI 5AOI 4 0 0 30 30 23 23 25 25 5.49 2.68 0.73 1.99 2.45 6.66 4.39 10.48 18 23 AOI 6AOI 5 0 0 30 30 23 24 25 25 2.68 1.05 1.99 7.85 6.66 26.17 10.48 25.54 23 24 AOI 6 0 30 24 25 1.05 7.85 26.17 25.54 24

The followingThe following conclusions conclusions can can be be drawn drawn from from thisthis summary: In theIn Educa the Educa packaging, packaging, the specificationthe specification of the of themesthe themes on the on cover the cover (AOI (AOI 1) draws 1) draws much much attention. In addition,attention. viewers In addition, look atviewers the recommended look at the recomme age (AOInded 3). age Not (AOI much 3). Not attention much attention is paid to is the paid brand to or the namethe brand of the or game the name (AOI of 2the and game 5). Product(AOI 2 and image 5). Product stands image out (AOI stands 6). out A (AOI priori, 6). the A priori, concepts the that attractconcepts the most that attention attract the are most the attention child’s hands are the and child’s the hands play setting and the (background play setting (background image) on theimage) cover of the Educaon the packaging, cover of the together Educa packaging, with the specification together with of the the specification themes. The of firstthe themes. thing observed The first bything users is the productobserved (AOI by users 6), with is the a timeproduct of 1.05(AOI s, 6), shown with a by time almost of 1.05 100% s, shown of users, by almost and a 100% number of users, of fixations and of a number of fixations of 25.5. The number of times the user looks at that area again is 6.4 and the most 25.5. The number of times the user looks at that area again is 6.4 and the most revisited by the user is revisited by the user is area AOI 5, with 7.0 revisits. area AOIFigure 5, with 3 7.0shows revisits. areas of interest (AOI) of the Diset packaging design. Figure3 shows areas of interest (AOI) of the Diset packaging design.

Figure 3. Product 02. Image of the Diset packaging with its areas of interest (AOI). Source: Prepared by Figure 3. Product 02. Image of the Diset packaging with its areas of interest (AOI). Source: Prepared the authors. by the authors. Some of the conclusions that can be drawn in this case are that the data is shown without Some of the conclusions that can be drawn in this case are that the data is shown without diﬀerentiatingdifferentiating the the gender, gender, overlapping overlapping thethe informationinformation of of each each individual individual heat heat map map (Figure (Figure 4) 4) correspondingcorresponding to eachto each user, user, and and using using the the team’steam’s own software software (sum (sum of ofdedication dedication times times to each to each regionregion of the of image,the image, generating generating a newa new added added heatheat map).map). The The parameters parameters configured conﬁgured for this for thistype typeof of representation,representation, for allfor analysisall analysis work, work, were were the the following: following: • Gaze (accumulatedGaze (accumulated time displayed), time displayed), 30 s; 30 s; • • Size (focus representation size), 35%; Size (focus representation size), 35%; • • Transparency (level of transparency of the representation), 40%. Transparency (level of transparency of the representation), 40%. • Soc. Sci. 2020, 9, 162 5 of 23 Soc. Sci. 2020, 9, x FOR PEER REVIEW 5 of 23

Figure 4. Heat map of Diset packaging for all consumers. Source: Prepared by the authors. Figure 4. Heat map of Diset packaging for all consumers. Source: Prepared by the authors. Once the areas of interest (AOI) were identified, which in this case corresponded to the Diset Once the areas of interest (AOI) were identified, which in this case corresponded to the Diset brand packaging (a total of seven areas of interest), the software allowed us to draw conclusions brand packaging (a total of seven areas of interest), the software allowed us to draw conclusions regarding the attention of users (Table2): regarding the attention of users (Table 2): Table 2. Information from the set of users, about areas of interest of the Diset toy, using eye tracking. Table 2. Information from the set of users, about areas of interest of the Diset toy, using eye tracking. Source: Prepared by the authors. Source: Prepared by the authors. AOI Ave Ave Ave AOI AOIDuration Total AveTime to Ave AOI Viewers Time AveTime Revisitors AOIStart Duration(sec—U = ViewersTime to1st FixationsAve NameAOI Viewers(#) Total AveViewed Time ViewedTime Revisitors(#) Start(sec) (sec—UUser = (#) 1stView Fixations(#) Name (#) Viewers (#) Viewed(s) (s) Viewed(%) (#) (sec) Controlled)User View(s) (#) (%) AOI 0 0Controlled) 30 23 25(s) 3.22 3.82 12.75 11.69 23 AOIAOI 1 0 0 0 30 30 23 25 25 253.22 6.503.82 2.08 12.75 6.94 11.69 6.04 23 22 AOIAOI 2 1 0 0 30 30 25 18 25 25 6.50 9.04 2.08 0.59 6.94 1.96 6.04 2.94 22 14 AOIAOI 3 2 0 0 30 30 18 22 25 25 9.04 9.68 0.59 1.24 1.96 4.12 2.94 3.77 14 15 AOIAOI 4 3 0 0 30 30 22 23 25 25 9.68 6.38 1.24 1.24 4.12 4.14 3.77 6.04 15 20 AOIAOI 5 4 0 0 30 30 23 23 25 25 6.38 2.42 1.24 1.56 4.14 5.20 6.04 7.69 20 23 AOIAOI 6 5 0 0 30 30 23 25 25 25 2.42 0.42 1.56 16.21 5.20 54.03 7.69 52.08 23 25 AOI 6 0 30 25 25 0.42 16.21 54.03 52.08 25 The following conclusions can be drawn from this summary: The following conclusions can be drawn from this summary: In the Diset packaging, the specification of the number of topics and questions/answers on the In the Diset packaging, the specification of the number of topics and questions/answers on the cover (AOI 0) is very striking. In addition, users looked at the message “when you hit...” (AOI 1) and cover (AOI 0) is very striking. In addition, users looked at the message “when you hit...” (AOI 1) and the product reference (AOI 5). Not much attention is paid to the brand, the recommended age, or the the product reference (AOI 5). Not much attention is paid to the brand, the recommended age, or the name of the game (AOI 2, 3, and 4). What is most striking, with more than 50% of the observation time, name of the game (AOI 2, 3, and 4). What is most striking, with more than 50% of the observation is the image of the product (AOI 6). A priori, the concepts that attract the most attention are the game time, is the image of the product (AOI 6). A priori, the concepts that attract the most attention are the setting (background image) of the Diset cover, along with the specification of the number of topics game setting (background image) of the Diset cover, along with the specification of the number of and questions. The first thing that users see is the image of the product (AOI 6), with a time of 0.42 s, topics and questions. The first thing that users see is the image of the product (AOI 6), with a time of shown by 100% of users and a number of fixations of 54.1. The number of times the user looks at the 0.42 s, shown by 100% of users and a number of fixations of 54.1. The number of times the user looks area again is 10.2, and it is the most revisited. These data are far superior to the Educa equivalents. at the area again is 10.2, and it is the most revisited. These data are far superior to the Educa equivalents.

