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Figure 3.2 This is the second in the series of industry group charts, all
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Abhay continues, "We then get all the data and we analyze it, we data mine it, we run statistical models on it, we actually build models of customer behavior, do segmentation and all that stuff. The main idea is we then come up with recommendations and define projects that would then increase the revenue from our site." At this point, my pupils are dilating and my breathing becomes labored. I have been looking for-hoping for-somebody, somewhere who is performing this level of data delving and is willing to talk about it. I want detail. I want the whole story. I want to reach deep into that vat of rich historical data that this group has been harvesting and aggregating for lo, these many years and lift out complex tapestries of human computer interaction and shopping and buying tendencies that Paco Underhill never dreamed of accessing. "Well, let me put it this way," Abhay says, letting me down easy. "We've only been doing this for about 6 months now. We went through first the stage of recognizing that we need to get really, really good data. "At first we decided to go down the log server route. I came across several problems, so this time around I decided not to do it that way. We went with Keylime to give us client-side data from browsers and spent a little bit of time implementing all the pages. "Keylime gives us two things. One is it gives us some out-of-the-box, off-the-shelf reports on our clickstream data. But more importantly for us, we get an 'x-track' from them which is our entire clickstream data which is coming up on a terabyte now, for the last quarter. And then we do a join with our internal data. The most important, the biggest piece is the order transaction data."
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searching information bases for useful data. Valuable information can be obtained by training salespeople to listen to and observe customers, suppliers, members of the distribution system, and the competition, then contributing this intelligence to the MIS. The intent should be to obtain usable marketing intelligence (information that is available to the public) and not to conduct industrial espionage (stealing information not available to the public). The latter is unethical and illegal. Marketers should be savvy enough to realize that as they are collecting information about their competition, the competition is probably collecting information about them. An additional external source of important information for the marketing MIS is the market for a company s products. A large amount of useful data can be obtained from the TPS for markets already being served by the company, but insights into buyer behaviors and preferences in new markets can only be obtained from sources outside the firm. The Internet may become the ultimate information source for both the competition and the market. It already provides access to information provided by government (.gov), for-profit business (.com), nonprofits (.org), universities (.edu), and individuals.
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The SciFinder program is a good tool for comprehensive searching of commercially available compounds. It includes the information that is published in Chemical Abstracts, but makes it much more accessible. This browser-like application permits searching by chemical structure, either as an exact match or based on substructure. While a comprehensive description of how to use SciFinder is not intended here, a few points should be kept in mind. Structures can be drawn in SciFinder itself, or in another chemical drawing program and pasted into the structure window in SciFinder. To avoid an unmanageable number of hits when searching by substructure, it may be necessary to limit the structure based on atoms that may be further substituted or rings that may be added. These parameters
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Public Agencies and Private Sector: Additional Roles
Remotely Accessing Files and Computers . . . . . . . . 187
The purchase step transaction group is behaving well (not slowing down much) throughout the workload. The log on/log off transaction group behaved well until the workload reached 400 users. At that point, the log on/log off performance jumped to the requirement maximum. The performance tester should use this information to alert the developer that the log on performance becomes marginal when the purchase step workload exceeds 400 active transactions. Figure 9.8 shows the results of ramping up the log on/log off along with launching purchase steps and catalog browser transactions to peak workload.
ImplementedCategories tells you what this object was designed for. You can easily find out whether it is suitable for scripting. ImplementedCategories contains Class-IDs that link to HKEY_CLASSES_ ROOT\Component Categories\ and tells you more about the abilities a
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