Online consumer behavior modeling

How to attract and win over the consumer in the highly competitive Internet marketplace?

What would the best marketer of the world do?

What would Sun Tzu do? 

What would Alexander the Great do? 

Well, the answer is not so simple, since online consumer behavior has become an emerging research area as well as a key factor for many companies. Digital ecosystems are evolving so rapidly and it seems that even the greatest strategists might face difficulties in consumer behavior modeling and optimizing consumer interactivity with the brand.

But how do we, marketers, approach the construct "online consumer behavior" in the first place? As suggested by Douglas et al. (1994), strong theoretical and conceptual frameworks can be developed through an integration of constructs from different disciplines. It is not coincidental that academic literature on online consumer modeling may be found in Journals like the ones of Electronic Commerce, Marketing Management, Decision Support Systems, Economic Psychology, Interactive Marketing, Management Information Systems and many more. Theory of Reasoned Action (TRA) and its family theories including the Technology Acceptance Model (TAM) and the Theory of Planned Behavior (TPB) are dominant theories in process of explaining online consumer actions. Expectation-Confirmation Theory (ECT) and Innovation Diffusion Theory (IDT) have also been repeatedly tested in the study of online consumer behavior.

However, Cheung et al. (2003) tried to approach it by integrating the three key concepts of intention, adoption, and continuance, based on Fishbein’s attitudinal theoretical model (Fishbein 1967) and the expectation-confirmation model (Oliver 1980). Their Model of Intention, Adoption, and Continuance (MIAC) seems an interesting framework for developing the framework of their theory.

MIAC

Thus, the modeling framework proposed, as an extension to Hoffman and Novak (1996), it the following:

The five domains of consumer, product, medium, intermediary characteristics as well as enviromental influences were integrated in MIAC (intention, adoption, repurchase) in order to provide a cohesive view on online consumer behavior.

It seems that a wise marketer may segment the market, target the correct target segment with the correct product, both core and augmented, and position it with the correct marketing mix, in order to win competition. However, medium characteristics should not be ignored; Trust and perceived risk have been widely investigated in the study of consumer online purchase intention. Some recent studies (Lee and Turban 2001) focused primarily on the trust formation process in the context of Internet shopping. Furthermore, environmental influences including norms, cultures and cultural contextual sets should also not be underestimated; A consumer behavior pattern, e.g. adoption and success of a fragrance e-shop in Mexico could be totally different in Greece or India.

Electronic commerce is rapidly changing the way people do business all over the globe. In the B2C segment, Internet sales have been increasing dramatically over the last few years. Customers, not only those from well-developed countries but also those from developing countries, are getting used to the new shopping channel. Understanding the factors that affect intention, adoption and repurchase are important both for researchers and practitioners. MIAC is a nice model to begin with.

Classical consumer behavioral theories provide researchers with a good starting point in understanding online consumer behavior. However, we should take the IT component into serious consideration when doing research in online consumer behavior. Instead of blindly borrowing theories and models from other disciplines, researchers and practitioners should test their own behavioral models declaring what is unique and specific to the context of consumer-based electronic commerce. This may be the only way that marketing can survive in the Digital Age.

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5+1 Successful Commercials of 2013!

The most successful commercials that really went viral so far in 2013!

5. GEICO - Hump Day

4. H&M - David Beckham Short Film

3. GoPro - Fireman Saves Kitten

2. MGM - Telekinetic Coffee Shop Surprise

1. Evian - Baby and Me

 

Lucky Star: Sony Playstation Instructional Video

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A Facebook for my Brand : Fan Pages and KPI's

Facebook fan pages can be regarded  as a virtual brand community. They are specialized, non geographically bound communities and they are based on a structured set of social relations among admirers of a brand. Therefore the theoretic explanations for a brand community are also suitable for explaining the fan page phenomenon. Social identity and  symbolic interactionism theories show that interaction with members of a reference group can lead to a strong feeling of belonging to this group (in this case the brand community) which in turn can lead to stronger buying behavior and positive brand attitude.

But how can we be sure that being a member of a facebook fan page has an impact on a brand admirer's buying behavior and brand attitude? Researchers from the University of Mannheim actually designed an experiment and assumed that members of the BMW fan page show stronger buying behavior and brand attitude than non-members and that within members, buying behavior and brand attitude are even stronger for active members than for passive ones.In order to carry out the experiment they conducted an online survey among 840 BMW admirers.