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Soc.Soc. Sci. Sci.2020 2020, 9,, 9 162, x FOR PEER REVIEW 6 of6 of 23 23 2.2. Computational Experiment in the Educational Toy Industry 2.2. Computational Experiment in the Educational Toy Industry 2.2.Preprocessing Computational Procedure Experiment in the Educational Toy Industry Preprocessing Procedure PreprocessingIf we assume Procedure that the variable Time to 1st View (sec) explains when a certain area catches our attention,If we we assume could saythat that the Areavariable 6 (game Time image) to 1st caViewptures (sec) the explains attention when of most a certain users areafrom catches second our0, If we assume that the variable Time to 1st View (sec) explains when a certain area catches our whileattention, Area we2 (game could brand) say that can Area be visualized 6 (game image) up to catheptures 30th secondthe attention of the ofobservation most users (Figure from second 5). 0, attention, we could say that Area 6 (game image) captures the attention of most users from second 0, while Area 2 (game brand) can be visualized up to the 30th second of the observation (Figure 5). while Area 2 (game brand) can be visualized up to the 30th second of the observation (Figure5).

Figure 5. Box plot of Time to 1st View by the area of interest. Source: Prepared by the authors. Figure 5. Box plot of Time to 1st View by the area of interest. Source: Prepared by the authors. Figure 5. Box plot of Time to 1st View by the area of interest. Source: Prepared by the authors. Observing the values that the variable Time to 1st View (sec) takes according to the user (Figure Observing the values that the variable Time to 1st View (sec) takes according to the user (Figure6), 6), we can see that these are very dispersed, although they have similar averages. That is, in the study, we canObserving see that these the values are very that dispersed, the variable although Time to they 1st haveView similar(sec) takes averages. according That to is, the in user the study,(Figure there are people who took very little time to see all the areas and others who needed more seconds there6), we are can people see that who these took are very very little dispersed, time to see although all the areasthey have and otherssimilar who averages. needed That more is, in seconds the study, to to visualize one in particular, or several of them. Outliers for each user mostly come from Area 2 and visualizethere are one people in particular, who took or very several little of time them. to see Outliers all the for areas each and user others mostly who come needed from more Area seconds 2 and some from Area 3 (child’s age). someto visualize from Area one 3 in (child’s particular, age). or several of them. Outliers for each user mostly come from Area 2 and some from Area 3 (child’s age).

Figure 6. Box plot of Time to 1st View by the user. Source: Prepared by the authors. Figure 6. Box plot of Time to 1st View by the user. Source: Prepared by the authors. Figure 6. Box plot of Time to 1st View by the user. Source: Prepared by the authors.

Soc. Sci. 2020, 9, x FOR PEER REVIEW 7 of 23 Soc. Sci. 2020, 9, 162 7 of 23 Soc. Sci. 2020, 9, x FOR PEER REVIEW 7 of 23 Comparing the values that the variable takes according to the brand of the game (Figure 7), it can ComparingbeComparing seen that the thethe values timevalues it that takesthat the the 75% variable variable of users takes takes to according se accordinge the areas to theto of the brand the brand Diset of the ofbrand gamethe gameis (Figure less (Figure than7), itthat can7), of it beEduca,can seen be thatseenwhich the that may time the mean ittime takes a it more 75%takes ofeasily 75% users ofseen tousers seedesign theto se areasore themore of areas the interest Diset of the in brand Disetcertain is brand lessareas, than is although less that than of Educa,it that takes of whichveryEduca, little may which time mean may to amove moremean from easilya more one seen easily area design to seen another. ordesign more In or order interest more to interest indraw certain conclusions, in certain areas, althoughareas, a systematic although it takes study it verytakes of littlethevery Time time little toViewed time move to (sec)move from variable onefrom area one is to arearequired. another. to another. In order In order to draw to draw conclusions, conclusions, a systematic a systematic study study of the of Timethe Time Viewed Viewed (sec) variable(sec) variable is required. is required.

Figure 7. Box plot of Time to 1st View by the product. Source: Prepared by the authors. Figure 7. Box plot of Time to 1st View by the product. Source: Prepared by the authors. Figure 7. Box plot of Time to 1st View by the product. Source: Prepared by the authors. Carrying out a similar analysis of the variable, to see the differences in the values according to Carrying out a similar analysis of the variable, to see the differences in the values according to genderCarrying (Figure out 8), personala similar situation analysis (Figureof the variable, 9), and the to seenumber the differences of children in (Figure the values 10), no according significant to gender (Figure8), personal situation (Figure9), and the number of children (Figure 10), no significant differencesgender (Figure were 8), seen personal between situation them, (Figuretaking into 9), and acco theunt number the sample of children bias (in (Figure the sample 10), nowe significant have only differences were seen between them, taking into account the sample bias (in the sample we have only 42differences values from were individuals seen between who them, live as taking a “couple” into acco as untcompared the sample with bias 280 (ininstances the sample of “married”. we have only 42 values from individuals who live as a “couple” as compared with 280 instances of “married”. 42 values from individuals who live as a “couple” as compared with 280 instances of “married”.

Figure 8. Box plot of Time to 1st View by gender. Source: Prepared by the authors. Figure 8. Box plot of Time to 1st View by gender. Source: Prepared by the authors. Figure 8. Box plot of Time to 1st View by gender. Source: Prepared by the authors.

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Figure 9. Box plot of Time to 1st View by personal situation. Source: Prepared by the authors. Figure 9. Box plot of Time to 1st View by personal situation. Source: Prepared by the authors. Figure 9. Box plot of Time to 1st View by personal situation. Source: Prepared by the authors.

FigureFigure 10. Box10. Box plot plot of Timeof Time to to 1st 1st View View by by numbernumber of children. children. Source: Source: Prepared Prepared by the by authors. the authors. Figure 10. Box plot of Time to 1st View by number of children. Source: Prepared by the authors. Finally,Finally, Figure Figure 11 shows11 shows the the distribution distribution ofof the variable is is studied studied without without considering considering other other factors.factors. InFinally, this In this case, Figure case, it is it 11 observedis observedshows the that that distribution the the outliersoutliers of come, come,the variable for for the the mostis moststudied part, part, fromwithout from Area Areaconsidering 2 and 2 andalso from alsoother from the Educathefactors. Educa brand. In brand.this It case, is It known isit knownis observed that that these thatthese data the data outliers can can negatively ne come,gatively for a theaffectffect most thethe part, subsequentsubsequent from Area study. study. 2 and Therefore, Therefore, also from the initialthethe sample initial Educa sample and brand. the and It sample is the known sample without that without these outliers data outliers arecan analysednearegatively analysed inaffect parallel.in parallel. the subsequent study. Therefore, the initial sample and the sample without outliers are analysed in parallel. In the same way, other variables of interest, i.e., time viewed (sec), fixations (#), and revisits (#) are analysed. For the subsequent classification study, as this type of method requires, the already discretized objective variables must be available. The discretize function and the “frequency” method are used to separate the data into intervals according to the frequency of the values that belong to it. The number of sections (breaks) used for the discretization of each variable has been achieved by progressively increasing the number of sections. Then, the chosen number of breaks is the one that achieves a greater agreement between the model and the probability of being correct if a fully “random” distribution was followed. Soc. Sci. 2020, 9, 162 9 of 23 Soc. Sci. 2020, 9, x FOR PEER REVIEW 9 of 23

FigureFigure 11. 11.Box Box plot plot of of Time Time toto 1st1st View. Source: Source: Prepared Prepared by bythe the authors. authors.

