What is it to you if people talk about your site in the internet? Should you really care if they say negative or positive things about your brand? Should you even take the time to consider if what is in your site, the content, be as detailed for transparency or just a basic structure for background for the reader? Personally, I question that myself. I really don't understand why people take the time in front of a laptop or PC taking hours and asking the people around them "What do you think about this?". But then, it hit me, maybe I'm just stuck in the Stone Age era thinking about the whole idea in a 2D manner. Just like that line in transformers, there's more to it than meets the eye. Brand reputation is just as much important, or even more important, than being visible to the public. It actually goes hand in hand. Put it this way, your brand is visible to the public, they know it's there, they have seen the site and actually liked the display images, they just don't mind your brand- what then is your output? Nada. Zilch. Zero. How about looking at it in the other direction, you have all the brand reputation you can ever imagine, the big time of all the brands, and yet the public doesn't know you exist online, what now is to become of you? Maybe your only clientele would be the people who are stuck in the Stone Age era just like me. And thinking about it, business/ your brand should be visible and reputable in all areas, period.
It's scary to realize that anyone can just write about you online or post pictures about your company online any day of the week and there's a 50/50 chance its going to be something you like or, to put it simply, your worst nightmare. Aside from the, communication via word-of-mouth is influenced 53% of the time from work related purchases. And as far as monitoring is concerned, only 20% of established corporations have a formal process for monitoring what is written or posted about them. For those applying to companies, 78% of executive recruiters routinely use search engines to learn more about the candidates while 35% of them eliminate candidates based from what they have found. Basically, what ever information available in the internet could be useful or harmful depending on who is on the bar stool. What matters more is it becomes more "truthful" when it is posted by someone who shares the same or similar interest, same profession or holds similar political beliefs. It is obvious that when an opinion speaks to you in a manner that agrees with your stand, your bias suddenly appears and your behavior is now strongly influenced by that opinion. Which then again is a double-edged sword that could be good or bad for the company with the product.
Like what I said at the start, reputation and being visible goes hand-in-hand. A company cannot ignore the other or they will pay the consequences. As of today, what others only hear about can now be personally accessed through the net. And what is more visible is more vulnerable to criticism. Just like movie stars compared to an average Joe like you and me. They get the spotlight, yet they gain from it and suffer from it as their life is placed under a microscope and dissected. Now, in context of companies under the microscope and vulnerable to public criticism, the only way to avoid negative feedback is to "show what you are doing, reveal your processes, acknowledge your mistakes, and participate fully in conversation that concerns you." Of course those are not my words, it's through a reading, but it states exactly what should be done.
Social media is another field that calls for your attention. This involves blogs, photo sharing, video sharing and forums. Why do you need to pay attention to this? The answer is exactly the same as what was mentioned before, opinions that influence the behavior of the consumer. Your neighbor might be the most influential person in forums about your product and your clueless about it. Those rumors that you consider JUST rumors might just alter your course from success to failure, and it all begins from your favorite media tools and forums.
Your reputation is the key to your existence as a company, an asset as important to the company as to your other assets. Just like what Abraham Lincoln said- "Character is like a tree and reputation like a shadow. The shadow is what we think of it; the tree is the real thing.". Reputation is not about what the company thinks, but what others think about the company. If no one knows you exist, then you have no reputation. If you are not subject to criticism, just you wait. And if you are under criticism, find a solution and build from it.
For our datamin class under Mr. Ramon Duremdes Jr.
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Friday, July 31, 2009
Friday, July 10, 2009
Competing with the help of data mining
Ever tried making decisions and wonder if they were the right ones? Ever tried going to business knowing exactly what will happen after a month or so? Obviously not. The instinct of a businessman can only help him so much, and from there, another question arises, "What now?". That has been the dilemma of most businessmen for quite some time. Business itself relies on current data and clientele to maintain a number of employees and equipment to handle, but that's just the surface of it all. Businessmen have to translate that data and be able to think at least two steps ahead to maintain and expand clientele being that there are certain variables which cannot be foreseen and trends that cannot be spotted right away. Corporations, schools, and other income generating businesses operating under strict competition environments must have that edge in order to stay competitive as well as flourish in the process. This is where data mining comes in.
Data mining, is defined by the article as the extraction of hidden predictive information from large databases. It is helpful in so many aspects especially business decisions. Data mining uses data stored from data warehouses and databases and with the use of data mining algorithms businesses are given special capabilities namely automated prediction of trends and behaviors, a handy tool in business especially when the scope of the business is service oriented, and automated discovery of previously unforeseen patterns which are very important as it will help boost product purchase( in sales) or usage (service). Although, without a powerful computer to analyze data takes so long to handle even by the best statisticians, that is why a prerequisite of data mining aside from mass collected data and data mining algorithms is a powerful computer. But even with visible patterns, there will also be the fact that not most of them are to be used as well as most of them has a low probability of coming into play. This, is resolved by the statistics involved in data mining. Statistics or statistical techniques as explained by the article are not data mining although it is very helpful in solving problems concerning data mining. With data mining, businesses can make informed decisions and take calculated risks.
Models, a very helpful tool in analyzing mass amounts of stored data in your data warehouse, are generated and sampled onto the computers. Models generally generate answers for your questions. And from broad questions, you can get to answer more in-depth questions answered and your once unknown territory, you will be able to do selective targeting. After making a model, data mining tools are used. Commonly used tools in data mining are the following: artificial neutral networks, decision trees, genetic algorithms, nearest neighborhood method and rule induction. As stated by the article, the classical techniques are statistics, neighborhoods and clustering while the next generation techniques are the trees, networks and rules.