The BMW Fan Page Survey

Membership and interaction were expected to influence psychographic dependent variables (brand loyalty, brand commitment) and economic dependent variables (purchase intention, willingness to cross-buy and  positive word of mouth). Willingness to cross-buy was polled in three categories, which were lifestyle products (e.g. apparel), financial services (e.g. leasing or insurance offers) and original BMW spare parts. Purchase intention was polled in two categories, which were automobiles and BMW car repair services. In order to analyze the influence of membership and interaction, two separate experiments were carried out. In the first study, respondents were split into  members (n=210)  and  non-members (n=630) according to the membership of the German BMW fan page. The non-members became the control group and members became the experimental group.

Furthermore, members had to answer questions about their usage of the BMW fan page . Using these responses, a weighted interaction level was determined for each member. A cut-off value was then applied to the weighted interaction level to classify members of the BMW fan page either in passive or active members. While passive members don't or rarely interact with the fan page, active members display a higher level of interaction.

Influence of Membership

In order to analyze the influence of membership on the dependent variables,  the nonmembers and members of the Facebook fan page were compared with each other. Non-members (n=215) were recruited in BMW internet forums to make sure they are admirers of the brand. In order to be able to analyze variances of members and non-members, the groups need to be about the same size (max factor 1.5). That is why a sample of n=315 was randomly drawn out of the total number of the BMW fan page members (n=630).

Then the data was analyzed using a multivariate analysis of variance (MANOVA) in order to examine differences between the responses of the two groups. The mean values were calculated and compared between the groups to evaluate whether membership and interaction do have an influence on the dependant variables. The F-Values of MANOVA indicate that there are differences between the groups. These values ranged from F = 23,608 to 89,195 woth significant values of p=0.00, allowing to proceed with the interpretation of MANOVA.


A comparison of the mean values of the two groups proved that there are significant differences between non-members and members  of the BMW fan page for all 10 dependent variables. Specifically, membership has a strong positive influence on the affective variables brand  trust, brand  loyalty, brand commitment and positive word of mouth (∆ between +0.646 and +0.733, average ∆ = +0.675). Its influence on the conative variables brand satisfaction, purchase intention (for both product and repair services), and willingness to cross-buy (for lifestyle products, financial services and spare parts) is also strong (∆ between +0.395 and +0.985, average ∆ =  +0.682). The highest difference of mean values exists for  purchase intention (∆ =  0.985)  and willingness to cross-buy (∆ = 0.923).

Influence of Interaction

Having analyzed the influence of membership, the next step was to look more closely at the members of the BMW fan page, with the objective of determining whether the level of interaction on Facebook fan sites has any impact on the dependent variables. For this purpose, the four types of interaction with the fan page (writing posts, clicking the “Like” button, uploading photos or videos and sharing photos or videos with other users) were weighted to determine the level of interaction. Based on this level, members of the BMW fan page with a low level were then classified as passive and with a high level of interaction as active members.

The two groups were also analyzed using a MANOVA. Unlike the first pass, the second one did not deliver significant F-values for all 10 constructs. While most of the constructs had values between F = 4,815 to 23,668, purchase intention (for both product and repair services) and brand satisfaction failed to deliver satisfying p-values (p = 0.065, 0.758 and 0.425).


The comparison of the mean values of the two groups makes it clear that the significance of the MANOVA F-tests stem from the differences between the groups. Specifically, interaction has a positive influence on the affective constructs brand trust, brand loyalty,  brand commitment and positive word of mouth (∆ between +0.160 and +0.316, average ∆ = +0.233). Its influence on the conative variable willingness to cross-buy (for lifestyle products, financial services and spare parts) is  also positive (∆ between +0.164 and +0.379, average  ∆ =  +0.248). An influence on brand satisfaction and purchase intention (for both product and repair Services) was not observable as can be seen from the p-values. Highest mean value differences were seen in willingness to cross-buy for lifestyle products (∆ = 0,379) and brand loyalty (∆ = 0,316).

These two KPI's

The results show that being a member of a Facebook fan page has a strong influence on both affective and conative variables. Non-members show lower brand attitude and buying intention than members do. Membership as a key performance indicator can thus be used to assess intended buying behavior (conative component) and emotional affinity of customers to the brand (affective component).