2.3. FeatureIn Selectionthe same way, Depending other variables on Target of Variable interest, i.e., time viewed (sec), fixations (#), and revisits (#) Theare analysed. selection of variables to incorporate in the predictive models is a process of key importance in the proposedFor the methodology, subsequent classification since not all study, explanatory as this type variables of method are highlyrequires correlated, the already with discretized the variable objective variables must be available. The discretize function and the “frequency” method are used to be predicted (nor are they to the same extent). to separate the data into intervals according to the frequency of the values that belong to it. The Innumber addition, of sections the automatic (breaks) used feature for selectionthe discreti processzation of becomes each variable especially has been relevant achieved in problems by whereprogressively there is a sampleincreasing with the fewnumber records, of sections. compared Then, tothe the chosen number number of variables of breaks is measured the one that in each record,achieves as is thea greater case at agreement hand, as confirmedbetween the in model (Dernoncourt and the probability et al. 2014 ).of To being approach correct this if a task,fully there are di“random”fferent methods distribution to carrywas followed. out the selection of relevant characteristics. One of the most widely used is the support vector machine (SVM) (Chapelle et al. 2002). Another is the principal component analysis2.3. Feature (PCA) Selection method, Depending which has on alsoTarget undergone Variable interesting updates from the applied point of view (Peres-NetoThe et selection al. 2005 of). variables to incorporate in the predictive models is a process of key importance Inin thisthe proposed study, the methodology, variable ranking since not method all expl (Repositoryanatory variables 2020) isare used highly to selectcorrelated the mostwith relevantthe variables,variable on to each be predicted of the possible (nor are target they to variables. the same extent). BelowIn addition, is the variable the automatic ranking feature for the selection target process variable becomes fixations. especially The restrelevant of the in problems rankings can where there is a sample with few records, compared to the number of variables measured in each be consulted in the AppendixA (before the References section), where it can be seen that the record, as is the case at hand, as confirmed in (Dernoncourt et al. 2014). To approach this task, there set of significant variables is not always the same, nor do they occupy the same positions in the are different methods to carry out the selection of relevant characteristics. One of the most widely differentused rankings.is the support vector machine (SVM) (Chapelle et al. 2002). Another is the principal component Ifanalysis the variables (PCA) method, that most which influence has also the undergone objective interesting variable updates fixations from are the analyzed, applied thepoint results of view shown inSoc. Figure Sci.(Peres-Neto 2020 12, 9 ,are x FOR obtained.et al.PEER 2005). REVIEW 10 of 23 In this study, the variable ranking method (Repository 2020) is used to select the most relevant variables, on each of the possible target variables. Below is the variable ranking for the target variable fixations. The rest of the rankings can be consulted in the Appendix A (before the References section), where it can be seen that the set of significant variables is not always the same, nor do they occupy the same positions in the different rankings. If the variables that most influence the objective variable fixations are analyzed, the results shown in Figure 12 are obtained.

Figure 12. FeatureFeature selection selection output output for for fixations ﬁxations on the full sample. Source: Source: Prepared Prepared by by the the authors. authors.

This data shows that the number of fixations in a given area is mainly conditioned by the area in question, thus, it depends on the user, his personal situation, and later the brand of the game. Eliminating the outliers of the variable the results shown in Figure 13 are obtained.

Figure 13. Feature selection output for fixations on the sample without outliers. Source: Prepared by the authors.

This figure shows that the Media.Name (framed in green) variable is now more important than Personal.situation (framed in red).

2.4. Generating Predictive Classification Models There are different techniques for modeling or predicting categorical variables (variables that are either originally discrete in nature or are discretized). The most used technique is Classification Trees, where the main references are the ID3 algorithm (J. R. M. l. Quinlan 1986) and its different evolutions, such as C4.5 (J. R. Quinlan 2014), which incorporates a series of improvements, such as, for example, the possibility of dealing with numerical values among the explanatory variables. Another reference in the literature is the CART algorithm (Breiman et al. 1984), which is capable of carrying out both classification (on a categorical objective variable) and regression (on a numerical objective variable) tasks. In the case at hand, and after trying different classifiers, the authors have opted for RPart (an implementation of CART) from the R “rpart” library, which provides accurate predictive models and fairly easy interpretation.

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Soc. Sci.Figure2020, 912., 162 Feature selection output for fixations on the full sample. Source: Prepared by the authors.10 of 23

This data shows that the number of fixations in a given area is mainly conditioned by the area in question,This data thus, shows it depends that the on number the user, of ﬁxations his personal in a givensituation, area and is mainly later the conditioned brand of the by thegame. area in question,Eliminating thus, it the depends outliers on of the the user, variable his personal the results situation, shown andin Figure later the13 are brand obtained. of the game. Eliminating the outliers of the variable the results shown in Figure 13 are obtained.

Figure 13. Feature selection output for fixationsfixations on the samplesample withoutwithout outliers.outliers. Source: Prepared by the authors. This figure shows that the Media.Name (framed in green) variable is now more important than This figure shows that the Media.Name (framed in green) variable is now more important than Personal.situation (framed in red). Personal.situation (framed in red). 2.4. Generating Predictive Classification Models 2.4. Generating Predictive Classification Models There are different techniques for modeling or predicting categorical variables (variables that are There are different techniques for modeling or predicting categorical variables (variables that either originally discrete in nature or are discretized). The most used technique is Classification Trees, are either originally discrete in nature or are discretized). The most used technique is Classification where the main references are the ID3 algorithm (J. R. M. l. Quinlan 1986) and its different evolutions, Trees, where the main references are the ID3 algorithm (J. R. M. l. Quinlan 1986) and its different such as C4.5 (J. R. Quinlan 2014), which incorporates a series of improvements, such as, for example, evolutions, such as C4.5 (J. R. Quinlan 2014), which incorporates a series of improvements, such as, the possibility of dealing with numerical values among the explanatory variables. Another reference for example, the possibility of dealing with numerical values among the explanatory variables. in the literature is the CART algorithm (Breiman et al. 1984), which is capable of carrying out both Another reference in the literature is the CART algorithm (Breiman et al. 1984), which is capable of classification (on a categorical objective variable) and regression (on a numerical objective variable) carrying out both classification (on a categorical objective variable) and regression (on a numerical tasks. In the case at hand, and after trying different classifiers, the authors have opted for RPart objective variable) tasks. In the case at hand, and after trying different classifiers, the authors have (an implementation of CART) from the R “rpart” library, which provides accurate predictive models opted for RPart (an implementation of CART) from the R “rpart” library, which provides accurate and fairly easy interpretation. predictive models and fairly easy interpretation. Taking the Time Viewed objective variable as an example, and using the complete sample of the data, a tree has been obtained with an accuracy of 60.95% versus 54.84% of the model generated from the sample without outliers. In other words, the elimination of outliers does not improve precision, due to the small sample size. Figure 14 shows that the relevant variable when predicting the viewing time of an area is the area itself, due to its very different natures and sizes. If the area is AOI 6, then the maximum possible time is displayed with the probability of 89%. If the area is AOI 0, AOI 1, or AOI 5, then, the viewing time depends on the age of the child. In the case that the child is 11 years old or older, the areas with the probability of 100% is not displayed. In the case that the child is less than 11 years old, the areas are displayed during an interval of 1.96 to 4.3 s. Each node contains the following information: in the first row, the range of the target variable; in the second row, the probability of occurrence of each value of the objective variable (.41 means 41%); and the third row contains the total percentage of the sample that accumulates in that node. Soc. Sci. 2020, 9, x FOR PEER REVIEW 11 of 23 Soc. Sci. 2020, 9, x FOR PEER REVIEW 11 of 23 Taking the Time Viewed objective variable as an example, and using the complete sample of the data, Takinga tree has the been Time obtained Viewed withobjective an accuracy variable of as 60.95% an example, versus and 54.84% using of the the complete model generated sample of from the thedata, sample a tree haswithout been outliers. obtained In with other an words, accuracy the of elimination 60.95% versus of outliers 54.84% ofdoes the not model improve generated precision, from duethe sampleto the small without sample outliers. size. In other words, the elimination of outliers does not improve precision, due toFigure the small 14 shows sample that size. the relevant variable when predicting the viewing time of an area is the area itself,Figure due 14 toshows its very that differ the relevantent natures variable and sizes. when If thepredicting area is AOIthe viewing6, then the time maximum of an area possible is the timearea itself,is displayed due to withits very the differ probabilityent natures of 89%. and If sizes. the area If the is AOIarea 0,is AOI 6,1, thenor AOI the 5, maximum then, the viewingpossible timetime dependsis displayed on thewith age the of probability the child. In of the 89%. case If ththeat area the childis AOI is 0, 11 AOI years 1, orold AOI or older, 5, then, the the areas viewing with thetime probability depends on of the100% age is of not the displayed. child. In the In thecase case that that the childthe child is 11 is years less thanold or 11 older, years theold, areas the areas with arethe displayedprobability during of 100% an isinterval not displayed. of 1.96 to In 4.3 the s. case that the child is less than 11 years old, the areas are displayedEach node during contains an intervalthe following of 1.96 information: to 4.3 s. in the first row, the range of the target variable; in theEach second node row, contains the probability the following of occurrence information: of ineach the value first row,of the the objective range of variable the target (.41 variable; means 41%);in the and second the thirdrow, rowthe probabilitycontains the of total occurrence percentage of each of the value sample of thethat objective accumulates variable in that (.41 node. means Soc. Sci. 2020, 9, 162 11 of 23 41%); and the third row contains the total percentage of the sample that accumulates in that node.

