Chloride by-products and elevated levels of chlorine and chlorine-byproducts that contribute to test and odor issues. Corrosion by-products related to the use of water that is corrosive to the distribution system , piping, and fixtures within the home. Internal plumbing that does not use NSF approved materials and the plumbing releases plasticizers into the water. Get Educated and Informed. In most regions private water wells and small water systems servicing less than 25 individuals and 15 service connections may not be regulated by the state or a federal authority. Therefore, it is up to these users to be proactive and get their water tested.
Our portal is used by private water supply systems worldwide, but this is a summary of what we have found in Pennsylvania. Get a copy of our New Booklet. Covers issues related to water conservation, the sources of pollution, ensuring that private water supply systems produce safe drinking water and protecting the long-term quality of our water sources. Our Current Sponsors. Which item did they buy first? Could you encourage people to buy X,Y and Z, thus boosting point-of-purchase sales? This looks at when customers bought, and tries to predict when they will buy again.
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You could use this type of analysis to determine a strategy of planned obsolescence or figure out complimentary products to sell. This also looks at the number of customers in your market and predicts how many will actually buy.
For example, imagine if you have a coffee shop in Seattle. Here are questions you might ask:. Take away: When it comes to forecasting sales, create three cash flow projections: realistic, optimistic and pessimistic.
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By examining customer purchasing patterns and looking at the demographics and psychographics of customers to build profiles, you can create products that will sell themselves. Of course for a marketer to get any value out of a database, it must continue to grow and evolve. You feed database information from sales, surveys, subscriptions and questionnaires.
And then you target customers based upon this intelligence. Take away : Database marketing begins with collecting information. For example, if you owned a coffee shop, your database might consist of these things:. As you collect this data, start to look for opportunities like best days to run a discount promotion. Ask yourself: Who are your local customers and how you can turn these customers in advocates for your store?
This is helpful for offline or online companies. For the offline, a company looking to grow by adding stores can evaluate the amount of merchandise they will need by looking at the exact layout of a current store. For an online business, merchandise planning can help you determine stocking options and inventory warehousing. If your business involves issuing credit cards, you can collect the information from usage, identify customer segments and then based on information on these segments build programs that improve retention , boost acquisition, target products to develop and design prices.
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A great example of this occurred when the UN decided to issue a Visa credit card to people who traveled overseas frequently. The agency marketers segmented their database into wealthy travelers—30, people in high-income households. That may sound small, but it actually exceeded industry standards. Large financial institutions typically see 0.
Analyzing customer buying patterns based on their credit card habits will give you insights into behavior that can lead to promotions and programs that will result in higher revenues and better customer loyalty. If your company depends upon telecommunications, then you can mine that incoming data to see use patterns, build customer profiles from these patterns and then construct a tiered pricing structure to maximize profit. Or you could build promotions that reflect your data. A China mobile operator with about , customers wanted to analyze their data to create offerings to fend off competition.
The first thing the project team behind collecting and analyzing the data did was create an index to describe caller behavior. That index then clustered the callers into 15 segments based on elements like this:. From that data the marketing department then created strategies directed at each segment, namely improving customer satisfaction, delivering quality SMS service for another group and encouraging another group to use more minutes. In a world where price wars occur, you will get customers jumping ship every time a competitor offers lower prices.
You can use data mining to help minimize this churn, especially with social media. Spigit uses different data mining techniques from your social media audience to help you acquire and retain more customers. Their programs include:. The author notes that cited reference data returned by the Web of Science are not limited to items indexed by the platform. The XML request must contain the accession number for the publication queried, along with several other parameters that inform the API as to what type of search to conduct.
One such parameter is a cited reference search, which returns the full reference list from the queried publication. Python is a free and open source programming language with thousands of user-developed modules that permit automation of a wide variety of tasks. The script accomplished the following tasks:. In this instance, the script took approximately eight minutes to return the data. The script and instructions for its use are freely available online.
The returned data represented citations to any material type—journals, books, government documents, or anything else. The data returned from the Web of Science API-cited reference queries required some cleaning and standardization. OpenRefine is a free and open source software application used for data refinement tasks such as transformations, pattern detection, mass editing, and detection of inconsistencies.
An obstacle of working with this data set is that a Web of Science—cited reference query returns textual content formatted in the preferred style of the journal in which the publication appeared. Reconciling journal titles from the two data sets was a semiautomated process. The Reconcile feature verified exact matches in journal title names automatically, but minor discrepancies between journal title punctuation and format between the two data sets meant that a portion of the reconciling process became a supervised procedure.
The rationale for this decision is that such a small amount of use does not justify adding an item to the collection. Finally, the study used Microsoft Excel to calculate bibliometrics of the faculty publication list, the cited reference data, and the reconciled citedWork-holdings data. The metrics calculated include:. The publication data included works from 31 unique authors from the Geological Sciences Department. The faculty averaged 86 publications per year indexed by Web of Science. The articles published in represented a high-water mark see table 2.
