What is online reputation?
Online reputation is a factor in any online community where trust is important. It affects a pseudonym rather than a person. Examples include eBay, an auction service which uses a system of customer feedback to publicly rate each member's reputation. Amazon.com has a similar reputation mechanism in place and merchants develop their reputations across different dimension. One study found that a good reputation added 7.6% to the price received. In addition, building and maintaining a good reputation can be a significant motivation for contributing to online communities. See Motivations for contributing to online communities for more information.To begin developing an online reputation, consider how your personal or company brand should be perceived. What is your brand identity / what is your value proposition / selling point / unique voice? Once you have developed the image you would like your constituencies to perceive, develop a strategy to build your brand. Are you seeking credibility in the marketplace (consider blogging, answering questions on LinkedIn), gain market leadership (create innovative tools for your industry) or connection (build a network of contacts in professional and/or social sites).Another way to look at online reputation is how well its being managed. This form of reputation is usually called web reputation to distinguish it from the online reputation.Indeed, Web reputation does not concern the virtual on-line reputation only, but the whole real reputation of a person or a company as it is affected by the Web. Nearly seven out of 10 global business executives see their reputations online as vulnerable. An online reputation is the perception that one has on the Internet based on their digital footprint. Digital footprints accumulate through all of the content shared, feedback provided and information that created online. People aspire to have a positive online reputation. If someone has a bad online reputation, he can easily change his pseudonym. This is why new accounts on ebay or amazon are usually untrusted. If a person or a company want to manage his web reputation, he will have many more difficulties. This is why a merchant on the web having a physical shop (with real name, real address) is usually more trusted.
http://en.wikipedia.org/wiki/Online_reputation#Online_Reputation
Wikipedia explains it pretty well and I see no need to paraphrase it since I will demonstrate that I understand the topic later in this blog, and if I didn't oh well.
That is online reputation
How do I build it?
As it says in the definition you need "TRUST".
That is to become trustworthy. As the students reiterated several times in different words, you have to be radically transparent. By being transparent, you can become trustworthy. What happens if you are not trustworthy you may ask? Then people will not listen to you and therefore you will not be able to build up an online reputation.
How can one become trustworthy?
Simply put, you have to invest effort into it. ALOT of effort. Put an effort into what and how you post, type, etc. Part of being radically transparent is also to be honest, so one always has to present their best self. As shallow as it sounds you have to at least ACT as a very respectable person to get people to consider listening to you. Make sure everything is easy to read and organized. At the very least use spell checker and allow spaces between paragraphs so you are not posting a big wall of text that is very difficult to read. Use the proper size and fonts, using wingdings or size 1 is not helping people notice you.
Following the surface things, appearance and whatnot. You also need substance. Now that you have gotten people's attention one way or the other you have to now keep their interest with the substance of whatever you discuss in your blog or whatever site you are building your reputation on. Some ways you can do this are; Discuss interesting and relevant topics, be spontaneous, use many visuals to support yourself.
As you build yourself and maintain your current level you will get to know more and more people inevitably. Assuming you have done well up to this point, since we are dealing with online reputation people are a part of the equation. as you get to know more people your reputation will spread to the people they know to the people they know and so on.
I have been going on and on about how and what you should be doing but I neglected to say where. Visit relevant sites to your goal. For example if you want to gain reputation as an expert in Basketball, visit sites related to NBA, Euroleague and other Basketball related topics. Visit forums and be active in them. All the while maintaining a steady radical transparency.
Online reputation is the fad these days and many people will if not now, later, depend on it for building their reputation. Soon, the old ways of reputation will end. By old reputation I mean by word of mouth or television. As technology continues to progress and improve upon itself, people will need the old ways less and less until it becomes non-existent. By making the most of online reputation as your tool to gain attention, one can accomplish many things.
Thursday, July 30, 2009
Friday, July 10, 2009
discovering hidden value through datamining
First off, everything I will blog about will be found here; http://www.thearling.com/text/dmwhite/dmwhite.htm http://www.thearling.com/text/dmtechniques/dmtechniques.htm
Anyway what, this blog is about is HOW we discover hidden value through datamining. Essentially, this refers to how datamining "scours databases for hidden patterns, finding predictive information that experts may miss because it lies outside their expectations". In the last blog everyone (as in the datamin class) talked about what datamin is. In my last post I talked about how datamining could be used by businesses as a tool to make them more efficient. This is what those hidden patterns and other predictive information do. for example, by being very shrewd, a person can predict demand of their product over a series of time and can produce in such a way that will maximize his profit by making the most of that demand. This is where the datamining makes money, by taking in more and more data, a person can then identify trends in the data collected. Seasonal things for example is a trend, for example like winter clothing being "in" during winter season (obviously). But, that is a simple exam people have more trends than that and are a lot less obvious such as fashion trends. In an ideal world, if you had enough data you could make a plan to maximize efficiency and profits in your business. Lets say your Mcdonald's, you could calculate when would be the best time (let's say season) to bring out your promotional foods, like twister fries. Specifically, you could decipher when people will most crave the product using past data to determine trends over several lengths of time.