Figure 14. Tree on the complete sample, to predict Time Viewed. Source: Prepared by the authors. Figure 14. Tree on the complete sample, to predict Time Viewed. Source: Prepared by the authors. Figure 14. Tree on the complete sample, to predict Time Viewed. Source: Prepared by the authors. Similarly, the decision tree using the sample can be interpreted, eliminating from it the outliers Similarly, the decision tree using the sample can be interpreted, eliminating from it the outliers of theSimilarly, Time Viewed the decision variable. tree In usingthis case, the sampleit is observed can be thatinterpreted, a new factor eliminating comes intofrom play, it the which outliers is of the Time Viewed variable. In this case, it is observed that a new factor comes into play, which is theof the brand Time of Viewed the game variable. and that In thethis age case, of itthe is childobserved changes that froma new 11 factor to 9 yearscomes of into age, play, as shown which inis the brand of the game and that the age of the child changes from 11 to 9 years of age, as shown in Figurethe brand 15 below. of the game and that the age of the child changes from 11 to 9 years of age, as shown in Figure 15 below. Figure 15 below.

Figure 15. Tree on the sample without outliers, to predict Time Viewed. Source: Prepared by the authors.

FigureThe same 15. Tree analysis on the was sample carried without out on outliers, the remaining to predict target Time variables Viewed. Source: and an Prepared improvement by the in the predictionauthors.Figure of15. the Tree values on the of sample the Time without to 1st outliers, View (sec) to pr variableedict Time by Viewed. 1% was Source: obtained, Prepared using theby the sample withoutauthors. outliers. Again, it is veriﬁed that inclusion or not of the outliers does not cause great changes in the precision achieved.

2.5. Comparison of Classiﬁcation Models The variables of interest that appear the most in each experiment are AOI (logically) and also Age 1 (the age of the oldest son). The ages of the other children are not relevant in any of the experiments. In addition, depending on the variable to be predicted, other explanatory variables could come into play, such as Media Name, which is relevant for predicting Time Viewed (without outliers) and Revisits (from full sample). In general, the accuracies achieved in each model are high enough, taking into account that the target variables were discretized in several sections. This is reﬂected in the kappa index. However, considering outliers in the sample sometimes improves precision and sometimes does not. It follows that it is necessary to carry out both models (with and without outliers) in each case. Soc. Sci. 2020, 9, x FOR PEER REVIEW 12 of 23

The same analysis was carried out on the remaining target variables and an improvement in the prediction of the values of the Time to 1st View (sec) variable by 1% was obtained, using the sample without outliers. Again, it is verified that inclusion or not of the outliers does not cause great changes in the precision achieved.

2.5. Comparison of Classification Models The variables of interest that appear the most in each experiment are AOI (logically) and also Age 1 (the age of the oldest son). The ages of the other children are not relevant in any of the experiments. In addition, depending on the variable to be predicted, other explanatory variables could come into play, such as Media Name, which is relevant for predicting Time Viewed (without outliers) and Revisits (from full sample). In general, the accuracies achieved in each model are high enough, taking into account that the target variables were discretized in several sections. This is reflected in the kappa index. However, considering outliers in the sample sometimes improves precision and sometimes does not. It follows that it is necessary to carry out both models (with and without outliers) in each case. Of all the target variables in the problem, the one that can be predicted with greater precision is Revisits with an average precision close to 64%. However, although accuracy is the one most frequently referred to, it is not the only metric of predictive models. In formal terms, accuracy is the percentage of correctly classified instances out of all instances and kappa or Cohen’s kappa is better thought of as classification accuracy, except that it isSoc. normalized Sci. 2020, 9, 162 at the baseline of random chance on the dataset. The confidence interval (95%12 ofCI) 23 represents the precision segment in which there is a 95% probability of predicting correctly. Finally, informationOf all the rate target is the variables precision in that the problem,can be achiev the oneed by that always can be predicting predicted the with one greater with the precision highest is probabilityRevisits with type, an averageor the interval precision in this close case. to 64%. Furthermore,However, although from a accuracy qualitative is the point one mostof view, frequently the models referred must to, be it is interpreted not the only with metric their of correspondingpredictive models. confusion In formal matrices, terms, which accuracy accumulate is the percentage the well-classifi of correctlyed instances classified on instances the diagonal out of and,all instances outside of and this, kappa we can or Cohen’ssee how kappaand where is better the errors thought of ofeach as predictive classification model accuracy, accumulate. except that it is normalizedReturning to at the the previous baseline example of random of a chanceprediction on themodel dataset. for the The fixations confidence variable, interval the following (95% CI) confusionrepresents matrix the precision is obtained segment (Figure in which16). The there cells is on a 95%the diagonal probability of the of predictingmatrix show correctly. the number Finally, of resultsinformation that were rate classified is the precision correctly, that while can be the achieved cells that by are always outside predicting the diagonal the one are with those the that highest were classifiedprobability incorrectly. type, or the interval in this case. TheFurthermore, confusion frommatrix a in qualitative the previous point figure of view,(Figure the 16) models shows mustthat 11 be instances interpreted of the with interval their [0.2]corresponding were correctly confusion classified matrices, and the which following accumulate were the incorrectly well-classified classified instances (framed onthe in red): diagonal 7 of and,the intervaloutside of[0.2] this, as we if they can see were how ofand the whereinterval the [2.4]; errors 0 ofas each[4.8],predictive 1 as [8.15] model and 1 accumulate. as [15.72]. Thus, the diagonalReturning represents to the the previous prediction example success of aof prediction each type, model and outsid for thee the fixations diagonal variable, the corresponding the following predictionconfusion matrixerrors isare obtained represented. (Figure It 16can). Thebe seen cells onthat the the diagonal largest oferror the matrixis made show by predicting the number the of intervalresults that from were 4 to classified 8 s, when correctly, it is classified while as the the cells interval that are from outside 8 to 15. the However, diagonal arethis those interval that is were the oneclassified with the incorrectly. most errors and the least hits (16 instances classified incorrectly versus 5 correct ones).