Interaction has an influence on the affective dependent variables as well as partly on willingness to cross-buy. But while membership has an influence on all dependent variables, an influence of interaction on purchase intention and brand satisfaction was not observable. Interaction lacks a significant influence on the conative dependent variables. It can be assumed that interacting with a Facebook fan page does not influence such variables like purchase intention and brand satisfaction since there are other factors playing a more important role in buying a car or being satisfied with it. In the case of brand satisfaction, it is likely that whether a  customer is satisfied with the brand or not depends on the  performance  of the  brand  and not necessarily by how he interacts with the fan page. If the brand performs above the customer's expectation then brand satisfaction will follow. Interaction as a key performance indicator can thus only be used to assess emotional closeness to the brand BMW but not intended buying behavior of members.

The findings of this survey also show that membership and interaction do have a strong influence on brand admirers. The number of members of a fan page and the level of their interaction can therefore be considered as key performance indicators which actually have an economic value for the company using them. The findings also have implications for companies wanting to use a fan page on Facebook. Companies need to implement and monitor these two KPIs in order to evaluate whether their efforts on Facebook are successful or not. This means that companies need to follow these two KPIs closely when conducting a marketing campaign on Facebook in order to evaluate whether said campaign was successful or not. Companies should also focus on acquiring new members for their fan pages since it has been shown that membership has an impact on brand attitude and buying behavior.

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Facebook Likes and Human Behavior

Earlier in 2013, the Psychometrics Centre of the University of Cambridge conducted an impressive research about how private attributes are predictable from digital records of human behavior, like Facebook likes. Digitally mediated behaviors like Facebook likes can easily be recorded and analyzed, fueling the emergence of computational social science and new services such as personalized search engines and targeted online marketing. However, the widespread availability of extensive records of individual behavior, together with the desire to learn more about customers and citizens, presents serious challenges related to privacy and data ownership. 

The researchers (Kosinski, Stinwell & Graepel) showed that easily accessible digital records of behavior, Facebook Likes, can be used to automatically and accurately predict a range of highly sensitive personal attributes including sexual orientation,ethnicity, religious and political views, personality traits, intelligence, happiness, use of addictive substances, parental separation, age, and gender. The analysis presented is based on a dataset of over 58,000 volunteers who provided their Facebook Likes, detailed demographic profiles, and the results of several psychometric tests. The proposed model uses dimensionality reduction for preprocessing the Likes data, which are then entered into linear regression to predict individual psychodemographic profiles from Likes. The model correctly discriminates between homosexual and heterosexual men in 88% of cases, African Americans and Caucasian Americans in 95% of cases, and between Democrat and Republican in 85% of cases.

Predicting individual traits and attributes based on various cues, such as samples of written text, answers to a psychometric test, or the appearance of spaces people inhabit, has a long history. Human migration to digital environment renders it possible to base such predictions on digital records of human behavior. It has been shown that age, gender, occupation, education level and even personality can be predicted from people’s Web site browsing logs. Similarly, it has been shown that personality can be predicted based on the contents of personal web sites, music collections, properties of Facebook or Twitter profiles such as the number of friends or the density of friendship networks, or language used by their users. Furthermore, location within a friendship network at Facebook was shown to be predictive of sexual orientation.

The research 

The Psychometrics Centre, University of Cambridge

The study was based on a sample of 58,466 volunteers from the United States, obtained through the my Personality Facebook application, which included their Facebook profile information, a list of their Likes (n = 170 Likes per person on average), psychometric test scores, and survey information. Users and their Likes were represented as a sparse user–Like matrix, the entries of which were set to 1 if there existed an association between a user and a Like and 0 otherwise. The dimensionility of the user–Like matrix was reduced using singular-value decomposition (SVD). 
Numeric variables such as age or intelligence were predicted using a linear regression model, whereas dichotomous variables such as gender or sexual orientation were predicted using logistic regression. In both cases,the researchers applied 10-fold cross-validation and used the k =100 top SVD components. For sexual orientation, parents’ relationship status, and drug consumption only k = 30 top SVD components were used because of the smaller number of users for which this information was available.

Prediction of Dichotomous Variables

The Psychometrics Centre, University of Cambridge

The aforementioned figure shows the prediction accuracy of dichotomous variables expressed in terms of the area under the receiver-operating characteristic curve (AUC), which is equivalent to the probability of correctly classifying two randomly selected users one from each class (e.g., male and female). The highest accuracy was achieved for ethnic origin and gender. African Americans and Caucasian Americans were correctly classified in 95% of cases, and males and females were correctly classified in 93% of cases, suggesting that patterns of online behavior as expressed by Likes significantly differ between those groups allowing for nearly perfect classification. Christians and Muslims were correctly classified in 82% of cases, and similar results were achieved for Democrats and Republicans (85%). Sexual orientation was easier to distinguish among males (88%) than females (75%), which may suggest a wider behavioral divide (as observed from online behavior) between hetero and homosexual males.
Good prediction accuracy was achieved for relationship status and substance use (between 65% and 73%). The relatively lower accuracy for relationship status may be explained by its temporal variability compared with other dichotomous variables (e.g., gender or sexual orientation).