So far, I've only mentioned a very single minded way of using the data for certain trends such as seasons. The application of datamining covers much more than that however. You could see trends and other predictive data in much larger scales. Such as seeing oh how men will react to your product as opposed to women, how african americans like it compared to asians and many other things. You could then choose to tailor your product to be most attractive to a certain race, sex, age, etc. Having this information will make it that much easier to plan and maximize whatever it is you do for business. The only thing that will be different among business is what their using this tool for. A business that will make some product will probably look at it's appeal to different groups. A business centered on service will look for where they are most wanted, for example, a massage company might want to relocate to a more industrial area where more people will be wanting their services after a hard day's work. An entrepreunership business will be looking for demand and be looking to supply to that place or groups of people to maximize their sales. The entertainment business will be looking for the fans who will be most receptive of whatever brand of entertainment they bring. The sports industry will be looking for mostly the same thing, for example, the NBA only sets up their teams to states in the U.S. where there are the most basketball fans, they have even stated that they might set up internationally if their sport gets more popular there.
This data will deal with mostly statistics. Simply put, a large count of people liking this and that. Arranged in such a way that there are several inferential data such as race, age, gender, sex (as mentioned earlier). Even without a complex program or mechanism that will simply tell you "oh this is the best choice for you". You could simply sort through the data to find which of the groups simply have the largest number of people which like or dislike whatever your selling. Like I said in my previous post; "knowledge is power, guard it well." All this data would be irrelevant if it could be found anywhere (shared on the internet) so in essence, this tool would only be helpful if only you and only you (and your co-workers) knew about it. This is probably one of the reasons there are laws such as patenting.
These days, there are subjects like gametheory, production control, operations management, etc. that deal with the application of the large amounts of data about people and other things. Datamining can be though of as the gathering tool and these subjects I have just mentioned as the application tool. I mention this to strengthen my next point, without datamining those application tools/subjects would have no value whatsoever.
I will end my blog here, with a little summary. How you discover hidden value is the next step in datamining, most of us in the datamin class probably talked about how datamining is a great tool to gathering large amounts of data. The next step is what all that gathering is for, to make inferences from statistical data to make predictions and to see trends. This can then be used as a great application to your business, assuming other people don't have you and your business have you in their own data (by knowing what data you have then taking advantage of that).
Again, "Knowledge is power, guard it well". :P
Anyway what, this blog is about is HOW we discover hidden value through datamining. Essentially, this refers to how datamining "scours databases for hidden patterns, finding predictive information that experts may miss because it lies outside their expectations". In the last blog everyone (as in the datamin class) talked about what datamin is. In my last post I talked about how datamining could be used by businesses as a tool to make them more efficient. This is what those hidden patterns and other predictive information do. for example, by being very shrewd, a person can predict demand of their product over a series of time and can produce in such a way that will maximize his profit by making the most of that demand. This is where the datamining makes money, by taking in more and more data, a person can then identify trends in the data collected. Seasonal things for example is a trend, for example like winter clothing being "in" during winter season (obviously). But, that is a simple exam people have more trends than that and are a lot less obvious such as fashion trends. In an ideal world, if you had enough data you could make a plan to maximize efficiency and profits in your business. Lets say your Mcdonald's, you could calculate when would be the best time (let's say season) to bring out your promotional foods, like twister fries. Specifically, you could decipher when people will most crave the product using past data to determine trends over several lengths of time.
So far, I've only mentioned a very single minded way of using the data for certain trends such as seasons. The application of datamining covers much more than that however. You could see trends and other predictive data in much larger scales. Such as seeing oh how men will react to your product as opposed to women, how african americans like it compared to asians and many other things. You could then choose to tailor your product to be most attractive to a certain race, sex, age, etc. Having this information will make it that much easier to plan and maximize whatever it is you do for business. The only thing that will be different among business is what their using this tool for. A business that will make some product will probably look at it's appeal to different groups. A business centered on service will look for where they are most wanted, for example, a massage company might want to relocate to a more industrial area where more people will be wanting their services after a hard day's work. An entrepreunership business will be looking for demand and be looking to supply to that place or groups of people to maximize their sales. The entertainment business will be looking for the fans who will be most receptive of whatever brand of entertainment they bring. The sports industry will be looking for mostly the same thing, for example, the NBA only sets up their teams to states in the U.S. where there are the most basketball fans, they have even stated that they might set up internationally if their sport gets more popular there.
This data will deal with mostly statistics. Simply put, a large count of people liking this and that. Arranged in such a way that there are several inferential data such as race, age, gender, sex (as mentioned earlier). Even without a complex program or mechanism that will simply tell you "oh this is the best choice for you". You could simply sort through the data to find which of the groups simply have the largest number of people which like or dislike whatever your selling. Like I said in my previous post; "knowledge is power, guard it well." All this data would be irrelevant if it could be found anywhere (shared on the internet) so in essence, this tool would only be helpful if only you and only you (and your co-workers) knew about it. This is probably one of the reasons there are laws such as patenting.
These days, there are subjects like gametheory, production control, operations management, etc. that deal with the application of the large amounts of data about people and other things. Datamining can be though of as the gathering tool and these subjects I have just mentioned as the application tool. I mention this to strengthen my next point, without datamining those application tools/subjects would have no value whatsoever.
I will end my blog here, with a little summary. How you discover hidden value is the next step in datamining, most of us in the datamin class probably talked about how datamining is a great tool to gathering large amounts of data. The next step is what all that gathering is for, to make inferences from statistical data to make predictions and to see trends. This can then be used as a great application to your business, assuming other people don't have you and your business have you in their own data (by knowing what data you have then taking advantage of that).
Again, "Knowledge is power, guard it well". :P
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