netrashetty
Netra Shetty
Citigroup Inc. (branded Citi) (NYSE: C, TYO: 8710) is an American multinational financial services company based in New York City. Citigroup was formed from one of the world's largest mergers in history by combining the banking giant Citicorp and financial conglomerate Travelers Group on April 7, 1998.[2]
Citigroup Inc. has the world's largest financial services network, spanning 140 countries with approximately 16,000 offices worldwide. The company employs approximately 260,000 staff around the world, and holds over 200 million customer accounts in more than 140 countries. It is a primary dealer in US Treasury securities.[3]
Citigroup suffered huge losses during the global financial crisis of 2008 and was rescued in November 2008 in a massive bailout by the U.S. government.[4] Its largest shareholders include funds from the Middle East and Singapore.[5] On February 27, 2009, Citigroup announced that the United States government would take a 36% equity stake in the company by converting $25 billion in emergency aid into common shares; the stake was reduced to 27% after Citigroup sold $21 billion of common shares and equity in the largest single share sale in US history, surpassing Bank of America's $19 billion share sale one month prior.
Citigroup is one of the Big Four banks in the United States, along with Bank of America, JP Morgan Chase and Wells Fargo
It must not be concluded, however, that either of these two very different perspectives is the "superior" one. Both perspectives have adaptive advantages, as well as attendant dangers. "Long-term" academic researchers are often criticized for being out of touch with the realities of the marketplace (the "ivory tower" criticism). "Short-term" marketers are often accused of doing research that is motivated by the fear of being judged solely responsible for a "bad" intuitive decision. They are merely seeking a place to "point a finger" if the consequences of their decisions don't pan out as expected. With apologies to comedian Flip Wilson, it is as though they want to be able to say, "The research made me do it!"
Synergistic relationships
It is often overlooked that most marketing research situations are of the form of a synergistic relationship between researcher and marketer. In this sense, marketing research is not "done" in the sense that a statistical analysis is "done." Rather, marketing research emerges as a joint function of the needs of the marketer and the skills of the researcher. One might argue that marketing research is more of a transition than a product or service. To the extent that marketers see research as a product, they will de-emphasize the understanding that can be gained. To the extent that researchers see marketing as a service, they will de-emphasize the important role it has in the non-academic world. The optimal situation is a dialogue between marketer and researcher that ensures mutually satisfactory transactions.
Without such dialogue, the analysis of a data set is often divorced from the original questions the survey was intended to address. From an objective standpoint, any statistical textbook could be consulted to determine the "proper" analysis. But the main questions might not be addressed even in the objectively "proper" analysis. "Proper" data is not necessarily useful data. Since the design of the survey and analysis of the data are inevitably interwoven, this dialogue between marketer and researcher should precede questionnaire development.
There can be no "magical" statistical solutions if the prior steps have not insured that the "proper" analyses can be performed. Worsening the situation is the widespread availability of statistical software. This encourages untrained individuals to apply statistical tests in an indiscriminate manner. The expectations generated in the minds of the owners of these statistical packages are oftentimes unrealistic. Owning a "statistical cookbook" does not make a person a "chef." And not even the greatest chef can make chocolate mousse from headcheese.
It is a lucky marketer who works with a researcher who is aware of the validity, and business necessity, of the "short-term" view. And it is an equally lucky researcher whose client appreciates that the "long-term" view can pay dividends in the future. Working together, this "team" of the marketer and researcher can address any challenge offered by the marketplace. They will not only find opportunities with a "long-term" view, but also will seize opportunities by dealing with "short-term" competitive threats with information rather than emotion.
Fueled by imagination and insight, the contribution of both marketers and researchers should lead to those "competitive advantages" that are so sought after in the world of business. So how does one go about finding such "gems" in the data? In some sense, what we seek is information rather than insight, but I would contend that the two go together more often than not.
Broad generalizations contribute little, and preoccupation with minutiae is equally counterproductive. Useful data should satisfy both the marketer and the researcher. The real challenge to those in marketing research is finding the right "level of focus" for the wisest "data use."
Vondruska's Postulates
What we need is a principled way in which analysis can be approached to maximize obtaining the desired information. The "level of focus" notion leads directly to Vondruska's Postulates, which are as follows:
Postulate 1: Lower levels of phenomenal organization are easier to detect than higher levels of phenomenal organization.
Postulate 2: Higher levels of phenomenal organization are easier to imagine than lower levels of phenomenal organization.
Obviously, the converse of each postulate is implied as well (e.g., it is difficult to detect organization at higher levels). What do I mean by "organization?" Simply that the world is not merely a collection of disjointed atoms in space. Hydrogen molecules organize into stars; people organize into market segments. We see patterns. We see constancy. We understand.