Predictive Power of Likes

Individual traits and attributes can be predicted to a high degree of accuracy based on records of users’ Likes. The best predictors of high intelligence include “Thunderstorms,” “The Colbert Report,” “Science,” and “Curly Fries,” whereas low intelligence was indicated by “Sephora,” “I Love Being A Mom,” “Harley Davidson,” and “Lady Antebellum.” Good predictors of male homosexuality included “No H8 Campaign,” “Mac Cosmetics,” and “Wicked The Musical,” whereas strong predictors of male heterosexuality included “Wu-Tang Clan,” “Shaq,” and “Being Confused After Waking Up From Naps.” 

Accuracy of selected predictions as a function of the number of available Likes. Accuracy is expressed as AUC (gender) and Pearson’s correlation coefficient (age and openness). About 50% of users in this sample had at least 100 Likes and about 20% had at least 250 Likes. Note, that for gender (dichotomous variable) the random guessing baseline corresponds to an AUC = 0.50. The Psychometrics Centre, University of Cambridge.

Moreover, note that few users were associated with Likes explicitly revealing their attributes. For example, less than 5% of users labeled as gay were connected with explicitly gay groups, such as No H8 Campaign, “Being Gay,” “Gay Marriage,” “I love Being Gay,” “We Didn’t Choose To Be Gay We Were Chosen.” Consequently, predictions rely on less informative but more popular Likes, such as “Britney Spears” or “Desperate Housewives” (both moderately indicative of being gay).

Conclusions

Similarity between Facebook Likes and other widespread kinds of digital records, such as browsing histories, search queries, or purchase histories suggests that the potential to reveal users’ attributes is unlikely to be limited to Likes. Moreover, the wide variety of attributes predicted in this study indicates that, given appropriate training data, it may be possible to reveal other attributes as well.
Predicting users’ individual attributes and preferences can be used to improve numerous products and services. For instance, digital systems and devices (such as online stores or cars) could be designed to adjust their behavior to best fit each user’s inferred profile. Also, the relevance of marketing and product recommendations could be improved by adding psychological dimensions to current user models. For example, online insurance advertisements might emphasize security when facing emotionally unstable (neurotic) users but stress potential threats when dealing with emotionally stable ones. 

Moreover, digital records of behavior may provide a convenient and reliable way to measure psychological traits. Automated assessment based on large samples of behavior may not only be more accurate and less prone to cheating and misrepresentation but may also permit assessment across time to detect trends. Moreover, inference based on observations of digitally recorded behavior may open new doors for research in human psychology.

On the other hand, the predictability of individual attributes from digital records of behavior may have considerable negative implications, because it can easily be applied to large numbers of people without obtaining their individual consent and without them noticing. Commercial companies, governmental institutions, or even one’s Facebook friends could use software to infer attributes such as intelligence, sexual orientation, or political views that an individual may not have intended to share. One can imagine situations in which such predictions, even if incorrect, could pose a threat to an individual’s well-being, freedom, or even life. Importantly, given the ever-increasing amount of digital traces people leave behind, it becomes difficult for individuals to control which of their attributes are being revealed.

There is a risk that the growing awareness of digital exposure may negatively affect people’s experience of digital technologies, decrease their trust in online services, or even completely deter them from using digital technology. It is our hope, however, that the trust and goodwill among parties interacting in the digital environment can be maintained by providing users with transparency and control over their information, leading to an individually controlled balance between the promises and perils of the Digital Age.

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Neuromarketing research for the win - pt1

Marketing research methods continuously develop and over the last decade technology offered solutions to improve this area. Traditional marketing research methods fail at some point in certain cases, and since emotions are mediators of how consumers process marketing messages, understanding of cognitive responses to advertisements have always been a challenge in methodology. Neuromarketing is the branch of neuroscience research that aims to better understand the consumer through his unconscious processes and has application in marketing, explaining consumer's preferences, motivations and expectations, predicting his behavior and evaluating successes or failures of advertising messages.