Figure 16. Confusion matrix and statistics for fixations variable on the sample without outliers. Source: Figure 16. Confusion matrix and statistics for fixations variable on the sample without outliers. Prepared by the authors. Source: Prepared by the authors. The confusion matrix in the previous figure (Figure 16) shows that 11 instances of the interval [0.2] wereFigure correctly 17 shows classified a comparative and the following summary were of incorrectly the results classified of the computational (framed in red): experiment 7 of the intervalcarried out[0.2] (results as if they models were for of each the intervaltarget variable [2.4]; 0 and as [4.8], for each 1 as type [8.15] of andsample 1 as collected). [15.72]. Thus, the diagonal represents the prediction success of each type, and outside the diagonal the corresponding prediction errors are represented. It can be seen that the largest error is made by predicting the interval from 4 to 8 s, when it is classified as the interval from 8 to 15. However, this interval is the one with the most errors and the least hits (16 instances classified incorrectly versus 5 correct ones). Figure 17 shows a comparative summary of the results of the computational experiment carried out (results models for each target variable and for each type of sample collected). Soc. Sci. 2020, 9, 162 13 of 23 Soc. Sci. 2020, 9, x FOR PEER REVIEW 13 of 23

Figure 17. Comparative table of classification model with improved results in the sample without Figureoutliers 17. framed Comparative in green. table Source: of classification Prepared by themodel authors. with improved results in the sample without outliers framed in green. Source: Prepared by the authors. 3. Discussion 3. Discussion The first part of this study analyzes the intersection between consumer behaviour and packaging designThe for first an part educational of this study toy ( Svanesanalyzes et the al. 2010inters),ection analyzing between the e fficonsumerciency of behaviour actual packaging and packaging for two designeducational for an toys educational which are toy competence. (Svanes et al. 2010), analyzing the efficiency of actual packaging for two educationalFrom the toys neuromarketing which are competence. point of view, the application of biometrics (neuromarketing analysis) (OhmeFrom et al.the 2011 neuromarketing), it is worth highlighting point of view, that the Diset’s application packaging of biometrics stands out (neuromarketing at the level of attractionanalysis) (Ohmeand interest et al. as2011), compared it is worth with highlighting Educa, but with that fewDise significantt’s packaging diff standserences, out due at tothe the level similarity of attraction of the anddesign. interest By gender, as compared male consumers with Educa, (33%) but focuswith few on thesignificant game itself differences, (the template due to shown the similarity on the cover of the of design.the package) By gender, and the male word consumers “English” (33%) (game focus reference). on the Womengame itself also (the focus template attention shown on the on name the cover of the ofgame the (frompackage) Diset) and and the the word brand “English” (Educa). (game reference). Women also focus attention on the name of theThe game packaging (from Diset) of the and Educa the brand game (Educa). leads, in general, to the fixation on the image of the game (the templateThe packaging shown of and the the Educa child’s game hands), leads, on in the general, themes to (shown the fixation graphically) on the image and the of recommendedthe game (the templateage. By gender, shownmen and alsothe lookchild’s at thehands), number on the of questions, themes (shown compared graphically) to women, and who the look recommended at the game age.reference By gender, (“English”). men also The look recommended at the number age of and qu themeestions, are compared key in choosing to women, an educational who look at toy, the ingame that referenceorder (Rundh (“English”). 2009). This The makes recommended educational age toys and a concerntheme are for key the developmentin choosing an of educational the child, both toy, their in thatown, order and those(Rundh of family2009). This and friends.makes educational In general, thetoys designs a concern are for similar the development and convey similar of the levels,child, bothbut Diset’s their own, seems and more those complete of family at and the friends. level of detailIn general, aimed the at designs the child are and similar Educa’s and at convey a higher similar level levels,of knowledge but Diset’s and seems focus onmore learning. complete The at greater the level number of detail of topics aimed and at questionsthe child inand Diset Educa’s leads at the a higherconsumer level to of be knowledge willing to payand more,focus on in additionlearning. toTh ae highergreater quality number perception of topics and (Velasco questions et al. in2014 Diset). leadsFrom the consumer the analytics to be and willing application to pay of more, machine in ad learningdition to models a higher (Vellido quality et al. perception 2012), the conclusions(Velasco et al.reached 2014). indicate that the classification models provide quite good precision (Alm et al. 2005) when predictingFrom variablesthe analytics that, althoughand application being numerical of machin in nature,e learning the contextmodels of (Velli the problemdo et al. suggests 2012), thatthe conclusionsthey be treated reached by segments indicate tothat facilitate the classification strategic decision models makingprovide inquite packaging good precision (Calver (Alm2004). et In al. a 2005)relatively when small predicting sample, variables where the that, percentage although of bein outliersg numerical does not in exceednature, 10%,the context the inclusion of the problem or not of suggeststhese for that the generationthey be treated of the by predictive segments model, to facilitate has a strategic variable decision incidence, making and therefore in packaging it is necessary (Calver 2004).to evaluate In a therelatively possibility small of includingsample, where them orthe not pe inrcentage the study of ofoutliers each objective does not variable. exceed 10%, the inclusion or not of these for the generation of the predictive model, has a variable incidence, and

Soc. Sci. 2020, 9, 162 14 of 23

During the eye tracking experiment (Ungureanu et al. 2017), the set of variables (and their relative weights) that help to predict the target variables, can change significantly, depending on the target variable chosen in each case. Some of the experiment times can be predicted more accurately than others. The need to evaluate the inclusion or not of outliers (John 1995), the convenience of discretizing the numerical variables in order to obtain classification models from which to make decisions, together with the fact that the relative weights of the explanatory variables cannot be established a priori for each objective variable, confirm that the data-driven approach is perfectly suited to predictive neuromarketing contexts where the samples have a high dispersion in their values, depending on each specific sample to be analysed. This methodology could be extrapolated to other types of products (with an emotional component, to apply neuromarketing biometrics) and even to other types of activities.

4. Materials and Methods The aim of this research was to determine, through neuromarketing techniques, the cognitive perception that Spanish parents, between 35 and 45 years old, with children between 4 and 8 years old, have regarding the elements contemplated in the design of toy packaging that is educational and age appropriate for your children. To do this, we used neuromarketing techniques that allowed us to analyse the attention of the subjects to the stimuli (eye tracking). Additionally, the data extracted from these experiments was studied using new analytical models based on machine learning techniques, capable of adapting to the context and establishing behavioural patterns that allowed the researcher to more eﬃciently identify the key aspects, because not all variables in the eye tracking experiment intervene with the same importance in the diﬀerent target variables, helping to make decisions in the design of packaging toys.