Relation of data mining and the data stored in the data warehouse require a relational database system. This will help in faster acquisition of desired data. On-line analytical processing or OLAP as defined in the article refers to array-oriented database applications that allow users to view, navigate through, manipulate, and analyze multidimensional databases. OLAP is more effective method of analysis compared to the usual decision support systems.
Data mining is a really effective technology in which gives long and short term benefits, it is actually a trend in which all large scale companies should follow, especially now that dependency to technology is becoming more and more a factor to society. Still, even if it is a tool for success, there are still factors to consider as data mining is still dependent factors in which it derives its success from: a large, well-integrated data warehouse and a well-defined understanding of the business process within which data mining is to be applied. When these factors are resolved, customer relations are improved and thus resulting to success for the business.
Techniques also fall in the same arena, data mining techniques such as histograms, rules, linear regression, clustering and so on cannot be used over and over again to solve a problem. Each problem can and most probably will be different from each other that is why, as stated in the article, techniques to be used are mostly based on trial and error. A constantly changing world means a company can only desire but not be able to formulate the perfect model for them. Some of these techniques are being taught to us right now in school. Methods like linear regression, linear relationship between the dependent and independent variable, which have applications (ex: QM for windows) that help understand the basic insight and know-how to manipulate and have a general knowledge on how linear regression works. Clustering which helps analyze competition properly and knowing the niche and competition in that niche. Although we may not know the total concept, the important thing is grasping the basic idea of it especially because we are future businessmen ourselves.
Concluding it all, data mining does have a lot of positive feedback, but there is also the element of surprise, an unpredictable turn of events (example given in the article: internet) as the world is, and will always be, continuously changing. Relying on technology does have a lot of benefits, but ingenuity of the human mind and entrepreneurial "gut feeling" should not be ignored.
submitted to Mr. Ramon Duremdes for our DATAMIN class.
DLSC
Data mining, is defined by the article as the extraction of hidden predictive information from large databases. It is helpful in so many aspects especially business decisions. Data mining uses data stored from data warehouses and databases and with the use of data mining algorithms businesses are given special capabilities namely automated prediction of trends and behaviors, a handy tool in business especially when the scope of the business is service oriented, and automated discovery of previously unforeseen patterns which are very important as it will help boost product purchase( in sales) or usage (service). Although, without a powerful computer to analyze data takes so long to handle even by the best statisticians, that is why a prerequisite of data mining aside from mass collected data and data mining algorithms is a powerful computer. But even with visible patterns, there will also be the fact that not most of them are to be used as well as most of them has a low probability of coming into play. This, is resolved by the statistics involved in data mining. Statistics or statistical techniques as explained by the article are not data mining although it is very helpful in solving problems concerning data mining. With data mining, businesses can make informed decisions and take calculated risks.
Models, a very helpful tool in analyzing mass amounts of stored data in your data warehouse, are generated and sampled onto the computers. Models generally generate answers for your questions. And from broad questions, you can get to answer more in-depth questions answered and your once unknown territory, you will be able to do selective targeting. After making a model, data mining tools are used. Commonly used tools in data mining are the following: artificial neutral networks, decision trees, genetic algorithms, nearest neighborhood method and rule induction. As stated by the article, the classical techniques are statistics, neighborhoods and clustering while the next generation techniques are the trees, networks and rules.
Relation of data mining and the data stored in the data warehouse require a relational database system. This will help in faster acquisition of desired data. On-line analytical processing or OLAP as defined in the article refers to array-oriented database applications that allow users to view, navigate through, manipulate, and analyze multidimensional databases. OLAP is more effective method of analysis compared to the usual decision support systems.
Data mining is a really effective technology in which gives long and short term benefits, it is actually a trend in which all large scale companies should follow, especially now that dependency to technology is becoming more and more a factor to society. Still, even if it is a tool for success, there are still factors to consider as data mining is still dependent factors in which it derives its success from: a large, well-integrated data warehouse and a well-defined understanding of the business process within which data mining is to be applied. When these factors are resolved, customer relations are improved and thus resulting to success for the business.
Techniques also fall in the same arena, data mining techniques such as histograms, rules, linear regression, clustering and so on cannot be used over and over again to solve a problem. Each problem can and most probably will be different from each other that is why, as stated in the article, techniques to be used are mostly based on trial and error. A constantly changing world means a company can only desire but not be able to formulate the perfect model for them. Some of these techniques are being taught to us right now in school. Methods like linear regression, linear relationship between the dependent and independent variable, which have applications (ex: QM for windows) that help understand the basic insight and know-how to manipulate and have a general knowledge on how linear regression works. Clustering which helps analyze competition properly and knowing the niche and competition in that niche. Although we may not know the total concept, the important thing is grasping the basic idea of it especially because we are future businessmen ourselves.
Concluding it all, data mining does have a lot of positive feedback, but there is also the element of surprise, an unpredictable turn of events (example given in the article: internet) as the world is, and will always be, continuously changing. Relying on technology does have a lot of benefits, but ingenuity of the human mind and entrepreneurial "gut feeling" should not be ignored.
submitted to Mr. Ramon Duremdes for our DATAMIN class.
DLSC
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