The journal most frequently published in was Geophysical Research Letters. Several high-impact, multidisciplinary journals such as Science and Nature are also present in the list of works most frequently published in. Table 4 shows the top 20 most frequently cited journals during the study period. Confirming its status as a preeminent geoscience publication, Geophysical Research Letters was the most cited title, with 1, citations. Science citations and Nature citations ranked numbers 2 and 3 for times cited, respectively.
While there was some overlap between the most cited journals and the most published in journals, 8 of the top 20 most published-in journals were not among the top 20 most cited journals. This finding suggests that faculty do not always publish in the journals they assign the most importance to—a reasonable assumption given that the most important journals typically have low acceptance rates. The Geological Sciences faculty overwhelmingly cite journals more than any other material resource. Basic counts and age calculations of the works cited in the articles underscore the importance of new and emerging research to the Geological Sciences faculty.
The reference lists for each of the articles contained 24, cited references, averaging 56 citations per paper see table 2. The faculty, however, most often cited much more recently published articles. The citation age occurring most often was three years old see figure 1 , and approximately 22 percent of all the citations were three years old or less at time of publication. In fact, the faculty cited items aged zero or less times—sometimes citing articles in press that were not due to be officially published for a year or more.
The implication of this finding is that the faculty rely heavily upon works that are between zero and two years old, considering the lag between the time articles are written and the time they are published. In this analysis, 20 percent of the journal titles received 85 percent of the citations. These results parallel a dispersion analysis of a related subject field conducted by Kimball et al.
While the papers reviewed for this study cited 3, unique titles, 80 percent of citations went to just 10 percent of titles journals. In fact, nearly half of all citations Figure 2 shows the relationship between number of citations and the percentage of titles cited, illustrating that a large portion of the cited references in this study cite a relatively small selection of all of the titles cited. This outcome indicates that, even though the Geological Sciences faculty cited a wide range of titles, they tended to rely the most on a small set of journals.
If the 80 percent mark is indicative of the core serials collection for a subject, as many have proposed, 50 then this finding suggests that the core earth sciences serials at CUB are composed of approximately the top 10 percent of titles cited. Future work comparing citations among similar faculty groups at different institutions such as Geological Science Departments with comparable teaching and research foci could identify a core earth sciences collection that could apply to many academic libraries.
Percentages of Total Citations vs. Total Titles Cited Citations to the left of the vertical dashed line represent 80 percent of all citations, which cited only 10 percent of all titles. The serial holdings offered by the library provided good coverage of the journals most often cited by the Geological Sciences faculty at the University of Colorado Boulder.
Figure 3 depicts the proportions of cited materials that the library provided access to for items cited at least 20, 10, and 5 times. At the point where the journal titles reached 80 percent of citations as discussed above , those journals received five citations during the five-year span of the study.
At that level, the library provided access to 92 percent of all titles cited. The author expected high holdings coverage of the most important journals in the field. The earth sciences at CUB have had a dedicated branch library for 20 years and a subject librarian performing collection development for even longer. Even though the library provided access to a high proportion of the most frequently cited materials, there were some exceptions.
The library did not have a current subscription to 36 items that the faculty cited five times or more from through Table 5 shows works missing from the collection cited 10 times or more. Each item in table 5 is within the top 7 percent of most frequently cited titles.
Notably, the fortieth most frequently cited work, Quaternary Research , is among the titles to which the library did not provide access. During the timespan of the study, the Geological Sciences faculty cited Quaternary Research times—within the top 1. While anecdotal, the request granted some cogency to the findings. Another noteworthy finding was that five out of the ten most frequently cited items not available from the library were Spanish language publications dealing with research in Patagonia.
A clear coverage gap in this subject area likely indicates that these titles are important to one or two faculty members. Academic librarians seeking to replicate this study locally might similarly discover works of importance to the faculty missing from their collections. In the case of this study, the analysis produced clear collection development priorities. A logical next step in the process is consulting with Geological Sciences faculty and graduate students to determine which items among those missing from the collection are most important to their work.
Future research should seek to identify the means by which faculty obtain cited materials that are inaccessible to them via the library and delineate the effects of their habits of obtaining these items on the library.
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Presumably, researchers in need of materials that are embargoed—or not in the collection—obtain these items from interlibrary loan, personal subscriptions, or other means. Combining cited reference data with a faculty survey and interlibrary loan statistics would provide a more complete assessment of how material usage and obtainment affects the library. Unavailable items cited by faculty may be reflected in interlibrary loan ILL request records. Conversely, if frequently cited but unavailable items do not turn up in ILL records, then one could assume faculty obtain these materials by some other means.
A further question might entertain the economic effects of authorized or unauthorized obtainment of unavailable library materials on the library. Subsequent studies could investigate the rate of ILL requests of these materials and the presence or lack of increased ILL costs. The methods advanced in this paper offer a compelling step forward in collecting citation data, and future work could expand and improve the techniques presented in this paper. Making use of the Web of Science API and a simple Python script significantly increased the speed with which an analysis of this scale can take place.