Saturday, August 31, 2013

Vertical Marketing Systems and Retail Industry

Vertical integration in a distribution channel places into focus the factors of cooperation such as the distribution of income among partners, the distribution of risk depending on the marketing functions and activities assumed, as well as margin increase through the cost reduction. The problem is to identify the model which is, from a theoretical point of view, based on gradation of integration and the retailers’ power. For efficient retail, it is necessary to adjust the retailer’s and the supplier’s distribution models, and this homogeneous structure represents a unique model of cooperation between the suppliers and the retailers, which will be discussed here. There are two basic functions that suppliers or mediators perform, them being the satisfaction of supply and demand. Other functions can be divided into the exchange of the market information, the presentation of suppliers to the market, technical back-up and help with product selling to the end user.

The development of retail with the purpose of forming the optimum cooperation among the participants in the distribution, places into focus the question what has to be improved, in what way the mutual cooperation should be modeled and in what way does a retailer influence the physical flow of the goods through the channel. There is a need for considering the mutual relationships among the distribution channel participants, i.e. the business subjects which distribute products and provide services with an emphasis on the cooperation of a retailer with other participants. The greater the income of the retailer, the greater their influence on the distribution channels and the greater their control over the flow of the goods and services, without taking the ownership of the channel. Retail management controls the suppliers, who, then, have to increase the level of their services, take over more marketing functions and activities, reduce the price and improve assortment in stores.

The structure of the Vertical Marketing System

The vertical marketing system is an inter-organizational structure which is based on contracts or trust, where each subject has a limited control over this mutual relationship. The purpose of the vertical marketing system is to connect, to a certain level, the marketing advantages of two or more separate organizational units which have different marketing interests and owners. The difference between the distribution channels are manifested in a different range of marketing functions, each with its own expenses, which especially come into focus if we are talking about outscoring marketing activities. If two or more levels of the distribution channel merge into one unit which is supervised and managed by a single managing structure, in that case we have vertical integration (Grossman & Hart,1986). These two authors link vertical integration with control and ownership. They also claim that in the case of vertical integration, we have the transfer of control over one’s assets to another subject, while the first subject still keeps the ownership over that assets.

The analysis of the distribution channel has shown that vertical, compared to traditional channels, have considerably greater dynamics, development opportunities and give a better background for a specialization of the marketing functions. With vertical marketing systems, we have a limited independence of the participants, and, under the pressure from the competition, ’looking down on the partners’ transforms into a closer relationship with the leader. The existence of traditional channels comes down to the benefits from a transaction and not from a cooperation which can be more profitable and advantageous in the long run. A vertical marketing channel provides a systematic view of the issue, which was not the case with conventional contracting in that they only dealt with the issue of the contact and the agreement between an end user and a supplier.

Vertical marketing channel is another descriptive, theoretical model of the distribution chain which, as opposed to the Porter’s theoretical model, emphasizes the role of the participant that would order and carry out the necessary functions. Verticalization stands for the process of unification of all the market levels within the channel, where each level is represented by independent business subjects which transfer their result to the next level that is closer to the consumer. What makes the vertical integration specific is the fact that the distribution of marketing activities is based on an agreement, on condition that all participants retain their identity and independence.

Vertical Integration Model Factors.

The performance of marketing functions in the channel depends on the following factors: costs, expected benefits and risks.The purpose of the above picture is to identify the connection of the verticalization of marketing functions in the channel with the limitations, in terms of costs, risks and benefits. The more flexible and looser the verticalization, the more bearing of the costs, the risk and more freedom will be transferred to the other negotiating party. The only phenomenon that can influence the distortion of a dotted line in Figure 1 is the moment of power within the channel. A powerful player puts the pressure on weaker suppliers in that he tries, through their resources, to achieve greater profit by reducing the risk and costs.

The influence of power on vertical marketing system models

The development in the channel manifests itself in the change of relationships within vertical integration, and this occurs if a retailer becomes the holder of a higher value margin in the market economy. The prerequisite for strengthening of a retailer’s position is, above all, the investment in financial assets. This investment is a result of the strategic goals of management and ownership with the purpose of using the acquired means for gaining new resources which should secure higher value margin and the profit in the long run. From the economic point of view, the return to the invested amount means that the capital of a business subject has provided the profit, and is used rationally and economically. In addition, viewed from a broader perspective, we must notice the connection of this financial category with the model of vertical integration. The retailers that get return on the invested capital, make an efficient use of strategic resources, or, in other words, the managing structure integrates and classifies those resources and activities which are profitable and executable.

The comprehension of power refers to putting one party’s interests before the interests of the other, in which case the latter one is unable to change this. Theoretically speaking, power is manifested in the ability of a business subject to, through his behavior and activities influence the course of cooperation and the behavior of other participants whom he interacts with. Since the purpose of cooperation is to make a bigger profit for the organization and the participants, individual striving towards this additional profit that the organization would not otherwise achieve, is always present. The goals of inter-organizational cooperation are set through negotiation, so it is only natural to conclude that what we are dealing with here is the negotiating power or the ability to influence the outcome of the negotiation.

Negotiating power has a great influence on all aspects, from the modeling of the vertical marketing system to the very way of arrangement of the products on the shelves, and that power comes from the volume economy and the growth of the sales potential.The cooperation at the very end of the channel for the distribution of goods and services will probably, in the future time, be ever more controlled by a retailer, while the others, manufacturers and mediators will be forced to accept their rules. The key resources for selling the product to the end user will be, even to a greater extent, under the dominance and the management of retail.