4.1. Objectives This research work aimed to help answer the question of which aspects are more relevant for consumers in purchasing educational toys, which will obviously be quite different from products more focused simply on leisure. This empirical research focused on an educational toy distributed in Spain by Educa brand (Conector family, reference “I learn English”), which was the brand’s best seller in this market area, and analysed how consumers made decisions regarding their choice in relation to other products designed by competitors. The study looked at customer reactions when looking at the products, generated by different aspects of product design and its influence on choice. The main objective of the research was to analyse the attention of parents towards the projection of images of the packaging of educational toys aimed at children between 4 and 8 years old, and proposed a methodology to predict which area of an advertisement was going to be observed in the first instance and which areas were never the focus of attention of potential customers. The methodology fully analysed and segmented these areas, according to social circumstance and which family member was observing. The specific objectives are as follows:

Analyze the attention generated by the different elements of the packaging of an educational toy • (comparison with 2 similar products of competing brands) between parents; Analyze and segment the areas, according to social circumstance and which family member • is observing; Determine what differences there are between parents, according to gender; • Analyze the attention of the different elements generated in the parents, according to the • purchase intention. Soc. Sci. 2020, 9, 162 15 of 23

4.2. Research Instrument Advances in neuroscience applied to traditional marketing have allowed the creation of a new discipline (neuromarketing) based on a deeper understanding of human behaviour as a consumer. Reimann (Reimann et al. 2011) formally defined consumer neuroscience as the study of neural conditions and the processes underlying consumption, their psychological significance, and their consequences for behaviour. As a result of combining neuroscience with marketing, neuromarketing emerges as a relatively new research discipline. Leveraging advances in technology, this new field goes beyond traditional quantitative and qualitative research tools and focuses on consumers’ brain reactions to marketing stimuli. Ariely (Ariely and Berns 2010) stated that the main objective of marketing was to help link products and people. Neuromarketing research aims to connect activity in the neural system with consumer behaviour, and has a wide variety of applications for brands, products, packaging, advertising, and marketing, such that retailers are able to determine the intention to buy, the level of novelty, and awareness or triggered emotions. Butler (Butler 2008) proposed a neuromarketing research model which interconnected marketing researchers, practitioners, and other stakeholders, and stated that more research was needed to establish its academic relevance. It is possible to consider that neuromarketing is the conjunction of neuroscience and marketing, with the aim of evaluating the conscious and unconscious mental states of consumers. This in turn allows marketing strategies to be designed which are more guaranteed to succeed, since these are addressed from a real and deep knowledge of how different stimuli act on the brain and how they influence behaviour and decision making. Consequently, neuromarketing is the marketing of the 21st century. Marketing concepts have not become obsolete, but rather that we have to work with the concepts that both disciplines provide in a holistic way and learn to apply them according to the context, objectives, and market strategies proposed. According to classic assumption, consumers in their decision-making process consider all the possible alternatives in the market and select the one that maximizes «marginal profit». This assumption is no longer valid, according to Daniel Kahneman (Kahneman 2002), psychologist and Nobel Prize winner in Economics in 2002. Neurosciences have shown that 97% of our decisions are unconscious. According to ESOMAR (ESOMAR 2017), International Association for Market Research and Opinion Studies, and Innerscope (Innerscope 2017), the 10 most used techniques in market research from neuroscience can be classified into the following 3 categories: Psychometric IAT (Implicit Association Tests); Biometrics FACS (facial coding), ET (eye tracking), HR (heartbeat), EDA-SCR or GSR (galvanic skin response), respiration patterns (motion, respiratory rate, and (VPA) voice pitch analyses); Neurometric EEG/SST (electroencephalography/steady state tomography) and fMRI (functional magnetic resonance imaging). These technologies developed from neuroscience are known under the name of psychometric, biometric, or neurometric tools depending on the applied technology. They allow the unconscious processes of the consumer’s mind to be identified and measured through the development of experiments (mostly in the laboratory). Regarding the different “approaches” to tackle complex analytical problems, for some time now, the scientific community has openly opted for the “data-driven” approach, which generates predictive models, solely from the data itself. This approach is fundamentally different from the model-driven approach, which is based on mathematical, physical, or economic equations that explain and define in advance the behaviour of the system. The data-driven approach has proven particularly well suited to be integrated into decision support systems (DSS) (Provost and Fawcett 2013). Furthermore, regarding the case study presented in this paper, the industrial field is a clear example of how the data-driven approach offers (by itself, or in combination with model-driven techniques) ideal solutions to analytical problems in any of the aspects of industrial processes, and is able to find data-based solutions for problems as different as anticipating mechanical (Li et al. 2019) or Soc. Sci. 2020, 9, 162 16 of 23 electronic (Li et al. 2019) failures in production, in simulation processes for remanufacturing (Goodall et al. 2019), in complex assembly tasks in automobile plants (Wang et al. 2011), or even to predict retailer/wholesaler behaviour (Radac and Precup 2015). In neuromarketing, the true importance of data analysis using brain metrics (EEGs) has already been confirmed in (Hakim and Levy 2019). The literature offers examples of very different analytical techniques. It is common to find classical statistical techniques such as component analysis from ANOVA for the prediction of consumption preferences (Goto et al. 2019), or the use of Naive Bayes to predict the acquisition of products (Taqwa et al. 2015). Since 2016, it has become increasingly frequent that studies on EEG data on neuromarketing (in general) and on advertisement scoring (in particular) have begun to incorporate predictive machine learning techniques, such as SVM in combination with random forest classifiers (Libert and Hulle 2019), C4.5 classifier together with ANN (Morillo et al. 2016) or SVM (Wei et al. 2018). Regarding the data from eye tracking experiments, there are a range of studies (Stark et al. 1962) that have shown the importance of being able to predict where and how the human eye will focus attention. From 2016, the literature offers some examples where the use of predictive machine learning techniques is verified, such as the case of ANN together with time series analysis on patterns in online advertisements or the use of hidden Markov models on AOIs in cases of prediction of attention in augmented reality contexts (Pierdicca et al. 2018). Big data has revolutionized decision making in many fields. The handling of a large amount of data, to analyze certain behaviours, is improving processes (Marín-Marín et al. 2019). Big-data analytics is gaining substantial attention, due to its contribution to the business strategy determination process and providing valuable information for the design and development of service innovation (Thuethongchai et al. 2020). Technology has allowed online sellers to make real-time price changes of high magnitude and proximity (Victor et al. 2018). The incorporation of information and communication technologies to education allows us to collect information on the teaching and learning process (Ruiz-Palmero et al. 2020), showing the importance of being able to forecast consumer trends, and then present an evaluation of prognosis (Silva et al. 2019). This article addresses the application of machine learning techniques in the eye tracking field, where there is a very high dispersion of data, produced by the heterogeneity of the users under study, with apparently arbitrary behaviour, which is difficult to predict. This leads to specific preprocessing of the experimental data, before applying discrete predictive techniques with greater tolerance to prediction error. Using confusion matrices, it is possible to measure where the hits and misses of our predictive model converge.

