Intro to Data Mining for Marketers - Part 1

Data mining can be defined as the process of "discovering patterns, meaning and insights in large datasets by using statistical and computational methods". Data mining works to analyze data stored in data warehouses that are used to store that data that is being analyzed. That particular data may come from all parts of business, from the production to the management. Managers also use data mining to decide upon marketing strategies for their product. They can use data to compare and contrast among competitors. Data mining interprets its data into real time analysis that can be used to increase sales, promote new product, or delete product that is not value-added to the company.

History

Data mining was born in the fields of Statistics and Computer Science (some might say Artificial Intelligence) and may also be referred as “Statistical Learning”. From a statistical perspective, most early and recent advances coming from Statistics have come from the Stanford Statistics department school of thoughts like  Bradley Efron, Jerome H. Friedman, Trevor Hastie and Robert Tibshirani. By the way, don’t forget that Stanford University is only 7 miles away from Google.

Stanford University ©

Data Mining Framework

Using data mining techniques, we, marketers, need to master an approach that will provide the decision makers with  a-priory knowledge about customers’ preferences and needs. Since there are many different kinds of customers with different kinds of needs and preferences, a simple, solid approach is meant to be a tool for performing market segmentation: divide the total market, choose the best segments, and design strategies for profitability serving the chosen segments better than the company’s competitors do. The example developed below is described for product development in auto industry, but it can be successfully implemented for any other applications where it is necessary to  find the correlations between the customer feelings or perceptions and the physical characteristics of a product. Yes, correlations, even through our statistics lenses. 

Yes,arithmophobia is over, my friend!

Understanding

Any data mining application should start by understanding the business goals of the application since the blind application of data mining techniques without  the requisite domain knowledge often leads to the discovery of irrelevant or meaningless patterns. In order to understand the target customers of an automotive company, it would be helpful to examine the relationships between the vehicle image/attributes and the customer emotional benefits that are tied to psychological needs, personality traits, and personal values. Thus, data mining can enable us to understand more completely how product specific characteristics relate to customer needs and the benefits a customer hopes to obtain from them. For instance, for many people, cars, homes, restaurants and vacations provide emotional benefits as well as rational benefits. However, for a wealthy person who has everything, the emotional benefits provided by status, prestige and superiority of an expensive automobile could outweigh rational benefits such as gas economy, lower maintenance and insurance costs, and resale value.  

A target audience perhaps? "Free to do anything, in control, confident, sporty but with family."

Therefore, it will be beneficial to have a tool that will help us to respond to questions such as: What and how many of the personality attributes used to describe the customer might be shaped by the vehicle’s image?  What kind of vehicle this customer or group of customers will buy?

Data selection

This step calls for targeting a database or selecting a subset of fields to be used for the data mining. The following issues should be considered in developing a plan for collecting data efficiently:

• Evaluation of existing data sources 
• Specification of research approaches 
• Data gathering (contact methods, sampling plans and instruments)

The survey research is a simple, efficient method to collect data. One of the advantages of the survey research is flexibility because it can be used to obtain many different kinds of information in many different situations. Furthermore, depending on the survey design, it may also provide information quicker at a lower cost compared to manual processing. The survey may be in the form of a questionnaire that is very flexible as there are many ways to ask questions. In preparing the questionnaire, only the questions contributing to the research objectives will be asked. The questions may be closed-ended, as they include all possible answers. In designing the survey, we also make sure that the questions are simple, direct and arranged in a logical order.  The first question should create interest if possible, and difficult or personal questions should be asked last so that respondents do not become defensive.

Instead of a traditional mail questionnaire, a more modern approach is the computer interviewing process, in which respondents sit down at a computer, read questions from a screen, and type their own answers into the computer at their own leisure. The beauty of this approach consists of its multiple benefits. As a first benefit, the respondents’ answers are automatically stored in a database. Furthermore, the survey is posted on the web and it can be accessible by an unlimited number of people. Filling out the survey becomes a non-time consuming task even for a busy person: the survey is on the web and it is accessible for anybody at any time; the submission of the completed survey requires only a ‘click on’ action executed by respondent, action possible  through an interactive survey implementation. 

Third, the computers might be located at different locations such as auto shows, dealerships, or retail locations. The biggest benefit is the collection of more relevant data since people present at those locations are most likely willing to answer correctly to the questions because they are interested in automobiles. The approach can be implemented such that the data is gathered from numerous computers  at different locations and stored in a unique and global database. As a fourth benefit, same survey format will be accessible to different categories of people: expert people (such as car designers) or people less familiar with auto domain characteristics. The large number of respondents and their diversity give more reliability on the results than small samples.


                                                      ...To be continued...

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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 

www.neurosciencemarketing.com

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.

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