Admittedly, the postulates are a bit abstract. So an illustrative analogy seems in order. Consider the following (familiar?) high school math formulas:
Ellipse:
x2 + y2
a2 + b2
Parabola:
y2 = 4 px.
Hyperbola:
X2 - y2
a2 - b2 =1
In terms of the postulates, these formulas can be considered at a "low" level of organization. They are useful unto themselves, but no relationship between the formulas is implied. Now consider the illustration of the conic sections in Figure 2.
By re-conceptualizing ellipses, parabolas, and hyperbolas at a "higher" level of "organization," we now see something new. Despite their distinct formulas, we see them as members if the family of plane figures. As the philosopher Ludwig Wittgenstein contended, sometimes things are related by family resemblance rather than common attributes. If we do not know that, we will not look for such resemblances.
The point here is that the same type of mental processes prevail when we work with data. Recasting the postulates in terms of the phrase "He cannot see the forest because of the trees" may help to explain them further.
Sometimes we can easily detect the "trees," but we miss imagining the "forest." And at other times, we get clobbered by "trees" as we dash through the "forest" of our preconceived notions.
The true power of these postulates is that they apply not only to marketing, but to most investigative endeavors. The proper "level of focus" for most meaningful investigations usually lies between the extremes of high and low levels of organization. Often, more than one "focus" is needed to thoroughly understand an array of data. Some, of course, will be more useful than others for particular purposes.
Facts vs. Ideas
Facts "need" ideas, and ideas "need" facts. Examples of the need for both measurement and theory abound in the history of science. The astronomer Johann Kepler spent many years of his life pursuing a mathematical/theoretical framework that would provide an account of planetary orbits. He immersed himself in the mysteries of mathematics in his attempt to bring order to astronomical phenomena. His driving intuition was that the perfection of mathematics must be hidden in the universe itself.
One of Kepler's contemporaries, the lesser known Tycho Brahe, approached the problem of determining the nature of the planetary orbits in a different way. He measured. He collected data. Night after night, he sat at his telescope and dutifully recorded the positions of the observable planets. But to his eye, no patterns emerged from the data. It was only when he and Kepler shared their different perspectives did the true usefulness of the data become apparent. Kepler is credited with the discovery that planets orbit the Sun in an elliptical pattern, but Tycho Brahe had no small contribution to that discovery.
Kepler's discovery of the elliptical orbits of the planets would not have been possible without the painstaking data collection of planetary positions by Tycho Brahe. The key is that Kepler had to consider the facts in his discovery. He would have much preferred the orbits to be perfect celestial circles, but the evidence mitigated against that theory. On a more mundane level, research realities such as these are encountered in marketing research on an everyday basis.
Hypothesis-driven research
It is not enough merely to subject data to rigorous analysis. The most useful data is gleaned from an analysis in which one already has a suspicion of what is sought. Hypothesis-driven research also yields the greatest insights from analysis. I have a personal rule that I apply to any analysis. After I have applied all of the "right" statistical tools, I look for "patterns" in the data. When I start to scour statistics manuals to find a procedure that will give me interesting results, I stop. This is a sure sign that I have "tortured" the data into confessing all of its secrets. Alas, sometimes there are no further secrets.
Higher level statistical analyses do not typically uncover relationships that are not at all apparent at lower levels. They simply "formalize" those relationships in a more elegant, and sometimes more useful way. A good example of this is hierarchical log linear analysis. Although there is the potential in this procedure for detecting very high level interactions between variables, these complex interactions are often impossible to interpret--for all practical purposes.
Obviously, there is a big difference between knowing what one ultimately wants to accomplish through marketing research and actually accomplishing it. Ambiguity in research design is especially common in the non-academic world. Invoking another astronomical analogy, it is as though many marketers fail to realize that even though they can see the planet Jupiter, that does not mean that they can get there directly. It takes a long time to get to Jupiter--and when you finally get there it will be in a new location! Both theoretical knowledge and technical knowledge are required to reach distant goals. Only then can the improbable become the possible.
There is a lesson to be learned here. Straightforward thinking does not always produce the desired result. Some research problems have solutions that possess a property that is denoted in the German language by the word "umweg." There is no suitable direct translation, but the idea is that only a roundabout approach will work. All direct approaches fail. Most puzzles and games incorporate this "umweg" principle. Indeed, Nature herself seems to have an immense sense of humor with regard to thwarting direct approaches.