4.3. Sample In the present research, the sample consisted of men and women, according to the indications of the manufacturer Educa, from current consumer data. A total of 30 people (33% men and 66% women) participated randomly and voluntarily as study subjects after meeting the requirements of being parents, aged between 35 and 45 with children of ages between 4 and 8 years old. Alicante (Spain) was chosen for the sample due to its status as a provincial capital. The sample size (consisting of 10 men and 20 women) was adequate for a neuromarketing study (Cuesta-Cambra et al. 2017; Juarez et al. 2020; Mañas-Viniegra et al. 2020; Mengual-Recuerda et al. 2020). After carrying out the empirical study, 5 users (all belonging to the female gender) were discarded, leaving 25 users (10 men and 15 women). The sample size was suﬃcient to be able to proceed to the study due to its representativeness, with unbiased and accurate standard errors (Maas and Hox 2005). Soc. Sci. 2020, 9, 162 17 of 23 Soc. Sci. 2020, 9, x FOR PEER REVIEW 17 of 23

4.4. Data Collection and Analysis The researchresearch phase phase with with packaging packaging was was performed performed using using the eye the tracker eye tracker model Gazepointmodel Gazepoint GP3HD, withGP3HD, a 150 with Hz a sampling 150 Hz sampling rate. For rate. data collection,For data collection, Gazepoint Gazepoint Analysis Analysis UX Edition UX v.5.3.0 Edition software v.5.3.0 wassoftware used. was used. The statistical analysis of the datadata waswas performedperformed withwith thethe RR software,software, v.3.6.3.v.3.6.3. The commoncommon elements (stimuli) between both packagespackages werewere deﬁneddefined (Figure(Figure 1818).). Subjects were exposed to 2 packages containing 7 stimuli each, comparable to eacheach other. The stimulus 02 of each brand is free (not(not equivalent).equivalent). Each Each package package had had a a maximum maximum time time limit limit of of 30 s, with 3 s ofof separationseparation betweenbetween stimuli, to prioritize the areas ofof interestinterest thatthat capturedcaptured thethe mostmost attentionattention ((Añaños-CarrascoAñaños-Carrasco 2015 2015).).

Figure 18. Packaging design for educational toys selected. Source: Prepared by the authors.

4.5. Dataset This studystudy begins begins with with two two data data sources. sources. The The ﬁrst first is the is data the obtaineddata obtained in eye trackingin eye tracking and consists and ofconsists 350 records of 350 andrecords the followingand the following 16 columns 16 (variables):columns (variables): Media ID, Media Media ID, Name, Media Media Name, Duration Media (sec—UDuration= (sec—UUserControlled), = UserControlled), AOI ID, AOI AOI Name, ID, AOIAOI Start, Name, AOI AOI Duration Start, (sec—UAOI Duration= UserControlled), (sec—U = UserUserControlled), ID, User Name, User User ID, Gender,User Name, User User Age, Gender, Time to User 1st View Age, (sec), Time Time to 1st Viewed View (sec), (sec), Time Time Viewed (%),(sec), Fixations Time Viewed (#) and (%), Revisits Fixations (#). (#) and Revisits (#). The secondsecond datadata source source refers refers to theto the users users who who participated participated in the in study the study and consists and consists of 25 records of 25 andrecords 8 columns and 8 columns that show that their show social their situation social situation (sex, personal (sex, situation,personal situation, number of number children, of age,children, etc.). age, etc.).These tables were cross-referenced in order to explain the data obtained by eye tracking, together withThese the characteristics tables were cross-referenced of each individual. in order to explain the data obtained by eye tracking, together with Afterthe characteristics eliminating theof each redundant, individual. empty, and repeated variables, and after assigning a suitable formatAfter to the eliminating remainder, the a datasetredundant, of 13 empty, columns and and repeated 350 rows variables, was obtained. and after assigning a suitable formatIn theto the Figure remainder, 19 we can a dataset see the of result 13 columns of the reduction and 350 ofrows columns was obtained. (the type of each variable and its basicIn descriptive the Figure statistics. 19 we can see the result of the reduction of columns (the type of each variable and its basicSubsequently, descriptivean statistics. exhaustive study was carried out on this data, in which it was important to look atSubsequently, the distribution an exhaustive of the following study variables was carried that weout wereon this interested data, in in which predicting it was (target important or class to variables,look at the framed distribution in red): of Timethe foll toowing 1st View variables (sec), Time that Viewedwe were (sec), interested Fixations in predicting (#), Revisits (target (#) and or class their behaviourvariables, framed according in tored): the Time antecedent to 1st orView explanatory (sec), Time variables Viewed (framed (sec), Fixations in green): (#), Media Revisits Name, (#) AOIand Name,their behaviour User Name, according User Gender, to the antecedent Personal situation, or explanatory Number variables of children, (framed Age in 1, green): Age 2, Media Age 3. Name, AOI Name, User Name, User Gender, Personal situation, Number of children, Age 1, Age 2, Age 3.

Soc. Sci. 2020, 9, x FOR PEER REVIEW 18 of 23 Soc. Sci. 2020, 9, 162 18 of 23 Soc. Sci. 2020, 9, x FOR PEER REVIEW 18 of 23

Figure 19. Input data set statistics. Summary of the data. Source: Prepared by the authors. Figure 19. Input data set statistics. Summary of the data. Source: Prepared by the authors. Figure 19. Input data set statistics. Summary of the data. Source: Prepared by the authors. 4.6.4.6. Analysis MethodologyMethodology 4.6. AnalysisThe data Methodology collected by observations on 25 users were subjected to preprocessing, detecting The data collected by observations on 25 users were subjected to preprocessing, detecting anomalous values (outliers), which allowed different subsamples of data to be generated based on anomalousThe data values collected (outliers), by observations which allowed on di25fferent users subsamples were subjected of data to topreprocessing, be generated baseddetecting on their distributions. The original sample and each of the subsamples was subjected to the same theiranomalous distributions. values The(outlier originals), which sample allowed and each different of the subsamples wasof data subjected to be generated to the same based process, on process, which consisted of the following: whichtheir distributions. consisted of the The following: original sample and each of the subsamples was subjected to the same process,• selection which of consisted the target of variable the following: (variable to be predicted); selection of the target variable (variable to be predicted); •• selectiondetection ofof thethe targetmost relevantvariable variab(variableles onto be the predicted); chosen target variable; detection of the most relevant variables on the chosen target variable; •• detectiongeneration of ofthe predictive most relevant models variab forles classifying on the chosen the targettarget variable; variable with the most influential generation of predictive models for classifying the target variable with the most influential •• generationvariables in ofeach predictive case. models for classifying the target variable with the most influential variables in each case. variablesThe different in each predictive case. models were compared with each other, in order to conclude which variablesTheThe didifferentwerefferent the predictivepredictiveones that best modelsmodels predicted werewere comparedcertaicomparedn objectives withwith eacheach and other, other,what indegreein orderorder of to toprecision concludeconclude could whichwhich be variablesvariablesobtained wereinwere predicting thethe onesones one thatthat or bestbest the predictedpredictedother. certaincertain objectivesobjectives andand whatwhat degreedegree ofof precisionprecision couldcould bebe obtainedobtainedIn the inin predictingpredictingfield of classification oneone oror thethe other.other. problems (predictions of discrete variables), the data science methodologyInIn thethe fieldfield is often ofof classificationclassification based on CRISP-DM problemsproblems (predictions(predictionsmodel (Chapman ofof discretediscrete and variables),Clintonvariables), 1999), thethe that datadata includes sciencescience methodologymethodologypreprocessing isisphases oftenoften (for basedbased cleaning onon CRISP-DMCRISP-DM and suitability modelmodel of ((ChapmanChapman the dataset), andand selection ClintonClinton of19991999), the), most thatthat includesincludesrelevant preprocessingpreprocessingattributes (for phasesthephases construction (for(for cleaningcleaning of the andand model), suitabilitysuitability and ofgenerationof thethe dataset),dataset), of classifica selectionselectiontion ofof models thethe mostmost (with relevantrelevant their attributesattributescorresponding (for(for thethedetails). constructionconstruction After evaluating ofof thethe model),model), the models, andand generationgeneration it is common ofof classificationclassificato allow tiona return modelsmodels to the (with(with attribute theirtheir correspondingcorrespondingselection phase. details).details). This scheme After has evaluating been applied the models, in several it is studies, common with to allowallowslight avariationsa returnreturn toto (Liu thethe attributeandattribute Han selectionselection2002; Rabasa phase.phase. and ThisThis Heavin schemescheme 2020), hashas An beenbeen outline appliedapplied of such inin severalseveral a methodology, studies,studies, withwith assumed slightslight variationsvariations on this paper, ((LiuLiu andisand shown HanHan 20022002;in Figure; RabasaRabasa 20. andand HeavinHeavin 2020 2020),), An An outline outline of of such such a a methodology, methodology, assumed assumed on on this this paper, paper, is shownis shown in Figurein Figure 20. 20.