Wednesday, August 21, 2013

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).


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.

Wednesday, August 07, 2013

Neuromarketing research for the win - pt2

The main question is: Should Neuromarketing research be closer to quantitative or qualitative approach?

Qualitative research is an in-depth exploration of what triggers people on a particular subject: their feelings, perceptions, decision-making processes, and so on. The most common forms of qualitative research are focus groups and depth interviews. Qualitative research will provide a much deeper understanding of how the target market thinks, but it does not provide data that can be projected and derived, so results cannot be generalized. 

On the other hand,  quantitative research  can be generalized, as it employs a larger sample (through mail, telephone or internet) which is representative of the entire population being researched, but it won't provide the depth of information available through qualitative research. 
Each approach has its drawbacks, as quantitative research often forces responses or people into categories that might not fit them, and qualitative research, on the other hand, sometimes focuses too closely on individual results and fails to make connections to larger situations or possible causes of  the results. But the solution would come in finding the most effective way to incorporate elements of both to ensure that their studies are accurate and valid[Bercea, 2013].

Neuromarketing and quantitative research

With regards to neuromarketing and quantitative research, there are some common points that are highlighted below: 

●  Psychophysiological techniques from neuromarketing research use a number of indicators to keep track of different psychological responses to stimuli, responses that can be represented by cognitive and affective processes. Quantitative measures of the cognitive processes include measures of beliefs, knowledge, attitudes, attention, memory, recall and everything that happens in the subject's mind. On the other hand, the affective process is a mental state that develops spontaneously without cognitive effort, and is involved with a set of emotional reactions.  

●  Rapid technological evolution enables marketing researchers to use more advanced equipment to conduct psychophysiological measurements. Researchers usually have to visually examine brain wave patterns recorded by EEG and also conduct brain wave mapping and statistical analyses using specific algorithms and software. Using computer-aided EEG, future marketing research may aim to identify the relationships between psychological processes and certain patterns of brain waves.  

●  Most data analysis in neuromarketing research includes preprocessing, statistical analysis, data interpretation (behavioral analysis and neuroimaging data analysis) and triangulation. Preprocessing includes having different phases which perform time correction (between appearance of stimuli and recording the signal of its effect), head motion correction, normalization (using algorithms in order to obtain a standard brain template) and smoothing (removing noises using Gaussian filters). Statistical analysis on the level of brain regions in order to find the Voxels (coordinates) for which the time series (fitting a general linear model) significantly correlates with a specific experimental condition. Data interpretation should confirm or infirm the hypothesis of the research, and triangulation should validate the research by correcting complementary sources and linking them to the data acquired with neuroimaging. 

● The purpose of neuromarketing studies is to test hypothesis, look at cause and effect and make predictions concerning consumer behavior, developing a quantitative approach.

● Although using a small sample size, findings can be generalized, as brain mechanisms of people are similar.

Neuromarketing and qualitative Research

Neuromarketing research passes the boundaries of traditional marketing research methods through the information provided and with the great advantage that it requires only 10% of subjects that would be necessary for traditional methods. Also, neuromarketing studies are small sample sized (not randomly selected) due to costs and complexity of the experiments, but taking into consideration that the data collected also contains noises that must be removed, at least 15 to 20 participants should be recruited to such studies in order to obtain internal validity. The reasearch of a small amount of subjects used make neuromarketing  come closer to the qualitative side and stay further from the quantitative one.

Invasive methods (such as PET or TMS - described in the previous post) change the role of the researcher, as he is able to activate or temporary disable areas of the brain or to add radioactive chemicals in the subject's blood.

Thus, we can consider neuromarketing research as being 

a triangulation of research, as it implies defining a problem (qualitative approach), 
defining and test hypothesis (quantitative approach) and exploring the results in depth (qualitative approach) [tribute to Monica Bercea, PhD, 2013] .

A sample Anti-smoking ad : Neuromarketing and UCLA fMRI 

Ad Campaign Comparison

A study published in Psychological Science brings us closer to that point: scientists using a UCLA fMRI facility analyzed anti-smoking ads by recording subject brain activity. They also asked subjects about the commercials and whether the ads were likely to change their behavior. The researchers found that activity in one specific area of the brain predicted the effectiveness of the ads in the larger population, while the self-reports didn’t.

The methodology involved comparing brain activity in subjects who viewed ads from three campaigns to actual performance of the campaigns in increasing call volumes. The researchers focused on a subregion of the medial prefrontal cortex (MPFC) but also compared activity in other brain regions for control purposes. They found that the ad campaign which created the greatest activity in the MPFC region generated significantly more calls to a stop-smoking hotline. The subjects failed to identify which ads would change their behavior; in fact, the most effective campaign, “C,” was the one judged to be least likely to work. The researchers also asked a group of industry experts to predict which campaign would work best. Like the experimental subjects, the so-called experts also predicted that “C” would be the least effective [Roger Dooley,2012] .

Even if this single, small study of smoker behavior can’t be readily extrapolated to campaigns for BMW or Pepsi, it’s still of great significance in proving neuromarketing studies can actually work. As the authors note, “The approach described here is novel because it directly links neural responses with behavioral responses to the ads at the population level.” Simply put, the brain scan data correctly predicted how the ads would perform in the real world – not just how the subjects would behave, but the broader public audience. Well, that’s a major milestone.