Of course, marketing research is not exempt from this "umweg" principle. An analysis plan which is too straightforward often founders on the rocks of perplexing findings. Luckily, by understanding the nature of data, we are still able to tease out the actionable information needed for practical marketing solutions.
Citigroup Inc. has the world's largest financial services network, spanning 140 countries with approximately 16,000 offices worldwide. The company employs approximately 260,000 staff around the world, and holds over 200 million customer accounts in more than 140 countries. It is a primary dealer in US Treasury securities.[3]
Citigroup suffered huge losses during the global financial crisis of 2008 and was rescued in November 2008 in a massive bailout by the U.S. government.[4] Its largest shareholders include funds from the Middle East and Singapore.[5] On February 27, 2009, Citigroup announced that the United States government would take a 36% equity stake in the company by converting $25 billion in emergency aid into common shares; the stake was reduced to 27% after Citigroup sold $21 billion of common shares and equity in the largest single share sale in US history, surpassing Bank of America's $19 billion share sale one month prior.
Citigroup is one of the Big Four banks in the United States, along with Bank of America, JP Morgan Chase and Wells Fargo
It must not be concluded, however, that either of these two very different perspectives is the "superior" one. Both perspectives have adaptive advantages, as well as attendant dangers. "Long-term" academic researchers are often criticized for being out of touch with the realities of the marketplace (the "ivory tower" criticism). "Short-term" marketers are often accused of doing research that is motivated by the fear of being judged solely responsible for a "bad" intuitive decision. They are merely seeking a place to "point a finger" if the consequences of their decisions don't pan out as expected. With apologies to comedian Flip Wilson, it is as though they want to be able to say, "The research made me do it!"
Synergistic relationships
It is often overlooked that most marketing research situations are of the form of a synergistic relationship between researcher and marketer. In this sense, marketing research is not "done" in the sense that a statistical analysis is "done." Rather, marketing research emerges as a joint function of the needs of the marketer and the skills of the researcher. One might argue that marketing research is more of a transition than a product or service. To the extent that marketers see research as a product, they will de-emphasize the understanding that can be gained. To the extent that researchers see marketing as a service, they will de-emphasize the important role it has in the non-academic world. The optimal situation is a dialogue between marketer and researcher that ensures mutually satisfactory transactions.
Without such dialogue, the analysis of a data set is often divorced from the original questions the survey was intended to address. From an objective standpoint, any statistical textbook could be consulted to determine the "proper" analysis. But the main questions might not be addressed even in the objectively "proper" analysis. "Proper" data is not necessarily useful data. Since the design of the survey and analysis of the data are inevitably interwoven, this dialogue between marketer and researcher should precede questionnaire development.
There can be no "magical" statistical solutions if the prior steps have not insured that the "proper" analyses can be performed. Worsening the situation is the widespread availability of statistical software. This encourages untrained individuals to apply statistical tests in an indiscriminate manner. The expectations generated in the minds of the owners of these statistical packages are oftentimes unrealistic. Owning a "statistical cookbook" does not make a person a "chef." And not even the greatest chef can make chocolate mousse from headcheese.
It is a lucky marketer who works with a researcher who is aware of the validity, and business necessity, of the "short-term" view. And it is an equally lucky researcher whose client appreciates that the "long-term" view can pay dividends in the future. Working together, this "team" of the marketer and researcher can address any challenge offered by the marketplace. They will not only find opportunities with a "long-term" view, but also will seize opportunities by dealing with "short-term" competitive threats with information rather than emotion.
Fueled by imagination and insight, the contribution of both marketers and researchers should lead to those "competitive advantages" that are so sought after in the world of business. So how does one go about finding such "gems" in the data? In some sense, what we seek is information rather than insight, but I would contend that the two go together more often than not.
Broad generalizations contribute little, and preoccupation with minutiae is equally counterproductive. Useful data should satisfy both the marketer and the researcher. The real challenge to those in marketing research is finding the right "level of focus" for the wisest "data use."
Vondruska's Postulates
What we need is a principled way in which analysis can be approached to maximize obtaining the desired information. The "level of focus" notion leads directly to Vondruska's Postulates, which are as follows:
Postulate 1: Lower levels of phenomenal organization are easier to detect than higher levels of phenomenal organization.
Postulate 2: Higher levels of phenomenal organization are easier to imagine than lower levels of phenomenal organization.
Obviously, the converse of each postulate is implied as well (e.g., it is difficult to detect organization at higher levels). What do I mean by "organization?" Simply that the world is not merely a collection of disjointed atoms in space. Hydrogen molecules organize into stars; people organize into market segments. We see patterns. We see constancy. We understand.