Neuroscience

Neuroscience gathers information on the structure and functions of the brain and its sub-domain called cognitive neuroscience seeks to understand the neural mechanisms behind thoughts, reasoning, emotions, memory or decision making. Using technology advances in neuroscience, researchers can obtain information on brain responses to marketing stimuli, not having full confidence in what they report. They provide new ways for understanding how consumers store, retrieve, develop and use information. Neuromarketing is an emerging interdisciplinary field that aims to investigate and understand consumer behavior by studying the brain. Thus, using neuroimaging techniques, researchers measure subjects' responses to marketing stimuli. Therefore, the development of this field depends on the advance of science, technology and computer science.



Neuroimaging tools

Functional Magnetic Resonance Imaging (fMRI) represents an appropriate methodology for uncovering the areas of the brain activation in response to a very simple experimental design with little potential for the temporal dimension to be a problem. fMRI combines magnetic field and radio waves, producing a signal that allows viewing brain structures in detail and following the metabolic activity in the brain. Τhe subject lies on a bed, with the head surrounded by a large magnet which causes the atom particles (protons) inside the subject's head to align with the magnetic field. As blood contains iron, the iron atoms that are not bound to oxygen produce small distortions in the nearer magnetic field and when a certain brain area is active, corresponding blood vessels dilate and more blood rushes in, reducing the amount of oxygen-fee hemoglobin and producing a change in the magnetic field in the active area.

Software allows viewing this change, displaying colored areas overlapping the grey-scale image of the brain and refreshing the image every 2 to 5 seconds. fMRI allows measuring brain activity and searching for patterns while subjects perform certain tasks or experience marketing stimuli. Data analysis can be conducted using specific software packages, as BrainVoyager QX or Statistical parametric Mapping (SPM5).

Electroencephalography (EEG) is one of the most used tools in neuromarketing research, after fMRI. The amplitudes of the recorded brain waves correspond to certain mental states, such as wakefulness (beta waves), relaxation (alpha waves), calmness (theta waves) and sleep (delta waves). A number of electrodes (up to 256) are placed on the scalp of the subjects, in certain areas, in order to measure and record the electricity for that certain spot. Technology allows EEG to be a portable device and record brain activity in any many circumstances, as for example in supermarkets. Also, EEG is able to record only activity data from superficial layers of the cortex.

Positron emission tomography (PET) is another expensive method to use that can obtain physiologic images with spatial resolution similar to fMRI by recording the radiation from the emission of positrons from the radioactive substance administered to the subject. A battery of detectors surrounds the subject's head and traces radiation pulse, without precisely identify the location of the signal. Technical issues involve obtaining the radioactive material and it's short life.

Transcranial magnetic stimulation (TMS) uses magnetic induction in order to modulate the activity of certain brain areas that are located 1-2 centimeters inside, without reaching the neocortex. New TMS technology allows also targeting lower brain areas and is less expensive than PET or fMRI scanners. A plastic case containing an electric coil is positioned near to the subject's head. TMS discharges a magnetic field that passes through the brain, allowing making changes in the brain tissue in certain locations and being able either to temporary activate neurons (using high frequency) or temporary disable neuronal activity (low frequency). TMS is able to highlight causal inferences by analyzing the subject in front of a marketing stimuli while certain brain areas are disabled, stimulated, or normal.

Neuromarketing methodology

Eye Tracking allows studying behavior and cognition without measuring brain activity, but where the subject is looking at, for how long he is looking, the path of the subject's view and changes in pupil dilation while the subjects looks at stimuli. Eye tracking allows measuring the attention focus and thus monitoring types of behavior. Eye movements fall into two categories: fixations and saccades. Fixation is when the eye movement pauses in a certain position and saccade is a switch to another position. The resulting series of fixations and saccades is called a scan path, and they are used in analyzing visual perception, cognitive intent, interest and salience. Eye tracking provides more accurate information than self-report, as research shows that claimed viewing is not always the same as measured actual viewing.

Measuring Physiological Responses to stimuli can provide information on the subject's emotional effects by monitoring the heart rate, blood pressure, skin conductivity (affected by sweat, measuring arousal level), stress hormone from saliva, facial muscles contractions, and inferring the emotional state for each moment. [Bercea,2013]

Response time measures computes the amount of time between stimuli appearance and it's response, informing researchers on the complexity of the stimulus to an individual and how the subject relates to it. This cheap method can be used on recall studies or on measuring subject's attitude towards certain stimuli.

                                                      To be continued...

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