Figure 20. Methodology. General schema. Source: Prepared by the authors.

Figure 21 showsFigure such 20. Methodology. a general methodology General schema. adapted Sour toce: thisPrepared speciﬁc by the problem. authors. Figure 20. Methodology. General schema. Source: Prepared by the authors.

Soc. Sci. 2020, 9, x FOR PEER REVIEW 19 of 23

Soc. Sci.Figure2020, 921, 162 shows such a general methodology adapted to this specific problem. 19 of 23

Figure 21. General diagram of the analytical methodology. Source: Prepared by the authors. Figure 21. General diagram of the analytical methodology. Source: Prepared by the authors. Due to the breadth of the study, motivated by the existence of multiple objective variables to be modeled,Due to the and breadth the great of the variety study, of motivated descriptive by statistics the existence on the of explanatorymultiple objective variables, variables the article to be modeled,focused on and the the most great significant variety cases of descriptive of each stage, statistics leaving on all the the explanatory others appropriately variables, described the article in focusedthe Appendix on theA most, before significant the References cases of section. each stage, leaving all the others appropriately described in the Appendix A, before the References section. 5. Conclusions 5. ConclusionsThis study has revealed the packaging design most observed aspects in the consumption of educationalThis study toys, has the revealed perception the of packaging the value design of the brandmost andobserved product aspects through in the packaging, consumption and theof educationalprojection on toys, the children’sthe perception entertainment of the value based of onthepackaging brand and design. product It through has also allowedpackaging, the and authors the projectionto compare on the the perception children’s/ codingentertainment of each based container on packaging by men and design. women, It has identify also allowed the level the of authors visual toattraction compare (time the perception/coding spent) towards the of producteach container and the by brand, men and and women, the levels identify of educational the level of value visual of attractioneach container, (time spent) perceived towards by the the customer product and objective the br (and,Enax and et al.the 2015 levels). Theof educational application value of machine of each container,learning models perceived provides by the a customer tight approximation objective (Ena inx the et al. prediction 2015). The of application the variables of and,machine even learning though modelsthey are provides numerical a intight nature, approximation the context in of the the predic problemtion suggests of the variables that they and, be treated even though using segments they are numericalto facilitate in strategic nature, decision the context making of the in theproblem design su ofggests the packaging. that they be treated using segments to facilitateThis strategic research decision has contributed making to in the the change design taking of the placepackaging. in the scientific literature on the design of educationalThis research toy packaging has contributed and the to models the change used taking to analyze place consumerin the scientific behaviour literature (Enax onet the al. design 2015), ofbased educational on the data toy packaging extracted from and the the models neuromarketing used to analyze analysis. consumer The recommendations behaviour (Enax drawn et al. 2015), from basedresearch on onthe the data design extracted of educational from the toyneuromarketing packaging aim analysis. to improve The graphicallyrecommendations those elements drawn from that researchreally attract on the consumer design of attention. educational The toy analysis packagin drawsg aim several to improve conclusions graphically that would those helpelements improve that reallythe perception attract consumer of the toy. attention. The need The toanalysis evaluate draws the several inclusion conclusions or not of that outliers would (Hawkins help improve 1980), the convenienceperception of of the discretizing toy. The need the numerical to evaluate variables the inclusion in order or tonot obtain of outliers classification (Hawkins models 1980), from the conveniencewhich to make of decisions,discretizing together the nu withmerical the factvariables that the in relativeorder to weights obtain ofclassification the explanatory models variables from whichcannot to be make established decisions, a priori together for eachwith objectivethe fact th variable,at the relative confirms weights that theof the data-driven explanatory approach variables is cannotperfectly be suited established to predictive a priori neuromarketingfor each objective contexts variable, where confirms the samples that the have data-driven a high dispersion approach inis perfectlytheir values, suited depending to predictive on each neuromarketing specific sample cont to beexts analyzed. where the It issamples important have to a focushigh attentiondispersion and in theirexplain values, the skills depending improved on byeach the specific child’s play,sample the to age be atanalyzed. which it isIt recommendedis important to to focus use, attention and eliminate and explainthe remaining the skills texts improved used. by the child’s play, the age at which it is recommended to use, and eliminateFinally, the this remaining study revealed texts used. that the knowledge of consumers’ conscious and unconscious mental statesFinally, allows this the study packaging revealed design that the of educationalknowledge of toys consumers’ to be much conscious more eandfficient. unconscious By taking mental into statesaccount allows that consumer the packaging habits design change, of organizations educational musttoys designto be much proposals more for efficient. each contact By taking they make into accountwith their that consumers, consumer through habits allchange, the aspects organization that accompanys must thedesign brand, proposals to achieve for a each greater contact perception they makeof these with creative their products.consumers, through all the aspects that accompany the brand, to achieve a greater perceptionFrom theof these data sciencecreative approach, products. the proposed methodology (originally based on CRISP-DM) has been successfully adapted to the specific problem of user’s preferences classification on a short life data sample. The classification techniques are absolutely dependent on the numeric target variables

Soc. Sci. 2020, 9, 162 20 of 23 discretization (on the preprocessing phase), and therefore this process must be specially adjusted in every case. As supervised learning methods are involved, classiﬁcation techniques such as the one proposed in this research achieve better precision ratios with larger training samples. In this sense, the authors have begun to launch other neuromarketing experiments in haute cuisine presentation, footwear packaging (and store decoration), food distribution in supermarkets, and football team store decoration, where the experiments report perfectly analyzable samples with slight adaptations of this data science methodology.

Author Contributions: Conceptualization, D.J.-V. and A.R.-D.; methodology, V.T.-V.; software, D.J.-V., A.R.-D. and K.P.; validation, D.J.-V., V.T.-V. and A.R.-D.; formal analysis, D.J.-V. and K.P.; investigation, D.J.-V.; resources, D.J.-V.; data curation, A.R.-D. and K.P.; writing—original draft preparation, D.J.-V. and A.R.-D.; writing—review and editing, V.T.-V. and D.J.-V.; visualization, D.J.-V.; supervision, D.J.-V.; project administration, D.J.-V. and A.R.-D. All authors have read and agreed to the published version of the manuscript. Funding: This research received no external funding. Conﬂicts of Interest: The authors declare no conﬂict of interest.

Appendix A 1. Descriptive statistics 1.2. Study of the variable Time Viewed (sec) 1.3. Study of the variable Fixations (#) 1.4. Study of the variable Revisits (#) 2. Feature selection 2.1. Study of the variable Time to 1st View (sec) 2.2. Study of the variable Time Viewed (sec) 2.3. Study of the variable Fixations (#) 2.4. Study of the variable Revisits (#) 3. Classiﬁcation models 3.1. Study of the variable Time to 1st View (sec) 3.2. Study of the variable Time Viewed (sec) 3.3. Study of the variable Fixations (#) 3.4. Study of the variable Revisits (#)

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