Admittedly, the postulates are a bit abstract. So an illustrative analogy seems in order. Consider the following (familiar?) high school math formulas:
Ellipse:
x2 + y2
a2 + b2
Parabola:
y2 = 4 px.
Hyperbola:
X2 - y2
a2 - b2 =1
In terms of the postulates, these formulas can be considered at a "low" level of organization. They are useful unto themselves, but no relationship between the formulas is implied. Now consider the illustration of the conic sections in Figure 2.
By re-conceptualizing ellipses, parabolas, and hyperbolas at a "higher" level of "organization," we now see something new. Despite their distinct formulas, we see them as members if the family of plane figures. As the philosopher Ludwig Wittgenstein contended, sometimes things are related by family resemblance rather than common attributes. If we do not know that, we will not look for such resemblances.
The point here is that the same type of mental processes prevail when we work with data. Recasting the postulates in terms of the phrase "He cannot see the forest because of the trees" may help to explain them further.
Sometimes we can easily detect the "trees," but we miss imagining the "forest." And at other times, we get clobbered by "trees" as we dash through the "forest" of our preconceived notions.
The true power of these postulates is that they apply not only to marketing, but to most investigative endeavors. The proper "level of focus" for most meaningful investigations usually lies between the extremes of high and low levels of organization. Often, more than one "focus" is needed to thoroughly understand an array of data. Some, of course, will be more useful than others for particular purposes.
Facts vs. Ideas
Facts "need" ideas, and ideas "need" facts. Examples of the need for both measurement and theory abound in the history of science. The astronomer Johann Kepler spent many years of his life pursuing a mathematical/theoretical framework that would provide an account of planetary orbits. He immersed himself in the mysteries of mathematics in his attempt to bring order to astronomical phenomena. His driving intuition was that the perfection of mathematics must be hidden in the universe itself.
One of Kepler's contemporaries, the lesser known Tycho Brahe, approached the problem of determining the nature of the planetary orbits in a different way. He measured. He collected data. Night after night, he sat at his telescope and dutifully recorded the positions of the observable planets. But to his eye, no patterns emerged from the data. It was only when he and Kepler shared their different perspectives did the true usefulness of the data become apparent. Kepler is credited with the discovery that planets orbit the Sun in an elliptical pattern, but Tycho Brahe had no small contribution to that discovery.
Kepler's discovery of the elliptical orbits of the planets would not have been possible without the painstaking data collection of planetary positions by Tycho Brahe. The key is that Kepler had to consider the facts in his discovery. He would have much preferred the orbits to be perfect celestial circles, but the evidence mitigated against that theory. On a more mundane level, research realities such as these are encountered in marketing research on an everyday basis.
Hypothesis-driven research
It is not enough merely to subject data to rigorous analysis. The most useful data is gleaned from an analysis in which one already has a suspicion of what is sought. Hypothesis-driven research also yields the greatest insights from analysis. I have a personal rule that I apply to any analysis. After I have applied all of the "right" statistical tools, I look for "patterns" in the data. When I start to scour statistics manuals to find a procedure that will give me interesting results, I stop. This is a sure sign that I have "tortured" the data into confessing all of its secrets. Alas, sometimes there are no further secrets.
Higher level statistical analyses do not typically uncover relationships that are not at all apparent at lower levels. They simply "formalize" those relationships in a more elegant, and sometimes more useful way. A good example of this is hierarchical log linear analysis. Although there is the potential in this procedure for detecting very high level interactions between variables, these complex interactions are often impossible to interpret--for all practical purposes.
Obviously, there is a big difference between knowing what one ultimately wants to accomplish through marketing research and actually accomplishing it. Ambiguity in research design is especially common in the non-academic world. Invoking another astronomical analogy, it is as though many marketers fail to realize that even though they can see the planet Jupiter, that does not mean that they can get there directly. It takes a long time to get to Jupiter--and when you finally get there it will be in a new location! Both theoretical knowledge and technical knowledge are required to reach distant goals. Only then can the improbable become the possible.
There is a lesson to be learned here. Straightforward thinking does not always produce the desired result. Some research problems have solutions that possess a property that is denoted in the German language by the word "umweg." There is no suitable direct translation, but the idea is that only a roundabout approach will work. All direct approaches fail. Most puzzles and games incorporate this "umweg" principle. Indeed, Nature herself seems to have an immense sense of humor with regard to thwarting direct approaches.
Of course, marketing research is not exempt from this "umweg" principle. An analysis plan which is too straightforward often founders on the rocks of perplexing findings. Luckily, by understanding the nature of data, we are still able to tease out the actionable information needed for practical marketing solutions.